system

Abstract

The system according to this embodiment aims to provide appropriate financial products to a specific demographic group. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user data. The analysis unit analyzes the data collected by the collection unit to identify the user's risk profile and needs. The generation unit generates financial products based on the risk profile and needs identified by the analysis unit. The provision unit provides the financial products generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to provide appropriate financial products for a specific population group, and financial inclusion has not been fully achieved.

[0005] The system according to the embodiment aims to provide appropriate financial products for a specific population group.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user data. The analysis unit analyzes the data collected by the collection unit to identify the user's risk profile and needs. The generation unit generates financial products based on the risk profile and needs identified by the analysis unit. The provision unit provides the financial products generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide appropriate financial products to a specific demographic group. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The financial product generation system according to an embodiment of the present invention is an innovative platform that utilizes generative AI to generate and provide financial products for people in Japan who have difficulty accessing financial services. This financial product generation system collects user data, and the generative AI analyzes that data to generate financial products tailored to individual needs. The generated financial products are provided through a multilingual interface, and users are educated using simple financial literacy tools. Furthermore, it utilizes non-traditional data sources to perform fair credit scoring and provide adaptive customer service. The system aims to promote financial inclusion, reduce inequality, and create new business opportunities. For example, the financial product generation system collects user data, including income, spending habits, and financial goals. For instance, mobile user data, transaction data, and user behavior data are collected. Next, the generative AI analyzes the collected data. Based on the collected data, the generative AI identifies the user's risk profile and needs and generates financial products accordingly. For example, the generative AI generates customized savings plans and loan products based on income and spending habits. The generated financial products are provided through a multilingual interface. This eliminates language barriers from hindering access to financial services. For example, services are offered in multiple languages, such as Japanese, English, and Chinese. Furthermore, users are educated using simple financial literacy tools. Generative AI generates interactive tutorials and guides on topics such as budget management, saving, and investing. This allows users to acquire financial knowledge and make better decisions. It also utilizes non-traditional data sources to provide fair credit scoring. Generative AI analyzes data such as mobile phone usage and utility payment history to calculate credit scores. This makes credit access possible even for people without a traditional credit history. Finally, it provides adaptive customer service. Generative AI provides personalized support through chatbots and voice assistants, adapting to the user's communication style and needs.This allows users to receive the support they need. As a result, the financial product generation system can promote financial inclusion, reduce inequality, and create new business opportunities.

[0029] The financial product generation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user data. For example, the collection unit collects mobile user data, transaction data, and user behavior data. For example, the collection unit can collect location information and app usage history as mobile user data. The collection unit can also collect purchase history and payment history as transaction data. Furthermore, the collection unit can collect website browsing history and click history as user behavior data. The analysis unit identifies the user's risk profile and needs based on the collected data. For example, the analysis unit can analyze the collected data to identify the user's risk profile. The analysis unit can also analyze the collected data to identify the user's needs. The generation unit generates customized savings plans and loan products based on income and spending habits. For example, the generation unit can generate customized savings plans based on income and spending habits. The generation unit can also generate customized loan products based on income and spending habits. The provision unit provides financial products through a multilingual interface. The service provider can offer financial products in multiple languages, such as Japanese, English, and Chinese. Furthermore, the service provider educates users using simple financial literacy tools. For example, the service provider can generate interactive tutorials and guides on topics such as budget management, savings, and investment. Thus, the financial product generation system according to this embodiment can provide financial products tailored to individual needs by collecting, analyzing, generating, and providing user data.

[0030] The data collection unit collects user data. For example, the data collection unit collects mobile user data, transaction data, and user behavior data. Specifically, as mobile user data, it can collect location information and app usage history. Location information is used to identify the user's movement patterns and destinations using GPS and Wi-Fi location services. App usage history records which apps a user uses and how often, and is used to understand the user's interests and preferences. The data collection unit can also collect transaction data, such as purchase history and payment history. Purchase history records which products and services a user has purchased and is used to analyze spending trends and consumption patterns. Payment history includes credit card and debit card usage history and bank account transaction history, and is important data for evaluating the user's financial situation and ability to pay. Furthermore, the data collection unit can collect user behavior data, such as website browsing history and click history. Website browsing history is used to understand which pages a user visits and what content they are interested in. Click history records which links and buttons users click, and is used to analyze user behavior patterns and interests in detail. This allows the data collection unit to gather a wide range of user data from diverse data sources, enabling a comprehensive understanding of user behavior, interests, and financial status. Furthermore, the data collection unit can centrally manage this data and integrate it with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and generation units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analytics unit identifies users' risk profiles and needs based on collected data. For example, the analytics unit can analyze collected data to identify users' risk profiles. Specifically, it uses AI to analyze collected data and evaluate users' risk tolerance and investment tendencies. For example, it can analyze users' transaction data and payment history to understand users' financial situation and spending patterns, thereby identifying risk profiles. It can also analyze users' behavioral data and mobile user data to understand users' interests and preferences, thereby identifying needs. For example, it can analyze website browsing history and app usage history to identify what financial products users are interested in. Furthermore, the analytics unit can utilize historical data and statistical information to predict users' future needs and risks. For example, it can predict users' spending patterns and income fluctuations based on past transaction data to evaluate future risks and needs. In addition, the analytics unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. This allows the analytics unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system. Furthermore, the analysis unit can collect user feedback and continuously improve the accuracy and effectiveness of the analysis results. For example, based on user feedback, it can review the methods for identifying risk profiles and needs, and optimize the analysis algorithm. This allows the analysis unit to accurately identify users' risk profiles and needs, thereby improving the overall system performance.

[0032] The generation unit generates customized savings plans and loan products based on income and spending habits. For example, it can generate customized savings plans based on income and spending habits. Specifically, it analyzes the user's income and spending data and designs an optimal savings plan that matches the user's savings goals and risk tolerance. For example, it sets monthly savings amounts and savings periods based on the user's monthly income and spending, ensuring that the user can continue saving without difficulty. The generation unit can also generate customized loan products based on income and spending habits. For example, it evaluates the user's repayment ability and risk profile based on the user's income and spending data and sets optimal loan terms. This allows the user to use a loan product that is best suited to their financial situation. Furthermore, the generation unit can use AI to predict future fluctuations in the user's income and spending and design long-term savings plans and loan products. For example, it predicts future increases or decreases in income and fluctuations in spending based on the user's past income and spending data and generates savings plans and loan products accordingly. Furthermore, the generation unit can collect user feedback and continuously improve the accuracy and effectiveness of the generated financial products. For example, it can review and optimize the terms and conditions of savings plans and loan products based on user feedback. This allows the generation unit to generate customized financial products based on the user's income and spending habits, providing the optimal financial products that meet the user's needs.

[0033] The service provider offers financial products through a multilingual interface. For example, it can offer financial products in multiple languages, such as Japanese, English, and Chinese. Specifically, it has a function that automatically switches the interface display content and explanations according to the language selected by the user. This allows users who speak different languages ​​to use the system intuitively. Furthermore, the service provider educates users using simple financial literacy tools. For example, it can generate interactive tutorials and guides on budget management, savings, and investment. This allows users to acquire financial knowledge at their own pace and make smarter financial decisions. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the interface and educational tools. For example, it can review and optimize the usability and display content of the interface based on user feedback. The service provider can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to quickly and reliably provide financial products to users and offer the most suitable financial products to meet their needs. Furthermore, the service provider can continuously review and optimize the content and conditions of the financial products it offers based on user behavior data and feedback. This allows the service provider to always provide highly accurate financial products based on the latest information, thereby improving user satisfaction.

[0034] The data collection unit can collect mobile user data, transaction data, and user behavior data. For example, the data collection unit can collect location information and app usage history as mobile user data. The data collection unit can also collect purchase history and payment history as transaction data. Furthermore, the data collection unit can collect website browsing history and click history as user behavior data. By collecting mobile user data, transaction data, and user behavior data, more detailed user information can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input mobile user data into a generating AI and have the generating AI perform data analysis.

[0035] The analysis unit can identify the user's risk profile and needs based on the collected data. For example, the analysis unit can analyze the collected data to identify the user's risk profile. The analysis unit can also analyze the collected data to identify the user's needs. This allows for the provision of more appropriate financial products by identifying the user's risk profile and needs based on the collected data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the identification of the user's risk profile and needs.

[0036] The generation unit can generate customized savings plans and loan products based on income and spending habits. For example, the generation unit can generate customized savings plans based on income and spending habits. Furthermore, the generation unit can generate customized loan products based on income and spending habits. This allows the system to provide users with the most suitable financial products by generating customized savings plans and loan products based on income and spending habits. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input income and spending habit data into a generation AI and have the generation AI generate customized savings plans and loan products.

[0037] The service provider can offer financial products through a multilingual interface. The service provider can offer financial products in multiple languages, such as Japanese, English, and Chinese. This allows for the provision of financial services across language barriers by offering financial products through a multilingual interface. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can have a generating AI perform translations of the generated financial products for provision through a multilingual interface.

[0038] The service provider can educate users using simple financial literacy tools. For example, the service provider can generate interactive tutorials and guides on topics such as budget management, saving, and investing. This allows for the improvement of users' financial knowledge by educating them using simple financial literacy tools. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have a generating AI perform the generation of financial literacy tools.

[0039] The analysis unit can calculate credit scores using non-traditional data sources such as mobile phone usage and utility bill payment history. For example, the analysis unit can analyze mobile phone usage and calculate a credit score. It can also analyze utility bill payment history and calculate a credit score. This allows individuals without a traditional credit history to access credit by calculating credit scores using non-traditional data sources. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not. For example, the analysis unit can input data on mobile phone usage and utility bill payment history into a generative AI and have the generative AI calculate the credit score.

[0040] The service provider can provide personalized support through chatbots and voice assistants. For example, the service provider can provide text-based support using a chatbot. Alternatively, the service provider can provide voice-based support using a voice assistant. This allows for personalized support tailored to user needs through chatbots and voice assistants. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can have a generating AI generate the responses for the chatbot or voice assistant.

[0041] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting the types of data that the user has frequently provided in the past. Furthermore, the data collection unit can avoid collecting data during times when the user has previously refused to provide data. In addition, the data collection unit can prioritize using methods in which the user has provided data with high accuracy in the past (e.g., voice input or text input). This allows for efficient data collection by analyzing the user's past data collection history and selecting the optimal collection method. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0042] The data collection unit can filter data based on the user's current living situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to items that the user considers important in their current living situation (e.g., health, education, work). It can also collect relevant data based on the user's areas of interest (e.g., investment, savings, loans). Furthermore, the data collection unit can select an appropriate data collection method according to the user's living situation (e.g., family structure, income). This allows for the collection of more relevant data by filtering data based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current living situation and areas of interest into a generating AI and have the generating AI perform data filtering.

[0043] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user lives in an urban area, the data collection unit can collect data related to financial needs specific to urban areas. Similarly, if the user lives in a rural area, the data collection unit can collect data related to financial needs specific to rural areas. Furthermore, if the user frequently visits a particular region, the data collection unit can prioritize the collection of data related to that region. This allows for the collection of more relevant data by considering the user's geographical location information during data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0044] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to topics that the user frequently mentions on social media. It can also analyze the activity of the user's friends and followers on social media and collect relevant data. Furthermore, the data collection unit can analyze the content of the user's social media posts and collect data related to their financial needs. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's social media activity into a generating AI and have the generating AI collect relevant data.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a concise analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data of moderate importance. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specific financial analysis algorithm to financial data. It can also apply a behavioral analysis algorithm to behavioral data. Furthermore, it can apply a social media analysis algorithm to social media data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of different analysis algorithms.

[0047] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit can prioritize the analysis of recently submitted data. It can also postpone the analysis of older data. Furthermore, it can appropriately prioritize data with a moderate submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data submission date into a generative AI and have the generative AI determine the priority of analysis.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of data with high relevance. It can also postpone the analysis of data with low relevance. Furthermore, it can appropriately prioritize data with moderate relevance. By adjusting the order of analysis based on the relevance of the data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0049] The generation unit can adjust the level of detail of the financial products it generates based on the user's risk profile during the generation process. For example, the generation unit can generate financial products with detailed risk descriptions for users with a high-risk profile. It can also generate financial products with concise risk descriptions for users with a low-risk profile. Furthermore, it can generate financial products with moderately detailed risk descriptions for users with a moderate risk profile. By adjusting the level of detail of financial products based on the user's risk profile, the generation unit can provide users with appropriate financial products. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's risk profile into a generation AI and have the generation AI adjust the level of detail of the financial products.

[0050] The generation unit can apply different generation algorithms during generation according to the user's needs. For example, the generation unit can apply a savings plan generation algorithm to users with high savings needs. It can also apply an investment plan generation algorithm to users with high investment needs. Furthermore, it can apply a loan plan generation algorithm to users with high loan needs. This allows the generation unit to provide users with the most suitable financial products by applying different generation algorithms according to their needs. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's needs into a generation AI and have the generation AI apply different generation algorithms.

[0051] The generation unit can generate highly relevant financial products by considering the user's geographical location information during the generation process. For example, the generation unit can generate financial products specific to urban areas for users living in urban areas. It can also generate financial products specific to rural areas for users living in rural areas. Furthermore, for users who frequently visit a particular region, the generation unit can generate financial products related to that region. This allows the generation unit to provide users with highly relevant financial products by considering their geographical location information during the generation process. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI generate highly relevant financial products.

[0052] The generation unit can analyze a user's social media activity and generate relevant financial products during the generation process. For example, the generation unit can generate financial products related to topics that the user frequently mentions on social media. It can also analyze the activities of the user's friends and followers on social media and generate relevant financial products. Furthermore, the generation unit can analyze the content of the user's social media posts and generate financial products related to their financial needs. This allows the generation unit to provide users with highly relevant financial products by analyzing their social media activity. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data on the user's social media activity into a generation AI and have the generation AI generate relevant financial products.

[0053] The delivery unit can select the optimal delivery method by referring to the user's past usage history at the time of delivery. For example, the delivery unit can prioritize using delivery methods that the user has frequently used in the past (e.g., email, SMS). It can also prioritize using delivery methods that the user has shown high satisfaction with in the past. Furthermore, the delivery unit can select the optimal delivery timing based on the user's past usage history. This enables efficient financial product delivery by selecting the optimal delivery method by referring to the user's past usage history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past usage history into a generating AI and have the generating AI select the optimal delivery method.

[0054] The service provider can customize the financial products offered based on the user's current living situation at the time of delivery. For example, if the user is busy, the service provider can offer financial products in a concise and easily understandable format. If the user is relaxed, the service provider can offer financial products in a format that includes detailed explanations. Furthermore, if the user is in a specific living situation (e.g., traveling), the service provider can offer financial products in a format suitable for that situation. This allows the service provider to offer the most suitable financial products to the user by customizing the financial products based on the user's current living situation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's current living situation into a generating AI and have the generating AI perform the customization of the financial products.

[0055] The service provider can select the optimal service delivery method at the time of delivery, taking into account the user's geographical location information. For example, the service provider can select a service delivery method specific to urban areas for users living in urban areas. Similarly, it can select a service delivery method specific to rural areas for users living in rural areas. Furthermore, for users who frequently visit a particular region, the service provider can select a service delivery method relevant to that region. This allows the service provider to offer the most suitable financial product to the user by selecting a delivery method that takes into account their geographical location information. Some or all of the above-described processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal service delivery method.

[0056] The service provider can analyze a user's social media activity at the time of provision and offer relevant financial products. For example, the service provider can offer financial products related to topics that the user frequently mentions on social media. Furthermore, the service provider can analyze the activity of the user's friends and followers on social media and offer relevant financial products. In addition, the service provider can analyze the content of the user's social media posts and offer financial products related to their financial needs. This allows the service provider to offer highly relevant financial products to the user by analyzing their social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's social media activity into a generating AI and have the generating AI provide relevant financial products.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] A financial product generation system can collect user health data and customize financial products based on their health status. For example, the collection unit can collect data from the user's fitness tracker or smartwatch, and the analysis unit can analyze this data to assess the user's health risks. The generation unit can generate financial products such as health insurance and medical expense assistance plans based on these health risks. The provision unit can provide advice and support tailored to the user's health status. In this way, the system can support users' health management by providing customized financial products based on their health status.

[0059] A financial product generation system can analyze a user's purchase history and customize financial products based on their purchasing patterns. For example, the collection unit can collect the user's online shopping data, and the analysis unit can analyze this data to identify the user's purchasing trends. The generation unit can generate financial products such as cashback and point reward plans based on these purchasing trends. The provision unit can offer benefits and promotions tailored to the user's purchase history. This improves the user's purchasing experience by providing customized financial products based on their purchasing patterns.

[0060] A financial product generation system can customize financial products based on a user's hobbies and interests. For example, the data collection unit can collect data from the user's social media activities and online communities, and the analysis unit can analyze this data to identify the user's hobbies and interests. The generation unit can generate investment plans and savings plans related to specific fields based on those hobbies and interests. The delivery unit can provide financial education content tailored to the user's hobbies and interests. This allows the system to attract user interest by providing financial products customized based on the user's hobbies and interests.

[0061] The financial product generation system can provide region-specific financial products by taking into account the user's geographical location. For example, the collection unit can collect the user's location information, and the analysis unit can analyze this data to identify region-specific financial needs. Based on these region-specific financial needs, the generation unit can generate region-specific investment plans and loan products. The delivery unit can collaborate with local financial institutions and service providers to deliver region-specific financial products. This allows the system to meet regional needs by providing customized financial products based on the user's geographical location.

[0062] The financial product generation system can adjust the content of financial product descriptions based on the user's education level. For example, the collection unit can collect the user's educational history and academic background data, and the analysis unit can analyze this data to determine the user's education level. Based on the education level, the generation unit can generate financial products that include simplified explanations avoiding technical jargon or detailed technical explanations. The provision unit can provide financial education content tailored to the user's education level. This allows for the provision of financial products customized based on the user's education level, making the information easier for the user to understand.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The collection unit collects user data. The collection unit collects, for example, mobile user data, transaction data, and user behavior data. Specifically, it can collect location information and app usage history as mobile user data, purchase history and payment history as transaction data, and website browsing history and click history as user behavior data. Step 2: The analysis unit identifies the user's risk profile and needs based on the collected data. For example, the analysis unit can analyze the collected data to identify the user's risk profile and needs. Step 3: The generation unit generates financial products based on the risk profiles and needs identified by the analysis unit. For example, the generation unit can generate customized savings plans or loan products based on income and spending habits. Step 4: The provider unit provides the financial products generated by the generator unit. The provider unit provides financial products through a multilingual interface and educates users using simple financial literacy tools. For example, it can provide financial products in multiple languages ​​such as Japanese, English, and Chinese, and generate interactive tutorials and guides on budget management, saving, investing, etc.

[0065] (Example of form 2) The financial product generation system according to an embodiment of the present invention is an innovative platform that utilizes generative AI to generate and provide financial products for people in Japan who have difficulty accessing financial services. This financial product generation system collects user data, and the generative AI analyzes that data to generate financial products tailored to individual needs. The generated financial products are provided through a multilingual interface, and users are educated using simple financial literacy tools. Furthermore, it utilizes non-traditional data sources to perform fair credit scoring and provide adaptive customer service. The system aims to promote financial inclusion, reduce inequality, and create new business opportunities. For example, the financial product generation system collects user data, including income, spending habits, and financial goals. For instance, mobile user data, transaction data, and user behavior data are collected. Next, the generative AI analyzes the collected data. Based on the collected data, the generative AI identifies the user's risk profile and needs and generates financial products accordingly. For example, the generative AI generates customized savings plans and loan products based on income and spending habits. The generated financial products are provided through a multilingual interface. This eliminates language barriers from hindering access to financial services. For example, services are offered in multiple languages, such as Japanese, English, and Chinese. Furthermore, users are educated using simple financial literacy tools. Generative AI generates interactive tutorials and guides on topics such as budget management, saving, and investing. This allows users to acquire financial knowledge and make better decisions. It also utilizes non-traditional data sources to provide fair credit scoring. Generative AI analyzes data such as mobile phone usage and utility payment history to calculate credit scores. This makes credit access possible even for people without a traditional credit history. Finally, it provides adaptive customer service. Generative AI provides personalized support through chatbots and voice assistants, adapting to the user's communication style and needs.This allows users to receive the support they need. As a result, the financial product generation system can promote financial inclusion, reduce inequality, and create new business opportunities.

[0066] The financial product generation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user data. For example, the collection unit collects mobile user data, transaction data, and user behavior data. For example, the collection unit can collect location information and app usage history as mobile user data. The collection unit can also collect purchase history and payment history as transaction data. Furthermore, the collection unit can collect website browsing history and click history as user behavior data. The analysis unit identifies the user's risk profile and needs based on the collected data. For example, the analysis unit can analyze the collected data to identify the user's risk profile. The analysis unit can also analyze the collected data to identify the user's needs. The generation unit generates customized savings plans and loan products based on income and spending habits. For example, the generation unit can generate customized savings plans based on income and spending habits. The generation unit can also generate customized loan products based on income and spending habits. The provision unit provides financial products through a multilingual interface. The service provider can offer financial products in multiple languages, such as Japanese, English, and Chinese. Furthermore, the service provider educates users using simple financial literacy tools. For example, the service provider can generate interactive tutorials and guides on topics such as budget management, savings, and investment. Thus, the financial product generation system according to this embodiment can provide financial products tailored to individual needs by collecting, analyzing, generating, and providing user data.

[0067] The data collection unit collects user data. For example, the data collection unit collects mobile user data, transaction data, and user behavior data. Specifically, as mobile user data, it can collect location information and app usage history. Location information is used to identify the user's movement patterns and destinations using GPS and Wi-Fi location services. App usage history records which apps a user uses and how often, and is used to understand the user's interests and preferences. The data collection unit can also collect transaction data, such as purchase history and payment history. Purchase history records which products and services a user has purchased and is used to analyze spending trends and consumption patterns. Payment history includes credit card and debit card usage history and bank account transaction history, and is important data for evaluating the user's financial situation and ability to pay. Furthermore, the data collection unit can collect user behavior data, such as website browsing history and click history. Website browsing history is used to understand which pages a user visits and what content they are interested in. Click history records which links and buttons users click, and is used to analyze user behavior patterns and interests in detail. This allows the data collection unit to gather a wide range of user data from diverse data sources, enabling a comprehensive understanding of user behavior, interests, and financial status. Furthermore, the data collection unit can centrally manage this data and integrate it with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and generation units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0068] The analytics unit identifies users' risk profiles and needs based on collected data. For example, the analytics unit can analyze collected data to identify users' risk profiles. Specifically, it uses AI to analyze collected data and evaluate users' risk tolerance and investment tendencies. For example, it can analyze users' transaction data and payment history to understand users' financial situation and spending patterns, thereby identifying risk profiles. It can also analyze users' behavioral data and mobile user data to understand users' interests and preferences, thereby identifying needs. For example, it can analyze website browsing history and app usage history to identify what financial products users are interested in. Furthermore, the analytics unit can utilize historical data and statistical information to predict users' future needs and risks. For example, it can predict users' spending patterns and income fluctuations based on past transaction data to evaluate future risks and needs. In addition, the analytics unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. This allows the analytics unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system. Furthermore, the analysis unit can collect user feedback and continuously improve the accuracy and effectiveness of the analysis results. For example, based on user feedback, it can review the methods for identifying risk profiles and needs, and optimize the analysis algorithm. This allows the analysis unit to accurately identify users' risk profiles and needs, thereby improving the overall system performance.

[0069] The generation unit generates customized savings plans and loan products based on income and spending habits. For example, it can generate customized savings plans based on income and spending habits. Specifically, it analyzes the user's income and spending data and designs an optimal savings plan that matches the user's savings goals and risk tolerance. For example, it sets monthly savings amounts and savings periods based on the user's monthly income and spending, ensuring that the user can continue saving without difficulty. The generation unit can also generate customized loan products based on income and spending habits. For example, it evaluates the user's repayment ability and risk profile based on the user's income and spending data and sets optimal loan terms. This allows the user to use a loan product that is best suited to their financial situation. Furthermore, the generation unit can use AI to predict future fluctuations in the user's income and spending and design long-term savings plans and loan products. For example, it predicts future increases or decreases in income and fluctuations in spending based on the user's past income and spending data and generates savings plans and loan products accordingly. Furthermore, the generation unit can collect user feedback and continuously improve the accuracy and effectiveness of the generated financial products. For example, it can review and optimize the terms and conditions of savings plans and loan products based on user feedback. This allows the generation unit to generate customized financial products based on the user's income and spending habits, providing the optimal financial products that meet the user's needs.

[0070] The service provider offers financial products through a multilingual interface. For example, it can offer financial products in multiple languages, such as Japanese, English, and Chinese. Specifically, it has a function that automatically switches the interface display content and explanations according to the language selected by the user. This allows users who speak different languages ​​to use the system intuitively. Furthermore, the service provider educates users using simple financial literacy tools. For example, it can generate interactive tutorials and guides on budget management, savings, and investment. This allows users to acquire financial knowledge at their own pace and make smarter financial decisions. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the interface and educational tools. For example, it can review and optimize the usability and display content of the interface based on user feedback. The service provider can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to quickly and reliably provide financial products to users and offer the most suitable financial products to meet their needs. Furthermore, the service provider can continuously review and optimize the content and conditions of the financial products it offers based on user behavior data and feedback. This allows the service provider to always provide highly accurate financial products based on the latest information, thereby improving user satisfaction.

[0071] The data collection unit can collect mobile user data, transaction data, and user behavior data. For example, the data collection unit can collect location information and app usage history as mobile user data. The data collection unit can also collect purchase history and payment history as transaction data. Furthermore, the data collection unit can collect website browsing history and click history as user behavior data. By collecting mobile user data, transaction data, and user behavior data, more detailed user information can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input mobile user data into a generating AI and have the generating AI perform data analysis.

[0072] The analysis unit can identify the user's risk profile and needs based on the collected data. For example, the analysis unit can analyze the collected data to identify the user's risk profile. The analysis unit can also analyze the collected data to identify the user's needs. This allows for the provision of more appropriate financial products by identifying the user's risk profile and needs based on the collected data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the identification of the user's risk profile and needs.

[0073] The generation unit can generate customized savings plans and loan products based on income and spending habits. For example, the generation unit can generate customized savings plans based on income and spending habits. Furthermore, the generation unit can generate customized loan products based on income and spending habits. This allows the system to provide users with the most suitable financial products by generating customized savings plans and loan products based on income and spending habits. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input income and spending habit data into a generation AI and have the generation AI generate customized savings plans and loan products.

[0074] The service provider can offer financial products through a multilingual interface. The service provider can offer financial products in multiple languages, such as Japanese, English, and Chinese. This allows for the provision of financial services across language barriers by offering financial products through a multilingual interface. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can have a generating AI perform translations of the generated financial products for provision through a multilingual interface.

[0075] The service provider can educate users using simple financial literacy tools. For example, the service provider can generate interactive tutorials and guides on topics such as budget management, saving, and investing. This allows for the improvement of users' financial knowledge by educating them using simple financial literacy tools. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have a generating AI perform the generation of financial literacy tools.

[0076] The analysis unit can calculate credit scores using non-traditional data sources such as mobile phone usage and utility bill payment history. For example, the analysis unit can analyze mobile phone usage and calculate a credit score. It can also analyze utility bill payment history and calculate a credit score. This allows individuals without a traditional credit history to access credit by calculating credit scores using non-traditional data sources. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not. For example, the analysis unit can input data on mobile phone usage and utility bill payment history into a generative AI and have the generative AI calculate the credit score.

[0077] The service provider can provide personalized support through chatbots and voice assistants. For example, the service provider can provide text-based support using a chatbot. Alternatively, the service provider can provide voice-based support using a voice assistant. This allows for personalized support tailored to user needs through chatbots and voice assistants. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can have a generating AI generate the responses for the chatbot or voice assistant.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can temporarily stop data collection and resume it when the user is relaxed. Also, if the user is relaxed, the data collection unit can actively collect detailed data. Furthermore, if the user is in a hurry, the data collection unit can quickly collect only the minimum necessary data. By adjusting the timing of data collection based on the user's emotions, it is possible to reduce user stress and enable efficient data collection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0079] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting the types of data that the user has frequently provided in the past. Furthermore, the data collection unit can avoid collecting data during times when the user has previously refused to provide data. In addition, the data collection unit can prioritize using methods in which the user has provided data with high accuracy in the past (e.g., voice input or text input). This allows for efficient data collection by analyzing the user's past data collection history and selecting the optimal collection method. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0080] The data collection unit can filter data based on the user's current living situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to items that the user considers important in their current living situation (e.g., health, education, work). It can also collect relevant data based on the user's areas of interest (e.g., investment, savings, loans). Furthermore, the data collection unit can select an appropriate data collection method according to the user's living situation (e.g., family structure, income). This allows for the collection of more relevant data by filtering data based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current living situation and areas of interest into a generating AI and have the generating AI perform data filtering.

[0081] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can postpone the collection of less important data. If the user is relaxed, the data collection unit can prioritize the collection of detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of only the most important data. This enables efficient data collection by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0082] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user lives in an urban area, the data collection unit can collect data related to financial needs specific to urban areas. Similarly, if the user lives in a rural area, the data collection unit can collect data related to financial needs specific to rural areas. Furthermore, if the user frequently visits a particular region, the data collection unit can prioritize the collection of data related to that region. This allows for the collection of more relevant data by considering the user's geographical location information during data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0083] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to topics that the user frequently mentions on social media. It can also analyze the activity of the user's friends and followers on social media and collect relevant data. Furthermore, the data collection unit can analyze the content of the user's social media posts and collect data related to their financial needs. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's social media activity into a generating AI and have the generating AI collect relevant data.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can provide concise and to-the-point analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results in a format that can be quickly understood. In this way, by adjusting the presentation of the analysis based on the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a concise analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data of moderate importance. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specific financial analysis algorithm to financial data. It can also apply a behavioral analysis algorithm to behavioral data. Furthermore, it can apply a social media analysis algorithm to social media data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of different analysis algorithms.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can provide concise and to-the-point analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results in a format that can be quickly understood. By adjusting the length of the analysis based on the user's emotions, the analysis results can be made easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0088] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit can prioritize the analysis of recently submitted data. It can also postpone the analysis of older data. Furthermore, it can appropriately prioritize data with a moderate submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data submission date into a generative AI and have the generative AI determine the priority of analysis.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of data with high relevance. It can also postpone the analysis of data with low relevance. Furthermore, it can appropriately prioritize data with moderate relevance. By adjusting the order of analysis based on the relevance of the data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0090] The generation unit can estimate the user's emotions and adjust the way financial products are presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate financial products with detailed explanations. If the user is stressed, the generation unit can generate concise and to-the-point financial products. Furthermore, if the user is in a hurry, the generation unit can generate financial products in a format that can be quickly understood. In this way, by adjusting the presentation of financial products based on the user's emotions, it is possible to provide financial products that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the presentation of financial products.

[0091] The generation unit can adjust the level of detail of the financial products it generates based on the user's risk profile during the generation process. For example, the generation unit can generate financial products with detailed risk descriptions for users with a high-risk profile. It can also generate financial products with concise risk descriptions for users with a low-risk profile. Furthermore, it can generate financial products with moderately detailed risk descriptions for users with a moderate risk profile. By adjusting the level of detail of financial products based on the user's risk profile, the generation unit can provide users with appropriate financial products. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's risk profile into a generation AI and have the generation AI adjust the level of detail of the financial products.

[0092] The generation unit can apply different generation algorithms during generation according to the user's needs. For example, the generation unit can apply a savings plan generation algorithm to users with high savings needs. It can also apply an investment plan generation algorithm to users with high investment needs. Furthermore, it can apply a loan plan generation algorithm to users with high loan needs. This allows the generation unit to provide users with the most suitable financial products by applying different generation algorithms according to their needs. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's needs into a generation AI and have the generation AI apply different generation algorithms.

[0093] The generation unit can estimate the user's emotions and determine the priority of financial products to generate based on the estimated emotions. For example, if the user is relaxed, the generation unit can prioritize generating financial products with detailed explanations. If the user is stressed, the generation unit can prioritize generating concise and to-the-point financial products. Furthermore, if the user is in a hurry, the generation unit can prioritize generating financial products in a format that can be quickly understood. This allows for the provision of financial products at the optimal time for the user by prioritizing them based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI determine the priority of financial products.

[0094] The generation unit can generate highly relevant financial products by considering the user's geographical location information during the generation process. For example, the generation unit can generate financial products specific to urban areas for users living in urban areas. It can also generate financial products specific to rural areas for users living in rural areas. Furthermore, for users who frequently visit a particular region, the generation unit can generate financial products related to that region. This allows the generation unit to provide users with highly relevant financial products by considering their geographical location information during the generation process. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI generate highly relevant financial products.

[0095] The generation unit can analyze a user's social media activity and generate relevant financial products during the generation process. For example, the generation unit can generate financial products related to topics that the user frequently mentions on social media. It can also analyze the activities of the user's friends and followers on social media and generate relevant financial products. Furthermore, the generation unit can analyze the content of the user's social media posts and generate financial products related to their financial needs. This allows the generation unit to provide users with highly relevant financial products by analyzing their social media activity. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data on the user's social media activity into a generation AI and have the generation AI generate relevant financial products.

[0096] The service provider can estimate the user's emotions and adjust the way financial products are presented based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a presentation that includes detailed explanations. If the user is stressed, the service provider can provide a concise and to-the-point presentation. Furthermore, if the user is in a hurry, the service provider can provide a presentation in a format that can be quickly understood. In this way, by adjusting the presentation of financial products based on the user's emotions, financial products that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the presentation of financial products.

[0097] The delivery unit can select the optimal delivery method by referring to the user's past usage history at the time of delivery. For example, the delivery unit can prioritize using delivery methods that the user has frequently used in the past (e.g., email, SMS). It can also prioritize using delivery methods that the user has shown high satisfaction with in the past. Furthermore, the delivery unit can select the optimal delivery timing based on the user's past usage history. This enables efficient financial product delivery by selecting the optimal delivery method by referring to the user's past usage history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past usage history into a generating AI and have the generating AI select the optimal delivery method.

[0098] The service provider can customize the financial products offered based on the user's current living situation at the time of delivery. For example, if the user is busy, the service provider can offer financial products in a concise and easily understandable format. If the user is relaxed, the service provider can offer financial products in a format that includes detailed explanations. Furthermore, if the user is in a specific living situation (e.g., traveling), the service provider can offer financial products in a format suitable for that situation. This allows the service provider to offer the most suitable financial products to the user by customizing the financial products based on the user's current living situation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's current living situation into a generating AI and have the generating AI perform the customization of the financial products.

[0099] The service provider can estimate the user's emotions and prioritize the financial products offered based on those emotions. For example, if the user is relaxed, the service provider can prioritize offering financial products with detailed explanations. If the user is stressed, the service provider can prioritize offering financial products that are concise and to the point. Furthermore, if the user is in a hurry, the service provider can prioritize offering financial products in a format that can be quickly understood. By prioritizing financial products based on the user's emotions, the service provider can offer financial products at the optimal time for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of financial products.

[0100] The service provider can select the optimal service delivery method at the time of delivery, taking into account the user's geographical location information. For example, the service provider can select a service delivery method specific to urban areas for users living in urban areas. Similarly, it can select a service delivery method specific to rural areas for users living in rural areas. Furthermore, for users who frequently visit a particular region, the service provider can select a service delivery method relevant to that region. This allows the service provider to offer the most suitable financial product to the user by selecting a delivery method that takes into account their geographical location information. Some or all of the above-described processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal service delivery method.

[0101] The service provider can analyze a user's social media activity at the time of provision and offer relevant financial products. For example, the service provider can offer financial products related to topics that the user frequently mentions on social media. Furthermore, the service provider can analyze the activity of the user's friends and followers on social media and offer relevant financial products. In addition, the service provider can analyze the content of the user's social media posts and offer financial products related to their financial needs. This allows the service provider to offer highly relevant financial products to the user by analyzing their social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's social media activity into a generating AI and have the generating AI provide relevant financial products.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] A financial product generation system can collect user health data and customize financial products based on their health status. For example, the collection unit can collect data from the user's fitness tracker or smartwatch, and the analysis unit can analyze this data to assess the user's health risks. The generation unit can generate financial products such as health insurance and medical expense assistance plans based on these health risks. The provision unit can provide advice and support tailored to the user's health status. In this way, the system can support users' health management by providing customized financial products based on their health status.

[0104] The financial product generation system can estimate the user's emotions and adjust the risk level of financial products based on those emotions. For example, if the user is stressed, the analysis unit can recommend low-risk financial products. Conversely, if the user is relaxed, it can suggest high-risk, high-return financial products. Furthermore, if the user is in a hurry, it can explain the risk level in a format that can be quickly understood. In this way, by adjusting the risk level based on the user's emotions, the system can provide the user with the most suitable financial product.

[0105] A financial product generation system can analyze a user's purchase history and customize financial products based on their purchasing patterns. For example, the collection unit can collect the user's online shopping data, and the analysis unit can analyze this data to identify the user's purchasing trends. The generation unit can generate financial products such as cashback and point reward plans based on these purchasing trends. The provision unit can offer benefits and promotions tailored to the user's purchase history. This improves the user's purchasing experience by providing customized financial products based on their purchasing patterns.

[0106] The financial product generation system can estimate a user's emotions and adjust customer service responses based on those emotions. For example, if a user is stressed, the service department can respond in a gentle tone and resolve the issue quickly. If the user is relaxed, they can provide detailed explanations and answer questions carefully. Furthermore, if the user is in a hurry, they can provide concise and to-the-point responses. By adjusting customer service responses based on the user's emotions, this system can improve user satisfaction.

[0107] A financial product generation system can customize financial products based on a user's hobbies and interests. For example, the data collection unit can collect data from the user's social media activities and online communities, and the analysis unit can analyze this data to identify the user's hobbies and interests. The generation unit can generate investment plans and savings plans related to specific fields based on those hobbies and interests. The delivery unit can provide financial education content tailored to the user's hobbies and interests. This allows the system to attract user interest by providing financial products customized based on the user's hobbies and interests.

[0108] The financial product generation system can estimate the user's emotions and adjust the way financial products are explained based on those emotions. For example, if the user is stressed, the system can provide a concise and to-the-point explanation. If the user is relaxed, it can provide a detailed explanation and carefully answer the user's questions. Furthermore, if the user is in a hurry, it can provide an explanation in a format that can be quickly understood. In this way, by adjusting the way financial products are explained based on the user's emotions, the system can provide information that is easy for the user to understand.

[0109] The financial product generation system can provide region-specific financial products by taking into account the user's geographical location. For example, the collection unit can collect the user's location information, and the analysis unit can analyze this data to identify region-specific financial needs. Based on these region-specific financial needs, the generation unit can generate region-specific investment plans and loan products. The delivery unit can collaborate with local financial institutions and service providers to deliver region-specific financial products. This allows the system to meet regional needs by providing customized financial products based on the user's geographical location.

[0110] The financial product generation system can estimate the user's emotions and adjust the timing of financial product delivery based on those emotions. For example, if the delivery unit is stressed, it can temporarily refrain from providing financial products and offer them again when the user is relaxed. Conversely, if the user is relaxed, it can proactively suggest financial products. Furthermore, if the user is in a hurry, it can quickly provide only the minimum necessary information. By adjusting the timing of financial product delivery based on the user's emotions, it is possible to reduce user stress and provide financial products effectively.

[0111] The financial product generation system can adjust the content of financial product descriptions based on the user's education level. For example, the collection unit can collect the user's educational history and academic background data, and the analysis unit can analyze this data to determine the user's education level. Based on the education level, the generation unit can generate financial products that include simplified explanations avoiding technical jargon or detailed technical explanations. The provision unit can provide financial education content tailored to the user's education level. This allows for the provision of financial products customized based on the user's education level, making the information easier for the user to understand.

[0112] The financial product generation system can estimate the user's emotions and adjust the customized content of financial products based on those emotions. For example, if the user is stressed, the generation unit can prioritize generating low-risk financial products. Conversely, if the user is relaxed, it can suggest high-risk financial products. Furthermore, if the user is in a hurry, it can customize financial products in a format that can be quickly understood. In this way, by adjusting the customized content of financial products based on the user's emotions, the system can provide the user with the most suitable financial products.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The collection unit collects user data. The collection unit collects, for example, mobile user data, transaction data, and user behavior data. Specifically, it can collect location information and app usage history as mobile user data, purchase history and payment history as transaction data, and website browsing history and click history as user behavior data. Step 2: The analysis unit identifies the user's risk profile and needs based on the collected data. For example, the analysis unit can analyze the collected data to identify the user's risk profile and needs. Step 3: The generation unit generates financial products based on the risk profiles and needs identified by the analysis unit. For example, the generation unit can generate customized savings plans or loan products based on income and spending habits. Step 4: The provider unit provides the financial products generated by the generator unit. The provider unit provides financial products through a multilingual interface and educates users using simple financial literacy tools. For example, it can provide financial products in multiple languages ​​such as Japanese, English, and Chinese, and generate interactive tutorials and guides on budget management, saving, investing, etc.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to identify the user's risk profile and needs. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and generates customized savings plans and loan products based on income and spending habits. The provision unit is implemented in the control unit 46A of the smart device 14, for example, and provides financial products through a multilingual interface. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to identify the user's risk profile and needs. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which generates customized savings plans and loan products based on income and spending habits. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides financial products through a multilingual interface. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to identify the user's risk profile and needs. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and generates customized savings plans and loan products based on income and spending habits. The provision unit is implemented in the control unit 46A of the headset terminal 314, for example, and provides financial products through a multilingual interface. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to identify the user's risk profile and needs. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which generates customized savings plans and loan products based on income and spending habits. The provision unit is implemented, for example, by the control unit 46A of the robot 414, which provides financial products through a multilingual interface. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0168] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A data collection unit that collects user data, An analysis unit analyzes the data collected by the aforementioned collection unit to identify the user's risk profile and needs, A generation unit that generates financial products based on the risk profile and needs identified by the analysis unit, The system comprises a supply unit that provides financial products generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect mobile user data, transaction data, and user behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, identify the user's risk profile and needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Create customized savings plans and loan products based on your income and spending habits. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Providing financial products through a multilingual interface. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Educate users using simple financial literacy tools. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, Credit scores are calculated using non-traditional data sources such as mobile phone usage and utility bill payment history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, We provide personalized support through chatbots and voice assistants. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is We estimate user sentiment and adjust the way financial products are represented based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, adjust the level of detail of the financial products generated based on the user's risk profile. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, different generation algorithms are applied according to the user's needs. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is It estimates user sentiment and determines the priority of financial products to generate based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, the system considers the user's geographical location to generate highly relevant financial products. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During generation, the system analyzes the user's social media activity and generates relevant financial products. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, We estimate the user's sentiment and adjust how financial products are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, At the time of delivery, the financial products offered will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, We estimate the user's emotions and determine the priority of financial products to offer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and offer relevant financial products. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user data, An analysis unit analyzes the data collected by the aforementioned collection unit to identify the user's risk profile and needs, A generation unit that generates financial products based on the risk profile and needs identified by the analysis unit, The system comprises a supply unit that provides financial products generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect mobile user data, transaction data, and user behavior data. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, identify the user's risk profile and needs. The system according to feature 1.

4. The generating unit is Create customized savings plans and loan products based on your income and spending habits. The system according to feature 1.

5. The aforementioned supply unit is, Providing financial products through a multilingual interface. The system according to feature 1.

6. The aforementioned supply unit is, Educate users using simple financial literacy tools. The system according to feature 1.

7. The aforementioned analysis unit, Credit scores are calculated using non-traditional data sources such as mobile phone usage and utility bill payment history. The system according to feature 1.

8. The aforementioned supply unit is, We provide personalized support through chatbots and voice assistants. The system according to feature 1.

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

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

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