System

Abstract

An object of the system according to the embodiment is to propose an appropriate investment strategy based on an investment goal and a risk tolerance of a user and to allow even a beginner to easily consult and confirm.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a consultation unit. The receiving unit receives information on an investment target, a risk tolerance, and an investment period of the user. The analysis unit analyzes the information received by the reception unit and proposes an appropriate investment strategy. The proposal unit provides the user with the investment strategy proposed by the analysis unit. The consultation unit performs consultation or confirmation regarding the investment strategy proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem of making it difficult to propose appropriate investment strategies based on a user's investment goals and risk tolerance, which is particularly difficult for beginners.

[0005] The system according to the embodiment aims to propose an appropriate investment strategy based on the user's investment goals and risk tolerance, and to enable even beginners to easily consult and confirm the strategy. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a consultation unit. The reception unit accepts information on the user's investment goals, risk tolerance, and investment period. The analysis unit analyzes the information accepted by the reception unit and proposes an appropriate investment strategy. The proposal unit provides the user with the investment strategy proposed by the analysis unit. The consultation unit provides consultation and confirmation regarding the investment strategy proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment proposes an appropriate investment strategy based on the user's investment goals and risk tolerance, and even beginners can easily consult and confirm the strategy. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An investment strategy proposal system according to an embodiment of the present invention proposes an optimal investment strategy based on a user's investment goals, risk tolerance, investment period, and other factors. In the investment strategy proposal system, a user inputs information such as their investment goals, risk tolerance, and investment period, and a generation AI analyzes this information to propose an optimal investment strategy. The proposal includes investment products and portfolios that best fit the user's investment goals. Users can also easily consult or confirm their investment strategy via LINE (registered trademark), anytime, anywhere. For example, in the investment strategy proposal system, a user inputs information such as "I want to earn 1 million yen in five years" or "I want to keep risk low." This information is input into the generation AI, which analyzes it and proposes an optimal investment strategy. For example, for users who want to keep risk low, the generation AI can suggest bonds or index funds that promise stable returns. Furthermore, users can consult with experts about their investment questions or concerns via LINE. Users can also easily check their investment progress via LINE. This allows even beginners without specialized knowledge to build assets with confidence. As a result, the investment strategy proposal system will propose the optimal investment strategy based on the user's investment goals, risk tolerance, investment period, etc., allowing even beginners to build assets with peace of mind. For example, users will be recommended the investment strategy that best suits their investment goals, allowing them to efficiently increase their assets. In addition, they can easily consult or check anything they want through LINE, anytime and anywhere, eliminating any concerns or questions they may have about investing.

[0029] An investment strategy proposal system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a consultation unit. The reception unit accepts information about a user's investment goals, risk tolerance, and investment period. For example, the user may input information such as "I want to aim for 1 million yen in five years" or "I want to keep risk low." The reception unit can input this information to the generation AI. The analysis unit uses the generation AI to analyze the information accepted by the reception unit and propose an optimal investment strategy. For example, the generation AI considers the user's investment goals, risk tolerance, investment period, etc. and proposes optimal investment products and portfolios. The generation AI analyzes the user's information and generates an investment strategy using a text generation AI (e.g., LLM) or a multimodal generation AI. The proposal unit provides the user with the investment strategy proposed by the analysis unit. For example, the proposal unit presents the investment products and portfolios proposed by the generation AI to the user. The proposal unit can also propose bonds or index funds that are expected to generate stable returns based on the user's risk tolerance. The consultation unit provides consultation and confirmation regarding the investment strategy proposed by the proposal unit. For example, users can consult with experts about their investment-related questions and concerns via LINE. Users can also easily check their investment progress via LINE. As a result, the investment strategy proposal system according to the embodiment proposes optimal investment strategies based on the user's investment goals, risk tolerance, investment period, etc., allowing even beginners to build assets with confidence.

[0030] The reception unit can analyze the user's past investment history and select an appropriate information input method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception unit analyzes the user's past investment history and selects the optimal information input method. The reception unit can also automatically input information about a specific investment product from the user's past investment history. For example, the reception unit suggests the optimal input procedure based on the user's past investment history. This enables efficient information input by selecting the optimal information input method based on the user's past investment history. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or without using AI.

[0031] When inputting investment information, the reception unit can perform filtering based on the user's current economic situation and areas of interest. The reception unit filters appropriate investment information, for example, taking into account the user's current income and expenditure situation. For example, the reception unit preferentially displays relevant investment information based on the user's areas of interest (e.g., environmentally related investments). The reception unit can also filter out unnecessary information and display only necessary information based on the user's economic situation and areas of interest. For example, the reception unit filters investment information based on the user's economic situation and areas of interest. This enables more effective investment by providing appropriate investment information based on the user's economic situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0032] When inputting investment information, the reception unit can select an appropriate input means depending on the user's input method. For example, if the user desires voice input, the reception unit inputs information using voice recognition technology. For example, if the user desires text input, the reception unit preferentially provides keyboard input. If the user desires image input, the reception unit can also input information using image recognition technology. For example, the reception unit selects the optimal input means depending on the user's input method. This improves the convenience of information input by providing the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0033] When inputting investment information, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. The reception unit, for example, prioritizes displaying region-specific investment information based on the user's place of residence. For example, the reception unit prioritizes displaying nearby investment opportunities based on the user's geographical location information. The reception unit can also prioritize displaying investment information related to the economic situation of the region in consideration of the user's geographical location information. For example, the reception unit prioritizes displaying investment information related to the economic situation of the region based on the user's geographical location information. In this way, by providing highly relevant information based on the user's geographical location information, it is possible to obtain region-specific investment information. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0034] When inputting investment information, the reception unit can analyze the user's social media activity and input related information. For example, the reception unit can preferentially display information about investment products in which the user has shown interest on social media. For example, the reception unit can analyze the content of the user's social media posts and input related investment information. The reception unit can also input related investment information by referring to the activities of the user's friends on social media. For example, the reception unit can analyze the user's social media activity and input related information. This makes it possible to provide more personalized information by providing related investment information based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0035] When inputting investment information, the reception unit can customize the input method by reflecting the user's past feedback. The reception unit, for example, preferentially provides an input method that the user has previously preferred. For example, the reception unit optimizes the input procedure based on the user's past feedback. The reception unit can also customize the input interface by reflecting the user's past feedback. For example, the reception unit customizes the input method by reflecting the user's past feedback. In this way, by reflecting the user's past feedback, an input method that is easier to use can be provided. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the investment information. The analysis unit, for example, performs a detailed analysis on investment information with high importance. For example, the analysis unit evaluates the importance of the investment information and performs a detailed analysis on the information with high importance. The analysis unit can also perform a simplified analysis on investment information with low importance. For example, the analysis unit gradually adjusts the level of detail of the analysis according to the importance of the investment information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the investment information. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of investment information. For example, the analysis unit applies a specific analysis algorithm to stock investments. For example, the analysis unit applies regression analysis to stock investments. The analysis unit can also apply a different analysis algorithm to bond investments. For example, the analysis unit applies clustering to bond investments. The analysis unit can also select the optimal analysis algorithm depending on the category of investment information. For example, the analysis unit selects the optimal analysis algorithm depending on the category of investment information. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the category of investment information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also optimize the analysis parameters by reflecting the user's past analysis results. For example, the analysis unit optimizes the analysis parameters by reflecting the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the investment information. The analysis unit, for example, prioritizes analysis of the most recent investment information. For example, the analysis unit evaluates the time of submission of the investment information and prioritizes analysis of the most recent information. The analysis unit can also lower the priority of analysis of older investment information. For example, the analysis unit gradually adjusts the priority of analysis depending on the time of submission of the investment information. In this way, by determining the priority of analysis depending on the time of submission of the investment information, the most recent information can be prioritized for analysis. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the investment information. The analysis unit, for example, prioritizes analysis of highly relevant investment information. For example, the analysis unit evaluates the relevance of the investment information and prioritizes analysis of highly relevant information. The analysis unit can also postpone the order of analysis of less relevant investment information. For example, the analysis unit gradually adjusts the order of analysis according to the relevance of the investment information. This enables efficient analysis by adjusting the order of analysis according to the relevance of the investment information. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0041] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is a beginner, the analysis unit avoids technical terminology and uses easy-to-understand expressions. For example, the analysis unit evaluates the user's level of expertise and uses easy-to-understand expressions, avoiding technical terminology, for beginners. The analysis unit can also use appropriate technical terminology if the user is an intermediate user. For example, the analysis unit uses appropriate technical terminology for intermediate users. For example, the analysis unit can provide detailed analysis results using a lot of technical terminology for advanced users. For example, the analysis unit provides detailed analysis results using a lot of technical terminology for advanced users. In this way, by adjusting the technical terminology in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without using AI.

[0042] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the investment product. For example, the proposal unit makes a detailed proposal for an investment product with a high level of importance. For example, the proposal unit evaluates the importance of the investment product and makes a detailed proposal for the product with a high level of importance. The proposal unit can also make a simplified proposal for an investment product with a low level of importance. For example, the proposal unit gradually adjusts the level of detail of the proposal according to the importance of the investment product. This enables efficient proposals by adjusting the level of detail of the proposal according to the importance of the investment product. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI or without using AI.

[0043] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the investment product. For example, the proposal unit applies a specific proposal algorithm to stock investments. For example, the proposal unit applies regression analysis to stock investments. The proposal unit can also apply a different proposal algorithm to bond investments. For example, the proposal unit applies clustering to bond investments. The proposal unit can also select an optimal proposal algorithm depending on the category of the investment product. For example, the proposal unit selects an optimal proposal algorithm depending on the category of the investment product. This improves the accuracy of the proposal by applying the optimal proposal algorithm depending on the category of the investment product. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI or without using AI.

[0044] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, adjusts the suggestion algorithm based on the user's past suggestion results. For example, the suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit can also optimize the suggestion parameters by reflecting the user's past suggestion results. For example, the suggestion unit optimizes the suggestion parameters by reflecting the user's past suggestion results. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0045] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the investment product. For example, the proposal unit prioritizes the proposal for the latest investment product. For example, the proposal unit evaluates the time of submission of the investment product and prioritizes the proposal for the latest product. The proposal unit can also lower the priority of the proposal for older investment products. For example, the proposal unit gradually adjusts the priority of the proposal depending on the time of submission of the investment product. In this way, by determining the priority of the proposal depending on the time of submission of the investment product, the latest information can be prioritized for proposal. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI or without using AI.

[0046] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the investment products. For example, the proposal unit prioritizes proposals for highly relevant investment products. For example, the proposal unit evaluates the relevance of investment products and prioritizes proposals for highly relevant products. The proposal unit can also postpone the order of proposals for less relevant investment products. For example, the proposal unit gradually adjusts the order of proposals according to the relevance of the investment products. This enables efficient proposals by adjusting the order of proposals according to the relevance of the investment products. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI.

[0047] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user is a beginner, the suggestion unit avoids technical terminology and uses easy-to-understand expressions. For example, the suggestion unit evaluates the user's level of expertise and uses easy-to-understand expressions, avoiding technical terminology, for beginners. The suggestion unit can also use appropriate technical terminology if the user is an intermediate user. For example, the suggestion unit uses appropriate technical terminology for intermediate users. For example, the suggestion unit can also make detailed proposals using a lot of technical terminology for advanced users. For example, the suggestion unit makes detailed proposals using a lot of technical terminology for advanced users. In this way, by adjusting the technical terminology in the proposal according to the user's level of expertise, it is possible to provide proposals that are easier to understand. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or without using AI.

[0048] At the time of consultation, the consultation unit can select the optimal response method by referring to the user's past consultation history. The consultation unit selects the optimal response method, for example, based on the content of past consultations made by the user. For example, the consultation unit selects the optimal response method by referring to the user's past consultation history. The consultation unit can also propose a solution to a specific problem from the user's past consultation history. For example, the consultation unit customizes the response method by referring to the user's past consultation history. This enables efficient response by selecting the optimal response method based on the user's past consultation history. Some or all of the above-described processing in the consultation unit may be performed, for example, using AI or without using AI.

[0049] The consultation unit can customize the consultation content based on the user's current investment situation during the consultation. The consultation unit, for example, provides appropriate advice taking into account the user's current investment situation. For example, the consultation unit evaluates the user's assets and investment performance and provides appropriate advice. The consultation unit can also propose specific improvement measures based on the user's investment situation. For example, the consultation unit reflects the user's investment situation in real time and customizes the consultation content. This allows for customizing the consultation content based on the user's current investment situation, thereby providing more appropriate advice. Some or all of the above-described processing in the consultation unit may be performed, for example, using AI, or may be performed without using AI.

[0050] The consultation unit can improve the consultation method by reflecting user feedback during the consultation. The consultation unit improves the consultation method based on user feedback, for example. For example, the consultation unit collects user feedback and improves the consultation method. The consultation unit can also optimize the response procedure by reflecting user feedback. For example, the consultation unit customizes the content of the consultation by referring to the user feedback. In this way, a more appropriate consultation method can be provided by reflecting the user feedback. Some or all of the above-mentioned processing in the consultation unit may be performed, for example, using AI, or may be performed without using AI.

[0051] During the consultation, the consultation unit can select the optimal consultation method taking into consideration the user's geographical location information. The consultation unit, for example, provides region-specific investment information based on the user's place of residence. For example, the consultation unit suggests nearby investment opportunities based on the user's geographical location information. The consultation unit can also select the optimal consultation method taking into consideration the user's geographical location information. For example, the consultation unit provides region-specific investment information based on the user's geographical location information. In this way, by providing the optimal consultation method based on the user's geographical location information, it is possible to obtain region-specific investment information. Some or all of the above-described processing in the consultation unit may be performed, for example, using AI or without using AI.

[0052] During a consultation, the consultation unit can analyze the user's social media activity to suggest consultation content. For example, the consultation unit can suggest consultation content related to an investment product in which the user has shown interest on social media. For example, the consultation unit can analyze the user's social media posts to suggest related consultation content. The consultation unit can also suggest related consultation content by referring to the activities of the user's friends on social media. For example, the consultation unit can analyze the user's social media activity to suggest related consultation content. This enables more personalized consultation by providing related consultation content based on the user's social media activity. Some or all of the above-described processing in the consultation unit may be performed, for example, using AI or without using AI.

[0053] The consultation unit can customize the consultation method by reflecting the user's past feedback during the consultation. The consultation unit customizes the consultation method based on, for example, the user's past feedback. For example, the consultation unit optimizes the response procedure by reflecting the user's past feedback. The consultation unit can also customize the consultation content by referring to the user's past feedback. For example, the consultation unit customizes the consultation method by reflecting the user's past feedback. In this way, a more appropriate consultation method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the consultation unit may be performed, for example, using AI or may be performed without using AI.

[0054] The proposal unit can propose bonds or index funds that are expected to provide stable returns based on the user's risk tolerance. The proposal unit, for example, evaluates the user's risk tolerance and proposes bonds or index funds that are expected to provide stable returns. For example, the proposal unit proposes investment products that are expected to provide stable returns based on the user's risk tolerance. The proposal unit can also propose investment products with reduced risk based on the user's risk tolerance. For example, the proposal unit proposes bonds or index funds that are expected to provide stable returns based on the user's risk tolerance. This enables investments with reduced risk by proposing investment products that are expected to provide stable returns based on the user's risk tolerance. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI or without using AI.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The reception unit can also adjust the investment strategy based on the user's health condition in addition to the user's investment goals and risk tolerance. For example, if the user's health condition is good, a long-term investment strategy can be proposed. Conversely, if the user's health condition is unstable, a short-term investment strategy can be proposed. Furthermore, it is possible to periodically monitor the user's health data and automatically adjust the investment strategy according to changes in the user's health condition. This makes it possible to provide a flexible investment strategy according to the user's health condition.

[0057] The suggestion unit can also adjust the investment strategy based on the user's life events (marriage, childbirth, retirement, etc.) in addition to the user's investment goals and risk tolerance. For example, if the user is planning to get married, an investment strategy can be suggested that takes into account the living expenses after marriage. Conversely, if the user is planning to retire, an investment strategy can be suggested that takes into account the income after retirement. Furthermore, it is possible to periodically update the user's life event data and automatically adjust the investment strategy in response to changes in life events. This makes it possible to provide a flexible investment strategy that corresponds to the user's life events.

[0058] The reception unit can also adjust the investment strategy based on the user's hobbies and interests, in addition to the user's investment goals and risk tolerance. For example, if the user is interested in environmental protection, environmental-related investment products can be suggested. Conversely, if the user is interested in technology, technology-related investment products can be suggested. Furthermore, the user's hobby and interest data can be periodically updated, and the investment strategy can be automatically adjusted according to changes in interests. This makes it possible to provide a flexible investment strategy that suits the user's hobbies and interests.

[0059] The suggestion unit can also adjust the investment strategy based on the user's family composition in addition to the user's investment goals and risk tolerance. For example, if the user has children, it can suggest an investment strategy that takes education expenses into consideration. Conversely, if the user is single, it can suggest an investment strategy for single people. Furthermore, it is possible to periodically update the user's family composition data and automatically adjust the investment strategy in response to changes in family composition. This makes it possible to provide a flexible investment strategy that suits the user's family composition.

[0060] The reception unit can also adjust the investment strategy based on the user's occupation, in addition to the user's investment goals and risk tolerance. For example, if the user has a stable job, it can propose a long-term investment strategy. Conversely, if the user is a freelancer, it can propose a short-term investment strategy. Furthermore, it is possible to periodically update the user's occupation data and automatically adjust the investment strategy according to changes in occupation. This makes it possible to provide a flexible investment strategy that suits the user's occupation.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit accepts information about the user's investment goals, risk tolerance, and investment period. For example, the user might enter information such as "I want to aim for 1 million yen in five years" or "I want to keep the risk low." The reception unit can then input this information into the generation AI. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and propose an optimal investment strategy. For example, the generation AI considers the user's investment goals, risk tolerance, investment period, etc., and proposes optimal investment products and portfolios. The generation AI analyzes the user's information and generates an investment strategy using text generation AI (e.g., LLM) or multimodal generation AI. Step 3: The proposal unit provides the user with the investment strategy proposed by the analysis unit. For example, the proposal unit presents the user with the investment products and portfolios proposed by the generation AI. The proposal unit can also suggest bonds or index funds that are expected to generate stable returns based on the user's risk tolerance. Step 4: The Consultation Department consults and confirms the investment strategy proposed by the Proposal Department. For example, users can consult with experts about their investment questions or concerns via LINE. Users can also easily check the progress of their investments via LINE.

[0063] (Example 2) An investment strategy proposal system according to an embodiment of the present invention proposes an optimal investment strategy based on a user's investment goals, risk tolerance, investment period, and other factors. In the investment strategy proposal system, a user inputs information such as their investment goals, risk tolerance, and investment period, and a generation AI analyzes this information to propose an optimal investment strategy. The proposal includes investment products and portfolios that best fit the user's investment goals. Users can also easily consult or confirm via LINE, anytime, anywhere. For example, in the investment strategy proposal system, a user inputs information such as "I want to earn 1 million yen in five years" or "I want to keep risk low." This information is input into the generation AI, which analyzes it and proposes an optimal investment strategy. For example, for users who want to keep risk low, the generation AI can suggest bonds or index funds that promise stable returns. Furthermore, users can consult with experts regarding their investment questions or concerns via LINE. Users can also easily check their investment progress via LINE. This allows even beginners without specialized knowledge to build assets with confidence. As a result, the investment strategy proposal system will propose the optimal investment strategy based on the user's investment goals, risk tolerance, investment period, etc., allowing even beginners to build assets with peace of mind. For example, users will be recommended the investment strategy that best suits their investment goals, allowing them to efficiently increase their assets. In addition, they can easily consult or check anything they want through LINE, anytime and anywhere, eliminating any concerns or questions they may have about investing.

[0064] An investment strategy proposal system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a consultation unit. The reception unit accepts information about a user's investment goals, risk tolerance, and investment period. For example, the user may input information such as "I want to aim for 1 million yen in five years" or "I want to keep risk low." The reception unit can input this information to the generation AI. The analysis unit uses the generation AI to analyze the information accepted by the reception unit and propose an optimal investment strategy. For example, the generation AI considers the user's investment goals, risk tolerance, investment period, etc. and proposes optimal investment products and portfolios. The generation AI analyzes the user's information and generates an investment strategy using a text generation AI (e.g., LLM) or a multimodal generation AI. The proposal unit provides the user with the investment strategy proposed by the analysis unit. For example, the proposal unit presents the investment products and portfolios proposed by the generation AI to the user. The proposal unit can also propose bonds or index funds that are expected to generate stable returns based on the user's risk tolerance. The consultation unit provides consultation and confirmation regarding the investment strategy proposed by the proposal unit. For example, users can consult with experts about their investment-related questions and concerns via LINE. Users can also easily check their investment progress via LINE. As a result, the investment strategy proposal system according to the embodiment proposes optimal investment strategies based on the user's investment goals, risk tolerance, investment period, etc., allowing even beginners to build assets with confidence.

[0065] The reception unit can estimate the user's emotions and adjust the timing of investment information input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay the input timing to allow the user to input in a relaxed state. For example, the reception unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. If the user is relaxed, the reception unit can prompt the user to input information immediately and smoothly accept information. For example, the reception unit can record the user's voice and estimate the user's emotions using voice analysis technology. If the user is in a hurry, the reception unit can accelerate the input timing to quickly accept information. For example, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the user to input investment information at a more appropriate time by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0066] The reception unit can analyze the user's past investment history and select an appropriate information input method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception unit analyzes the user's past investment history and selects the optimal information input method. The reception unit can also automatically input information about a specific investment product from the user's past investment history. For example, the reception unit suggests the optimal input procedure based on the user's past investment history. This enables efficient information input by selecting the optimal information input method based on the user's past investment history. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or without using AI.

[0067] When inputting investment information, the reception unit can perform filtering based on the user's current economic situation and areas of interest. The reception unit filters appropriate investment information, for example, taking into account the user's current income and expenditure situation. For example, the reception unit preferentially displays relevant investment information based on the user's areas of interest (e.g., environmentally related investments). The reception unit can also filter out unnecessary information and display only necessary information based on the user's economic situation and areas of interest. For example, the reception unit filters investment information based on the user's economic situation and areas of interest. This enables more effective investment by providing appropriate investment information based on the user's economic situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0068] When inputting investment information, the reception unit can select an appropriate input means depending on the user's input method. For example, if the user desires voice input, the reception unit inputs information using voice recognition technology. For example, if the user desires text input, the reception unit preferentially provides keyboard input. If the user desires image input, the reception unit can also input information using image recognition technology. For example, the reception unit selects the optimal input means depending on the user's input method. This improves the convenience of information input by providing the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0069] The reception unit can estimate the user's emotions and prioritize the investment information to be input based on the estimated user emotions. For example, if the user is feeling anxious, the reception unit can prioritize displaying low-risk investment information. For example, the reception unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. For example, if the user is excited, the reception unit can prioritize displaying high-risk investment information. For example, the reception unit can record the user's voice and estimate the user's emotions using voice analysis technology. For example, if the user is relaxed, the reception unit can prioritize displaying balanced investment information. For example, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the user to prioritize investment information according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0070] When inputting investment information, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. The reception unit, for example, prioritizes displaying region-specific investment information based on the user's place of residence. For example, the reception unit prioritizes displaying nearby investment opportunities based on the user's geographical location information. The reception unit can also prioritize displaying investment information related to the economic situation of the region in consideration of the user's geographical location information. For example, the reception unit prioritizes displaying investment information related to the economic situation of the region based on the user's geographical location information. In this way, by providing highly relevant information based on the user's geographical location information, it is possible to obtain region-specific investment information. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0071] When inputting investment information, the reception unit can analyze the user's social media activity and input related information. For example, the reception unit can preferentially display information about investment products in which the user has shown interest on social media. For example, the reception unit can analyze the content of the user's social media posts and input related investment information. The reception unit can also input related investment information by referring to the activities of the user's friends on social media. For example, the reception unit can analyze the user's social media activity and input related information. This makes it possible to provide more personalized information by providing related investment information based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0072] When inputting investment information, the reception unit can customize the input method by reflecting the user's past feedback. The reception unit, for example, preferentially provides an input method that the user has previously preferred. For example, the reception unit optimizes the input procedure based on the user's past feedback. The reception unit can also customize the input interface by reflecting the user's past feedback. For example, the reception unit customizes the input method by reflecting the user's past feedback. In this way, by reflecting the user's past feedback, an input method that is easier to use can be provided. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0073] The analysis unit can estimate the user's emotions and adjust the analysis presentation method based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit uses a simple and easy-to-understand presentation method. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, the analysis unit records the user's voice and estimates the emotion using voice analysis technology. If the user is excited, the analysis unit can use a visually stimulating presentation method. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the analysis presentation method to be adjusted according to the user's emotions, thereby providing more understandable analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0074] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the investment information. The analysis unit, for example, performs a detailed analysis on investment information with high importance. For example, the analysis unit evaluates the importance of the investment information and performs a detailed analysis on the information with high importance. The analysis unit can also perform a simplified analysis on investment information with low importance. For example, the analysis unit gradually adjusts the level of detail of the analysis according to the importance of the investment information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the investment information. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0075] During analysis, the analysis unit can apply different analysis algorithms depending on the category of investment information. For example, the analysis unit applies a specific analysis algorithm to stock investments. For example, the analysis unit applies regression analysis to stock investments. The analysis unit can also apply a different analysis algorithm to bond investments. For example, the analysis unit applies clustering to bond investments. The analysis unit can also select the optimal analysis algorithm depending on the category of investment information. For example, the analysis unit selects the optimal analysis algorithm depending on the category of investment information. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the category of investment information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0076] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also optimize the analysis parameters by reflecting the user's past analysis results. For example, the analysis unit optimizes the analysis parameters by reflecting the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0077] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, the analysis unit records the user's voice and estimates the emotion using voice analysis technology. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis result. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0078] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the investment information. The analysis unit, for example, prioritizes analysis of the most recent investment information. For example, the analysis unit evaluates the time of submission of the investment information and prioritizes analysis of the most recent information. The analysis unit can also lower the priority of analysis of older investment information. For example, the analysis unit gradually adjusts the priority of analysis depending on the time of submission of the investment information. In this way, by determining the priority of analysis depending on the time of submission of the investment information, the most recent information can be prioritized for analysis. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0079] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the investment information. The analysis unit, for example, prioritizes analysis of highly relevant investment information. For example, the analysis unit evaluates the relevance of the investment information and prioritizes analysis of highly relevant information. The analysis unit can also postpone the order of analysis of less relevant investment information. For example, the analysis unit gradually adjusts the order of analysis according to the relevance of the investment information. This enables efficient analysis by adjusting the order of analysis according to the relevance of the investment information. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0080] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is a beginner, the analysis unit avoids technical terminology and uses easy-to-understand expressions. For example, the analysis unit evaluates the user's level of expertise and uses easy-to-understand expressions, avoiding technical terminology, for beginners. The analysis unit can also use appropriate technical terminology if the user is an intermediate user. For example, the analysis unit uses appropriate technical terminology for intermediate users. For example, the analysis unit can provide detailed analysis results using a lot of technical terminology for advanced users. For example, the analysis unit provides detailed analysis results using a lot of technical terminology for advanced users. In this way, by adjusting the technical terminology in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without using AI.

[0081] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is feeling anxious, the suggestion unit uses a simple and easy-to-understand expression. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The suggestion unit can also provide detailed suggestions if the user is relaxed. For example, the suggestion unit records the user's voice and estimates the emotion using voice analysis technology. If the user is excited, the suggestion unit can also use a visually stimulating expression. For example, the suggestion unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the suggestion unit to adjust the way suggestions are expressed based on the user's emotions, thereby providing more understandable suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the proposing unit may be performed using AI, for example, or may be performed without using AI.

[0082] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the investment product. For example, the proposal unit makes a detailed proposal for an investment product with a high level of importance. For example, the proposal unit evaluates the importance of the investment product and makes a detailed proposal for the product with a high level of importance. The proposal unit can also make a simplified proposal for an investment product with a low level of importance. For example, the proposal unit gradually adjusts the level of detail of the proposal according to the importance of the investment product. This enables efficient proposals by adjusting the level of detail of the proposal according to the importance of the investment product. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI or without using AI.

[0083] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the investment product. For example, the proposal unit applies a specific proposal algorithm to stock investments. For example, the proposal unit applies regression analysis to stock investments. The proposal unit can also apply a different proposal algorithm to bond investments. For example, the proposal unit applies clustering to bond investments. The proposal unit can also select an optimal proposal algorithm depending on the category of the investment product. For example, the proposal unit selects an optimal proposal algorithm depending on the category of the investment product. This improves the accuracy of the proposal by applying the optimal proposal algorithm depending on the category of the investment product. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI or without using AI.

[0084] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, adjusts the suggestion algorithm based on the user's past suggestion results. For example, the suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit can also optimize the suggestion parameters by reflecting the user's past suggestion results. For example, the suggestion unit optimizes the suggestion parameters by reflecting the user's past suggestion results. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0085] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. For example, the suggestion unit can provide detailed suggestions if the user is relaxed. For example, the suggestion unit can record the user's voice and estimate the user's emotions using voice analysis technology. For example, the suggestion unit can provide visually stimulating suggestions if the user is excited. For example, the suggestion unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the length of the suggestions to be adjusted according to the user's emotions, thereby providing more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the proposing unit may be performed using AI, for example, or may be performed without using AI.

[0086] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the investment product. For example, the proposal unit prioritizes the proposal for the latest investment product. For example, the proposal unit evaluates the time of submission of the investment product and prioritizes the proposal for the latest product. The proposal unit can also lower the priority of the proposal for older investment products. For example, the proposal unit gradually adjusts the priority of the proposal depending on the time of submission of the investment product. In this way, by determining the priority of the proposal depending on the time of submission of the investment product, the latest information can be prioritized for proposal. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI or without using AI.

[0087] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the investment products. For example, the proposal unit prioritizes proposals for highly relevant investment products. For example, the proposal unit evaluates the relevance of investment products and prioritizes proposals for highly relevant products. The proposal unit can also postpone the order of proposals for less relevant investment products. For example, the proposal unit gradually adjusts the order of proposals according to the relevance of the investment products. This enables efficient proposals by adjusting the order of proposals according to the relevance of the investment products. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI.

[0088] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user is a beginner, the suggestion unit avoids technical terminology and uses easy-to-understand expressions. For example, the suggestion unit evaluates the user's level of expertise and uses easy-to-understand expressions, avoiding technical terminology, for beginners. The suggestion unit can also use appropriate technical terminology if the user is an intermediate user. For example, the suggestion unit uses appropriate technical terminology for intermediate users. For example, the suggestion unit can also make detailed proposals using a lot of technical terminology for advanced users. For example, the suggestion unit makes detailed proposals using a lot of technical terminology for advanced users. In this way, by adjusting the technical terminology in the proposal according to the user's level of expertise, it is possible to provide proposals that are easier to understand. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or without using AI.

[0089] The consultation unit can estimate the user's emotions and adjust the consultation response method based on the estimated user emotions. For example, if the user is feeling anxious, the consultation unit responds in a gentle tone. For example, the consultation unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. If the user is relaxed, the consultation unit can also provide a detailed explanation. For example, the consultation unit records the user's voice and estimates the user's emotions using voice analysis technology. If the user is in a hurry, the consultation unit can also respond quickly. For example, the consultation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. This enables a more appropriate response by adjusting the consultation response method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the consultation unit may be performed using, for example, AI, or without AI.

[0090] At the time of consultation, the consultation unit can select the optimal response method by referring to the user's past consultation history. The consultation unit selects the optimal response method, for example, based on the content of past consultations made by the user. For example, the consultation unit selects the optimal response method by referring to the user's past consultation history. The consultation unit can also propose a solution to a specific problem from the user's past consultation history. For example, the consultation unit customizes the response method by referring to the user's past consultation history. This enables efficient response by selecting the optimal response method based on the user's past consultation history. Some or all of the above-described processing in the consultation unit may be performed, for example, using AI or without using AI.

[0091] The consultation unit can customize the consultation content based on the user's current investment situation during the consultation. The consultation unit, for example, provides appropriate advice taking into account the user's current investment situation. For example, the consultation unit evaluates the user's assets and investment performance and provides appropriate advice. The consultation unit can also propose specific improvement measures based on the user's investment situation. For example, the consultation unit reflects the user's investment situation in real time and customizes the consultation content. This allows for customizing the consultation content based on the user's current investment situation, thereby providing more appropriate advice. Some or all of the above-described processing in the consultation unit may be performed, for example, using AI, or may be performed without using AI.

[0092] The consultation unit can improve the consultation method by reflecting user feedback during the consultation. The consultation unit improves the consultation method based on user feedback, for example. For example, the consultation unit collects user feedback and improves the consultation method. The consultation unit can also optimize the response procedure by reflecting user feedback. For example, the consultation unit customizes the content of the consultation by referring to the user feedback. In this way, a more appropriate consultation method can be provided by reflecting the user feedback. Some or all of the above-mentioned processing in the consultation unit may be performed, for example, using AI, or may be performed without using AI.

[0093] The consultation unit can estimate the user's emotions and determine the priority of consultations based on the estimated user emotions. For example, the consultation unit prioritizes responses when the user is feeling anxious. For example, the consultation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. If the user is relaxed, the consultation unit can also respond in a normal order of response. For example, the consultation unit records the user's voice and estimates the emotion using voice analysis technology. If the user is in a hurry, the consultation unit can also respond quickly. For example, the consultation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This enables faster response by determining the priority of consultations based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the consultation unit may be performed using, for example, AI, or without AI.

[0094] During the consultation, the consultation unit can select the optimal consultation method taking into consideration the user's geographical location information. The consultation unit, for example, provides region-specific investment information based on the user's place of residence. For example, the consultation unit suggests nearby investment opportunities based on the user's geographical location information. The consultation unit can also select the optimal consultation method taking into consideration the user's geographical location information. For example, the consultation unit provides region-specific investment information based on the user's geographical location information. In this way, by providing the optimal consultation method based on the user's geographical location information, it is possible to obtain region-specific investment information. Some or all of the above-described processing in the consultation unit may be performed, for example, using AI or without using AI.

[0095] During a consultation, the consultation unit can analyze the user's social media activity to suggest consultation content. For example, the consultation unit can suggest consultation content related to an investment product in which the user has shown interest on social media. For example, the consultation unit can analyze the user's social media posts to suggest related consultation content. The consultation unit can also suggest related consultation content by referring to the activities of the user's friends on social media. For example, the consultation unit can analyze the user's social media activity to suggest related consultation content. This enables more personalized consultation by providing related consultation content based on the user's social media activity. Some or all of the above-described processing in the consultation unit may be performed, for example, using AI or without using AI.

[0096] The consultation unit can customize the consultation method by reflecting the user's past feedback during the consultation. The consultation unit customizes the consultation method based on, for example, the user's past feedback. For example, the consultation unit optimizes the response procedure by reflecting the user's past feedback. The consultation unit can also customize the consultation content by referring to the user's past feedback. For example, the consultation unit customizes the consultation method by reflecting the user's past feedback. In this way, a more appropriate consultation method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the consultation unit may be performed, for example, using AI or may be performed without using AI.

[0097] The proposal unit can propose bonds or index funds that are expected to provide stable returns based on the user's risk tolerance. The proposal unit, for example, evaluates the user's risk tolerance and proposes bonds or index funds that are expected to provide stable returns. For example, the proposal unit proposes investment products that are expected to provide stable returns based on the user's risk tolerance. The proposal unit can also propose investment products with reduced risk based on the user's risk tolerance. For example, the proposal unit proposes bonds or index funds that are expected to provide stable returns based on the user's risk tolerance. This enables investments with reduced risk by proposing investment products that are expected to provide stable returns based on the user's risk tolerance. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI or without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, proposal unit, and consultation unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives information on the user's investment goals, risk tolerance, and investment period. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information using a generating AI to propose an optimal investment strategy. The proposal unit is implemented, for example, by the control unit 46A of the smart device 14 and provides the user with the investment strategy proposed by the analysis unit. The consultation unit is implemented, for example, by the control unit 46A of the smart device 14 and provides consultation and confirmation regarding investments via LINE. The reception unit, for example, can estimate the user's emotions and adjust the timing of inputting investment information based on the estimated emotions. Emotion estimation is performed, for example, using the camera 42 and microphone 38B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, proposal unit, and consultation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives information on the user's investment goals, risk tolerance, and investment period. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information using a generative AI to propose an optimal investment strategy. The proposal unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the user with the investment strategy proposed by the analysis unit. The consultation unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides consultation and confirmation regarding investments via LINE. The reception unit, for example, can estimate the user's emotions and adjust the timing of inputting investment information based on the estimated emotions. Emotion estimation is performed, for example, using the camera 42 and microphone 238 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, proposal unit, and consultation unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives information on the user's investment goals, risk tolerance, and investment period. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information using a generating AI to propose an optimal investment strategy. The proposal unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides the user with the investment strategy proposed by the analysis unit. The consultation unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides consultation and confirmation regarding investments via LINE. The reception unit, for example, can estimate the user's emotions and adjust the timing of inputting investment information based on the estimated emotions. Emotion estimation is performed, for example, using the camera 42 and microphone 238 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, proposal unit, and consultation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives information on the user's investment goals, risk tolerance, and investment period. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information using a generative AI to propose an optimal investment strategy. The proposal unit is realized, for example, by the control unit 46A of the robot 414 and provides the user with the investment strategy proposed by the analysis unit. The consultation unit is realized, for example, by the control unit 46A of the robot 414 and provides consultation and confirmation regarding investments via LINE. The reception unit, for example, can estimate the user's emotions and adjust the timing of inputting investment information based on the estimated emotions. Emotion estimation is performed, for example, using the camera 42 and microphone 238 of the robot 414.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The reception unit can also adjust the investment strategy based on the user's health condition in addition to the user's investment goals and risk tolerance. For example, if the user's health condition is good, a long-term investment strategy can be proposed. Conversely, if the user's health condition is unstable, a short-term investment strategy can be proposed. Furthermore, it is possible to periodically monitor the user's health data and automatically adjust the investment strategy according to changes in the user's health condition. This makes it possible to provide a flexible investment strategy according to the user's health condition.

[0100] The analysis unit can estimate the user's emotions and adjust the risk level of the investment strategy based on the estimated user emotions. For example, if the user is feeling stressed, a low-risk investment strategy can be suggested. Conversely, if the user is relaxed, a high-risk investment strategy can be suggested. Furthermore, it is possible to periodically collect user emotion data and automatically adjust the investment strategy according to changes in emotion. This makes it possible to provide a flexible investment strategy that corresponds to the user's emotional state.

[0101] The suggestion unit can also adjust the investment strategy based on the user's life events (marriage, childbirth, retirement, etc.) in addition to the user's investment goals and risk tolerance. For example, if the user is planning to get married, an investment strategy can be suggested that takes into account the living expenses after marriage. Conversely, if the user is planning to retire, an investment strategy can be suggested that takes into account the income after retirement. Furthermore, it is possible to periodically update the user's life event data and automatically adjust the investment strategy in response to changes in life events. This makes it possible to provide a flexible investment strategy that corresponds to the user's life events.

[0102] The consultation unit can estimate the user's emotions and adjust the consultation response method based on the estimated user emotions. For example, if the user is feeling anxious, the consultation unit can respond in a gentle tone. Conversely, if the user is relaxed, the consultation unit can provide a detailed explanation. Furthermore, it is also possible to periodically collect user emotion data and automatically adjust the consultation response method according to changes in emotion. This makes it possible to provide flexible consultation responses that correspond to the user's emotional state.

[0103] The reception unit can also adjust the investment strategy based on the user's hobbies and interests, in addition to the user's investment goals and risk tolerance. For example, if the user is interested in environmental protection, environmental-related investment products can be suggested. Conversely, if the user is interested in technology, technology-related investment products can be suggested. Furthermore, the user's hobby and interest data can be periodically updated, and the investment strategy can be automatically adjusted according to changes in interests. This makes it possible to provide a flexible investment strategy that suits the user's hobbies and interests.

[0104] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is feeling anxious, a simple and easy-to-understand presentation can be used. Conversely, if the user is relaxed, detailed analysis results can be provided. Furthermore, it is possible to periodically collect user emotion data and automatically adjust the way the analysis is presented according to changes in emotion. This makes it possible to provide flexible analysis results that correspond to the user's emotional state.

[0105] The suggestion unit can also adjust the investment strategy based on the user's family composition in addition to the user's investment goals and risk tolerance. For example, if the user has children, it can suggest an investment strategy that takes education expenses into consideration. Conversely, if the user is single, it can suggest an investment strategy for single people. Furthermore, it is possible to periodically update the user's family composition data and automatically adjust the investment strategy in response to changes in family composition. This makes it possible to provide a flexible investment strategy that suits the user's family composition.

[0106] The consultation unit can estimate the user's emotions and determine the priority of consultations based on the estimated user's emotions. For example, if the user is feeling anxious, the consultation can be prioritized. Conversely, if the user is relaxed, the consultation can be handled in the normal order of priority. Furthermore, it is also possible to periodically collect user emotion data and automatically adjust the priority of consultations according to changes in emotion. This makes it possible to provide flexible consultation responses according to the user's emotional state.

[0107] The reception unit can also adjust the investment strategy based on the user's occupation, in addition to the user's investment goals and risk tolerance. For example, if the user has a stable job, it can propose a long-term investment strategy. Conversely, if the user is a freelancer, it can propose a short-term investment strategy. Furthermore, it is possible to periodically update the user's occupation data and automatically adjust the investment strategy according to changes in occupation. This makes it possible to provide a flexible investment strategy that suits the user's occupation.

[0108] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is feeling anxious, a simple and easy-to-understand expression can be used. Conversely, if the user is relaxed, detailed suggestions can be provided. Furthermore, it is also possible to periodically collect user emotion data and automatically adjust the way suggestions are expressed in response to changes in emotion. This makes it possible to provide flexible suggestions that correspond to the user's emotional state.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The reception unit accepts information about the user's investment goals, risk tolerance, and investment period. For example, the user might enter information such as "I want to aim for 1 million yen in five years" or "I want to keep the risk low." The reception unit can then input this information into the generation AI. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and propose an optimal investment strategy. For example, the generation AI considers the user's investment goals, risk tolerance, investment period, etc., and proposes optimal investment products and portfolios. The generation AI analyzes the user's information and generates an investment strategy using text generation AI (e.g., LLM) or multimodal generation AI. Step 3: The proposal unit provides the user with the investment strategy proposed by the analysis unit. For example, the proposal unit presents the user with the investment products and portfolios proposed by the generation AI. The proposal unit can also suggest bonds or index funds that are expected to generate stable returns based on the user's risk tolerance. Step 4: The Consultation Department consults and confirms the investment strategy proposed by the Proposal Department. For example, users can consult with experts about their investment questions or concerns via LINE. Users can also easily check the progress of their investments via LINE.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0158] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] 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.

[0174] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0176] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0180] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0182] [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives information on a user's investment goals, risk tolerance, and investment period; an analysis unit that analyzes the information received by the reception unit and proposes an appropriate investment strategy; a proposal unit that provides a user with the investment strategy proposed by the analysis unit; a consultation unit that provides consultation and confirmation regarding the investment strategy proposed by the proposal unit. A system characterized by:

2. The reception unit To estimate a user's emotions and adjust the timing of inputting investment information based on the estimated user's emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past investment history and select the appropriate information entry method 2. The system of claim 1.

4. The reception unit Filter investment information based on your current financial situation and interests 2. The system of claim 1.

5. The reception unit When inputting investment information, select the appropriate input method depending on the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's feelings and determine the priority of the investment information to be input based on the estimated user's feelings.

2. The system of claim 1.

7. The reception unit When entering investment information, the system takes into account the user's geographic location to prioritize the most relevant information.

2. The system of claim 1.

8. The reception unit When entering investment information, analyze the user's social media activity and enter relevant information 2. The system of claim 1.

Images