system

Abstract

The system according to this embodiment aims to provide users with a concrete plan for understanding their current asset formation status and making appropriate financial investments. [Solution] The system according to this embodiment comprises a reception unit, a generation unit, an awareness guidance unit, and a suggestion unit. The reception unit receives the user's required information. The generation unit analyzes the information received by the reception unit and generates an asset formation planning table. The awareness guidance unit makes the user aware of the need for financial investment based on the planning table generated by the generation unit. The suggestion unit suggests individual stocks from the planning table generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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 was a problem that it was difficult for a user to grasp the current situation of their asset formation and make a specific plan for appropriate financial investment.

[0005] The system according to the embodiment aims to provide a specific plan for a user to grasp the current situation of their asset formation and make appropriate financial investments.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, an awareness guidance unit, and a suggestion unit. The reception unit receives the user's required information. The generation unit analyzes the information entered by the reception unit and generates an asset formation planning table. The awareness guidance unit makes the user aware of the need for financial investment based on the planning table generated by the generation unit. The suggestion unit suggests individual stocks from the planning table generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide users with a concrete plan to understand their current asset formation status and make appropriate financial investments. [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 controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The asset formation planning system according to an embodiment of the present invention is a system that uses a generating AI to perform asset formation planning, visualizes future asset formation for the user, and makes them aware of the need for financial investment. This asset formation planning system requires the user to input necessary information such as age, annual income, and current financial assets. The generating AI analyzes this information and generates an asset formation planning table. This planning table visualizes the user's future asset formation and shows the financial assets at age 65, when pension payments begin. The generating AI calculates an ideal asset amount and compares it to the actual asset amount to make the user aware of the need for financial investment. The generating AI proposes monthly or annual investment amounts. The generating AI automatically generates a MyPage and suggests individual stocks (stocks and investment trusts) from the planning table. The generating AI also provides information on individual companies and the market, and reflects this information in MyPage. The user can update the asset formation planning table any number of times, making it possible to generate a more detailed planning table. The generating AI implements integrated marketing strategies and conducts marketing activities tailored to the user's stage. This will improve the quality of marketing activities and reduce costs. The asset formation planning system will be able to concretely understand the user's future asset formation and recognize the need for financial investment. Furthermore, the AI-generated suggestions for individual stocks and the provision of market information will enable users to make appropriate investment decisions. In addition, integrated marketing strategies will enable effective marketing activities tailored to the user's stage of development.

[0029] The asset formation planning system according to this embodiment comprises a reception unit, a generation unit, an awareness guidance unit, and a suggestion unit. The reception unit receives the user's required information. This required information includes, but is not limited to, age, annual income, and current financial assets. The reception unit stores the information entered by the user in a database, for example. The reception unit can also analyze the information entered by the user in real time. The generation unit analyzes the information entered by the reception unit and generates an asset formation planning table. The generation unit analyzes the user's information using, for example, data mining technology. The generation unit can also generate the asset formation planning table using statistical analysis technology. The generation unit uses a generation AI to generate an optimal asset formation planning table based on the user's information. The awareness guidance unit makes the user aware of the need for financial investment based on the planning table generated by the generation unit. The awareness guidance unit informs the user of the need for financial investment using, for example, a notification function. The awareness guidance unit can also emphasize the need for financial investment to the user using an alert function. The awareness guidance unit can also use a reporting function to explain in detail to the user the necessity of financial investment. The suggestion unit suggests individual stocks from the planning table generated by the generation unit. The suggestion unit selects the optimal individual stock, for example, using an algorithm. The suggestion unit can also suggest individual stocks suitable for the user using filtering criteria. The suggestion unit uses a generation AI to suggest the optimal individual stock based on the user's information. As a result, the asset formation planning system according to this embodiment can perform asset formation planning based on the user's necessary information, make the user aware of the necessity of financial investment, and suggest individual stocks.

[0030] The reception desk inputs the user's required information. This information may include, but is not limited to, age, annual income, and current financial assets. The reception desk stores the information entered by the user in a database. Specifically, information entered by the user through a dedicated interface is stored in a secure database in real time. This ensures that the user's information is always up-to-date and used for subsequent processing. The reception desk can also analyze the information entered by the user in real time. For example, when a user enters their annual income or financial assets, the information can be immediately analyzed and initial feedback can be provided. This makes it easier for the user to understand how their information will be used. Furthermore, the reception desk has a function to detect user input errors and prompt appropriate corrections. For example, if data in an incorrect format is entered in the annual income input field, an error message will be immediately displayed, prompting the user to re-enter the data in the correct format. This ensures data accuracy and improves the precision of subsequent analysis and planning.

[0031] The generation unit analyzes the information entered by the reception unit and generates an asset formation planning table. The generation unit analyzes user information using, for example, data mining technology. Specifically, it analyzes past data and market trends based on data such as the user's age, annual income, and current financial assets to derive the optimal asset formation plan. The generation unit can also generate an asset formation planning table using statistical analysis technology. For example, it uses regression analysis and clustering technology to propose the plan best suited to the user's asset formation goals. Furthermore, the generation unit uses a generation AI to generate the optimal asset formation planning table based on the user's information. The generation AI receives user input data as a prompt and generates the optimal plan using a model that has learned from past success stories and market trends. For example, if a user is in their 30s, has an annual income of 5 million yen, and current financial assets of 1 million yen, the generation AI will propose an asset formation plan that takes into account risk tolerance and investment period based on this information. In this way, the generation unit can provide an asset formation plan that is best suited to the user's individual circumstances.

[0032] The awareness guidance unit raises awareness of the need for financial investment based on the planning table generated by the generation unit. For example, the awareness guidance unit informs the user of the need for financial investment using a notification function. Specifically, it sends notifications to the user's smartphone or email address, explaining the importance of the asset building plan and the benefits of investment. The awareness guidance unit can also emphasize the need for financial investment to the user using an alert function. For example, if there are changes in specific market trends or economic indicators, it will immediately issue an alert to inform the user of the timing for investment. Furthermore, the awareness guidance unit can also explain the need for financial investment to the user in detail using a reporting function. For example, regularly generated reports will include the progress of the user's asset building plan and the latest market information, making it easier for the user to understand the need for investment. In this way, the awareness guidance unit can effectively communicate the importance of financial investment to the user and raise awareness of asset building.

[0033] The suggestion unit suggests individual stocks from the planning table generated by the generation unit. The suggestion unit selects the optimal individual stocks using algorithms, for example. Specifically, an algorithm is executed to select the optimal stocks based on the user's risk tolerance and investment goals. The suggestion unit can also suggest individual stocks suitable for the user using filtering criteria. For example, it optimizes the user's investment portfolio by selecting stocks limited to specific industries or market segments. Furthermore, the suggestion unit uses a generation AI to suggest the optimal individual stocks based on the user's information. The generation AI receives user input data as a prompt and proposes the optimal stocks using a model that has learned from past market data and stock performance. For example, if the user desires long-term investment with reduced risk, the generation AI will suggest stocks that are expected to grow steadily. In this way, the suggestion unit can provide investment stocks that are best suited to the user's individual needs and support their success in wealth building.

[0034] The generation unit can analyze information such as the user's age, annual income, and current financial assets to generate an asset formation planning table. For example, the generation unit collects information such as the user's age, annual income, current financial assets, and debt situation, and analyzes it using data mining techniques. Furthermore, the generation unit can use statistical analysis techniques to generate an optimal asset formation planning table based on the user's information. The generation unit uses a generation AI to generate an asset formation planning table based on the user's information. For example, the generation unit inputs information such as the user's age, annual income, and current financial assets into the generation AI, which then analyzes the data and generates an asset formation planning table. This allows the generation of an asset formation planning table by analyzing information such as the user's age, annual income, and current financial assets.

[0035] The awareness guidance unit can make the user aware of the need for financial investment based on the generated planning table. The awareness guidance unit can, for example, inform the user of the need for financial investment using a notification function. It can also emphasize the need for financial investment to the user using an alert function. The awareness guidance unit can also explain the need for financial investment to the user in detail using a reporting function. In this way, the user can be made aware of the need for financial investment based on the generated planning table. Some or all of the above processing in the awareness guidance unit may be performed using AI, for example, or not using AI. For example, the awareness guidance unit can input the generated planning table into AI, and the AI ​​can generate notifications or alerts to make the user aware of the need for financial investment.

[0036] The suggestion unit can suggest individual stocks (stocks and investment trusts) from the generated planning table. For example, the suggestion unit can select the optimal individual stock using an algorithm. It can also suggest individual stocks suitable for the user using filtering criteria. The suggestion unit uses a generation AI to suggest the optimal individual stock based on the user's information. For example, the suggestion unit inputs the generated planning table into the generation AI, which analyzes it and suggests the optimal individual stock. This allows the suggestion unit to suggest individual stocks (stocks and investment trusts) from the generated planning table.

[0037] The suggestion function can provide information on individual companies and markets and reflect it in MyPage. For example, the suggestion function can collect financial information and market trends of individual companies and provide them to users. Furthermore, the suggestion function can use a generation AI to analyze information on individual companies and markets and present it to users in an easy-to-understand format. The suggestion function uses the generation AI to reflect information on individual companies and markets in MyPage. For example, the suggestion function inputs financial information of individual companies into the generation AI, which analyzes it and reflects it in MyPage in an easy-to-understand format. This allows the function to provide information on individual companies and markets and reflect it in MyPage.

[0038] The generation unit allows users to update their asset formation planning table as many times as they like. For example, the generation unit automatically updates the asset formation planning table whenever the user enters new information. The generation unit can also allow users to update their asset formation planning table periodically. The generation unit uses a generation AI to update the asset formation planning table in real time based on the user's information. For example, whenever the user enters new information, the generation AI analyzes it and automatically updates the asset formation planning table. This allows users to update their asset formation planning table as many times as they like.

[0039] The marketing department can develop integrated marketing strategies tailored to the user's stage in life. For example, the marketing department can propose optimal marketing strategies based on the user's life stage and investment experience. Furthermore, the marketing department can use generative AI to analyze user information and develop marketing strategies tailored to their stage. The marketing department uses generative AI to develop integrated marketing strategies tailored to the user's stage. For example, the marketing department inputs the user's life stage and investment experience into the generative AI, which then analyzes the data and proposes optimal marketing strategies. This enables the development of integrated marketing strategies tailored to the user's stage in life.

[0040] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (such as voice or text) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. The reception desk can also simplify the input process by automatically completing information previously entered by the user. This allows the reception desk to analyze the user's past input history and select the optimal input method. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into a generating AI, which can then select the optimal input method.

[0041] The reception desk can customize input fields based on the user's current living situation and areas of interest when information is entered. For example, if a user starts a new job, the reception desk will prioritize displaying input fields related to that job. The reception desk can also add input fields related to a user's hobby if the user is interested in a particular hobby. If a user changes their family structure, the reception desk can adjust the input fields based on the new family structure. This allows for the customization of input fields based on the user's current living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's living situation data into a generating AI, which can then customize the input fields.

[0042] The reception desk can prioritize the input of highly relevant information by considering the user's geographical location during information entry. For example, if the user lives in a specific region, the reception desk can prioritize displaying input fields related to that region. Furthermore, if the user is traveling, the reception desk can add input fields related to their travel destination. If the user is planning to move, the reception desk can prioritize displaying input fields related to their new address. This allows the reception desk to prioritize the input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI, which can then prioritize the input of highly relevant information.

[0043] The reception desk can analyze the user's social media activity during information entry and prompt the user to input relevant information. For example, the reception desk can prioritize displaying input fields related to topics the user frequently mentions on social media. It can also add input fields related to events if the user plans to attend a specific event. The reception desk can also customize input fields based on information the user has shared on social media, thereby analyzing the user's social media activity and prompting them to input relevant information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's social media activity data into a generating AI, which can then prompt the AI ​​to input relevant information.

[0044] The generation unit can adjust the level of detail in the planning table based on the user's importance. For example, if a user has a specific asset-building goal, the generation unit can generate a planning table that includes detailed information related to that goal. Alternatively, if a user is aiming for general asset building, the generation unit can generate a planning table that includes basic information. If a user has short-term goals, the generation unit can also generate a planning table that includes detailed information related to those goals. This allows the level of detail in the planning table to be adjusted based on the user's importance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user importance data into a generation AI, which can then adjust the level of detail in the planning table.

[0045] The generation unit can apply different generation algorithms depending on the user's category when generating the planning table. For example, if the user is young, the generation unit can apply an algorithm that proposes an investment plan with a high risk tolerance. If the user is middle-aged, the generation unit can also apply an algorithm that proposes a balanced investment plan. If the user is elderly, the generation unit can also apply an algorithm that proposes an investment plan that prioritizes safety. This allows different generation algorithms to be applied depending on the user's category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user category data into a generation AI, and the generation AI can apply different generation algorithms.

[0046] The generation unit can determine priorities based on the user's submission timing when generating planning tables. For example, if the user urgently needs a planning table, the generation unit will generate it with the highest priority. The generation unit can also generate a planning table to match a specific deadline if the user needs it by that deadline. If the user desires long-term planning, the generation unit can also generate a planning table that includes detailed information. This allows the generation unit to determine the priority of planning tables based on the user's submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input user submission timing data into a generation AI, which can then determine the priority of the planning tables.

[0047] The generation unit can adjust the order of the planning table based on the user's relevance when generating it. For example, if the user has a specific asset-building goal, the generation unit will prioritize displaying information related to that goal. It can also prioritize displaying basic information if the user is aiming for general asset building. Furthermore, if the user has short-term goals, the generation unit can prioritize displaying information related to those goals. This allows the order of the planning table to be adjusted based on the user's relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user relevance data into a generation AI, which can then adjust the order of the planning table.

[0048] The consciousness guidance unit can analyze the user's past behavioral history and select the optimal guidance method during consciousness guidance. For example, the consciousness guidance unit may prioritize suggesting guidance methods that the user has frequently used in the past. The consciousness guidance unit can also predict and suggest guidance methods to be used during specific time periods based on the user's past behavioral history. The consciousness guidance unit can also select the optimal guidance method based on the user's past actions. This allows the unit to analyze the user's past behavioral history and select the optimal guidance method. Some or all of the above processing in the consciousness guidance unit may be performed using AI, for example, or without AI. For example, the consciousness guidance unit can input the user's past behavioral history data into a generating AI, which can then select the optimal guidance method.

[0049] The consciousness guidance unit can customize the guidance methods based on the user's current living situation during consciousness guidance. For example, if the user starts a new job, the consciousness guidance unit will prioritize suggesting guidance methods related to that job. The consciousness guidance unit can also add guidance methods related to a hobby if the user is interested in a particular hobby. If the user changes their family structure, the consciousness guidance unit can adjust the guidance methods based on the new family structure. This allows the guidance methods to be customized based on the user's current living situation. Some or all of the above processing in the consciousness guidance unit may be performed using AI, for example, or without AI. For example, the consciousness guidance unit can input the user's living situation data into a generating AI, which can then customize the guidance methods.

[0050] The consciousness guidance unit can select the optimal guidance method while considering the user's geographical location information. For example, if the user lives in a specific area, the consciousness guidance unit will prioritize suggesting guidance methods related to that area. Furthermore, if the user is traveling, the consciousness guidance unit can also add guidance methods related to the travel destination. If the user is planning to move, the consciousness guidance unit can also prioritize suggesting guidance methods related to the new address. This allows the unit to select the optimal guidance method while considering the user's geographical location information. Some or all of the above processing in the consciousness guidance unit may be performed using AI, for example, or without AI. For example, the consciousness guidance unit can input the user's geographical location information into a generating AI, which can then select the optimal guidance method.

[0051] The awareness guidance unit can analyze the user's social media activity and propose guidance methods during awareness guidance. For example, the awareness guidance unit may prioritize proposing guidance methods related to topics that the user frequently mentions on social media. Furthermore, if the user plans to attend a specific event, the awareness guidance unit can also add guidance methods related to that event. The awareness guidance unit can also customize guidance methods based on information shared by the user on social media. This allows it to analyze the user's social media activity and propose guidance methods. Some or all of the above processing in the awareness guidance unit may be performed using AI, for example, or without AI. For example, the awareness guidance unit can input the user's social media activity data into a generating AI, which can then propose guidance methods.

[0052] The suggestion unit can analyze the user's past investment history to select the optimal suggestion method. For example, the suggestion unit can prioritize suggesting stocks that the user has frequently invested in in the past. Furthermore, the suggestion unit can predict investment tendencies at specific time periods based on the user's past investment history and make suggestions accordingly. The suggestion unit can also select the optimal suggestion method based on the user's past investments. This allows the system to analyze the user's past investment history and select the most suitable suggestion method. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past investment history data into a generating AI, which can then select the optimal suggestion method.

[0053] The suggestion unit can customize its suggestions based on the user's current living situation. For example, if a user starts a new job, the suggestion unit will prioritize suggesting investment stocks related to that job. It can also add investment stocks related to a user's hobby if the user is interested in that hobby. Furthermore, if a user changes their family structure, the suggestion unit can adjust its investment stocks based on the new family structure. This allows the suggestion unit to customize its suggestions based on the user's current living situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user living situation data into a generating AI, which can then customize the suggestion method.

[0054] The suggestion unit can select the optimal suggestion method by considering the user's geographical location information when making suggestions. For example, if the user lives in a specific region, the suggestion unit will prioritize suggesting investment products related to that region. Furthermore, if the user is traveling, the suggestion unit can add investment products related to the travel destination. If the user is planning to move, the suggestion unit can prioritize suggesting investment products related to the new address. This allows the system to select the optimal suggestion method by considering the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI, which can then select the optimal suggestion method.

[0055] The suggestion unit can analyze the user's social media activity and propose suggestion methods at the time of suggestion. For example, the suggestion unit can prioritize suggesting investment stocks related to topics that the user frequently mentions on social media. The suggestion unit can also add investment stocks related to events if the user plans to attend a specific event. The suggestion unit can also customize investment stocks based on information shared by the user on social media. This allows it to analyze the user's social media activity and propose suggestion methods. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity data into a generating AI, which can then propose suggestion methods.

[0056] The marketing department can analyze a user's past behavior history to select the most suitable marketing strategy. For example, the marketing department can prioritize suggesting marketing strategies that the user has frequently used in the past. Furthermore, the marketing department can predict and suggest marketing strategies to be used during specific time periods based on the user's past behavior history. The marketing department can also select the most suitable marketing strategy based on the user's past actions. This allows for the selection of the most suitable strategy by analyzing the user's past behavior history. Some or all of the above processes in the marketing department may be performed using AI, or not. For example, the marketing department can input the user's past behavior history data into a generating AI, which can then select the most suitable strategy.

[0057] The marketing department can customize marketing strategies based on the user's current life circumstances. For example, if a user starts a new job, the marketing department will prioritize suggesting marketing strategies related to that job. The marketing department can also add marketing strategies related to a user's hobby if the user has an interest in that hobby. Furthermore, if a user changes their family structure, the marketing department can adjust marketing strategies based on the new family structure. This allows for the customization of strategies based on the user's current life circumstances. Some or all of the above processes in the marketing department may be performed using AI, for example, or not. For instance, the marketing department could input user life data into a generating AI, which could then customize the strategies.

[0058] The marketing department can select the most suitable marketing strategies by considering the user's geographical location. For example, if a user lives in a specific region, the marketing department can prioritize suggesting marketing strategies related to that region. Furthermore, if a user is traveling, the marketing department can add marketing strategies related to their travel destination. If a user is planning to move, the marketing department can prioritize suggesting marketing strategies related to their new address. This allows the marketing department to select the most suitable strategies by considering the user's geographical location. Some or all of the above processes in the marketing department may be performed using AI, or not. For example, the marketing department can input the user's geographical location information into a generating AI, which can then select the most suitable strategies.

[0059] The marketing department can analyze users' social media activity to select the most suitable marketing strategies. For example, the marketing department can prioritize suggesting marketing strategies related to topics that users frequently mention on social media. Furthermore, if a user plans to attend a specific event, the marketing department can add marketing strategies related to that event. The marketing department can also customize marketing strategies based on information shared by users on social media, allowing for the selection of optimal strategies based on an analysis of users' social media activity. Some or all of the above processes performed by the marketing department may be carried out using AI, or not. For example, the marketing department could input user social media activity data into a generating AI, which could then select the most suitable strategies.

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

[0061] The asset building planning system can create plans that take the user's health condition into consideration. For example, by inputting the results of a health checkup, the system can analyze the information and propose an asset building plan tailored to the user's health. If the user is in good health, it can recommend high-risk investments, while if their health is deteriorating, it can propose investments that prioritize safety. Furthermore, based on the user's health condition, the system can also create plans that take into account future medical and nursing care expenses. This allows users to obtain an optimal asset building plan that suits their own health condition.

[0062] The asset building planning system can create plans that take into account the user's family structure. For example, by inputting the user's family structure, the system analyzes that information and can propose an asset building plan tailored to the number and ages of family members. It can also create plans that take into account children's education expenses and family living expenses. Furthermore, the plan can be updated in response to changes in family structure. This allows users to obtain an optimal asset building plan that suits their family's situation.

[0063] The asset building planning system can create plans that take into account the user's hobbies and interests. For example, by inputting the user's hobbies and interests, the generation unit can analyze that information and propose investment plans related to those hobbies and interests. For instance, if the user is interested in sports, it can suggest sports-related companies or investment trusts. Similarly, if the user is interested in travel, it can propose travel-related investment plans. This allows users to obtain an optimal asset building plan tailored to their hobbies and interests.

[0064] The asset formation planning system can create plans that take into account the user's geographical location. For example, by inputting information on the economic situation and real estate market of the area where the user lives, the generation unit can analyze that information and propose an asset formation plan tailored to the region. For instance, if the user lives in an urban area, it can recommend urban real estate investment, and if they live in a rural area, it can propose an investment plan that suits the characteristics of that area. This allows users to obtain an optimal asset formation plan that is appropriate for their region.

[0065] The asset building planning system can create plans that take into account the user's past investment history. For example, by inputting the user's past investment history, the generation unit can analyze that information and propose an asset building plan based on past investment patterns. For instance, it can analyze patterns of successful investments in the past and propose similar investment plans. It can also provide advice on how to avoid past failed investments. As a result, the user can obtain an optimal asset building plan based on their past investment history.

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

[0067] Step 1: The reception desk enters the user's required information. This information may include, for example, age, annual income, and current financial assets. The reception desk can also save the information entered by the user to a database and analyze it in real time. Step 2: The generation unit analyzes the information entered by the reception unit and generates an asset formation planning table. The generation unit uses data mining technology, statistical analysis technology, and generation AI to analyze the user's information and generate an optimal asset formation planning table. Step 3: The awareness guidance unit makes the user aware of the need for financial investment based on the planning table generated by the generation unit. The awareness guidance unit uses notification, alert, and reporting functions to inform, emphasize, and explain in detail the need for financial investment to the user. Step 4: The suggestion unit suggests individual stocks from the planning table generated by the generation unit. The suggestion unit uses algorithms, filtering criteria, and generation AI to select and suggest the most suitable individual stocks for the user.

[0068] (Example of form 2) The asset formation planning system according to an embodiment of the present invention is a system that uses a generating AI to perform asset formation planning, visualizes future asset formation for the user, and makes them aware of the need for financial investment. This asset formation planning system requires the user to input necessary information such as age, annual income, and current financial assets. The generating AI analyzes this information and generates an asset formation planning table. This planning table visualizes the user's future asset formation and shows the financial assets at age 65, when pension payments begin. The generating AI calculates an ideal asset amount and compares it to the actual asset amount to make the user aware of the need for financial investment. The generating AI proposes monthly or annual investment amounts. The generating AI automatically generates a MyPage and suggests individual stocks (stocks and investment trusts) from the planning table. The generating AI also provides information on individual companies and the market, and reflects this information in MyPage. The user can update the asset formation planning table any number of times, making it possible to generate a more detailed planning table. The generating AI implements integrated marketing strategies and conducts marketing activities tailored to the user's stage. This will improve the quality of marketing activities and reduce costs. The asset formation planning system will be able to concretely understand the user's future asset formation and recognize the need for financial investment. Furthermore, the AI-generated suggestions for individual stocks and the provision of market information will enable users to make appropriate investment decisions. In addition, integrated marketing strategies will enable effective marketing activities tailored to the user's stage of development.

[0069] The asset formation planning system according to this embodiment comprises a reception unit, a generation unit, an awareness guidance unit, and a suggestion unit. The reception unit receives the user's required information. This required information includes, but is not limited to, age, annual income, and current financial assets. The reception unit stores the information entered by the user in a database, for example. The reception unit can also analyze the information entered by the user in real time. The generation unit analyzes the information entered by the reception unit and generates an asset formation planning table. The generation unit analyzes the user's information using, for example, data mining technology. The generation unit can also generate the asset formation planning table using statistical analysis technology. The generation unit uses a generation AI to generate an optimal asset formation planning table based on the user's information. The awareness guidance unit makes the user aware of the need for financial investment based on the planning table generated by the generation unit. The awareness guidance unit informs the user of the need for financial investment using, for example, a notification function. The awareness guidance unit can also emphasize the need for financial investment to the user using an alert function. The awareness guidance unit can also use a reporting function to explain in detail to the user the necessity of financial investment. The suggestion unit suggests individual stocks from the planning table generated by the generation unit. The suggestion unit selects the optimal individual stock, for example, using an algorithm. The suggestion unit can also suggest individual stocks suitable for the user using filtering criteria. The suggestion unit uses a generation AI to suggest the optimal individual stock based on the user's information. As a result, the asset formation planning system according to this embodiment can perform asset formation planning based on the user's necessary information, make the user aware of the necessity of financial investment, and suggest individual stocks.

[0070] The reception desk inputs the user's required information. This information may include, but is not limited to, age, annual income, and current financial assets. The reception desk stores the information entered by the user in a database. Specifically, information entered by the user through a dedicated interface is stored in a secure database in real time. This ensures that the user's information is always up-to-date and used for subsequent processing. The reception desk can also analyze the information entered by the user in real time. For example, when a user enters their annual income or financial assets, the information can be immediately analyzed and initial feedback can be provided. This makes it easier for the user to understand how their information will be used. Furthermore, the reception desk has a function to detect user input errors and prompt appropriate corrections. For example, if data in an incorrect format is entered in the annual income input field, an error message will be immediately displayed, prompting the user to re-enter the data in the correct format. This ensures data accuracy and improves the precision of subsequent analysis and planning.

[0071] The generation unit analyzes the information entered by the reception unit and generates an asset formation planning table. The generation unit analyzes user information using, for example, data mining technology. Specifically, it analyzes past data and market trends based on data such as the user's age, annual income, and current financial assets to derive the optimal asset formation plan. The generation unit can also generate an asset formation planning table using statistical analysis technology. For example, it uses regression analysis and clustering technology to propose the plan best suited to the user's asset formation goals. Furthermore, the generation unit uses a generation AI to generate the optimal asset formation planning table based on the user's information. The generation AI receives user input data as a prompt and generates the optimal plan using a model that has learned from past success stories and market trends. For example, if a user is in their 30s, has an annual income of 5 million yen, and current financial assets of 1 million yen, the generation AI will propose an asset formation plan that takes into account risk tolerance and investment period based on this information. In this way, the generation unit can provide an asset formation plan that is best suited to the user's individual circumstances.

[0072] The awareness guidance unit raises awareness of the need for financial investment based on the planning table generated by the generation unit. For example, the awareness guidance unit informs the user of the need for financial investment using a notification function. Specifically, it sends notifications to the user's smartphone or email address, explaining the importance of the asset building plan and the benefits of investment. The awareness guidance unit can also emphasize the need for financial investment to the user using an alert function. For example, if there are changes in specific market trends or economic indicators, it will immediately issue an alert to inform the user of the timing for investment. Furthermore, the awareness guidance unit can also explain the need for financial investment to the user in detail using a reporting function. For example, regularly generated reports will include the progress of the user's asset building plan and the latest market information, making it easier for the user to understand the need for investment. In this way, the awareness guidance unit can effectively communicate the importance of financial investment to the user and raise awareness of asset building.

[0073] The suggestion unit suggests individual stocks from the planning table generated by the generation unit. The suggestion unit selects the optimal individual stocks using algorithms, for example. Specifically, an algorithm is executed to select the optimal stocks based on the user's risk tolerance and investment goals. The suggestion unit can also suggest individual stocks suitable for the user using filtering criteria. For example, it optimizes the user's investment portfolio by selecting stocks limited to specific industries or market segments. Furthermore, the suggestion unit uses a generation AI to suggest the optimal individual stocks based on the user's information. The generation AI receives user input data as a prompt and proposes the optimal stocks using a model that has learned from past market data and stock performance. For example, if the user desires long-term investment with reduced risk, the generation AI will suggest stocks that are expected to grow steadily. In this way, the suggestion unit can provide investment stocks that are best suited to the user's individual needs and support their success in wealth building.

[0074] The generation unit can analyze information such as the user's age, annual income, and current financial assets to generate an asset formation planning table. For example, the generation unit collects information such as the user's age, annual income, current financial assets, and debt situation, and analyzes it using data mining techniques. Furthermore, the generation unit can use statistical analysis techniques to generate an optimal asset formation planning table based on the user's information. The generation unit uses a generation AI to generate an asset formation planning table based on the user's information. For example, the generation unit inputs information such as the user's age, annual income, and current financial assets into the generation AI, which then analyzes the data and generates an asset formation planning table. This allows the generation of an asset formation planning table by analyzing information such as the user's age, annual income, and current financial assets.

[0075] The awareness guidance unit can make the user aware of the need for financial investment based on the generated planning table. The awareness guidance unit can, for example, inform the user of the need for financial investment using a notification function. It can also emphasize the need for financial investment to the user using an alert function. The awareness guidance unit can also explain the need for financial investment to the user in detail using a reporting function. In this way, the user can be made aware of the need for financial investment based on the generated planning table. Some or all of the above processing in the awareness guidance unit may be performed using AI, for example, or not using AI. For example, the awareness guidance unit can input the generated planning table into AI, and the AI ​​can generate notifications or alerts to make the user aware of the need for financial investment.

[0076] The suggestion unit can suggest individual stocks (stocks and investment trusts) from the generated planning table. For example, the suggestion unit can select the optimal individual stock using an algorithm. It can also suggest individual stocks suitable for the user using filtering criteria. The suggestion unit uses a generation AI to suggest the optimal individual stock based on the user's information. For example, the suggestion unit inputs the generated planning table into the generation AI, which analyzes it and suggests the optimal individual stock. This allows the suggestion unit to suggest individual stocks (stocks and investment trusts) from the generated planning table.

[0077] The suggestion function can provide information on individual companies and markets and reflect it in MyPage. For example, the suggestion function can collect financial information and market trends of individual companies and provide them to users. Furthermore, the suggestion function can use a generation AI to analyze information on individual companies and markets and present it to users in an easy-to-understand format. The suggestion function uses the generation AI to reflect information on individual companies and markets in MyPage. For example, the suggestion function inputs financial information of individual companies into the generation AI, which analyzes it and reflects it in MyPage in an easy-to-understand format. This allows the function to provide information on individual companies and markets and reflect it in MyPage.

[0078] The generation unit allows users to update their asset formation planning table as many times as they like. For example, the generation unit automatically updates the asset formation planning table whenever the user enters new information. The generation unit can also allow users to update their asset formation planning table periodically. The generation unit uses a generation AI to update the asset formation planning table in real time based on the user's information. For example, whenever the user enters new information, the generation AI analyzes it and automatically updates the asset formation planning table. This allows users to update their asset formation planning table as many times as they like.

[0079] The marketing department can develop integrated marketing strategies tailored to the user's stage in life. For example, the marketing department can propose optimal marketing strategies based on the user's life stage and investment experience. Furthermore, the marketing department can use generative AI to analyze user information and develop marketing strategies tailored to their stage. The marketing department uses generative AI to develop integrated marketing strategies tailored to the user's stage. For example, the marketing department inputs the user's life stage and investment experience into the generative AI, which then analyzes the data and proposes optimal marketing strategies. This enables the development of integrated marketing strategies tailored to the user's stage in life.

[0080] The reception desk can estimate the user's emotions and adjust the timing of information input based on the estimated emotions. For example, if the user is stressed, the reception desk can temporarily suspend input and display a relaxing interface. If the user is relaxed, the reception desk can quickly display the next input item to facilitate smooth input. If the user is in a hurry, the reception desk can prioritize the most important information input and allow details to be entered later. This allows the timing of information input to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI, which can estimate emotions and adjust the timing of information input.

[0081] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (such as voice or text) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. The reception desk can also simplify the input process by automatically completing information previously entered by the user. This allows the reception desk to analyze the user's past input history and select the optimal input method. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into a generating AI, which can then select the optimal input method.

[0082] The reception desk can customize input fields based on the user's current living situation and areas of interest when information is entered. For example, if a user starts a new job, the reception desk will prioritize displaying input fields related to that job. The reception desk can also add input fields related to a user's hobby if the user is interested in a particular hobby. If a user changes their family structure, the reception desk can adjust the input fields based on the new family structure. This allows for the customization of input fields based on the user's current living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's living situation data into a generating AI, which can then customize the input fields.

[0083] The reception desk can estimate the user's emotions and prioritize the information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk can prioritize the most important information, allowing for more detailed information to be entered later. If the user is relaxed, the reception desk can quickly display the next input fields to facilitate more detailed information entry. If the user is in a hurry, the reception desk can also prioritize the most important information, allowing for more detailed information to be entered later. This allows the system to prioritize the information to be entered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI, which can estimate emotions and determine the priority of information.

[0084] The reception desk can prioritize the input of highly relevant information by considering the user's geographical location during information entry. For example, if the user lives in a specific region, the reception desk can prioritize displaying input fields related to that region. Furthermore, if the user is traveling, the reception desk can add input fields related to their travel destination. If the user is planning to move, the reception desk can prioritize displaying input fields related to their new address. This allows the reception desk to prioritize the input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI, which can then prioritize the input of highly relevant information.

[0085] The reception desk can analyze the user's social media activity during information entry and prompt the user to input relevant information. For example, the reception desk can prioritize displaying input fields related to topics the user frequently mentions on social media. It can also add input fields related to events if the user plans to attend a specific event. The reception desk can also customize input fields based on information the user has shared on social media, thereby analyzing the user's social media activity and prompting them to input relevant information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's social media activity data into a generating AI, which can then prompt the AI ​​to input relevant information.

[0086] The generation unit can estimate the user's emotions and adjust the presentation of the planning table based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a planning table with detailed explanations. If the user is in a hurry, the generation unit can also generate a concise planning table that gets straight to the point. If the user is excited, the generation unit can also generate a planning table with visually stimulating effects. This allows the presentation of the planning table to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user facial expression data into the generation AI, which can estimate emotions and adjust the presentation of the planning table.

[0087] The generation unit can adjust the level of detail in the planning table based on the user's importance. For example, if a user has a specific asset-building goal, the generation unit can generate a planning table that includes detailed information related to that goal. Alternatively, if a user is aiming for general asset building, the generation unit can generate a planning table that includes basic information. If a user has short-term goals, the generation unit can also generate a planning table that includes detailed information related to those goals. This allows the level of detail in the planning table to be adjusted based on the user's importance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user importance data into a generation AI, which can then adjust the level of detail in the planning table.

[0088] The generation unit can apply different generation algorithms depending on the user's category when generating the planning table. For example, if the user is young, the generation unit can apply an algorithm that proposes an investment plan with a high risk tolerance. If the user is middle-aged, the generation unit can also apply an algorithm that proposes a balanced investment plan. If the user is elderly, the generation unit can also apply an algorithm that proposes an investment plan that prioritizes safety. This allows different generation algorithms to be applied depending on the user's category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user category data into a generation AI, and the generation AI can apply different generation algorithms.

[0089] The generation unit can estimate the user's emotions and adjust the length of the planning table based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise planning table. If the user is relaxed, the generation unit can also generate a longer planning table with detailed explanations. If the user is excited, the generation unit can also generate a planning table with visually stimulating effects. This allows the length of the planning table to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user facial expression data into the generation AI, which can estimate emotions and adjust the length of the planning table.

[0090] The generation unit can determine priorities based on the user's submission timing when generating planning tables. For example, if the user urgently needs a planning table, the generation unit will generate it with the highest priority. The generation unit can also generate a planning table to match a specific deadline if the user needs it by that deadline. If the user desires long-term planning, the generation unit can also generate a planning table that includes detailed information. This allows the generation unit to determine the priority of planning tables based on the user's submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input user submission timing data into a generation AI, which can then determine the priority of the planning tables.

[0091] The generation unit can adjust the order of the planning table based on the user's relevance when generating it. For example, if the user has a specific asset-building goal, the generation unit will prioritize displaying information related to that goal. It can also prioritize displaying basic information if the user is aiming for general asset building. Furthermore, if the user has short-term goals, the generation unit can prioritize displaying information related to those goals. This allows the order of the planning table to be adjusted based on the user's relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user relevance data into a generation AI, which can then adjust the order of the planning table.

[0092] The consciousness guidance unit can estimate the user's emotions and adjust the method of consciousness guidance based on the estimated emotions. For example, if the user is relaxed, the consciousness guidance unit can provide consciousness guidance that includes detailed explanations. If the user is in a hurry, the consciousness guidance unit can also provide concise consciousness guidance that gets straight to the point. If the user is excited, the consciousness guidance unit can also provide consciousness guidance with visually stimulating effects. This allows the method of consciousness guidance to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the consciousness guidance unit may be performed using AI, for example, or not using AI. For example, the consciousness guidance unit can input the user's facial expression data into the generative AI, which can estimate emotions and adjust the method of consciousness guidance.

[0093] The consciousness guidance unit can analyze the user's past behavioral history and select the optimal guidance method during consciousness guidance. For example, the consciousness guidance unit may prioritize suggesting guidance methods that the user has frequently used in the past. The consciousness guidance unit can also predict and suggest guidance methods to be used during specific time periods based on the user's past behavioral history. The consciousness guidance unit can also select the optimal guidance method based on the user's past actions. This allows the unit to analyze the user's past behavioral history and select the optimal guidance method. Some or all of the above processing in the consciousness guidance unit may be performed using AI, for example, or without AI. For example, the consciousness guidance unit can input the user's past behavioral history data into a generating AI, which can then select the optimal guidance method.

[0094] The consciousness guidance unit can customize the guidance methods based on the user's current living situation during consciousness guidance. For example, if the user starts a new job, the consciousness guidance unit will prioritize suggesting guidance methods related to that job. The consciousness guidance unit can also add guidance methods related to a hobby if the user is interested in a particular hobby. If the user changes their family structure, the consciousness guidance unit can adjust the guidance methods based on the new family structure. This allows the guidance methods to be customized based on the user's current living situation. Some or all of the above processing in the consciousness guidance unit may be performed using AI, for example, or without AI. For example, the consciousness guidance unit can input the user's living situation data into a generating AI, which can then customize the guidance methods.

[0095] The consciousness guidance unit can estimate the user's emotions and determine the priority of consciousness guidance based on the estimated emotions. For example, if the user is stressed, the consciousness guidance unit will prioritize the most important consciousness guidance and explain the details later. The consciousness guidance unit can also provide consciousness guidance with detailed explanations if the user is relaxed. If the user is in a hurry, the consciousness guidance unit can provide concise consciousness guidance that gets straight to the point. This allows the system to determine the priority of consciousness guidance based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the consciousness guidance unit may be performed using AI or not. For example, the consciousness guidance unit can input user facial expression data into a generative AI, which can estimate emotions and determine the priority of consciousness guidance.

[0096] The consciousness guidance unit can select the optimal guidance method while considering the user's geographical location information. For example, if the user lives in a specific area, the consciousness guidance unit will prioritize suggesting guidance methods related to that area. Furthermore, if the user is traveling, the consciousness guidance unit can also add guidance methods related to the travel destination. If the user is planning to move, the consciousness guidance unit can also prioritize suggesting guidance methods related to the new address. This allows the unit to select the optimal guidance method while considering the user's geographical location information. Some or all of the above processing in the consciousness guidance unit may be performed using AI, for example, or without AI. For example, the consciousness guidance unit can input the user's geographical location information into a generating AI, which can then select the optimal guidance method.

[0097] The awareness guidance unit can analyze the user's social media activity and propose guidance methods during awareness guidance. For example, the awareness guidance unit may prioritize proposing guidance methods related to topics that the user frequently mentions on social media. Furthermore, if the user plans to attend a specific event, the awareness guidance unit can also add guidance methods related to that event. The awareness guidance unit can also customize guidance methods based on information shared by the user on social media. This allows it to analyze the user's social media activity and propose guidance methods. Some or all of the above processing in the awareness guidance unit may be performed using AI, for example, or without AI. For example, the awareness guidance unit can input the user's social media activity data into a generating AI, which can then propose guidance methods.

[0098] The suggestion unit can estimate the user's emotions and adjust its suggestion method based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide suggestions with detailed explanations. If the user is in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows the suggestion method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI, which can estimate emotions and adjust the suggestion method.

[0099] The suggestion unit can analyze the user's past investment history to select the optimal suggestion method. For example, the suggestion unit can prioritize suggesting stocks that the user has frequently invested in in the past. Furthermore, the suggestion unit can predict investment tendencies at specific time periods based on the user's past investment history and make suggestions accordingly. The suggestion unit can also select the optimal suggestion method based on the user's past investments. This allows the system to analyze the user's past investment history and select the most suitable suggestion method. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past investment history data into a generating AI, which can then select the optimal suggestion method.

[0100] The suggestion unit can customize its suggestions based on the user's current living situation. For example, if a user starts a new job, the suggestion unit will prioritize suggesting investment stocks related to that job. It can also add investment stocks related to a user's hobby if the user is interested in that hobby. Furthermore, if a user changes their family structure, the suggestion unit can adjust its investment stocks based on the new family structure. This allows the suggestion unit to customize its suggestions based on the user's current living situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user living situation data into a generating AI, which can then customize the suggestion method.

[0101] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize the most important suggestions and explain the details later. If the user is relaxed, the suggestion unit can also provide suggestions with detailed explanations. If the user is in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. This allows the suggestion unit to prioritize suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI, which can estimate emotions and determine the priority of suggestions.

[0102] The suggestion unit can select the optimal suggestion method by considering the user's geographical location information when making suggestions. For example, if the user lives in a specific region, the suggestion unit will prioritize suggesting investment products related to that region. Furthermore, if the user is traveling, the suggestion unit can add investment products related to the travel destination. If the user is planning to move, the suggestion unit can prioritize suggesting investment products related to the new address. This allows the system to select the optimal suggestion method by considering the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI, which can then select the optimal suggestion method.

[0103] The suggestion unit can analyze the user's social media activity and propose suggestion methods at the time of suggestion. For example, the suggestion unit can prioritize suggesting investment stocks related to topics that the user frequently mentions on social media. The suggestion unit can also add investment stocks related to events if the user plans to attend a specific event. The suggestion unit can also customize investment stocks based on information shared by the user on social media. This allows it to analyze the user's social media activity and propose suggestion methods. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity data into a generating AI, which can then propose suggestion methods.

[0104] The marketing department can estimate user emotions and adjust marketing strategies based on those estimated emotions. For example, if a user is relaxed, the marketing department can implement marketing strategies that include detailed explanations. If a user is in a hurry, the marketing department can implement concise marketing strategies that get straight to the point. If a user is excited, the marketing department can implement marketing strategies that incorporate visually stimulating effects. This allows marketing strategies to be adjusted based on user emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processes in the marketing department may be performed using AI or not. For example, the marketing department can input user facial expression data into a generative AI, which can estimate emotions and adjust marketing strategies accordingly.

[0105] The marketing department can analyze a user's past behavior history to select the most suitable marketing strategy. For example, the marketing department can prioritize suggesting marketing strategies that the user has frequently used in the past. Furthermore, the marketing department can predict and suggest marketing strategies to be used during specific time periods based on the user's past behavior history. The marketing department can also select the most suitable marketing strategy based on the user's past actions. This allows for the selection of the most suitable strategy by analyzing the user's past behavior history. Some or all of the above processes in the marketing department may be performed using AI, or not. For example, the marketing department can input the user's past behavior history data into a generating AI, which can then select the most suitable strategy.

[0106] The marketing department can customize marketing strategies based on the user's current life circumstances. For example, if a user starts a new job, the marketing department will prioritize suggesting marketing strategies related to that job. The marketing department can also add marketing strategies related to a user's hobby if the user has an interest in that hobby. Furthermore, if a user changes their family structure, the marketing department can adjust marketing strategies based on the new family structure. This allows for the customization of strategies based on the user's current life circumstances. Some or all of the above processes in the marketing department may be performed using AI, for example, or not. For instance, the marketing department could input user life data into a generating AI, which could then customize the strategies.

[0107] The marketing department can estimate user emotions and prioritize marketing initiatives based on those emotions. For example, if a user is stressed, the marketing department can prioritize the most important marketing initiatives and provide details later. If a user is relaxed, the marketing department can also implement marketing initiatives that include detailed explanations. If a user is in a hurry, the marketing department can implement concise marketing initiatives that get straight to the point. This allows for prioritizing marketing initiatives based on user emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processes in the marketing department may be performed using AI or not. For example, the marketing department can input user facial expression data into a generative AI, which can estimate emotions and determine the priority of marketing initiatives.

[0108] The marketing department can select the most suitable marketing strategies by considering the user's geographical location. For example, if a user lives in a specific region, the marketing department can prioritize suggesting marketing strategies related to that region. Furthermore, if a user is traveling, the marketing department can add marketing strategies related to their travel destination. If a user is planning to move, the marketing department can prioritize suggesting marketing strategies related to their new address. This allows the marketing department to select the most suitable strategies by considering the user's geographical location. Some or all of the above processes in the marketing department may be performed using AI, or not. For example, the marketing department can input the user's geographical location information into a generating AI, which can then select the most suitable strategies.

[0109] The marketing department can analyze users' social media activity to select the most suitable marketing strategies. For example, the marketing department can prioritize suggesting marketing strategies related to topics that users frequently mention on social media. Furthermore, if a user plans to attend a specific event, the marketing department can add marketing strategies related to that event. The marketing department can also customize marketing strategies based on information shared by users on social media, allowing for the selection of optimal strategies based on an analysis of users' social media activity. Some or all of the above processes performed by the marketing department may be carried out using AI, or not. For example, the marketing department could input user social media activity data into a generating AI, which could then select the most suitable strategies.

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

[0111] The asset building planning system can create plans that take the user's health condition into consideration. For example, by inputting the results of a health checkup, the system can analyze the information and propose an asset building plan tailored to the user's health. If the user is in good health, it can recommend high-risk investments, while if their health is deteriorating, it can propose investments that prioritize safety. Furthermore, based on the user's health condition, the system can also create plans that take into account future medical and nursing care expenses. This allows users to obtain an optimal asset building plan that suits their own health condition.

[0112] The asset building planning system can create plans that take into account the user's family structure. For example, by inputting the user's family structure, the system analyzes that information and can propose an asset building plan tailored to the number and ages of family members. It can also create plans that take into account children's education expenses and family living expenses. Furthermore, the plan can be updated in response to changes in family structure. This allows users to obtain an optimal asset building plan that suits their family's situation.

[0113] The asset building planning system can create plans that take into account the user's hobbies and interests. For example, by inputting the user's hobbies and interests, the generation unit can analyze that information and propose investment plans related to those hobbies and interests. For instance, if the user is interested in sports, it can suggest sports-related companies or investment trusts. Similarly, if the user is interested in travel, it can propose travel-related investment plans. This allows users to obtain an optimal asset building plan tailored to their hobbies and interests.

[0114] The asset formation planning system can create plans that take into account the user's geographical location. For example, by inputting information on the economic situation and real estate market of the area where the user lives, the generation unit can analyze that information and propose an asset formation plan tailored to the region. For instance, if the user lives in an urban area, it can recommend urban real estate investment, and if they live in a rural area, it can propose an investment plan that suits the characteristics of that area. This allows users to obtain an optimal asset formation plan that is appropriate for their region.

[0115] The asset building planning system can create plans that take into account the user's past investment history. For example, by inputting the user's past investment history, the generation unit can analyze that information and propose an asset building plan based on past investment patterns. For instance, it can analyze patterns of successful investments in the past and propose similar investment plans. It can also provide advice on how to avoid past failed investments. As a result, the user can obtain an optimal asset building plan based on their past investment history.

[0116] The asset building planning system can estimate the user's emotions and adjust the planning approach based on those emotions. For example, if the user is stressed, the system can suggest a low-risk investment plan; if the user is relaxed, it can suggest a high-risk investment plan. Furthermore, if the user is excited, it can generate a planning table with visually stimulating effects. This allows the system to provide an optimal asset building plan tailored to the user's emotions.

[0117] The asset building planning system can estimate the user's emotions and adjust the timing of information delivery based on those emotions. For example, if the user is feeling stressed, it can temporarily suspend information delivery and display a relaxing interface. Conversely, if the user is relaxed, it can quickly display the next piece of information to ensure a smooth flow of information delivery. This allows the system to adjust the timing of information delivery based on the user's emotions.

[0118] The asset building planning system can estimate the user's emotions and adjust the presentation of the planning table based on those emotions. For example, if the user is relaxed, it can generate a planning table with detailed explanations. Conversely, if the user is in a hurry, it can generate a concise planning table that gets straight to the point. This allows the system to adjust the presentation of the planning table based on the user's emotions.

[0119] The asset building planning system can estimate the user's emotions and adjust the length of the planning table based on those emotions. For example, if the user is in a hurry, it can generate a short, concise planning table. Conversely, if the user is relaxed, it can generate a longer planning table with more detailed explanations. This allows the system to adjust the length of the planning table based on the user's emotions.

[0120] The asset building planning system can estimate the user's emotions and adjust its guidance methods based on those emotions. For example, if the user is relaxed, it can provide guidance that includes detailed explanations. Conversely, if the user is in a hurry, it can provide concise guidance that gets straight to the point. This allows the guidance method to be adjusted based on the user's emotions.

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

[0122] Step 1: The reception desk enters the user's required information. This information may include, for example, age, annual income, and current financial assets. The reception desk can also save the information entered by the user to a database and analyze it in real time. Step 2: The generation unit analyzes the information entered by the reception unit and generates an asset formation planning table. The generation unit uses data mining technology, statistical analysis technology, and generation AI to analyze the user's information and generate an optimal asset formation planning table. Step 3: The awareness guidance unit makes the user aware of the need for financial investment based on the planning table generated by the generation unit. The awareness guidance unit uses notification, alert, and reporting functions to inform, emphasize, and explain in detail the need for financial investment to the user. Step 4: The suggestion unit suggests individual stocks from the planning table generated by the generation unit. The suggestion unit uses algorithms, filtering criteria, and generation AI to select and suggest the most suitable individual stocks for the user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the reception unit, generation unit, awareness guidance unit, and suggestion unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, which inputs information such as the user's age, annual income, and current financial assets. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input information and generates an asset formation planning table. The awareness guidance unit is implemented by the specific processing unit 290 of the data processing unit 12, which makes the user aware of the need for financial investment based on the generated planning table. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, which suggests individual stocks from the generated planning table. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, generation unit, awareness guidance unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, which inputs information such as the user's age, annual income, and current financial assets. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input information and generates an asset formation planning table. The awareness guidance unit is implemented by the specific processing unit 290 of the data processing unit 12, which makes the user aware of the need for financial investment based on the generated planning table. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, which suggests individual stocks from the generated planning table. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the reception unit, generation unit, awareness guidance unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, which inputs information such as the user's age, annual income, and current financial assets. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input information and generates an asset formation planning table. The awareness guidance unit is implemented by the specific processing unit 290 of the data processing unit 12, which makes the user aware of the need for financial investment based on the generated planning table. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, which suggests individual stocks from the generated planning table. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the reception unit, generation unit, awareness guidance unit, and suggestion unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, which inputs information such as the user's age, annual income, and current financial assets. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input information and generates an asset formation planning table. The awareness guidance unit is implemented by the specific processing unit 290 of the data processing unit 12, which makes the user aware of the need for financial investment based on the generated planning table. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, which suggests individual stocks from the generated planning table. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A reception area where users enter their required information, A generation unit analyzes the information entered by the reception unit and generates an asset formation planning table, Based on the planning table generated by the generation unit, an awareness guidance unit makes the user aware of the need for financial investment, The system includes a suggestion unit that suggests individual stocks from the planning table generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is The system analyzes user information such as age, annual income, and current financial assets to generate an asset formation planning table. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned consciousness induction unit is Based on the generated planning table, the system aims to make users aware of the need for financial investment. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned suggestion section is, Suggest individual stocks (stocks and mutual funds) from the generated planning table. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned suggestion section is, Answer questions about individual companies and market information, and have them reflected in your MyPage. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Allow users to update their asset building plan as many times as they like. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned marketing department, We will implement integrated marketing strategies tailored to the user's stage of development. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering information, the input fields are customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users enter information, the system prioritizes inputting highly relevant information by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When users enter information, the system analyzes their social media activity and prompts them to enter relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is The system estimates the user's emotions and adjusts the presentation of the planning table based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating the planning table, adjust the level of detail based on the user's importance. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating the planning table, different generation algorithms are applied depending on the user's category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is The system estimates the user's emotions and adjusts the length of the planning table based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating the planning table, prioritization is determined based on the user's submission timing. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating the planning table, the order is adjusted based on user relationships. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned consciousness induction unit is The system estimates the user's emotions and adjusts the method of guiding their awareness based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned consciousness induction unit is During the process of inducing awareness, the system analyzes the user's past behavioral history to select the optimal induction method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned consciousness induction unit is During consciousness induction, the induction method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned consciousness induction unit is It estimates the user's emotions and determines the priority of attention guidance based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned consciousness induction unit is During consciousness guidance, the optimal guidance method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned consciousness induction unit is During the process of inducing awareness, we analyze the user's social media activity and propose methods for inducing that awareness. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned suggestion section is, It estimates the user's emotions and adjusts the suggestion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned suggestion section is, When suggesting products, the system analyzes the user's past investment history to select the most suitable suggestion method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned suggestion section is, When suggesting, the suggestion method is customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned suggestion section is, It estimates the user's emotions and determines the priority of suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned suggestion section is, When suggesting results, the system selects the optimal suggestion method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned suggestion section is, When suggesting, the system analyzes the user's social media activity to propose appropriate suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned marketing department, We estimate user emotions and adjust marketing strategies based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned marketing department, When implementing marketing strategies, we analyze users' past behavioral history to select the most suitable strategies. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned marketing department, When implementing marketing strategies, customize the strategies based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned marketing department, We estimate user emotions and prioritize marketing initiatives based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned marketing department, When implementing marketing strategies, select the most suitable strategy by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned marketing department, When implementing marketing strategies, we analyze users' social media activity to select the most effective strategies. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users enter their required information, A generation unit analyzes the information entered by the reception unit and generates an asset formation planning table, Based on the planning table generated by the generation unit, an awareness guidance unit makes the user aware of the need for financial investment, The system includes a suggestion unit that suggests individual stocks from the planning table generated by the generation unit. A system characterized by the following features.

2. The generating unit is The system analyzes user information such as age, annual income, and current financial assets to generate an asset formation planning table. The system according to feature 1.

3. The aforementioned consciousness induction unit is Based on the generated planning table, the system aims to make users aware of the need for financial investment. The system according to feature 1.

4. The aforementioned suggestion section is, Suggest individual stocks from the generated planning table. The system according to feature 1.

5. The aforementioned suggestion section is, Answer questions about individual companies and market information, and have them reflected in your MyPage. The system according to feature 1.

6. The generating unit is Allow users to update their asset building plan as many times as they like. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information input based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When entering information, the input fields are customized based on the user's current lifestyle and areas of interest. The system according to feature 1.

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