system

Abstract

To enable an investor to easily receive asset management advice optimized in real time.SOLUTION: A system includes an analysis unit, a generation unit, a provision unit, and a reception unit. The analysis part analyzes the asset situation of the investor or market data. The generation part generates asset management advice on the basis of the data analyzed by the analysis part. The provision unit provides the advice generated by the generation unit to the investor using the VR / AR technology. In the reception unit, the investor accesses the service using a smartphone.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 technology has the drawback of making it difficult for investors to easily receive real-time, optimized asset management advice.

[0005] The system according to the embodiment aims to enable investors to easily receive asset management advice that is optimized in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a generation unit, a provision unit, and a reception unit. The analysis unit analyzes the asset status or market data of the investor. The generation unit generates asset management advice based on the data analyzed by the analysis unit. The provision unit provides the advice generated by the generation unit to the investor using VR / AR technology. The reception unit allows the investor to access the service using a smartphone. [Effects of the Invention]

[0007] The system according to the embodiment allows investors to easily receive asset management advice that is optimized in real time. [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 asset management advice system according to an embodiment of the present invention allows investors to more easily receive optimized asset management advice in real time. In this asset management advice system, investors access the service using their smartphones, and a generating AI analyzes the investor's current asset status and market data to generate optimal asset management advice. This advice is visually provided to the investor using VR / AR technology. For example, investors can manipulate a virtual investment portfolio via their smartphone and receive advice in real time. This service allows investors to more easily manage their assets and receive optimized advice in real time. For example, when an investor adjusts their portfolio in response to market fluctuations, the generating AI proposes an optimal investment strategy and allows them to visually confirm the effectiveness of the strategy using VR / AR technology. Furthermore, this service is available to investors wherever they are. For example, they can access the service using their smartphones while commuting or traveling and receive advice in real time. This allows investors to always manage their assets based on the latest information. Furthermore, the generating AI provides individually optimized advice taking into account the investor's past investment history and risk tolerance. For example, stable investments are suggested for investors seeking low-risk management, while high-growth investments are suggested for investors seeking high-risk investments for high-return returns. In this way, by using VR / AR technology combined with smartphones and generative AI, investors can more easily receive optimized asset management advice in real time. This allows the asset management advice system to more easily provide investors with advice on their asset management in real time. For example, it can quickly and accurately grade answer sheets written by investors, reducing the burden on teachers. In addition, investors can learn specific areas for improvement in their asset management, improving learning effectiveness.

[0029] An asset management advice system according to an embodiment includes an analysis unit, a generation unit, a provision unit, and a reception unit. The analysis unit analyzes an investor's asset status or market data. The investor's asset status includes, but is not limited to, cash, stocks, real estate, and the like. The market data includes, but is not limited to, stock price data, economic indicators, and trading volume. The analysis unit analyzes data using, for example, statistical analysis or a machine learning algorithm. The generation unit generates asset management advice based on the data analyzed by the analysis unit. The generation unit generates an investment strategy using, for example, a generation AI. The generation AI generates an optimal investment strategy using, for example, a deep learning model or a reinforcement learning algorithm. The provision unit provides the advice generated by the generation unit to the investor using VR / AR technology. The provision unit provides the advice visually using, for example, a head-mounted display or an augmented reality application. The reception unit allows the investor to access the service using a smartphone. The reception unit allows the investor to access the service using, for example, a specific application or interface. This allows the asset management advice system according to an embodiment to more easily receive optimized asset management advice in real time. For example, investors can access the service using their smartphones, and the AI ​​generator will analyze the investor's current asset situation and market data to generate optimal asset management advice. This advice is visually provided to the investor using VR / AR technology. For example, investors can manipulate a virtual investment portfolio via their smartphone and receive advice in real time. This allows investors to more easily manage their assets and receive optimized advice in real time.

[0030] The analysis unit can analyze data taking into account the investor's past investment history or risk tolerance. The analysis unit, for example, analyzes the investor's past trading records and investment targets. The analysis unit can also analyze survey results and past investment behavior to evaluate the investor's risk tolerance. For example, the analysis unit can extract investment patterns based on the investor's past trading records and evaluate the investor's risk tolerance. The analysis unit can also propose an optimal investment strategy based on the investor's risk tolerance. This enables analysis tailored to the investor's individual circumstances. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the investor's past investment history and risk tolerance into the generation AI, which then performs the analysis.

[0031] The generation unit can generate an investment strategy using a generation AI. The generation unit generates the investment strategy using, for example, a deep learning model. The generation unit can also generate the investment strategy using a reinforcement learning algorithm. For example, the generation unit can use a deep learning model to learn past market data and generate an optimal investment strategy. The generation unit can also use a reinforcement learning algorithm to analyze market data in real time and generate an optimal investment strategy. This makes it possible to generate an optimal investment strategy using the generation AI. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input data analyzed by the analysis unit into the generation AI, and the generation AI can generate an investment strategy.

[0032] The providing unit can provide advice to investors using VR / AR technology. The providing unit can provide advice to investors using, for example, a head-mounted display. The providing unit can also provide advice to investors using an augmented reality application. For example, the providing unit can use a head-mounted display to allow investors to receive advice while operating a virtual investment portfolio. The providing unit can also use an augmented reality application to allow investors to receive advice via their smartphone. In this way, by using VR / AR technology, it is possible to provide advice that is visually easy to understand. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the advice generated by the generation unit into the generation AI, which can visually provide the advice.

[0033] The reception unit allows investors to access the service using a smartphone. The reception unit, for example, allows investors to access the service using a specific application. The reception unit can also allow investors to access the service using an interface. For example, the reception unit provides an application for smartphones, allowing investors to access the service with one tap. The reception unit can also allow investors to access the service using voice commands. This allows investors to easily access the service anywhere using their smartphone. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit can input the investor's access data into the generation AI, which can then suggest the optimal access method.

[0034] The providing unit can provide advice to investors when they operate a virtual investment portfolio. For example, the providing unit provides advice in real time when investors operate a virtual investment portfolio. The providing unit can also suggest optimal investment strategies when investors adjust their portfolios in response to market fluctuations. For example, the providing unit allows the generation AI to provide advice in real time while investors operate the virtual investment portfolio. The providing unit can also allow the generation AI to suggest optimal investment strategies when investors adjust their portfolios in response to market fluctuations. This allows investors to respond immediately by providing advice in real time. Some or all of the above-described processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input investor operation data into the generation AI, which then provides advice in real time.

[0035] The analysis unit can perform a detailed analysis of an investor's past investment history and extract investment behavior patterns under specific market conditions. For example, the analysis unit can perform a detailed analysis of an investor's past trading records and extract successful investment patterns. The analysis unit can also perform a detailed analysis of an investor's past trading records and extract unsuccessful investment patterns. For example, the analysis unit can make recommendations under similar market conditions based on the investor's past successful investment patterns. The analysis unit can also warn of risks under similar market conditions based on the investor's past unsuccessful investment patterns. This enables analysis based on past investment behavior patterns. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the investor's past investment history data into the generation AI, which can then extract investment behavior patterns.

[0036] The analysis unit can apply an analysis algorithm according to the investor's risk tolerance. For example, the analysis unit can apply an algorithm that analyzes stable investment destinations to investors with low risk tolerance. The analysis unit can also apply an algorithm that analyzes high-risk, high-return investment destinations to investors with high risk tolerance. For example, the analysis unit can apply an algorithm that analyzes stable investment destinations to investors with low risk tolerance and propose an investment strategy with reduced risk. The analysis unit can also apply an algorithm that analyzes high-risk, high-return investment destinations to investors with high risk tolerance and propose an investment strategy aiming for high returns. This enables analysis according to risk tolerance. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the investor's risk tolerance data into the generation AI, which can then apply an appropriate analysis algorithm.

[0037] The analysis unit can update the investor's asset status and perform analysis based on the latest data. For example, when the investor's asset status changes, the analysis unit updates the data in real time and reflects the data in the analysis results. Furthermore, when the investor makes a new investment, the analysis unit can immediately reflect that information in the analysis. For example, when the investor's asset status changes, the analysis unit updates the data in real time and reflects the data in the analysis results. Furthermore, when the investor makes a new investment, the analysis unit can immediately reflect that information in the analysis. This enables analysis based on the latest data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the investor's asset status data into the generation AI, which can update the data in real time and perform analysis.

[0038] The analysis unit can reflect region-specific market data in the analysis based on the investor's geographical location information. The analysis unit, for example, performs the analysis taking into account the economic situation of the region where the investor lives. The analysis unit can also prioritize analyzing market data of regions in which the investor is interested. For example, the analysis unit performs the analysis taking into account the economic situation of the region where the investor lives. The analysis unit can also prioritize analyzing market data of regions in which the investor is interested. This enables analysis that takes region-specific market data into account. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the investor's geographical location information data into the generation AI, and the generation AI can reflect region-specific market data in the analysis.

[0039] The analysis unit can analyze investors' social media activities and incorporate related market trends into the analysis. For example, the analysis unit can reflect the trends of companies that investors follow on social media into the analysis. The analysis unit can also incorporate market trends that investors are interested in on social media into the analysis. For example, the analysis unit can reflect the trends of companies that investors follow on social media into the analysis. The analysis unit can also incorporate market trends that investors are interested in on social media into the analysis. This enables analysis that takes social media activities into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI, for example. For example, the analysis unit can input investors' social media activity data into the generation AI, and the generation AI can incorporate related market trends into the analysis.

[0040] The analysis unit can customize the analysis algorithm based on the investor's past feedback. The analysis unit, for example, adjusts the analysis algorithm based on feedback provided by the investor in the past. Furthermore, if the investor is satisfied with a particular analysis result, the analysis unit can also preferentially use that algorithm. For example, the analysis unit adjusts the analysis algorithm based on feedback provided by the investor in the past. Furthermore, if the investor is satisfied with a particular analysis result, the analysis unit can also preferentially use that algorithm. This makes it possible to perform analysis that reflects past feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI, for example. For example, the analysis unit can input investor feedback data into the generation AI, which can then customize the analysis algorithm.

[0041] The generation unit can simulate the investor's asset allocation when generating an investment strategy. The generation unit, for example, simulates an optimal asset allocation based on the investor's current asset allocation. The generation unit can also simulate different asset allocation scenarios taking into account the investor's risk tolerance. For example, the generation unit simulates an optimal asset allocation based on the investor's current asset allocation. The generation unit can also simulate different asset allocation scenarios taking into account the investor's risk tolerance. This enables optimization of asset allocation. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input the investor's asset allocation data into the generation AI, which then performs the simulation.

[0042] When generating an investment strategy, the generation unit can generate multiple strategies based on market scenarios. The generation unit, for example, generates an investment strategy when the market is in an uptrend. The generation unit can also generate an investment strategy when the market is in a downtrend. For example, the generation unit generates an investment strategy when the market is in an uptrend. The generation unit can also generate an investment strategy when the market is in a downtrend. This makes it possible to generate strategies that correspond to different market scenarios. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input market scenario data into the generation AI, which then generates multiple strategies.

[0043] When generating an investment strategy, the generation unit can improve the accuracy of the strategy based on the investor's past successes and failures. For example, the generation unit generates a strategy for similar market conditions based on the investor's past successes. The generation unit can also generate a risk-avoiding strategy based on the investor's past failures. For example, the generation unit generates a strategy for similar market conditions based on the investor's past successes. The generation unit can also generate a risk-avoiding strategy based on the investor's past failures. This makes it possible to improve the accuracy of the strategy by taking past cases into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the investor's past successes and failures into the generation AI, which can then improve the accuracy of the strategy.

[0044] When generating investment strategies, the generation unit can determine the priority of strategies based on the investor's submission time. For example, if the investor is in a hurry, the generation unit can quickly generate a strategy. Also, if the investor has time, the generation unit can perform a detailed analysis and generate a strategy. For example, if the investor is in a hurry, the generation unit can quickly generate a strategy. Also, if the investor has time, the generation unit can perform a detailed analysis and generate a strategy. This makes it possible to determine the priority of strategies based on the submission time. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input investor submission time data into the generation AI, and the generation AI can determine the priority of strategies.

[0045] When generating an investment strategy, the generation unit can adjust the use of technical terminology in the strategy based on the investor's level of expertise. For example, if the investor is a beginner, the generation unit can generate a strategy using simple terminology. Also, if the investor is an intermediate investor, the generation unit can generate a strategy using appropriate technical terminology. For example, if the investor is a beginner, the generation unit can generate a strategy using simple terminology. Also, if the investor is an intermediate investor, the generation unit can generate a strategy using appropriate technical terminology. This makes it possible to generate strategies according to the level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input investor's expertise level data into the generation AI, which can adjust the use of technical terminology in the strategy.

[0046] When generating an investment strategy, the generation unit can generate a strategy based on the market value of an investor. For example, if the investor has a high market value, the generation unit generates a risky strategy. Also, if the investor has a medium market value, the generation unit can generate a balanced strategy. For example, if the investor has a high market value, the generation unit generates a risky strategy. Also, if the investor has a medium market value, the generation unit can generate a balanced strategy. This makes it possible to generate strategies according to market value. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input investor market value data into the generation AI, which then generates a strategy.

[0047] When providing advice, the providing unit can select a display method based on the investor's past operation history. For example, the providing unit can prioritize and provide a display method that the investor has used favorably in the past. The providing unit can also avoid a display method that the investor has expressed dissatisfaction with in the past. For example, the providing unit can prioritize and provide a display method that the investor has used favorably in the past. The providing unit can also avoid a display method that the investor has expressed dissatisfaction with in the past. This makes it possible to select an optimal display method based on the past operation history. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input the investor's operation history data into the generation AI, which can select the optimal display method.

[0048] When providing advice, the providing unit can customize the display content based on the investor's current task. For example, if the investor is adjusting their portfolio, the providing unit can prioritize displaying advice related to the task. In addition, if the investor is considering a new investment, the providing unit can also display advice related to the task. For example, if the investor is adjusting their portfolio, the providing unit can prioritize displaying advice related to the task. In addition, if the investor is considering a new investment, the providing unit can also display advice related to the task. This makes it possible to customize the display content according to the current task. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the investor's task data into the generation AI, which can customize the display content.

[0049] The providing unit can improve the display method based on investor feedback when providing advice. For example, if an investor provides feedback on the advice provided, the providing unit improves the display method based on the feedback. Furthermore, if an investor is satisfied with a particular display method, the providing unit can continue to use that method. For example, if an investor provides feedback on the advice provided, the providing unit improves the display method based on the feedback. Furthermore, if an investor is satisfied with a particular display method, the providing unit can continue to use that method. This makes it possible to improve the display method based on feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input investor feedback data into the generation AI, which can improve the display method.

[0050] When providing advice, the providing unit can select a display method based on the investor's device information. For example, if the investor is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the investor is using a tablet, the providing unit can also provide a display method that is optimized for a larger screen. For example, if the investor is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the investor is using a tablet, the providing unit can also provide a display method that is optimized for a larger screen. This makes it possible to select the optimal display method according to the device information. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI, for example. For example, the providing unit can input the investor's device information data into the generation AI, which can then select the optimal display method.

[0051] When providing advice, the providing unit can make the displayed content multilingual based on the investor's language setting. The providing unit, for example, automatically sets the language of the advice based on the language setting of the investor's device. The providing unit can also provide a language switching function when the investor uses multiple languages. For example, the providing unit automatically sets the language of the advice based on the language setting of the investor's device. The providing unit can also provide a language switching function when the investor uses multiple languages. This enables multilingual display. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the investor's language setting data into the generation AI, which can then make the displayed content multilingual.

[0052] When providing advice, the providing unit can select a display method based on the investor's geographical location information. The providing unit, for example, displays advice taking into account the economic situation in the area where the investor lives. The providing unit can also prioritize displaying market data for areas in which the investor is interested. For example, the providing unit displays advice taking into account the economic situation in the area where the investor lives. The providing unit can also prioritize displaying market data for areas in which the investor is interested. This makes it possible to select an optimal display method according to the geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input the investor's geographical location information data into the generation AI, which can then select the optimal display method.

[0053] The reception unit can analyze the investor's past access history and select an access method. For example, the reception unit can prioritize and provide access methods that the investor has frequently used in the past. The reception unit can also avoid access methods that the investor has expressed dissatisfaction with in the past. For example, the reception unit can prioritize and provide access methods that the investor has frequently used in the past. The reception unit can also avoid access methods that the investor has expressed dissatisfaction with in the past. This makes it possible to select an optimal access method based on the past access history. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the investor's access history data into the generation AI, which can select the optimal access method.

[0054] The reception unit can customize the access method based on the investor's current living situation and areas of interest. For example, if the investor is busy, the reception unit can provide a method for quick access. Also, if the investor is relaxed, the reception unit can provide a method for detailed information. For example, if the investor is busy, the reception unit can provide a method for quick access. Also, if the investor is relaxed, the reception unit can provide a method for detailed information. This makes it possible to customize the access method according to the investor's living situation and areas of interest. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, the generation AI, for example. For example, the reception unit can input data on the investor's living situation and areas of interest into the generation AI, which can then customize the access method.

[0055] The reception unit can select an access means based on the investor's input method. For example, if the investor prefers voice input, the reception unit can provide an access method using voice recognition. Furthermore, if the investor prefers text input, the reception unit can also provide a text-based access method. For example, if the investor prefers voice input, the reception unit can provide an access method using voice recognition. Furthermore, if the investor prefers text input, the reception unit can also provide a text-based access method. This makes it possible to select the optimal access means depending on the input method. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI, for example. For example, the reception unit can input the investor's input method data into the generation AI, which can then select the optimal access means.

[0056] The reception unit can prioritize providing highly relevant access methods based on the investor's geographical location information. For example, the reception unit can prioritize access to information related to the economic situation in the region where the investor lives. The reception unit can also prioritize access to market data for regions in which the investor is interested. For example, the reception unit can prioritize access to information related to the economic situation in the region where the investor lives. The reception unit can also prioritize access to market data for regions in which the investor is interested. This makes it possible to provide highly relevant access methods according to the geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the investor's geographical location information data into the generation AI, which can then prioritize providing highly relevant access methods.

[0057] The reception unit can analyze the investor's social media activity and provide a related access method. For example, the reception unit can provide access to information on companies that the investor follows on social media. The reception unit can also provide access to market trends that the investor is interested in on social media. For example, the reception unit can provide access to information on companies that the investor follows on social media. The reception unit can also provide access to market trends that the investor is interested in on social media. This makes it possible to provide an access method that takes social media activity into consideration. Some or all of the above-described processing in the reception unit can be performed, for example, using or without the generation AI. For example, the reception unit can input the investor's social media activity data into the generation AI, which can then provide a related access method.

[0058] The reception unit can customize the access method based on the investor's past feedback. For example, the reception unit adjusts the access method based on feedback provided by the investor in the past. Furthermore, if the investor is satisfied with a particular access method, the reception unit can continue to use that method. For example, the reception unit adjusts the access method based on feedback provided by the investor in the past. Furthermore, if the investor is satisfied with a particular access method, the reception unit can continue to use that method. This makes it possible to customize the access method based on past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI, for example. For example, the reception unit can input the investor's feedback data into the generation AI, which can customize the access method.

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

[0060] The analysis unit can incorporate an investor's health data into its analysis. For example, it can analyze an investor's stress level and sleep patterns to identify factors that may affect investment decisions. The analysis unit can also adjust an investor's risk tolerance based on their health. For example, if the investor is highly stressed, it can suggest a low-risk investment strategy, and if the investor's health is good, it can suggest a riskier strategy. This makes it possible to provide asset management advice that takes into account the investor's health.

[0061] The generation unit can generate an investment strategy taking into account the investor's hobbies and interests. For example, if an investor is interested in environmental protection, the generation unit can suggest investment in eco-friendly companies. If an investor is interested in technology, the generation unit can suggest investment in companies with the latest technology. Furthermore, if an investor is interested in a specific region, the generation unit can generate an investment strategy that focuses on companies and markets in that region. This makes it possible to provide investment strategies based on the individual interests of investors.

[0062] The reception unit can suggest the optimal access time based on the investor's past access history. For example, if the investor has often accessed the site at night in the past, it can provide investment information that is optimal for that time. Also, if the investor has often accessed the site on weekends, it can provide investment advice tailored to the weekend. Furthermore, if the investor has often accessed the site on a specific day of the week, it can provide investment information tailored to that day of the week. This makes it possible to provide services at the optimal time based on the investor's access patterns.

[0063] The generation unit can generate an investment strategy taking into account the investor's social network. For example, it can propose an investment strategy based on the companies and markets in which the investor's friends and family are investing. It can also generate a strategy taking into account the investment trends of the community or group to which the investor belongs. Furthermore, it can propose an investment strategy based on the opinions of influencers and experts that the investor follows. This makes it possible to provide an investment strategy that utilizes the investor's social network.

[0064] The reception unit can suggest the optimal device based on the investor's device usage history. For example, if the investor frequently uses a smartphone, it can suggest an application optimized for smartphones. Also, if the investor uses a tablet, it can provide an interface optimized for tablets. Furthermore, if the investor uses a desktop, it can suggest a web application optimized for desktops. This makes it possible to suggest the optimal device based on the investor's device usage history.

[0065] The providing unit can customize the format of advice based on the investor's past feedback. For example, if the investor has preferred text-format advice in the past, the advice can be provided in text format. Also, if the investor has preferred visual advice, the advice can be provided using graphs and charts. Furthermore, if the investor has preferred audio advice, the advice can be provided audio. This makes it possible to provide the optimal advice format based on the investor's past feedback.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The analysis unit analyzes the investor's asset status or market data. The investor's asset status includes cash, stocks, real estate, etc., and market data includes stock price data, economic indicators, trading volume, etc. The analysis unit analyzes the data using statistical analysis and machine learning algorithms. Step 2: The generation unit generates asset management advice based on the data analyzed by the analysis unit. The generation unit generates an investment strategy using a generation AI, and generates an optimal investment strategy using a deep learning model, reinforcement learning algorithm, etc. Step 3: The provider provides the advice generated by the generator to investors using VR / AR technology. The provider provides the advice visually using a head-mounted display or an augmented reality application. Step 4: The reception unit allows the investor to access the service using a smartphone. The reception unit allows the investor to access the service using a specific application or interface.

[0068] (Example 2) An asset management advice system according to an embodiment of the present invention allows investors to more easily receive optimized asset management advice in real time. In this asset management advice system, investors access the service using their smartphones, and a generating AI analyzes the investor's current asset status and market data to generate optimal asset management advice. This advice is visually provided to the investor using VR / AR technology. For example, investors can manipulate a virtual investment portfolio via their smartphone and receive advice in real time. This service allows investors to more easily manage their assets and receive optimized advice in real time. For example, when an investor adjusts their portfolio in response to market fluctuations, the generating AI proposes an optimal investment strategy and allows them to visually confirm the effectiveness of the strategy using VR / AR technology. Furthermore, this service is available to investors wherever they are. For example, they can access the service using their smartphones while commuting or traveling and receive advice in real time. This allows investors to always manage their assets based on the latest information. Furthermore, the generating AI provides individually optimized advice taking into account the investor's past investment history and risk tolerance. For example, stable investments are suggested for investors seeking low-risk management, while high-growth investments are suggested for investors seeking high-risk investments for high-return returns. In this way, by using VR / AR technology combined with smartphones and generative AI, investors can more easily receive optimized asset management advice in real time. This allows the asset management advice system to more easily provide investors with advice on their asset management in real time. For example, it can quickly and accurately grade answer sheets written by investors, reducing the burden on teachers. In addition, investors can learn specific areas for improvement in their asset management, improving learning effectiveness.

[0069] An asset management advice system according to an embodiment includes an analysis unit, a generation unit, a provision unit, and a reception unit. The analysis unit analyzes an investor's asset status or market data. The investor's asset status includes, but is not limited to, cash, stocks, real estate, and the like. The market data includes, but is not limited to, stock price data, economic indicators, and trading volume. The analysis unit analyzes data using, for example, statistical analysis or a machine learning algorithm. The generation unit generates asset management advice based on the data analyzed by the analysis unit. The generation unit generates an investment strategy using, for example, a generation AI. The generation AI generates an optimal investment strategy using, for example, a deep learning model or a reinforcement learning algorithm. The provision unit provides the advice generated by the generation unit to the investor using VR / AR technology. The provision unit provides the advice visually using, for example, a head-mounted display or an augmented reality application. The reception unit allows the investor to access the service using a smartphone. The reception unit allows the investor to access the service using, for example, a specific application or interface. This allows the asset management advice system according to an embodiment to more easily receive optimized asset management advice in real time. For example, investors can access the service using their smartphones, and the AI ​​generator will analyze the investor's current asset situation and market data to generate optimal asset management advice. This advice is visually provided to the investor using VR / AR technology. For example, investors can manipulate a virtual investment portfolio via their smartphone and receive advice in real time. This allows investors to more easily manage their assets and receive optimized advice in real time.

[0070] The analysis unit can analyze data taking into account the investor's past investment history or risk tolerance. The analysis unit, for example, analyzes the investor's past trading records and investment targets. The analysis unit can also analyze survey results and past investment behavior to evaluate the investor's risk tolerance. For example, the analysis unit can extract investment patterns based on the investor's past trading records and evaluate the investor's risk tolerance. The analysis unit can also propose an optimal investment strategy based on the investor's risk tolerance. This enables analysis tailored to the investor's individual circumstances. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the investor's past investment history and risk tolerance into the generation AI, which then performs the analysis.

[0071] The generation unit can generate an investment strategy using a generation AI. The generation unit generates the investment strategy using, for example, a deep learning model. The generation unit can also generate the investment strategy using a reinforcement learning algorithm. For example, the generation unit can use a deep learning model to learn past market data and generate an optimal investment strategy. The generation unit can also use a reinforcement learning algorithm to analyze market data in real time and generate an optimal investment strategy. This makes it possible to generate an optimal investment strategy using the generation AI. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input data analyzed by the analysis unit into the generation AI, and the generation AI can generate an investment strategy.

[0072] The providing unit can provide advice to investors using VR / AR technology. The providing unit can provide advice to investors using, for example, a head-mounted display. The providing unit can also provide advice to investors using an augmented reality application. For example, the providing unit can use a head-mounted display to allow investors to receive advice while operating a virtual investment portfolio. The providing unit can also use an augmented reality application to allow investors to receive advice via their smartphone. In this way, by using VR / AR technology, it is possible to provide advice that is visually easy to understand. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the advice generated by the generation unit into the generation AI, which can visually provide the advice.

[0073] The reception unit allows investors to access the service using a smartphone. The reception unit, for example, allows investors to access the service using a specific application. The reception unit can also allow investors to access the service using an interface. For example, the reception unit provides an application for smartphones, allowing investors to access the service with one tap. The reception unit can also allow investors to access the service using voice commands. This allows investors to easily access the service anywhere using their smartphone. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit can input the investor's access data into the generation AI, which can then suggest the optimal access method.

[0074] The providing unit can provide advice to investors when they operate a virtual investment portfolio. For example, the providing unit provides advice in real time when investors operate a virtual investment portfolio. The providing unit can also suggest optimal investment strategies when investors adjust their portfolios in response to market fluctuations. For example, the providing unit allows the generation AI to provide advice in real time while investors operate the virtual investment portfolio. The providing unit can also allow the generation AI to suggest optimal investment strategies when investors adjust their portfolios in response to market fluctuations. This allows investors to respond immediately by providing advice in real time. Some or all of the above-described processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input investor operation data into the generation AI, which then provides advice in real time.

[0075] The analysis unit can estimate the investor's emotions and adjust the analysis priority based on the estimated emotions. For example, the analysis unit can capture the investor's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also record the investor's voice and estimate the emotions using voice analysis technology. For example, the analysis unit can calculate an emotion score based on changes in facial expressions and adjust the analysis priority. The analysis unit can also analyze the tone and speed of voice to calculate an emotion score and adjust the analysis priority. This makes it possible to adjust the analysis priority according to the investor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input investor's emotion data into the generation AI, which can then adjust the analysis priority.

[0076] The analysis unit can perform a detailed analysis of an investor's past investment history and extract investment behavior patterns under specific market conditions. For example, the analysis unit can perform a detailed analysis of an investor's past trading records and extract successful investment patterns. The analysis unit can also perform a detailed analysis of an investor's past trading records and extract unsuccessful investment patterns. For example, the analysis unit can make recommendations under similar market conditions based on the investor's past successful investment patterns. The analysis unit can also warn of risks under similar market conditions based on the investor's past unsuccessful investment patterns. This enables analysis based on past investment behavior patterns. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the investor's past investment history data into the generation AI, which can then extract investment behavior patterns.

[0077] The analysis unit can apply an analysis algorithm according to the investor's risk tolerance. For example, the analysis unit can apply an algorithm that analyzes stable investment destinations to investors with low risk tolerance. The analysis unit can also apply an algorithm that analyzes high-risk, high-return investment destinations to investors with high risk tolerance. For example, the analysis unit can apply an algorithm that analyzes stable investment destinations to investors with low risk tolerance and propose an investment strategy with reduced risk. The analysis unit can also apply an algorithm that analyzes high-risk, high-return investment destinations to investors with high risk tolerance and propose an investment strategy aiming for high returns. This enables analysis according to risk tolerance. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the investor's risk tolerance data into the generation AI, which can then apply an appropriate analysis algorithm.

[0078] The analysis unit can update the investor's asset status and perform analysis based on the latest data. For example, when the investor's asset status changes, the analysis unit updates the data in real time and reflects the data in the analysis results. Furthermore, when the investor makes a new investment, the analysis unit can immediately reflect that information in the analysis. For example, when the investor's asset status changes, the analysis unit updates the data in real time and reflects the data in the analysis results. Furthermore, when the investor makes a new investment, the analysis unit can immediately reflect that information in the analysis. This enables analysis based on the latest data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the investor's asset status data into the generation AI, which can update the data in real time and perform analysis.

[0079] The analysis unit can estimate the investor's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit can capture the investor's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also record the investor's voice and estimate the emotions using voice analysis technology. For example, the analysis unit can calculate an emotion score based on changes in facial expressions and adjust the display method of the analysis results. The analysis unit can also analyze the tone and speed of voice, calculate an emotion score, and adjust the display method of the analysis results. This makes it possible to adjust the display method according to the investor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input investor's emotion data into the generation AI, which can then adjust the display method of the analysis results.

[0080] The analysis unit can reflect region-specific market data in the analysis based on the investor's geographical location information. The analysis unit, for example, performs the analysis taking into account the economic situation of the region where the investor lives. The analysis unit can also prioritize analyzing market data of regions in which the investor is interested. For example, the analysis unit performs the analysis taking into account the economic situation of the region where the investor lives. The analysis unit can also prioritize analyzing market data of regions in which the investor is interested. This enables analysis that takes region-specific market data into account. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the investor's geographical location information data into the generation AI, and the generation AI can reflect region-specific market data in the analysis.

[0081] The analysis unit can analyze investors' social media activities and incorporate related market trends into the analysis. For example, the analysis unit can reflect the trends of companies that investors follow on social media into the analysis. The analysis unit can also incorporate market trends that investors are interested in on social media into the analysis. For example, the analysis unit can reflect the trends of companies that investors follow on social media into the analysis. The analysis unit can also incorporate market trends that investors are interested in on social media into the analysis. This enables analysis that takes social media activities into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI, for example. For example, the analysis unit can input investors' social media activity data into the generation AI, and the generation AI can incorporate related market trends into the analysis.

[0082] The analysis unit can customize the analysis algorithm based on the investor's past feedback. The analysis unit, for example, adjusts the analysis algorithm based on feedback provided by the investor in the past. Furthermore, if the investor is satisfied with a particular analysis result, the analysis unit can also preferentially use that algorithm. For example, the analysis unit adjusts the analysis algorithm based on feedback provided by the investor in the past. Furthermore, if the investor is satisfied with a particular analysis result, the analysis unit can also preferentially use that algorithm. This makes it possible to perform analysis that reflects past feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI, for example. For example, the analysis unit can input investor feedback data into the generation AI, which can then customize the analysis algorithm.

[0083] The generation unit can estimate the investor's emotions and adjust the expression method of the advice to be generated based on the estimated emotions. For example, the generation unit can capture the investor's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The generation unit can also record the investor's voice and estimate the emotions using voice analysis technology. For example, the generation unit can calculate an emotion score based on changes in facial expressions and adjust the expression method of the advice. The generation unit can also analyze the tone and speed of the voice, calculate an emotion score, and adjust the expression method of the advice. This makes it possible to adjust the expression method of the advice according to the investor's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the investor's emotional data into the generation AI, which can then adjust the expression method of the advice.

[0084] The generation unit can simulate the investor's asset allocation when generating an investment strategy. The generation unit, for example, simulates an optimal asset allocation based on the investor's current asset allocation. The generation unit can also simulate different asset allocation scenarios taking into account the investor's risk tolerance. For example, the generation unit simulates an optimal asset allocation based on the investor's current asset allocation. The generation unit can also simulate different asset allocation scenarios taking into account the investor's risk tolerance. This enables optimization of asset allocation. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input the investor's asset allocation data into the generation AI, which then performs the simulation.

[0085] When generating an investment strategy, the generation unit can generate multiple strategies based on market scenarios. The generation unit, for example, generates an investment strategy when the market is in an uptrend. The generation unit can also generate an investment strategy when the market is in a downtrend. For example, the generation unit generates an investment strategy when the market is in an uptrend. The generation unit can also generate an investment strategy when the market is in a downtrend. This makes it possible to generate strategies that correspond to different market scenarios. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input market scenario data into the generation AI, which then generates multiple strategies.

[0086] When generating an investment strategy, the generation unit can improve the accuracy of the strategy based on the investor's past successes and failures. For example, the generation unit generates a strategy for similar market conditions based on the investor's past successes. The generation unit can also generate a risk-avoiding strategy based on the investor's past failures. For example, the generation unit generates a strategy for similar market conditions based on the investor's past successes. The generation unit can also generate a risk-avoiding strategy based on the investor's past failures. This makes it possible to improve the accuracy of the strategy by taking past cases into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the investor's past successes and failures into the generation AI, which can then improve the accuracy of the strategy.

[0087] The generation unit can estimate the investor's emotions and adjust the length of the advice to be generated based on the estimated emotions. For example, the generation unit captures the investor's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The generation unit can also record the investor's voice and estimate the emotions using voice analysis technology. For example, the generation unit calculates an emotion score based on changes in facial expressions and adjusts the length of the advice. The generation unit can also analyze the tone and speed of the voice, calculate an emotion score, and adjust the length of the advice. This makes it possible to adjust the length of the advice according to the investor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the investor's emotion data into the generation AI, which can then adjust the length of the advice.

[0088] When generating investment strategies, the generation unit can determine the priority of strategies based on the investor's submission time. For example, if the investor is in a hurry, the generation unit can quickly generate a strategy. Also, if the investor has time, the generation unit can perform a detailed analysis and generate a strategy. For example, if the investor is in a hurry, the generation unit can quickly generate a strategy. Also, if the investor has time, the generation unit can perform a detailed analysis and generate a strategy. This makes it possible to determine the priority of strategies based on the submission time. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input investor submission time data into the generation AI, and the generation AI can determine the priority of strategies.

[0089] When generating an investment strategy, the generation unit can adjust the use of technical terminology in the strategy based on the investor's level of expertise. For example, if the investor is a beginner, the generation unit can generate a strategy using simple terminology. Also, if the investor is an intermediate investor, the generation unit can generate a strategy using appropriate technical terminology. For example, if the investor is a beginner, the generation unit can generate a strategy using simple terminology. Also, if the investor is an intermediate investor, the generation unit can generate a strategy using appropriate technical terminology. This makes it possible to generate strategies according to the level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input investor's expertise level data into the generation AI, which can adjust the use of technical terminology in the strategy.

[0090] When generating an investment strategy, the generation unit can generate a strategy based on the market value of an investor. For example, if the investor has a high market value, the generation unit generates a risky strategy. Also, if the investor has a medium market value, the generation unit can generate a balanced strategy. For example, if the investor has a high market value, the generation unit generates a risky strategy. Also, if the investor has a medium market value, the generation unit can generate a balanced strategy. This makes it possible to generate strategies according to market value. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input investor market value data into the generation AI, which then generates a strategy.

[0091] The providing unit can estimate the investor's emotions and adjust the display method of the advice based on the estimated emotions. For example, the providing unit can capture the investor's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The providing unit can also record the investor's voice and estimate the emotions using voice analysis technology. For example, the providing unit can calculate an emotion score based on changes in facial expressions and adjust the display method of the advice. The providing unit can also analyze the tone and speed of the voice, calculate an emotion score, and adjust the display method of the advice. This makes it possible to adjust the display method according to the investor'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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the investor's emotion data into the generation AI, which can then adjust the display method of the advice.

[0092] When providing advice, the providing unit can select a display method based on the investor's past operation history. For example, the providing unit can prioritize and provide a display method that the investor has used favorably in the past. The providing unit can also avoid a display method that the investor has expressed dissatisfaction with in the past. For example, the providing unit can prioritize and provide a display method that the investor has used favorably in the past. The providing unit can also avoid a display method that the investor has expressed dissatisfaction with in the past. This makes it possible to select an optimal display method based on the past operation history. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input the investor's operation history data into the generation AI, which can select the optimal display method.

[0093] When providing advice, the providing unit can customize the display content based on the investor's current task. For example, if the investor is adjusting their portfolio, the providing unit can prioritize displaying advice related to the task. In addition, if the investor is considering a new investment, the providing unit can also display advice related to the task. For example, if the investor is adjusting their portfolio, the providing unit can prioritize displaying advice related to the task. In addition, if the investor is considering a new investment, the providing unit can also display advice related to the task. This makes it possible to customize the display content according to the current task. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the investor's task data into the generation AI, which can customize the display content.

[0094] The providing unit can improve the display method based on investor feedback when providing advice. For example, if an investor provides feedback on the advice provided, the providing unit improves the display method based on the feedback. Furthermore, if an investor is satisfied with a particular display method, the providing unit can continue to use that method. For example, if an investor provides feedback on the advice provided, the providing unit improves the display method based on the feedback. Furthermore, if an investor is satisfied with a particular display method, the providing unit can continue to use that method. This makes it possible to improve the display method based on feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input investor feedback data into the generation AI, which can improve the display method.

[0095] The providing unit can estimate the investor's emotions and adjust the advice operation procedures based on the estimated emotions. For example, the providing unit can capture the investor's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The providing unit can also record the investor's voice and estimate the emotions using voice analysis technology. For example, the providing unit can calculate an emotion score based on changes in facial expressions and adjust the advice operation procedures. The providing unit can also analyze the tone and speed of the voice, calculate an emotion score, and adjust the advice operation procedures. This makes it possible to adjust the operation procedures according to the investor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the investor's emotion data into the generation AI, which can then adjust the advice operation procedures.

[0096] When providing advice, the providing unit can select a display method based on the investor's device information. For example, if the investor is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the investor is using a tablet, the providing unit can also provide a display method that is optimized for a larger screen. For example, if the investor is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the investor is using a tablet, the providing unit can also provide a display method that is optimized for a larger screen. This makes it possible to select the optimal display method according to the device information. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI, for example. For example, the providing unit can input the investor's device information data into the generation AI, which can then select the optimal display method.

[0097] When providing advice, the providing unit can make the displayed content multilingual based on the investor's language setting. The providing unit, for example, automatically sets the language of the advice based on the language setting of the investor's device. The providing unit can also provide a language switching function when the investor uses multiple languages. For example, the providing unit automatically sets the language of the advice based on the language setting of the investor's device. The providing unit can also provide a language switching function when the investor uses multiple languages. This enables multilingual display. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the investor's language setting data into the generation AI, which can then make the displayed content multilingual.

[0098] When providing advice, the providing unit can select a display method based on the investor's geographical location information. The providing unit, for example, displays advice taking into account the economic situation in the area where the investor lives. The providing unit can also prioritize displaying market data for areas in which the investor is interested. For example, the providing unit displays advice taking into account the economic situation in the area where the investor lives. The providing unit can also prioritize displaying market data for areas in which the investor is interested. This makes it possible to select an optimal display method according to the geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input the investor's geographical location information data into the generation AI, which can then select the optimal display method.

[0099] The reception unit can estimate the investor's emotions and adjust the access method based on the estimated emotions. For example, the reception unit can capture the investor's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The reception unit can also record the investor's voice and estimate the emotions using voice analysis technology. For example, the reception unit can calculate an emotion score based on changes in facial expressions and adjust the access method. The reception unit can also analyze the tone and speed of the voice to calculate an emotion score and adjust the access method. This makes it possible to adjust the access method according to the investor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the investor's emotion data into the generation AI, which can then adjust the access method.

[0100] The reception unit can analyze the investor's past access history and select an access method. For example, the reception unit can prioritize and provide access methods that the investor has frequently used in the past. The reception unit can also avoid access methods that the investor has expressed dissatisfaction with in the past. For example, the reception unit can prioritize and provide access methods that the investor has frequently used in the past. The reception unit can also avoid access methods that the investor has expressed dissatisfaction with in the past. This makes it possible to select an optimal access method based on the past access history. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the investor's access history data into the generation AI, which can select the optimal access method.

[0101] The reception unit can customize the access method based on the investor's current living situation and areas of interest. For example, if the investor is busy, the reception unit can provide a method for quick access. Also, if the investor is relaxed, the reception unit can provide a method for detailed information. For example, if the investor is busy, the reception unit can provide a method for quick access. Also, if the investor is relaxed, the reception unit can provide a method for detailed information. This makes it possible to customize the access method according to the investor's living situation and areas of interest. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, the generation AI, for example. For example, the reception unit can input data on the investor's living situation and areas of interest into the generation AI, which can then customize the access method.

[0102] The reception unit can select an access means based on the investor's input method. For example, if the investor prefers voice input, the reception unit can provide an access method using voice recognition. Furthermore, if the investor prefers text input, the reception unit can also provide a text-based access method. For example, if the investor prefers voice input, the reception unit can provide an access method using voice recognition. Furthermore, if the investor prefers text input, the reception unit can also provide a text-based access method. This makes it possible to select the optimal access means depending on the input method. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI, for example. For example, the reception unit can input the investor's input method data into the generation AI, which can then select the optimal access means.

[0103] The reception unit can estimate the investor's emotions and determine access priorities based on the estimated emotions. For example, the reception unit can capture the investor's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The reception unit can also record the investor's voice and estimate the emotions using voice analysis technology. For example, the reception unit can calculate an emotion score based on changes in facial expressions and determine access priorities. The reception unit can also analyze the tone and speed of voice to calculate an emotion score and determine access priorities. This enables access priorities to be determined based on the investor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input investor's emotion data into the generation AI, which can then determine access priorities.

[0104] The reception unit can prioritize providing highly relevant access methods based on the investor's geographical location information. For example, the reception unit can prioritize access to information related to the economic situation in the region where the investor lives. The reception unit can also prioritize access to market data for regions in which the investor is interested. For example, the reception unit can prioritize access to information related to the economic situation in the region where the investor lives. The reception unit can also prioritize access to market data for regions in which the investor is interested. This makes it possible to provide highly relevant access methods according to the geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the investor's geographical location information data into the generation AI, which can then prioritize providing highly relevant access methods.

[0105] The reception unit can analyze the investor's social media activity and provide a related access method. For example, the reception unit can provide access to information on companies that the investor follows on social media. The reception unit can also provide access to market trends that the investor is interested in on social media. For example, the reception unit can provide access to information on companies that the investor follows on social media. The reception unit can also provide access to market trends that the investor is interested in on social media. This makes it possible to provide an access method that takes social media activity into consideration. Some or all of the above-described processing in the reception unit can be performed, for example, using or without the generation AI. For example, the reception unit can input the investor's social media activity data into the generation AI, which can then provide a related access method.

[0106] The reception unit can customize the access method based on the investor's past feedback. For example, the reception unit adjusts the access method based on feedback provided by the investor in the past. Furthermore, if the investor is satisfied with a particular access method, the reception unit can continue to use that method. For example, the reception unit adjusts the access method based on feedback provided by the investor in the past. Furthermore, if the investor is satisfied with a particular access method, the reception unit can continue to use that method. This makes it possible to customize the access method based on past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI, for example. For example, the reception unit can input the investor's feedback data into the generation AI, which can customize the access method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, provision unit, and reception unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart device 14. For example, the reception unit is realized by the reception device 38 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, provision unit, and reception unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart glasses 214. For example, the reception unit is realized by the microphone 238 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, provision unit, and reception unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the headset type terminal 314. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, provision unit, and reception unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the robot 414. For example, the reception unit is realized by the microphone 238 of the robot 414.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The analysis unit can incorporate an investor's health data into its analysis. For example, it can analyze an investor's stress level and sleep patterns to identify factors that may affect investment decisions. The analysis unit can also adjust an investor's risk tolerance based on their health. For example, if the investor is highly stressed, it can suggest a low-risk investment strategy, and if the investor's health is good, it can suggest a riskier strategy. This makes it possible to provide asset management advice that takes into account the investor's health.

[0109] The generation unit can generate an investment strategy taking into account the investor's hobbies and interests. For example, if an investor is interested in environmental protection, the generation unit can suggest investment in eco-friendly companies. If an investor is interested in technology, the generation unit can suggest investment in companies with the latest technology. Furthermore, if an investor is interested in a specific region, the generation unit can generate an investment strategy that focuses on companies and markets in that region. This makes it possible to provide investment strategies based on the individual interests of investors.

[0110] The advice providing unit can estimate the investor's emotions and adjust the timing of advice based on the estimated emotions. For example, if the investor is feeling stressed, the advice providing unit can temporarily refrain from providing advice. Alternatively, if the investor is relaxed, the advice providing unit can proactively provide advice. Furthermore, if the investor is excited, the advice providing unit can suggest a high-risk investment strategy. This makes it possible to provide advice at the appropriate time according to the investor's emotional state.

[0111] The reception unit can suggest the optimal access time based on the investor's past access history. For example, if the investor has often accessed the site at night in the past, it can provide investment information that is optimal for that time. Also, if the investor has often accessed the site on weekends, it can provide investment advice tailored to the weekend. Furthermore, if the investor has often accessed the site on a specific day of the week, it can provide investment information tailored to that day of the week. This makes it possible to provide services at the optimal time based on the investor's access patterns.

[0112] The analysis unit can estimate the investor's emotions and adjust the level of detail in the analysis results based on the estimated emotions. For example, if the investor is feeling anxious, detailed analysis results can be provided to reassure the investor. If the investor is feeling confident, concise analysis results can be provided. Furthermore, if the investor is excited, high-risk investment strategies can be explained in detail. This makes it possible to provide analysis results that correspond to the investor's emotional state.

[0113] The generation unit can generate an investment strategy taking into account the investor's social network. For example, it can propose an investment strategy based on the companies and markets in which the investor's friends and family are investing. It can also generate a strategy taking into account the investment trends of the community or group to which the investor belongs. Furthermore, it can propose an investment strategy based on the opinions of influencers and experts that the investor follows. This makes it possible to provide an investment strategy that utilizes the investor's social network.

[0114] The advice providing unit can estimate the investor's emotions and adjust the tone of the advice based on the estimated emotions. For example, if the investor is feeling anxious, the advice can be provided in a gentle tone. If the investor is feeling confident, the advice can be provided in a positive tone. Furthermore, if the investor is excited, the advice can be explained about risks in a calm tone. This makes it possible to provide advice in an appropriate tone according to the investor's emotional state.

[0115] The reception unit can suggest the optimal device based on the investor's device usage history. For example, if the investor frequently uses a smartphone, it can suggest an application optimized for smartphones. Also, if the investor uses a tablet, it can provide an interface optimized for tablets. Furthermore, if the investor uses a desktop, it can suggest a web application optimized for desktops. This makes it possible to suggest the optimal device based on the investor's device usage history.

[0116] The analysis unit can estimate the investor's emotions and adjust the frequency of analysis based on the estimated emotions. For example, if the investor is feeling stressed, the analysis frequency can be reduced to reduce the burden. Also, if the investor is relaxed, the analysis frequency can be increased to provide more detailed information. Furthermore, if the investor is excited, real-time analysis can be performed to provide immediate advice. This makes it possible to adjust the analysis frequency according to the investor's emotional state.

[0117] The providing unit can customize the format of advice based on the investor's past feedback. For example, if the investor has preferred text-format advice in the past, the advice can be provided in text format. Also, if the investor has preferred visual advice, the advice can be provided using graphs and charts. Furthermore, if the investor has preferred audio advice, the advice can be provided audio. This makes it possible to provide the optimal advice format based on the investor's past feedback.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The analysis unit analyzes the investor's asset status or market data. The investor's asset status includes cash, stocks, real estate, etc., and market data includes stock price data, economic indicators, trading volume, etc. The analysis unit analyzes the data using statistical analysis and machine learning algorithms. Step 2: The generation unit generates asset management advice based on the data analyzed by the analysis unit. The generation unit generates an investment strategy using a generation AI, and generates an optimal investment strategy using a deep learning model, reinforcement learning algorithm, etc. Step 3: The provider provides the advice generated by the generator to investors using VR / AR technology. The provider provides the advice visually using a head-mounted display or an augmented reality application. Step 4: The reception unit allows the investor to access the service using a smartphone. The reception unit allows the investor to access the service using a specific application or interface.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0134] 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.

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

[0136] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0138] The data processing system 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.

[0139] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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 AI 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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 AI 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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, in order to avoid confusion and to 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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. an analysis unit that analyzes the asset status of investors or market data; a generation unit that generates asset management advice based on the data analyzed by the analysis unit; a providing unit that provides the advice generated by the generating unit to investors using VR / AR technology; A reception unit where investors can access the service using their smartphones. A system characterized by:

2. The analysis unit Analyze data taking into account the investor's past investment history or risk tolerance 2. The system of claim 1.

3. The generation unit Generate investment strategies using generative AI 2. The system of claim 1.

4. The providing unit Providing advice to investors using VR / AR technology 2. The system of claim 1.

5. The reception unit Investors access the service using their smartphones 2. The system of claim 1.

6. The providing unit Advising investors on managing their hypothetical investment portfolios 2. The system of claim 1.

7. The analysis unit Estimate investor sentiment and adjust analysis priorities based on the estimated sentiment 2. The system of claim 1.

8. The analysis unit Analyzing investors' past investment history and extracting investment behavior patterns under specific market conditions 2. The system of claim 1.

9. The analysis unit Apply analytical algorithms according to the investor's risk tolerance 2. The system of claim 1.

10. The analysis unit Update investors' asset status and conduct analysis based on the latest data 2. The system of claim 1.

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