System

Abstract

An object of a system according to an embodiment is to provide an appropriate commodity proposal and information for improving money literacy on the basis of a purchase behavior and an economic situation of a user.SOLUTION: A system includes a purchase action analysis unit, a commodity proposal unit, and an information provision unit. The purchase behavior analysis unit analyzes the purchase behavior and the economic situation of the user. The product proposal unit proposes a product purchase on the basis of the result analyzed by the purchase behavior analysis unit. An information providing part provides information for improving money literacy on the basis of a result analyzed by the purchase action analysis part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of not providing adequate information to suggest appropriate products based on users' purchasing behavior and economic situation, or to improve financial literacy.

[0005] The system according to the embodiment aims to provide appropriate product suggestions and information for improving financial literacy based on the user's purchasing behavior and economic situation. [Means for solving the problem]

[0006] The system according to the embodiment includes a purchasing behavior analysis unit, a product proposal unit, and an information provision unit. The purchasing behavior analysis unit analyzes a user's purchasing behavior and economic situation. The product proposal unit makes product purchase proposals based on the results of the analysis by the purchasing behavior analysis unit. The information provision unit provides information for improving financial literacy based on the results of the analysis by the purchasing behavior analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate product suggestions and information to improve financial literacy based on the user's purchasing behavior and economic situation. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A financial literacy improvement system according to an embodiment of the present invention is a system that analyzes a user's purchasing behavior and economic situation, and a generation AI makes product purchase suggestions and provides information for improving financial literacy. As a result, the financial literacy improvement system can analyze a user's purchasing behavior and economic situation, and provide appropriate product suggestions and information for improving financial literacy.

[0029] A financial literacy improvement system according to an embodiment includes a purchasing behavior analysis unit, a product proposal unit, and an information provision unit. The purchasing behavior analysis unit analyzes a user's purchasing behavior and financial situation. For example, the purchasing behavior analysis unit collects a user's purchasing history and analyzes the history of past product purchases. The purchasing behavior analysis unit can also analyze the user's income and spending patterns. For example, the purchasing behavior analysis unit evaluates the user's financial situation based on data on the user's income and spending. The purchasing behavior analysis unit can also collect and analyze data related to the user's financial situation. For example, the purchasing behavior analysis unit analyzes the user's savings and investment status. The product proposal unit makes product purchase suggestions based on the results of the analysis by the purchasing behavior analysis unit. For example, the product proposal unit proposes products that the user regularly purchases. The product proposal unit can also propose products tailored to specific events. For example, the product proposal unit proposes products tailored to the user's birthday or anniversary. The product proposal unit can also propose optimal products based on the user's purchasing history. For example, the product proposal unit proposes related products based on the user's history of past purchases. The information providing unit provides information for improving financial literacy based on the results of the analysis by the purchasing behavior analysis unit. For example, the information providing unit provides basic knowledge about saving methods and investing. The information providing unit can also provide tips for managing household finances. For example, the information providing unit provides advice on household finances based on the user's financial situation. Furthermore, the information providing unit can also provide information necessary for improving the user's financial literacy. For example, the information providing unit provides information for taking appropriate financial actions based on the user's financial situation. As a result, the financial literacy improvement system according to the embodiment can analyze the user's purchasing behavior and financial situation, and provide appropriate product suggestions and information for improving financial literacy. For example, the user can purchase products efficiently and use the saved time for other valuable activities. Furthermore, the user can better understand their own financial situation and take appropriate financial actions.

[0030] The purchasing behavior analysis unit analyzes a user's social media posts and search history to understand consumer behavior patterns. For example, the purchasing behavior analysis unit analyzes the content of a user's social media posts to identify factors that influence purchasing behavior. For example, if there are many positive comments about a particular product, it will recommend that product. The purchasing behavior analysis unit also analyzes a user's search history to identify products and services in which the user is interested. For example, it will suggest related products based on frequently searched keywords. The purchasing behavior analysis unit also integrates data from social media and search history to analyze a user's consumer behavior patterns in detail. For example, it can understand purchasing trends related to specific events or seasons. This makes it possible to understand more detailed consumer behavior patterns by analyzing a user's social media posts and search history.

[0031] The purchasing behavior analysis unit can predict future risks based on the impact of past economic crises and market fluctuations. For example, the purchasing behavior analysis unit analyzes data on past economic crises and market fluctuations to evaluate their impact on the user's economic situation. For example, it predicts future risks based on data from the Lehman Shock. The purchasing behavior analysis unit also analyzes the user's income and expenditure patterns to predict future economic risks. For example, if income is unstable, it makes suggestions to avoid high-risk investments. The purchasing behavior analysis unit also collects market fluctuation data in real time and immediately reflects it in the user's economic situation. For example, it provides risk avoidance advice when stock prices suddenly fall. In this way, future risks can be predicted by taking into account the impact of past economic crises and market fluctuations.

[0032] The purchasing behavior analysis unit can compare the analysis results of a user's purchasing behavior and economic situation with those of other users to find common patterns or different trends. The purchasing behavior analysis unit, for example, compares purchasing behavior data of multiple users to identify common patterns or different trends. For example, it analyzes the purchasing trends of users in the same age or income bracket. The purchasing behavior analysis unit also compares users' economic situation data to identify common risk factors or different economic behaviors. For example, it analyzes the economic situations of users in the same occupation or region. The purchasing behavior analysis unit also integrates data on purchasing behavior and economic situation and makes individual suggestions based on the comparison results with other users. For example, it makes common suggestions to users with the same purchasing patterns. In this way, by comparing a user's purchasing behavior and economic situation with those of other users, it is possible to find common patterns or different trends.

[0033] The purchasing behavior analysis unit integrates the analysis results of a user's purchasing behavior and financial situation with health data and fitness data, thereby clarifying the relationship between health status and consumption behavior. The purchasing behavior analysis unit, for example, integrates a user's health data and purchasing behavior data to analyze the impact of health status on consumption behavior. For example, users in good health tend to purchase more health foods. The purchasing behavior analysis unit also integrates fitness data and financial situation data to analyze the impact of exercise habits on financial behavior. For example, users who exercise regularly tend to be more frugal. The purchasing behavior analysis unit also integrates health data, fitness data, and purchasing behavior data to perform a detailed analysis of the relationship between health status and consumption behavior. For example, it identifies a tendency for consumption behavior to change as health status deteriorates. In this way, by integrating with health data and fitness data, it is possible to clarify the relationship between health status and consumption behavior.

[0034] The product suggestion unit can make product suggestions according to the season and weather based on the user's purchasing history. The product suggestion unit, for example, analyzes the user's purchasing history and makes product suggestions according to the season. For example, cooling products and sunglasses are suggested in the summer, and heating appliances and cold weather gear are suggested in the winter. The product suggestion unit also makes product suggestions linked to the user's purchasing history based on weather data. For example, umbrellas and raincoats are suggested on rainy days, and outdoor gear on sunny days. The product suggestion unit also analyzes the user's purchasing history in accordance with changes in the season and weather, and builds a system that makes appropriate product suggestions. For example, suggestions are made based on seasonal purchasing patterns. This makes it possible to make product suggestions according to the season and weather based on the user's purchasing history.

[0035] The product proposal unit analyzes the user's purchasing history and economic situation, predicts long-term purchasing patterns, and can propose products that meet future needs. The product proposal unit, for example, analyzes the user's purchasing history and economic situation, and predicts long-term purchasing patterns. For example, it identifies products and services that are purchased regularly, and makes proposals that meet future needs. The product proposal unit also builds a system that predicts the user's purchasing patterns, taking into account changes in economic situations. For example, it proposes more expensive products when income increases. The product proposal unit also proposes products that meet future needs based on the long-term purchasing patterns. For example, it proposes products that correspond to changes in the user's life stage. In this way, by analyzing the user's purchasing history and economic situation, it is possible to predict long-term purchasing patterns and propose products that meet future needs.

[0036] The information providing unit can provide a customized money literacy improvement program according to the user's financial situation. The information providing unit provides a customized money literacy improvement program based on, for example, data on the user's income and expenses. For example, it focuses on teaching saving methods to users with unstable incomes. The information providing unit also builds a system that provides money literacy improvement programs according to economic situations. For example, it provides basic investment knowledge to users whose income has increased. The information providing unit also analyzes the user's financial situation in real time and provides information for improving money literacy at the appropriate time. For example, it teaches tips on household management when expenses increase. In this way, by providing a customized money literacy improvement program according to the user's financial situation, it is possible to improve the user's financial knowledge.

[0037] The information providing unit can analyze the user's past economic behavior and present specific improvements and success stories. For example, the information providing unit analyzes the user's past income and expenditure data and presents specific improvements. For example, it provides advice on reducing wasteful spending. The information providing unit also builds a system that presents success stories based on the user's past economic behavior. For example, it introduces examples of successful investments from the past and recommends similar actions. The information providing unit also analyzes the user's economic behavior data in detail and presents specific improvements and success stories in real time. For example, it analyzes spending patterns and teaches tips for saving money. In this way, it is possible to present specific improvements and success stories by analyzing the user's past economic behavior.

[0038] The information providing unit can gamify the provision of information for improving financial literacy to increase the user's motivation to learn. The information providing unit, for example, gamifies the provision of information for improving financial literacy to build a system that increases the user's motivation to learn. For example, it provides a game in the form of a quiz to teach saving methods. The information providing unit also provides a gamified financial literacy improvement program so that the user can learn while having fun. For example, learning the basics of investing through an investment simulation game. The information providing unit also visualizes the user's learning progress within the game, allowing the user to feel a sense of accomplishment. For example, it introduces a system that allows the user to earn points each time a savings goal is achieved. In this way, gamifying the provision of information for improving financial literacy can increase the user's motivation to learn.

[0039] The information providing unit can share information for improving financial literacy with the user's family and friends and provide a collaborative learning program. The information providing unit, for example, builds a system that allows information for improving financial literacy to be shared with family and friends. For example, it provides a household management workshop that the entire family can participate in. The information providing unit also provides a collaborative learning program, allowing the user to improve financial literacy together with family and friends. For example, it provides a game in which the user learns how to save money while competing with friends. The information providing unit also provides a program that improves learning effectiveness by sharing with family and friends. For example, it introduces a mechanism in which the entire family sets savings goals and shares the level of achievement. In this way, the learning effectiveness can be improved by sharing information for improving financial literacy with family and friends and learning together.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The purchasing behavior analysis unit analyzes the user's purchasing behavior and economic situation. For example, the purchasing behavior analysis unit collects the user's purchasing history and analyzes the history of past product purchases. The purchasing behavior analysis unit can also analyze the user's income and spending patterns. For example, the purchasing behavior analysis unit evaluates the user's economic situation based on the user's income and spending data. Furthermore, the purchasing behavior analysis unit can collect and analyze data related to the user's economic situation. For example, the purchasing behavior analysis unit analyzes the user's savings and investment status. The product proposal unit makes product purchase suggestions based on the results of the analysis by the purchasing behavior analysis unit. For example, the product proposal unit proposes products that the user regularly purchases. The product proposal unit can also propose products tailored to specific events. For example, the product proposal unit proposes products tailored to the user's birthday or anniversary. Furthermore, the product proposal unit can propose optimal products based on the user's purchasing history. For example, the product proposal unit proposes related products based on the user's history of past product purchases. The information provision unit provides information for improving financial literacy based on the results of the analysis by the purchasing behavior analysis unit. For example, the information providing unit provides basic knowledge about saving methods and investments. The information providing unit can also provide tips for managing household finances. For example, the information providing unit provides advice on household finances according to the user's financial situation. Furthermore, the information providing unit can also provide information necessary for improving the user's financial literacy. For example, the information providing unit provides information for taking appropriate financial actions based on the user's financial situation. As a result, the financial literacy improvement system according to the embodiment can analyze the user's purchasing behavior and financial situation, and provide appropriate product suggestions and information for improving financial literacy. For example, the user can purchase products efficiently and use the saved time for other valuable activities. Furthermore, the user can better understand his or her financial situation and take appropriate financial actions.

[0042] The purchasing behavior analysis unit analyzes a user's social media posts and search history to understand consumer behavior patterns. For example, the purchasing behavior analysis unit analyzes the content of a user's social media posts to identify factors that influence purchasing behavior. For example, if there are many positive comments about a particular product, it will recommend that product. The purchasing behavior analysis unit also analyzes a user's search history to identify products and services in which the user is interested. For example, it will suggest related products based on frequently searched keywords. The purchasing behavior analysis unit also integrates social media and search history data to analyze a user's consumer behavior patterns in detail. For example, it can identify purchasing trends related to specific events or seasons. This makes it possible to understand more detailed consumer behavior patterns by analyzing a user's social media posts and search history.

[0043] The purchasing behavior analysis unit can predict future risks based on the impact of past economic crises and market fluctuations. For example, the purchasing behavior analysis unit analyzes data on past economic crises and market fluctuations to evaluate their impact on the user's economic situation. For example, it predicts future risks based on data from the Lehman Shock. The purchasing behavior analysis unit also analyzes the user's income and expenditure patterns to predict future economic risks. For example, if income is unstable, it will make suggestions to avoid high-risk investments. The purchasing behavior analysis unit also collects data on market fluctuations in real time and immediately reflects this in the user's economic situation. For example, it will provide advice on risk avoidance when stock prices suddenly fall. In this way, future risks can be predicted by taking into account the impact of past economic crises and market fluctuations.

[0044] The purchasing behavior analysis unit can compare the analysis results of a user's purchasing behavior and economic situation with those of other users to find common patterns or different trends. For example, the purchasing behavior analysis unit compares purchasing behavior data of multiple users to identify common patterns or different trends. For example, it analyzes the purchasing trends of users in the same age or income bracket. The purchasing behavior analysis unit also compares users' economic situation data to identify common risk factors or different economic behaviors. For example, it analyzes the economic situations of users in the same occupation or region. The purchasing behavior analysis unit also integrates data on purchasing behavior and economic situation and makes individual suggestions based on the comparison results with other users. For example, it makes common suggestions to users with the same purchasing patterns. In this way, by comparing a user's purchasing behavior and economic situation with those of other users, it is possible to find common patterns or different trends.

[0045] The purchasing behavior analysis unit integrates the analysis results of a user's purchasing behavior and financial situation with health data and fitness data, thereby clarifying the relationship between health status and consumption behavior. For example, the purchasing behavior analysis unit integrates a user's health data and purchasing behavior data to analyze the impact of health status on consumption behavior. For example, users in good health tend to purchase more health foods. The purchasing behavior analysis unit also integrates fitness data and financial situation data to analyze the impact of exercise habits on financial behavior. For example, users who exercise regularly tend to be more frugal. The purchasing behavior analysis unit also integrates health data, fitness data, and purchasing behavior data to perform a detailed analysis of the relationship between health status and consumption behavior. For example, it identifies a tendency for consumption behavior to change as health status deteriorates. In this way, by integrating the health data and fitness data, it is possible to clarify the relationship between health status and consumption behavior.

[0046] The product suggestion unit can make product suggestions according to the season and weather based on the user's purchasing history. For example, the product suggestion unit analyzes the user's purchasing history and makes product suggestions according to the season. For example, cooling products and sunglasses are suggested in the summer, and heating appliances and cold weather gear are suggested in the winter. The product suggestion unit also makes product suggestions linked to the user's purchasing history based on weather data. For example, umbrellas and raincoats are suggested on rainy days, and outdoor gear on sunny days. The product suggestion unit also builds a system that analyzes the user's purchasing history in accordance with changes in the season and weather, and makes appropriate product suggestions. For example, suggestions are made based on seasonal purchasing patterns. This makes it possible to make product suggestions according to the season and weather based on the user's purchasing history.

[0047] The product proposal unit can analyze a user's purchasing history and economic situation, predict long-term purchasing patterns, and make product proposals that meet future needs. For example, the product proposal unit analyzes a user's purchasing history and economic situation to predict long-term purchasing patterns. For example, it identifies products and services that are purchased regularly and makes proposals that meet future needs. The product proposal unit also builds a system that predicts a user's purchasing patterns, taking into account changes in economic situations. For example, it proposes more expensive products when income increases. The product proposal unit also makes product proposals that meet future needs based on long-term purchasing patterns. For example, it proposes products that correspond to changes in the user's life stage. In this way, by analyzing a user's purchasing history and economic situation, it is possible to predict long-term purchasing patterns and make product proposals that meet future needs.

[0048] The information providing unit can provide a customized money literacy improvement program according to the user's financial situation. For example, the information providing unit provides a customized money literacy improvement program based on the user's income and expenditure data. For example, the information providing unit may focus on teaching saving methods to a user with an unstable income. The information providing unit also builds a system that provides money literacy improvement programs according to the user's financial situation. For example, the information providing unit provides basic investment knowledge to a user whose income has increased. The information providing unit also analyzes the user's financial situation in real time and provides information to improve money literacy at the appropriate time. For example, the information providing unit may teach tips on household management when expenses increase. In this way, the user's financial knowledge can be improved by providing a customized money literacy improvement program according to the user's financial situation.

[0049] The information providing unit can analyze the user's past economic behavior and present specific improvements and success stories. For example, the information providing unit can analyze the user's past income and expenditure data and present specific improvements. For example, it can provide advice on reducing wasteful spending. The information providing unit can also build a system that presents success stories based on the user's past economic behavior. For example, it can introduce examples of successful investments from the past and recommend similar actions. The information providing unit can also analyze the user's economic behavior data in detail and present specific improvements and success stories in real time. For example, it can analyze spending patterns and teach tips for saving money. In this way, it can present specific improvements and success stories by analyzing the user's past economic behavior.

[0050] The information providing unit can gamify the provision of information for improving financial literacy to increase users' motivation to learn. For example, the information providing unit gamifies the provision of information for improving financial literacy to build a system that increases users' motivation to learn. For example, the information providing unit provides a game in the form of a quiz to teach saving methods. The information providing unit also provides a gamified financial literacy improvement program so that users can learn while having fun. For example, learning the basics of investing through an investment simulation game. The information providing unit also visualizes the user's learning progress within the game, allowing them to feel a sense of accomplishment. For example, a system can be introduced that allows users to earn points each time they achieve a savings goal. In this way, gamifying the provision of information for improving financial literacy can increase users' motivation to learn.

[0051] The information providing unit can share information for improving financial literacy with the user's family and friends and provide a collaborative learning program. For example, the information providing unit builds a system that allows information for improving financial literacy to be shared with family and friends. For example, it provides a household management workshop that the entire family can participate in. The information providing unit also provides a collaborative learning program, allowing the user to improve financial literacy together with family and friends. For example, it provides a game in which the user learns how to save money while competing with friends. The information providing unit also provides a program that improves learning effectiveness by sharing with family and friends. For example, it introduces a mechanism in which the entire family sets savings goals and shares the level of achievement. In this way, the learning effectiveness can be improved by sharing information for improving financial literacy with family and friends and learning together.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The purchasing behavior analysis unit analyzes the user's purchasing behavior and economic situation. For example, the purchasing behavior analysis unit collects the user's purchasing history and analyzes the history of past purchases. The purchasing behavior analysis unit can also analyze the user's income and expenditure patterns. For example, the purchasing behavior analysis unit evaluates the user's economic situation based on the user's income and expenditure data. Furthermore, the purchasing behavior analysis unit can collect and analyze data related to the user's economic situation. For example, the purchasing behavior analysis unit analyzes the user's savings and investment status. Step 2: The product suggestion unit makes product purchase suggestions based on the results of the analysis by the purchasing behavior analysis unit. For example, the product suggestion unit suggests products that the user regularly purchases. The product suggestion unit can also suggest products that match specific events. For example, the product suggestion unit suggests products that match the user's birthday or anniversary. Furthermore, the product suggestion unit can also suggest optimal products based on the user's purchasing history. For example, the product suggestion unit suggests related products based on the user's history of past purchases. Step 3: The information providing unit provides information for improving financial literacy based on the results of the analysis by the purchasing behavior analysis unit. For example, the information providing unit provides basic knowledge about saving methods and investing. The information providing unit can also provide tips for managing household finances. For example, the information providing unit provides advice on household finances based on the user's financial situation. Furthermore, the information providing unit can also provide information necessary for improving the user's financial literacy. For example, the information providing unit provides information for taking appropriate financial actions based on the user's financial situation.

[0054] (Example 2) A financial literacy improvement system according to an embodiment of the present invention is a system that analyzes a user's purchasing behavior and economic situation, and a generation AI makes product purchase suggestions and provides information for improving financial literacy. As a result, the financial literacy improvement system can analyze a user's purchasing behavior and economic situation, and provide appropriate product suggestions and information for improving financial literacy.

[0055] A financial literacy improvement system according to an embodiment includes a purchasing behavior analysis unit, a product proposal unit, and an information provision unit. The purchasing behavior analysis unit analyzes a user's purchasing behavior and financial situation. For example, the purchasing behavior analysis unit collects a user's purchasing history and analyzes the history of past product purchases. The purchasing behavior analysis unit can also analyze the user's income and spending patterns. For example, the purchasing behavior analysis unit evaluates the user's financial situation based on data on the user's income and spending. The purchasing behavior analysis unit can also collect and analyze data related to the user's financial situation. For example, the purchasing behavior analysis unit analyzes the user's savings and investment status. The product proposal unit makes product purchase suggestions based on the results of the analysis by the purchasing behavior analysis unit. For example, the product proposal unit proposes products that the user regularly purchases. The product proposal unit can also propose products tailored to specific events. For example, the product proposal unit proposes products tailored to the user's birthday or anniversary. The product proposal unit can also propose optimal products based on the user's purchasing history. For example, the product proposal unit proposes related products based on the user's history of past purchases. The information providing unit provides information for improving financial literacy based on the results of the analysis by the purchasing behavior analysis unit. For example, the information providing unit provides basic knowledge about saving methods and investing. The information providing unit can also provide tips for managing household finances. For example, the information providing unit provides advice on household finances based on the user's financial situation. Furthermore, the information providing unit can also provide information necessary for improving the user's financial literacy. For example, the information providing unit provides information for taking appropriate financial actions based on the user's financial situation. As a result, the financial literacy improvement system according to the embodiment can analyze the user's purchasing behavior and financial situation, and provide appropriate product suggestions and information for improving financial literacy. For example, the user can purchase products efficiently and use the saved time for other valuable activities. Furthermore, the user can better understand their own financial situation and take appropriate financial actions.

[0056] The purchasing behavior analysis unit analyzes a user's social media posts and search history to understand consumer behavior patterns. For example, the purchasing behavior analysis unit analyzes the content of a user's social media posts to identify factors that influence purchasing behavior. For example, if there are many positive comments about a particular product, it will recommend that product. The purchasing behavior analysis unit also analyzes a user's search history to identify products and services in which the user is interested. For example, it will suggest related products based on frequently searched keywords. The purchasing behavior analysis unit also integrates data from social media and search history to analyze a user's consumer behavior patterns in detail. For example, it can understand purchasing trends related to specific events or seasons. This makes it possible to understand more detailed consumer behavior patterns by analyzing a user's social media posts and search history.

[0057] The purchasing behavior analysis unit can predict future risks based on the impact of past economic crises and market fluctuations. For example, the purchasing behavior analysis unit analyzes data on past economic crises and market fluctuations to evaluate their impact on the user's economic situation. For example, it predicts future risks based on data from the Lehman Shock. The purchasing behavior analysis unit also analyzes the user's income and expenditure patterns to predict future economic risks. For example, if income is unstable, it makes suggestions to avoid high-risk investments. The purchasing behavior analysis unit also collects market fluctuation data in real time and immediately reflects it in the user's economic situation. For example, it provides risk avoidance advice when stock prices suddenly fall. In this way, future risks can be predicted by taking into account the impact of past economic crises and market fluctuations.

[0058] The purchasing behavior analysis unit uses the emotion estimation function to analyze emotions associated with a user's purchasing behavior and evaluate the impact of emotional factors on purchasing behavior. The purchasing behavior analysis unit, for example, analyzes a user's emotions during purchasing behavior in real time and evaluates the impact of emotional factors on purchasing decisions. For example, if positive emotions are strong, that product is recommended. The purchasing behavior analysis unit also uses the emotion estimation function to analyze the emotions a user has when purchasing a specific product. For example, it monitors changes in emotions before and after purchase and evaluates purchasing satisfaction. The purchasing behavior analysis unit also integrates the user's purchasing history and emotion data to perform a detailed analysis of the impact of emotional factors on purchasing behavior. For example, it prioritizes the suggestion of products with high emotion scores. In this way, the emotion estimation function can be used to evaluate the impact of emotional factors on purchasing behavior.

[0059] The purchasing behavior analysis unit can compare the analysis results of a user's purchasing behavior and economic situation with those of other users to find common patterns or different trends. The purchasing behavior analysis unit, for example, compares purchasing behavior data of multiple users to identify common patterns or different trends. For example, it analyzes the purchasing trends of users in the same age or income bracket. The purchasing behavior analysis unit also compares users' economic situation data to identify common risk factors or different economic behaviors. For example, it analyzes the economic situations of users in the same occupation or region. The purchasing behavior analysis unit also integrates data on purchasing behavior and economic situation and makes individual suggestions based on the comparison results with other users. For example, it makes common suggestions to users with the same purchasing patterns. In this way, by comparing a user's purchasing behavior and economic situation with those of other users, it is possible to find common patterns or different trends.

[0060] The purchasing behavior analysis unit integrates the analysis results of a user's purchasing behavior and financial situation with health data and fitness data, thereby clarifying the relationship between health status and consumption behavior. The purchasing behavior analysis unit, for example, integrates a user's health data and purchasing behavior data to analyze the impact of health status on consumption behavior. For example, users in good health tend to purchase more health foods. The purchasing behavior analysis unit also integrates fitness data and financial situation data to analyze the impact of exercise habits on financial behavior. For example, users who exercise regularly tend to be more frugal. The purchasing behavior analysis unit also integrates health data, fitness data, and purchasing behavior data to perform a detailed analysis of the relationship between health status and consumption behavior. For example, it identifies a tendency for consumption behavior to change as health status deteriorates. In this way, by integrating with health data and fitness data, it is possible to clarify the relationship between health status and consumption behavior.

[0061] The purchasing behavior analysis unit uses the emotion estimation function to monitor the user's emotional response to purchasing behavior in real time and can make purchasing suggestions based on the emotion. The purchasing behavior analysis unit, for example, uses the emotion estimation function to monitor the user's emotional response to purchasing behavior in real time. For example, it makes a next purchase suggestion based on the emotion score at the time of purchase. The purchasing behavior analysis unit also makes product suggestions that elicit a positive emotional response based on the user's emotion data. For example, it re-suggests products that have previously evoked positive emotions. The purchasing behavior analysis unit also collects emotion estimation data in real time and builds a system that makes purchasing suggestions based on the user's emotions. For example, it prioritizes suggestions for products with high emotion scores. In this way, using the emotion estimation function makes it possible to make purchasing suggestions based on the user's emotions.

[0062] The product suggestion unit can make product suggestions according to the season and weather based on the user's purchasing history. The product suggestion unit, for example, analyzes the user's purchasing history and makes product suggestions according to the season. For example, cooling products and sunglasses are suggested in the summer, and heating appliances and cold weather gear are suggested in the winter. The product suggestion unit also makes product suggestions linked to the user's purchasing history based on weather data. For example, umbrellas and raincoats are suggested on rainy days, and outdoor gear on sunny days. The product suggestion unit also analyzes the user's purchasing history in accordance with changes in the season and weather, and builds a system that makes appropriate product suggestions. For example, suggestions are made based on seasonal purchasing patterns. This makes it possible to make product suggestions according to the season and weather based on the user's purchasing history.

[0063] The product proposal unit analyzes the user's purchasing history and economic situation, predicts long-term purchasing patterns, and can propose products that meet future needs. The product proposal unit, for example, analyzes the user's purchasing history and economic situation, and predicts long-term purchasing patterns. For example, it identifies products and services that are purchased regularly, and makes proposals that meet future needs. The product proposal unit also builds a system that predicts the user's purchasing patterns, taking into account changes in economic situations. For example, it proposes more expensive products when income increases. The product proposal unit also proposes products that meet future needs based on the long-term purchasing patterns. For example, it proposes products that correspond to changes in the user's life stage. In this way, by analyzing the user's purchasing history and economic situation, it is possible to predict long-term purchasing patterns and propose products that meet future needs.

[0064] The product suggestion unit uses the emotion estimation function to suggest products according to the user's emotional state, thereby increasing emotional satisfaction. The product suggestion unit, for example, uses the emotion estimation function to suggest products according to the user's emotional state. For example, if stress is high, relaxation products are suggested. The product suggestion unit also builds a system that makes product suggestions that increase emotional satisfaction based on the user's emotional data. For example, products that elicit positive emotions are suggested. The product suggestion unit also collects emotion estimation data in real time and makes product suggestions according to the user's emotional state. For example, if the emotion score is low, products that will help change your mood are suggested. In this way, by using the emotion estimation function, it is possible to suggest products according to the user's emotional state, thereby increasing emotional satisfaction.

[0065] The information providing unit can provide a customized money literacy improvement program according to the user's financial situation. The information providing unit provides a customized money literacy improvement program based on, for example, data on the user's income and expenses. For example, it focuses on teaching saving methods to users with unstable incomes. The information providing unit also builds a system that provides money literacy improvement programs according to economic situations. For example, it provides basic investment knowledge to users whose income has increased. The information providing unit also analyzes the user's financial situation in real time and provides information for improving money literacy at the appropriate time. For example, it teaches tips on household management when expenses increase. In this way, by providing a customized money literacy improvement program according to the user's financial situation, it is possible to improve the user's financial knowledge.

[0066] The information providing unit can analyze the user's past economic behavior and present specific improvements and success stories. For example, the information providing unit analyzes the user's past income and expenditure data and presents specific improvements. For example, it provides advice on reducing wasteful spending. The information providing unit also builds a system that presents success stories based on the user's past economic behavior. For example, it introduces examples of successful investments from the past and recommends similar actions. The information providing unit also analyzes the user's economic behavior data in detail and presents specific improvements and success stories in real time. For example, it analyzes spending patterns and teaches tips for saving money. In this way, it is possible to present specific improvements and success stories by analyzing the user's past economic behavior.

[0067] The information providing unit can use the emotion estimation function to analyze the user's emotional response to financial behavior and provide advice based on the emotion. The information providing unit, for example, uses the emotion estimation function to analyze the user's emotional response to financial behavior. For example, saving advice is provided to a user who feels stressed when spending a lot. The information providing unit also builds a system that provides emotion-based advice based on the user's emotion data. For example, investment advice is provided to elicit positive emotions. The information providing unit also collects emotion estimation data in real time and analyzes the user's emotional response to financial behavior. For example, risk avoidance advice is provided if the emotion score is low. In this way, the emotion estimation function can be used to analyze the user's emotional response to financial behavior and provide emotion-based advice.

[0068] The information providing unit can gamify the provision of information for improving financial literacy to increase the user's motivation to learn. The information providing unit, for example, gamifies the provision of information for improving financial literacy to build a system that increases the user's motivation to learn. For example, it provides a game in the form of a quiz to teach saving methods. The information providing unit also provides a gamified financial literacy improvement program so that the user can learn while having fun. For example, learning the basics of investing through an investment simulation game. The information providing unit also visualizes the user's learning progress within the game, allowing the user to feel a sense of accomplishment. For example, it introduces a system that allows the user to earn points each time a savings goal is achieved. In this way, gamifying the provision of information for improving financial literacy can increase the user's motivation to learn.

[0069] The information providing unit can share information for improving financial literacy with the user's family and friends and provide a collaborative learning program. The information providing unit, for example, builds a system that allows information for improving financial literacy to be shared with family and friends. For example, it provides a household management workshop that the entire family can participate in. The information providing unit also provides a collaborative learning program, allowing the user to improve financial literacy together with family and friends. For example, it provides a game in which the user learns how to save money while competing with friends. The information providing unit also provides a program that improves learning effectiveness by sharing with family and friends. For example, it introduces a mechanism in which the entire family sets savings goals and shares the level of achievement. In this way, the learning effectiveness can be improved by sharing information for improving financial literacy with family and friends and learning together.

[0070] The information providing unit uses the emotion estimation function to analyze the emotions a user has toward information for improving financial literacy, and can provide information that elicits a positive emotional response. The information providing unit, for example, uses the emotion estimation function to analyze the emotions a user has toward information for improving financial literacy. For example, it provides a saving method that elicits positive emotions. The information providing unit also builds a system that provides information that elicits a positive emotional response based on the user's emotion data. For example, it introduces success stories to increase the user's motivation. The information providing unit also collects emotion estimation data in real time and analyzes the emotions a user has toward information for improving financial literacy. For example, it prioritizes the provision of information with a high emotion score. As a result, by using the emotion estimation function, it is possible to analyze the emotions a user has toward information for improving financial literacy, and provide information that elicits a positive emotional response.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The purchasing behavior analysis unit analyzes the user's purchasing behavior and economic situation. For example, the purchasing behavior analysis unit collects the user's purchasing history and analyzes the history of past product purchases. The purchasing behavior analysis unit can also analyze the user's income and spending patterns. For example, the purchasing behavior analysis unit evaluates the user's economic situation based on the user's income and spending data. Furthermore, the purchasing behavior analysis unit can collect and analyze data related to the user's economic situation. For example, the purchasing behavior analysis unit analyzes the user's savings and investment status. The product proposal unit makes product purchase suggestions based on the results of the analysis by the purchasing behavior analysis unit. For example, the product proposal unit proposes products that the user regularly purchases. The product proposal unit can also propose products tailored to specific events. For example, the product proposal unit proposes products tailored to the user's birthday or anniversary. Furthermore, the product proposal unit can propose optimal products based on the user's purchasing history. For example, the product proposal unit proposes related products based on the user's history of past product purchases. The information provision unit provides information for improving financial literacy based on the results of the analysis by the purchasing behavior analysis unit. For example, the information providing unit provides basic knowledge about saving methods and investments. The information providing unit can also provide tips for managing household finances. For example, the information providing unit provides advice on household finances according to the user's financial situation. Furthermore, the information providing unit can also provide information necessary for improving the user's financial literacy. For example, the information providing unit provides information for taking appropriate financial actions based on the user's financial situation. As a result, the financial literacy improvement system according to the embodiment can analyze the user's purchasing behavior and financial situation, and provide appropriate product suggestions and information for improving financial literacy. For example, the user can purchase products efficiently and use the saved time for other valuable activities. Furthermore, the user can better understand his or her financial situation and take appropriate financial actions.

[0073] The purchasing behavior analysis unit analyzes a user's social media posts and search history to understand consumer behavior patterns. For example, the purchasing behavior analysis unit analyzes the content of a user's social media posts to identify factors that influence purchasing behavior. For example, if there are many positive comments about a particular product, it will recommend that product. The purchasing behavior analysis unit also analyzes a user's search history to identify products and services in which the user is interested. For example, it will suggest related products based on frequently searched keywords. The purchasing behavior analysis unit also integrates social media and search history data to analyze a user's consumer behavior patterns in detail. For example, it can identify purchasing trends related to specific events or seasons. This makes it possible to understand more detailed consumer behavior patterns by analyzing a user's social media posts and search history.

[0074] The purchasing behavior analysis unit can predict future risks based on the impact of past economic crises and market fluctuations. For example, the purchasing behavior analysis unit analyzes data on past economic crises and market fluctuations to evaluate their impact on the user's economic situation. For example, it predicts future risks based on data from the Lehman Shock. The purchasing behavior analysis unit also analyzes the user's income and expenditure patterns to predict future economic risks. For example, if income is unstable, it will make suggestions to avoid high-risk investments. The purchasing behavior analysis unit also collects data on market fluctuations in real time and immediately reflects this in the user's economic situation. For example, it will provide advice on risk avoidance when stock prices suddenly fall. In this way, future risks can be predicted by taking into account the impact of past economic crises and market fluctuations.

[0075] The purchasing behavior analysis unit uses the emotion estimation function to analyze emotions associated with a user's purchasing behavior and evaluate the impact of emotional factors on purchasing behavior. For example, the purchasing behavior analysis unit analyzes a user's emotions during purchasing behavior in real time and evaluates the impact of emotional factors on purchasing decisions. For example, if positive emotions are strong, that product is recommended. The purchasing behavior analysis unit also uses the emotion estimation function to analyze the emotions a user has when purchasing a specific product. For example, it monitors changes in emotions before and after purchase and evaluates purchasing satisfaction. The purchasing behavior analysis unit also integrates the user's purchasing history and emotion data to perform a detailed analysis of the impact of emotional factors on purchasing behavior. For example, it prioritizes the suggestion of products with high emotion scores. In this way, the emotion estimation function can be used to evaluate the impact of emotional factors on purchasing behavior.

[0076] The purchasing behavior analysis unit can compare the analysis results of a user's purchasing behavior and economic situation with those of other users to find common patterns or different trends. For example, the purchasing behavior analysis unit compares purchasing behavior data of multiple users to identify common patterns or different trends. For example, it analyzes the purchasing trends of users in the same age or income bracket. The purchasing behavior analysis unit also compares users' economic situation data to identify common risk factors or different economic behaviors. For example, it analyzes the economic situations of users in the same occupation or region. The purchasing behavior analysis unit also integrates data on purchasing behavior and economic situation and makes individual suggestions based on the comparison results with other users. For example, it makes common suggestions to users with the same purchasing patterns. In this way, by comparing a user's purchasing behavior and economic situation with those of other users, it is possible to find common patterns or different trends.

[0077] The purchasing behavior analysis unit integrates the analysis results of a user's purchasing behavior and financial situation with health data and fitness data, thereby clarifying the relationship between health status and consumption behavior. For example, the purchasing behavior analysis unit integrates a user's health data and purchasing behavior data to analyze the impact of health status on consumption behavior. For example, users in good health tend to purchase more health foods. The purchasing behavior analysis unit also integrates fitness data and financial situation data to analyze the impact of exercise habits on financial behavior. For example, users who exercise regularly tend to be more frugal. The purchasing behavior analysis unit also integrates health data, fitness data, and purchasing behavior data to perform a detailed analysis of the relationship between health status and consumption behavior. For example, it identifies a tendency for consumption behavior to change as health status deteriorates. In this way, by integrating the health data and fitness data, it is possible to clarify the relationship between health status and consumption behavior.

[0078] The purchasing behavior analysis unit uses the emotion estimation function to monitor the user's emotional response to purchasing behavior in real time and can make purchasing suggestions based on the user's emotions. For example, the purchasing behavior analysis unit uses the emotion estimation function to monitor the user's emotional response to purchasing behavior in real time. For example, it makes a next purchase suggestion based on the emotion score at the time of purchase. The purchasing behavior analysis unit also makes product suggestions that elicit a positive emotional response based on the user's emotion data. For example, it re-suggests products that have previously elicited positive emotions. The purchasing behavior analysis unit also collects emotion estimation data in real time and builds a system that makes purchasing suggestions based on the user's emotions. For example, it prioritizes the suggestions of products with high emotion scores. In this way, using the emotion estimation function makes it possible to make purchasing suggestions based on the user's emotions.

[0079] The product suggestion unit can make product suggestions according to the season and weather based on the user's purchasing history. For example, the product suggestion unit analyzes the user's purchasing history and makes product suggestions according to the season. For example, cooling products and sunglasses are suggested in the summer, and heating appliances and cold weather gear are suggested in the winter. The product suggestion unit also makes product suggestions linked to the user's purchasing history based on weather data. For example, umbrellas and raincoats are suggested on rainy days, and outdoor gear on sunny days. The product suggestion unit also builds a system that analyzes the user's purchasing history in accordance with changes in the season and weather, and makes appropriate product suggestions. For example, suggestions are made based on seasonal purchasing patterns. This makes it possible to make product suggestions according to the season and weather based on the user's purchasing history.

[0080] The product proposal unit can analyze a user's purchasing history and economic situation, predict long-term purchasing patterns, and make product proposals that meet future needs. For example, the product proposal unit analyzes a user's purchasing history and economic situation to predict long-term purchasing patterns. For example, it identifies products and services that are purchased regularly and makes proposals that meet future needs. The product proposal unit also builds a system that predicts a user's purchasing patterns, taking into account changes in economic situations. For example, it proposes more expensive products when income increases. The product proposal unit also makes product proposals that meet future needs based on long-term purchasing patterns. For example, it proposes products that correspond to changes in the user's life stage. In this way, by analyzing a user's purchasing history and economic situation, it is possible to predict long-term purchasing patterns and make product proposals that meet future needs.

[0081] The product suggestion unit uses the emotion estimation function to suggest products according to the user's emotional state, thereby increasing emotional satisfaction. For example, the product suggestion unit uses the emotion estimation function to suggest products according to the user's emotional state. For example, if stress is high, relaxation products are suggested. The product suggestion unit also builds a system that makes product suggestions that increase emotional satisfaction based on the user's emotional data. For example, products that elicit positive emotions are suggested. The product suggestion unit also collects emotion estimation data in real time and makes product suggestions according to the user's emotional state. For example, if the emotion score is low, products that will help change your mood are suggested. In this way, by using the emotion estimation function, it is possible to suggest products according to the user's emotional state, thereby increasing emotional satisfaction.

[0082] The information providing unit can provide a customized money literacy improvement program according to the user's financial situation. For example, the information providing unit provides a customized money literacy improvement program based on the user's income and expenditure data. For example, the information providing unit may focus on teaching saving methods to a user with an unstable income. The information providing unit also builds a system that provides money literacy improvement programs according to the user's financial situation. For example, the information providing unit provides basic investment knowledge to a user whose income has increased. The information providing unit also analyzes the user's financial situation in real time and provides information to improve money literacy at the appropriate time. For example, the information providing unit may teach tips on household management when expenses increase. In this way, the user's financial knowledge can be improved by providing a customized money literacy improvement program according to the user's financial situation.

[0083] The information providing unit can analyze the user's past economic behavior and present specific improvements and success stories. For example, the information providing unit can analyze the user's past income and expenditure data and present specific improvements. For example, it can provide advice on reducing wasteful spending. The information providing unit can also build a system that presents success stories based on the user's past economic behavior. For example, it can introduce examples of successful investments from the past and recommend similar actions. The information providing unit can also analyze the user's economic behavior data in detail and present specific improvements and success stories in real time. For example, it can analyze spending patterns and teach tips for saving money. In this way, it can present specific improvements and success stories by analyzing the user's past economic behavior.

[0084] The information providing unit can use the emotion estimation function to analyze a user's emotional response to financial behavior and provide advice based on the emotion. For example, the information providing unit uses the emotion estimation function to analyze a user's emotional response to financial behavior. For example, saving advice is provided to a user who feels stressed when spending a lot. The information providing unit also builds a system that provides emotion-based advice based on the user's emotion data. For example, investment advice is provided to elicit positive emotions. The information providing unit also collects emotion estimation data in real time and analyzes a user's emotional response to financial behavior. For example, risk avoidance advice is provided if the emotion score is low. In this way, the emotion estimation function can be used to analyze a user's emotional response to financial behavior and provide emotion-based advice.

[0085] The information providing unit can gamify the provision of information for improving financial literacy to increase users' motivation to learn. For example, the information providing unit gamifies the provision of information for improving financial literacy to build a system that increases users' motivation to learn. For example, the information providing unit provides a game in the form of a quiz to teach saving methods. The information providing unit also provides a gamified financial literacy improvement program so that users can learn while having fun. For example, learning the basics of investing through an investment simulation game. The information providing unit also visualizes the user's learning progress within the game, allowing them to feel a sense of accomplishment. For example, a system can be introduced that allows users to earn points each time they achieve a savings goal. In this way, gamifying the provision of information for improving financial literacy can increase users' motivation to learn.

[0086] The information providing unit can share information for improving financial literacy with the user's family and friends and provide a collaborative learning program. For example, the information providing unit builds a system that allows information for improving financial literacy to be shared with family and friends. For example, it provides a household management workshop that the entire family can participate in. The information providing unit also provides a collaborative learning program, allowing the user to improve financial literacy together with family and friends. For example, it provides a game in which the user learns how to save money while competing with friends. The information providing unit also provides a program that improves learning effectiveness by sharing with family and friends. For example, it introduces a mechanism in which the entire family sets savings goals and shares the level of achievement. In this way, the learning effectiveness can be improved by sharing information for improving financial literacy with family and friends and learning together.

[0087] The information providing unit can use the emotion estimation function to analyze the emotions a user has toward information for improving financial literacy, and provide information that elicits a positive emotional response. For example, the information providing unit uses the emotion estimation function to analyze the emotions a user has toward information for improving financial literacy. For example, the information providing unit provides a saving method that elicits positive emotions. The information providing unit also builds a system that provides information that elicits a positive emotional response based on the user's emotion data. For example, it introduces success stories to increase the user's motivation. The information providing unit also collects emotion estimation data in real time and analyzes the emotions a user has toward information for improving financial literacy. For example, it prioritizes the provision of information with a high emotion score. As a result, by using the emotion estimation function, it is possible to analyze the emotions a user has toward information for improving financial literacy, and provide information that elicits a positive emotional response.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The purchasing behavior analysis unit analyzes the user's purchasing behavior and economic situation. For example, the purchasing behavior analysis unit collects the user's purchasing history and analyzes the history of past purchases. The purchasing behavior analysis unit can also analyze the user's income and expenditure patterns. For example, the purchasing behavior analysis unit evaluates the user's economic situation based on the user's income and expenditure data. Furthermore, the purchasing behavior analysis unit can collect and analyze data related to the user's economic situation. For example, the purchasing behavior analysis unit analyzes the user's savings and investment status. Step 2: The product suggestion unit makes product purchase suggestions based on the results of the analysis by the purchasing behavior analysis unit. For example, the product suggestion unit suggests products that the user regularly purchases. The product suggestion unit can also suggest products that match specific events. For example, the product suggestion unit suggests products that match the user's birthday or anniversary. Furthermore, the product suggestion unit can also suggest optimal products based on the user's purchasing history. For example, the product suggestion unit suggests related products based on the user's history of past purchases. Step 3: The information providing unit provides information for improving financial literacy based on the results of the analysis by the purchasing behavior analysis unit. For example, the information providing unit provides basic knowledge about saving methods and investing. The information providing unit can also provide tips for managing household finances. For example, the information providing unit provides advice on household finances based on the user's financial situation. Furthermore, the information providing unit can also provide information necessary for improving the user's financial literacy. For example, the information providing unit provides information for taking appropriate financial actions based on the user's financial situation.

[0090] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0096] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0097] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0098] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0099] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0100] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0101] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0102] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0104] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0105] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0110] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 7, a 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.

[0125] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0131] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0134] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0135] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0137] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0140] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0141] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0142] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0143] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0144] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0145] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0146] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0147] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0148] 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.

[0149] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0150] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0151] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0152] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0153] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0154] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0155] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0156] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0157] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a purchasing behavior analysis unit that analyzes users' purchasing behavior and economic situations; a product suggestion unit that suggests product purchases based on the results of the analysis by the purchasing behavior analysis unit; an information providing unit that provides information for improving financial literacy based on the results of the analysis by the purchasing behavior analysis unit. A system characterized by:

2. The purchasing behavior analysis unit Analyzing the user's social media posts and search history to understand consumer behavior patterns 2. The system of claim 1.

3. The purchasing behavior analysis unit The analysis results of the user's purchasing behavior and financial situation will be integrated with health and fitness data to clarify the relationship between health status and consumption behavior.

2. The system of claim 1.

4. The product proposal unit Based on the user's purchase history, products are proposed according to the season and weather.

2. The system of claim 1.

5. The information providing unit To provide a customized financial literacy improvement program according to the user's economic situation.

2. The system of claim 1.

6. The purchasing behavior analysis unit Analyzing emotions associated with the user's purchasing behavior and evaluating the influence of emotional factors on the purchasing behavior.

2. The system of claim 1.

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