System

Abstract

An object of a system according to an embodiment is to analyze behavior data of an individual and provide a financial service based on the analysis.SOLUTION: A system includes a collection part, an analysis part, a stock price display part, a credit index calculation part, and an investment interface part. The collection unit collects action data of a user. The analysis unit analyzes the data collected by the collection unit. The stock price display unit displays a stock price related to the user based on the data analyzed by the analysis unit. The trust index calculation unit calculates a trust index of the user based on the data obtained by the analysis unit. The investment interface unit makes an investment based on the credit index calculated by the credit index calculation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not yet fully utilized individual behavioral data in financial services, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze individual behavioral data and provide financial services based on the data. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a stock price display unit, a credit index calculation unit, and an investment interface unit. The collection unit collects user behavioral data. The analysis unit analyzes the data collected by the collection unit. The stock price display unit displays stock prices related to the user based on the data analyzed by the analysis unit. The credit index calculation unit calculates the user's credit index based on the data obtained by the analysis unit. The investment interface unit makes investments based on the credit index calculated by the credit index calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze individual behavioral data and provide financial services based on the data. [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) A personal stock market system according to an embodiment of the present invention collects user behavioral data, analyzes it with a generation AI, displays stock prices, calculates a credit index, and allows investments. The personal stock market system collects user behavioral data, analyzes it with a generation AI, displays stock prices, calculates a credit index, and allows investments. For example, the personal stock market system collects various data from a user's daily life, including the user's purchasing history, social media posts, and exercise habits. This data is analyzed by a generation AI to reveal the user's behavioral patterns and choices. The personal stock market system then displays the user's stock price based on the data analyzed by the generation AI. For example, users with healthy lifestyles and those who actively contribute to society tend to have higher stock prices. On the other hand, users with unhealthy lifestyles and negative behaviors may have lower stock prices. This allows users to see in real time how their behavior affects stock prices. Furthermore, the personal stock market system uses a generation AI to predict future stock prices based on past data and calculates the user's credit index based on the prediction. Users can then make investments based on this credit index. For example, by investing in users whose stock prices are predicted to rise in the future, a return can be obtained. This allows the personal stock market system to collect and analyze users' behavioral data, display stock prices, calculate a credit index, and make investments. This allows the personal stock market system to collect and analyze users' behavioral data, display stock prices, calculate a credit index, and make investments. For example, by users being aware of how their actions affect stock prices, they will adopt better lifestyle habits and behavior. Also, by referring to the actions and credit indexes of other users, it can be an opportunity to reconsider one's own behavior. This is expected to lead to improvements in the lifestyle habits and behavior of society as a whole.

[0029] The individual stock market system according to the embodiment includes a collection unit, an analysis unit, a stock price display unit, a credit index calculation unit, and an investment interface unit. The collection unit collects user behavioral data. The user behavioral data includes, but is not limited to, location information, purchase history, and browsing history. The collection unit collects data from, for example, the user's smartphone or wearable device. The collection unit can also anonymize and encrypt the collected data. For example, the collection unit can delete personal information and mask the data. The collection unit can also encrypt the collected data using an encryption algorithm such as AES (Advanced Encryption Standard). The analysis unit uses a generation AI to analyze the data collected by the collection unit. The analysis can be performed using, for example, a machine learning algorithm, but is not limited to, an example. For example, the analysis unit can analyze the data using deep learning. The analysis unit can also analyze the data using a support vector machine. The analysis unit can also use the generation AI to extract patterns from the data and identify user behavioral patterns. The stock price display unit displays the user's stock price based on the data analyzed by the analysis unit. The stock price display includes, for example, a real-time graph display and a notification function, but is not limited to these examples. For example, the stock price display unit displays the user's stock price in a graph in real time. The stock price display unit can also include a function for notifying the user of stock price fluctuations. The stock price display unit can also display the user's stock price in text format. The credit index calculation unit calculates the user's credit index based on the data obtained by the analysis unit. The credit index is calculated using, for example, a prediction model based on past data, but is not limited to these examples. For example, the credit index calculation unit calculates the credit index using a regression model. The credit index calculation unit can also calculate the credit index using time series analysis. The credit index calculation unit can also calculate the user's credit index using a generation AI. The investment interface unit makes an investment based on the credit index calculated by the credit index calculation unit.The investment interface may, for example, include an investment interface and an investment history management function via an application, but is not limited to such examples. For example, the investment interface unit provides an interface for a user to make investments via an application. The investment interface unit may also include a function for managing the user's investment history. The investment interface unit may also include a function for displaying the user's investment status in real time. As a result, the personal stock market system according to the embodiment can collect and analyze user behavioral data, display stock prices, calculate a credit index, and make investments. For example, by being aware of how their own behavior affects stock prices, users will adopt better lifestyle habits and behavior. Furthermore, referring to the behavior and credit index of other users can provide an opportunity to reconsider their own behavior. This is expected to lead to improvements in the lifestyle habits and behavior of society as a whole.

[0030] The collection unit can collect data from the user's smartphone or wearable device. For example, the collection unit collects location information and app usage history from the user's smartphone. The collection unit can also collect heart rate and step count data from the user's wearable device. For example, the collection unit collects heart rate data from a smartwatch to understand the user's health condition. The collection unit can also collect step count data from a fitness tracker to analyze the user's exercise habits. In this way, by collecting data from the user's smartphone or wearable device, more detailed behavioral data can be obtained. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input location information acquired from the smartphone to a generation AI and have the generation AI analyze the location information.

[0031] The analysis unit can analyze data using a machine learning algorithm. The analysis unit analyzes data using, for example, deep learning. For example, the analysis unit learns large amounts of data and performs advanced pattern recognition. The analysis unit can also analyze data using a support vector machine. For example, the analysis unit performs data classification and regression analysis. The analysis unit can also use a generation AI to extract patterns from the data and reveal user behavior patterns. For example, the analysis unit can input data into the generation AI and have the generation AI analyze the behavior patterns. In this way, the use of a machine learning algorithm improves the accuracy of data analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0032] The collection unit can anonymize and encrypt the collected data. For example, the collection unit deletes personal information and masks the data. For example, the collection unit anonymizes the data by deleting personal information such as the user's name and address. The collection unit can also encrypt the collected data using an encryption algorithm such as AES (Advanced Encryption Standard). For example, the collection unit encrypts the data to prevent unauthorized access by third parties. This protects the user's privacy by anonymizing and encrypting the data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the collected data into a generation AI and have the generation AI anonymize and encrypt the data.

[0033] The stock price display unit may have a real-time graph display or notification function. The stock price display unit, for example, displays a graph of the user's stock price in real time. For example, the stock price display unit displays fluctuations in the user's stock price in real time, allowing the user to immediately understand the fluctuations in the stock price. The stock price display unit may also have a function of notifying the user of stock price fluctuations. For example, the stock price display unit notifies the user when the user's stock price exceeds a certain threshold. The stock price display unit may also display the user's stock price in text format. For example, the stock price display unit displays the user's stock price in text, allowing the user to check the details of the stock price. This allows the user to immediately understand fluctuations in the stock price through the real-time graph display or notification function. Some or all of the above-mentioned processing in the stock price display unit may be performed, for example, using AI, or may be performed without using AI. For example, the stock price display unit may display stock prices graphically using a generation AI.

[0034] The credit index calculation unit can calculate the credit index using a prediction model based on past data. The credit index calculation unit can calculate the credit index using, for example, a regression model. For example, the credit index calculation unit performs regression analysis based on past data to calculate the credit index. The credit index calculation unit can also calculate the credit index using time series analysis. For example, the credit index calculation unit analyzes time series data and predicts a future credit index. The credit index calculation unit can also calculate the user's credit index using a generation AI. For example, the credit index calculation unit can input data to the generation AI and cause the generation AI to calculate the credit index. As a result, by using a prediction model based on past data, the calculation accuracy of the credit index is improved. Some or all of the above-mentioned processing in the credit index calculation unit can be performed, for example, using AI or without using AI.

[0035] The investment interface unit may have an investment interface and investment history management function through an application. The investment interface unit, for example, provides an interface through which a user makes an investment through an application. For example, the investment interface unit provides an intuitive user interface so that the user can easily make an investment. The investment interface unit may also have a function to manage the user's investment history. For example, the investment interface unit stores the user's past investment history so that the user can check it at any time. The investment interface unit may also have a function to display the user's investment status in real time. For example, the investment interface unit displays the user's current investment status in graphs or text so that the user can understand the progress of their investments. This allows the user to easily make and manage their investments through the investment interface and investment history management function through the application. Some or all of the above-mentioned processing in the investment interface unit may be performed, for example, using AI, or may be performed without using AI. For example, the investment interface unit may display the investment interface and manage the investment history using a generation AI.

[0036] The collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, the collection unit prioritizes collecting data from devices that the user has frequently used in the past. For example, the collection unit collects data from the user's smartphone or wearable device. The collection unit can also analyze the user's past behavioral patterns and select the most efficient timing for collecting data. For example, the collection unit determines the optimal timing for collecting data based on the user's past behavioral data. The collection unit can also select the optimal data collection method (audio, text, image, etc.) based on the user's past data collection history. For example, the collection unit analyzes the user's past data collection history and determines the optimal data collection method. In this way, the optimal data collection method can be selected by analyzing the user's past behavioral data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavioral data into a generation AI and cause the generation AI to select the optimal data collection method.

[0037] The collection unit can filter data based on the user's current activity status and areas of interest when collecting data. For example, when the user is exercising, the collection unit prioritizes collecting data related to exercise. For example, the collection unit collects the user's exercise data and prioritizes analyzing the exercise-related data. Furthermore, when the user is working, the collection unit can prioritize collecting data related to work. For example, the collection unit collects data related to the user's work and prioritizes analyzing the work-related data. Furthermore, when the user is immersed in a hobby, the collection unit can prioritize collecting data related to the hobby. For example, the collection unit collects data related to the user's hobby and prioritizes analyzing the hobby-related data. This allows highly relevant data to be collected by filtering data based on the user's current activity status and areas of interest. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can cause a generation AI to filter data based on the user's current activity status and areas of interest.

[0038] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit prioritizes collecting voice data. For example, the collection unit collects the user's voice data and analyzes the data based on the voice input. Furthermore, when the user uses text input, the collection unit can also prioritize collecting text data. For example, the collection unit collects the user's text data and analyzes the data based on the text input. Furthermore, when the user uses image input, the collection unit can also prioritize collecting image data. For example, the collection unit collects the user's image data and analyzes the data based on the image input. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause a generation AI to select the optimal collection means depending on the user's input method.

[0039] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. For example, the collection unit collects the user's location information and prioritizes analyzing data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the user's travel destination. For example, the collection unit collects the user's location information and prioritizes analyzing data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to the user's home. For example, the collection unit collects the user's location information and prioritizes analyzing data related to the user's home. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's location information to the generation AI and cause the generation AI to collect highly relevant data.

[0040] During data collection, the collection unit can analyze the user's social media activities and collect related data. The collection unit, for example, collects data related to places where the user has checked in on social media. For example, the collection unit analyzes the user's social media activities and prioritizes analyzing data related to the checked-in places. The collection unit can also analyze the content of the user's social media posts and collect related data. For example, the collection unit analyzes the content of the user's posts and prioritizes analyzing related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the activities of the user's friends and prioritizes analyzing related data. In this way, related data can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activities into a generation AI and cause the generation AI to collect related data.

[0041] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, adjusts the frequency of data collection based on feedback provided by the user in the past. For example, the collection unit analyzes the user's feedback and optimizes the frequency of data collection. The collection unit can also select the type of data to collect by referring to the user's past feedback. For example, the collection unit determines the type of data to collect based on the user's feedback. The collection unit can also adjust the timing of data collection by reflecting the user's past feedback. For example, the collection unit determines the optimal timing for data collection based on the user's feedback. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's feedback to a generation AI and cause the generation AI to customize the collection method.

[0042] During data analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the user's behavioral pattern. For example, if the user's behavioral pattern is consistent, the analysis unit performs a detailed analysis. For example, the analysis unit analyzes the user's behavioral pattern in detail and extracts important data. Furthermore, if the user's behavioral pattern fluctuates, the analysis unit can perform a simplified analysis. For example, the analysis unit analyzes the user's behavioral pattern in a simplified manner and extracts basic data. Furthermore, if the user's behavioral pattern matches a specific condition, the analysis unit can adjust the level of detail of the analysis based on the condition. For example, if the user's behavioral pattern matches a specific condition, the analysis unit performs a detailed analysis based on the condition. This enables efficient data analysis by adjusting the level of detail of the analysis based on the importance of the user's behavioral pattern. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's behavioral pattern into a generation AI and have the generation AI adjust the level of detail of the analysis.

[0043] When analyzing data, the analysis unit can apply different analysis algorithms depending on the user's category. For example, if the user belongs to a health category, the analysis unit applies a health-related analysis algorithm. For example, the analysis unit analyzes the user's health data and evaluates the user's health condition. Furthermore, if the user belongs to a financial category, the analysis unit can also apply a finance-related analysis algorithm. For example, the analysis unit analyzes the user's financial data and evaluates financial risk. Furthermore, if the user belongs to an entertainment category, the analysis unit can also apply an entertainment-related analysis algorithm. For example, the analysis unit analyzes the user's entertainment data and evaluates entertainment preferences. By applying different analysis algorithms depending on the user's category, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause a generation AI to apply different analysis algorithms depending on the user's category.

[0044] When analyzing data, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, improves the accuracy of the current analysis based on the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and reflects them in the current analysis. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit adjusts the parameters of the analysis algorithm based on the user's past analysis results. The analysis unit can also reduce analysis errors by using the user's past analysis results. For example, the analysis unit minimizes analysis errors based on the user's past analysis results. This improves the accuracy of the current analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0045] During data analysis, the analysis unit can determine analysis priorities based on the user's behavioral history. For example, the analysis unit prioritizes analysis of the most important data from the user's behavioral history. For example, the analysis unit analyzes the user's behavioral history and prioritizes analysis of important data. The analysis unit can also dynamically adjust analysis priorities based on the user's behavioral history. For example, the analysis unit adjusts analysis priorities in real time based on the user's behavioral history. The analysis unit can also determine the order of analysis with reference to the user's behavioral history. For example, the analysis unit optimizes the order of analysis based on the user's behavioral history. This allows important data to be analyzed preferentially by determining analysis priorities based on the user's behavioral history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's behavioral history into a generation AI and have the generation AI determine the analysis priorities.

[0046] The analysis unit can adjust the order of analysis based on the user's relevance during data analysis. For example, the analysis unit prioritizes analysis of data with high relevance to the user. For example, the analysis unit evaluates the user's relevance and prioritizes analysis of highly relevant data. The analysis unit can also dynamically adjust the order of analysis based on the user's relevance. For example, the analysis unit evaluates the user's relevance in real time and adjusts the order of analysis. The analysis unit can also determine the priority of analysis taking the user's relevance into consideration. For example, the analysis unit optimizes the priority of analysis based on the user's relevance. This enables efficient data analysis by adjusting the order of analysis based on the user's relevance. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's relevance to a generation AI and cause the generation AI to adjust the order of analysis.

[0047] During data analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user's level of expertise is high, the analysis unit provides analysis results that use a lot of technical terms. For example, the analysis unit evaluates the user's level of expertise and provides analysis results that use a lot of technical terms. Furthermore, if the user's level of expertise is low, the analysis unit can also provide analysis results that avoid technical terms. For example, the analysis unit evaluates the user's level of expertise and provides analysis results that avoid technical terms. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. For example, the analysis unit optimizes the way in which the analysis results are presented based on the user's level of expertise. This allows for the provision of analysis results that are easy to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms in the analysis.

[0048] When displaying stock prices, the stock price display unit can adjust the level of detail of the display based on the importance of the user's behavioral pattern. For example, if the user's behavioral pattern is consistent, the stock price display unit displays detailed stock prices. For example, the stock price display unit analyzes the user's behavioral pattern in detail and displays important data. Furthermore, if the user's behavioral pattern fluctuates, the stock price display unit can display simplified stock prices. For example, the stock price display unit analyzes the user's behavioral pattern in simple terms and displays basic data. Furthermore, if the user's behavioral pattern matches a specific condition, the stock price display unit can adjust the level of detail of the display based on the condition. For example, if the user's behavioral pattern matches a specific condition, the stock price display unit displays detailed stock prices based on the condition. This enables efficient stock price display by adjusting the level of detail of the display based on the importance of the user's behavioral pattern. Some or all of the above-described processing in the stock price display unit may be performed using, or without, AI. For example, the stock price display unit can input the user's behavioral pattern into a generation AI and have the generation AI adjust the level of detail of the display.

[0049] The stock price display unit can apply different display algorithms depending on the user's category when displaying stock prices. For example, if the user belongs to a health category, the stock price display unit applies a health-related stock price display algorithm. For example, the stock price display unit analyzes the user's health data and evaluates the user's health condition. Also, if the user belongs to a finance category, the stock price display unit can apply a finance-related stock price display algorithm. For example, the stock price display unit analyzes the user's financial data and evaluates financial risk. Also, if the user belongs to an entertainment category, the stock price display unit can apply an entertainment-related stock price display algorithm. For example, the stock price display unit analyzes the user's entertainment data and evaluates the user's entertainment preferences. This improves the accuracy of the display by applying different display algorithms depending on the user's category. Some or all of the above-described processing in the stock price display unit may be performed using, for example, AI, or may be performed without using AI. For example, the stock price display unit can cause a generation AI to apply different display algorithms depending on the user's category.

[0050] When displaying stock prices, the stock price display unit can improve the accuracy of the display by referring to the user's past display results. The stock price display unit, for example, improves the accuracy of the current stock price display based on the user's past display results. For example, the stock price display unit analyzes the user's past display results and reflects them in the current display. The stock price display unit can also adjust the display algorithm by referring to the user's past display results. For example, the stock price display unit adjusts the parameters of the display algorithm based on the user's past display results. The stock price display unit can also reduce display errors by using the user's past display results. For example, the stock price display unit minimizes display errors based on the user's past display results. This improves the accuracy of the current display by referring to the user's past display results. Some or all of the above-mentioned processing in the stock price display unit may be performed using, for example, AI, or may be performed without using AI. For example, the stock price display unit can input the user's past display results into a generation AI and cause the generation AI to improve the display accuracy.

[0051] When displaying stock prices, the stock price display unit can determine display priorities based on the user's behavioral history. For example, the stock price display unit prioritizes displaying the most important stock price information based on the user's behavioral history. For example, the stock price display unit analyzes the user's behavioral history and prioritizes displaying important stock price information. The stock price display unit can also dynamically adjust display priorities based on the user's behavioral history. For example, the stock price display unit adjusts display priorities in real time based on the user's behavioral history. The stock price display unit can also determine the display order with reference to the user's behavioral history. For example, the stock price display unit optimizes the display order based on the user's behavioral history. This allows important stock price information to be prioritized by determining display priorities based on the user's behavioral history. Some or all of the above-described processing in the stock price display unit may be performed using, for example, AI, or may be performed without using AI. For example, the stock price display unit can input the user's behavioral history into a generation AI and have the generation AI determine the display priorities.

[0052] The stock price display unit can adjust the display order based on the user's relevance when displaying stock prices. For example, the stock price display unit prioritizes displaying stock price information that is highly relevant to the user. For example, the stock price display unit evaluates the user's relevance and prioritizes displaying highly relevant stock price information. The stock price display unit can also dynamically adjust the display order based on the user's relevance. For example, the stock price display unit evaluates the user's relevance in real time and adjusts the display order. The stock price display unit can also determine the display priority taking the user's relevance into consideration. For example, the stock price display unit optimizes the display priority based on the user's relevance. This enables efficient stock price display by adjusting the display order based on the user's relevance. Some or all of the above-described processing in the stock price display unit may be performed using, for example, AI, or may be performed without using AI. For example, the stock price display unit can input the user's relevance to a generation AI and have the generation AI adjust the display order.

[0053] The stock price display unit can adjust the use of technical terms in the display according to the user's level of expertise when displaying stock prices. For example, if the user's level of expertise is high, the stock price display unit provides a stock price display that uses a lot of technical terms. For example, the stock price display unit evaluates the user's level of expertise and provides a stock price display that uses a lot of technical terms. Furthermore, if the user's level of expertise is low, the stock price display unit can provide a stock price display that avoids technical terms. For example, the stock price display unit evaluates the user's level of expertise and provides a stock price display that avoids technical terms. Furthermore, the stock price display unit can adjust the way the stock price display is displayed according to the user's level of expertise. For example, the stock price display unit optimizes the way the stock price display is displayed based on the user's level of expertise. This allows for adjusting the use of technical terms in the display according to the user's level of expertise, thereby providing a stock price display that is easy to understand. Some or all of the above-described processing in the stock price display unit may be performed using, for example, AI, or may be performed without AI. For example, the stock price display unit can input the user's level of expertise into a generation AI and cause the generation AI to adjust the use of technical terms in the display.

[0054] The trust index calculation unit can adjust the level of detail of the calculation based on the importance of the user's behavioral pattern when calculating the trust index. For example, if the user's behavioral pattern is consistent, the trust index calculation unit calculates a detailed trust index. For example, the trust index calculation unit analyzes the user's behavioral pattern in detail and calculates the trust index based on important data. Furthermore, if the user's behavioral pattern fluctuates, the trust index calculation unit can calculate a simplified trust index. For example, the trust index calculation unit analyzes the user's behavioral pattern in a simplified manner and calculates the trust index based on basic data. Furthermore, if the user's behavioral pattern matches a specific condition, the trust index calculation unit can adjust the level of detail of the calculation based on the condition. For example, if the user's behavioral pattern matches a specific condition, the trust index calculation unit calculates a detailed trust index based on the condition. This enables efficient trust index calculation by adjusting the level of detail of the calculation based on the importance of the user's behavioral pattern. Some or all of the above-mentioned processing in the trust index calculation unit may be performed using AI, for example, or without AI. For example, the credit index calculation unit can input the user's behavioral patterns into the generation AI and have the generation AI adjust the level of detail of the calculation.

[0055] The credit index calculation unit can apply different calculation algorithms depending on the user category when calculating the credit index. For example, if the user belongs to a health category, the credit index calculation unit applies a health-related credit index calculation algorithm. For example, the credit index calculation unit analyzes the user's health data and evaluates the user's health condition. Furthermore, if the user belongs to a finance category, the credit index calculation unit can also apply a finance-related credit index calculation algorithm. For example, the credit index calculation unit analyzes the user's financial data and evaluates financial risk. Furthermore, if the user belongs to an entertainment category, the credit index calculation unit can also apply an entertainment-related credit index calculation algorithm. For example, the credit index calculation unit analyzes the user's entertainment data and evaluates the user's entertainment preferences. Thus, by applying different calculation algorithms depending on the user category, the accuracy of the calculation is improved. Some or all of the above-described processing in the credit index calculation unit may be performed using, or without, AI. For example, the credit index calculation unit can cause a generation AI to apply different calculation algorithms depending on the user category.

[0056] The trust index calculation unit can improve the accuracy of the calculation when calculating the trust index by referring to the user's past calculation results. The trust index calculation unit, for example, improves the accuracy of the current trust index calculation based on the user's past calculation results. For example, the trust index calculation unit analyzes the user's past calculation results and reflects them in the current calculation. The trust index calculation unit can also adjust the calculation algorithm by referring to the user's past calculation results. For example, the trust index calculation unit adjusts the parameters of the calculation algorithm based on the user's past calculation results. The trust index calculation unit can also reduce calculation errors by using the user's past calculation results. For example, the trust index calculation unit minimizes calculation errors based on the user's past calculation results. This improves the accuracy of the current calculation by referring to the user's past calculation results. Some or all of the above-mentioned processing in the trust index calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the trust index calculation unit can input the user's past calculation results into the generation AI and cause the generation AI to improve the calculation accuracy.

[0057] The trust index calculation unit can weight the calculation based on the user's behavioral history when calculating the trust index. The trust index calculation unit, for example, calculates the trust index by weighting the most important data from the user's behavioral history. For example, the trust index calculation unit analyzes the user's behavioral history and weights important data. The trust index calculation unit can also dynamically adjust the calculation weighting based on the user's behavioral history. For example, the trust index calculation unit evaluates the user's behavioral history in real time and adjusts the weighting. The trust index calculation unit can also determine the calculation weighting with reference to the user's behavioral history. For example, the trust index calculation unit optimizes the weighting based on the user's behavioral history. In this way, by weighting the calculation based on the user's behavioral history, it is possible to calculate a trust index that emphasizes important data. Some or all of the above-described processing in the trust index calculation unit may be performed using, for example, AI, or may be performed without AI. For example, the trust index calculation unit can input the user's behavioral history to a generation AI and cause the generation AI to adjust the weighting.

[0058] The trust index calculation unit can adjust the calculation order based on the user's relevance when calculating the trust index. The trust index calculation unit, for example, prioritizes calculation of data with high user relevance. For example, the trust index calculation unit evaluates the user's relevance and prioritizes calculation of data with high relevance. The trust index calculation unit can also dynamically adjust the calculation order based on the user's relevance. For example, the trust index calculation unit evaluates the user's relevance in real time and adjusts the calculation order. The trust index calculation unit can also determine the calculation priority taking the user's relevance into consideration. For example, the trust index calculation unit optimizes the calculation priority based on the user's relevance. This enables efficient trust index calculation by adjusting the calculation order based on the user's relevance. Some or all of the above-described processing in the trust index calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the trust index calculation unit can input the user's relevance to a generation AI and cause the generation AI to adjust the calculation order.

[0059] The trust index calculation unit can adjust the use of technical terms in the calculation according to the user's level of expertise when calculating the trust index. For example, if the user's level of expertise is high, the trust index calculation unit calculates the trust index using a lot of technical terms. For example, the trust index calculation unit evaluates the user's level of expertise and calculates the trust index using a lot of technical terms. Furthermore, if the user's level of expertise is low, the trust index calculation unit can also calculate the trust index while avoiding technical terms. For example, the trust index calculation unit evaluates the user's level of expertise and calculates the trust index while avoiding technical terms. Furthermore, the trust index calculation unit can adjust the expression method for the trust index calculation according to the user's level of expertise. For example, the trust index calculation unit optimizes the expression method for the trust index calculation based on the user's level of expertise. This adjusts the use of technical terms in the calculation according to the user's level of expertise, making it possible to provide an easy-to-understand trust index. Some or all of the above-mentioned processing in the trust index calculation unit may be performed, for example, using AI or without AI. For example, the trust index calculation unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology in the calculation.

[0060] When displaying the investment interface, the investment interface unit can select the optimal display method by referring to the user's past investment history. The investment interface unit provides the optimal investment interface, for example, based on the user's past investment history. For example, the investment interface unit analyzes the user's past investment history and selects the optimal display method. The investment interface unit can also adjust the display algorithm by referring to the user's past investment history. For example, the investment interface unit adjusts the parameters of the display algorithm based on the user's past investment history. The investment interface unit can also reduce display errors by using the user's past investment history. For example, the investment interface unit minimizes display errors based on the user's past investment history. In this way, the optimal investment interface can be provided by referring to the user's past investment history. Some or all of the above-described processing in the investment interface unit may be performed using, for example, AI, or may be performed without using AI. For example, the investment interface unit can input the user's past investment history into a generation AI and have the generation AI select a display method.

[0061] When displaying the investment interface, the investment interface unit can customize the display content according to the user's current investment situation. The investment interface unit, for example, provides an optimal investment interface based on the user's current investment situation. For example, the investment interface unit analyzes the user's current investment situation and customizes the display content. The investment interface unit can also adjust the display algorithm based on the user's current investment situation. For example, the investment interface unit adjusts the parameters of the display algorithm based on the user's current investment situation. The investment interface unit can also reduce display errors using the user's current investment situation. For example, the investment interface unit minimizes display errors based on the user's current investment situation. This enables efficient investment management by customizing the display content according to the user's current investment situation. Some or all of the above-described processing in the investment interface unit may be performed using, for example, AI, or may be performed without AI. For example, the investment interface unit can input the user's current investment situation into a generation AI and have the generation AI customize the display content.

[0062] The investment interface unit can improve the display method by reflecting user feedback when displaying the investment interface. The investment interface unit, for example, improves the display method of the investment interface based on user feedback. For example, the investment interface unit analyzes user feedback and optimizes the display method. The investment interface unit can also adjust the display algorithm by referring to user feedback. For example, the investment interface unit adjusts parameters of the display algorithm based on user feedback. The investment interface unit can also reduce display errors by using user feedback. For example, the investment interface unit minimizes display errors based on user feedback. In this way, the display method of the investment interface can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the investment interface unit may be performed using AI, for example, or may be performed without using AI. For example, the investment interface unit can input user feedback into a generation AI and cause the generation AI to improve the display method.

[0063] When displaying the investment interface, the investment interface unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the investment interface unit provides a display method that matches the screen size. For example, the investment interface unit provides a display method optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the investment interface unit can also provide a display method optimized for a larger screen. For example, the investment interface unit provides a display method optimized for the tablet screen size. Furthermore, if the user is using a smartwatch, the investment interface unit can also provide a simple and highly visible display method. For example, the investment interface unit provides a display method optimized for the smartwatch screen size. This makes it possible to provide an optimal investment interface by taking into account the user's device information. Some or all of the above-described processing in the investment interface unit may be performed using AI, for example, or may be performed without using AI. For example, the investment interface unit can input the user's device information into the generation AI and have the generation AI select the display method.

[0064] When displaying the investment interface, the investment interface unit can make the display content multilingual according to the user's language setting. The investment interface unit, for example, automatically sets the language of the investment interface based on the language setting of the user's device. For example, the investment interface unit detects the language setting of the user's device and provides display content corresponding to that language. The investment interface unit can also provide a language switching function if the user uses multiple languages. For example, the investment interface unit provides an interface that allows the user to select the language they want to use. Furthermore, if the user selects a specific language, the investment interface unit can provide the investment interface in that language. For example, the investment interface unit provides display content corresponding to the language selected by the user. This improves user convenience by making the display content multilingual according to the user's language setting. Some or all of the above-described processing in the investment interface unit may be performed using, for example, AI, or may be performed without AI. For example, the investment interface unit can input the user's language setting into a generation AI and have the generation AI perform multilingual support for the display content.

[0065] The investment interface unit can customize the display content based on the user's investment goals when displaying the investment interface. The investment interface unit, for example, provides an optimal investment interface based on the user's investment goals. For example, the investment interface unit analyzes the user's investment goals and customizes the display content. The investment interface unit can also adjust the display algorithm based on the user's investment goals. For example, the investment interface unit adjusts the parameters of the display algorithm based on the user's investment goals. The investment interface unit can also reduce display errors using the user's investment goals. For example, the investment interface unit minimizes display errors based on the user's investment goals. This enables efficient investment management by customizing the display content based on the user's investment goals. Some or all of the above-described processing in the investment interface unit may be performed using, for example, AI, or may be performed without using AI. For example, the investment interface unit can input the user's investment goals into a generation AI and have the generation AI customize the display content.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The personal stock market system can further include a sensor network for collecting user behavioral data. The sensor network is installed in the user's living environment and collects environmental data such as temperature, humidity, and light intensity. For example, sensors installed in the user's home can monitor the indoor temperature and humidity in real time and evaluate the user's comfort level. Sensors installed in offices and public facilities can also provide data for detailed understanding of user behavioral patterns. Furthermore, the sensor network can collect biometric data such as heart rate and blood pressure to monitor the user's health. This allows for more detailed collection of user behavioral data and improved analysis accuracy.

[0068] When analyzing user behavior data, the analysis unit can adjust the analysis algorithm taking into account the user's past behavior patterns. For example, if the user has repeatedly performed a specific behavior pattern in the past, the analysis algorithm can be optimized based on that pattern. Also, if the user's behavior pattern is fluctuating, the analysis algorithm can be dynamically adjusted to perform analysis based on the latest data. Furthermore, if the user's behavior pattern matches certain conditions, the level of analysis detail can be adjusted based on those conditions. In this way, by adjusting the analysis algorithm based on the user's behavior pattern, the accuracy of the analysis can be improved.

[0069] The personal stock market system can further include a drone network for collecting user behavioral data. The drone network flies over the user's living environment and collects data from the air. For example, a drone flies around the user's home and collects environmental data and user behavioral data. The drone can also provide data to understand the user's behavioral patterns in detail while they are out. Furthermore, the drone can collect biometric data such as heart rate and body temperature to monitor the user's health. This allows user behavioral data to be collected over a wider area and improves the accuracy of analysis.

[0070] When analyzing user behavior data, the analysis unit can adjust the analysis algorithm by taking into account the user's social network data. For example, the analysis unit can analyze the behavioral patterns of the user's friends and followers to evaluate their influence on the user's behavior. It can also analyze the user's comments and posts on social networks to gain a more detailed understanding of the user's behavioral patterns. Furthermore, it can predict fluctuations in the user's behavioral patterns based on the user's social network data and dynamically adjust the analysis algorithm. By taking into account the user's social network data, the accuracy of the analysis can be improved.

[0071] The collection unit may be equipped with a privacy filtering function to protect the user's privacy when collecting the user's behavioral data. For example, the collection unit may automatically mask and anonymize the user's personal information. The collection unit may also limit the scope of data collection based on specific privacy settings when collecting the user's data. Furthermore, the collection unit may comply with the user's privacy policy and ensure transparency regarding data collection and use. This allows the behavioral data to be collected while protecting the user's privacy.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The collection unit collects user behavioral data. The user behavioral data includes, for example, location information, purchase history, browsing history, etc. The collection unit collects data from the user's smartphone or wearable device. The collection unit can also anonymize and encrypt the collected data. For example, the collection unit can delete personal information and mask the data. The collection unit can also encrypt the data using an encryption algorithm such as AES (Advanced Encryption Standard). Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit. The analysis is performed using, for example, machine learning algorithms, deep learning, or support vector machines. The analysis unit also extracts patterns from the data and identifies user behavior patterns. Step 3: The stock price display unit displays the user's stock price based on the data analyzed by the analysis unit. The stock price display has a real-time graph display and notification function. For example, the stock price display unit can have a function to display the user's stock price in a graph in real time and notify the user of stock price fluctuations. The stock price display unit can also display the user's stock price in text format. Step 4: The credit index calculation unit calculates the user's credit index based on the data obtained by the analysis unit. The credit index is calculated using, for example, a predictive model or regression model based on past data, time series analysis, or generation AI. Step 5: The investment interface unit invests based on the credit index calculated by the credit index calculation unit. The investment interface has an investment interface and an investment history management function through an application. For example, it can provide an interface for users to invest through an application, and have a function to manage the user's investment history and a function to display the investment status in real time.

[0074] (Example 2) A personal stock market system according to an embodiment of the present invention collects user behavioral data, analyzes it with a generation AI, displays stock prices, calculates a credit index, and allows investments. The personal stock market system collects user behavioral data, analyzes it with a generation AI, displays stock prices, calculates a credit index, and allows investments. For example, the personal stock market system collects various data from a user's daily life, including the user's purchasing history, social media posts, and exercise habits. This data is analyzed by a generation AI to reveal the user's behavioral patterns and choices. The personal stock market system then displays the user's stock price based on the data analyzed by the generation AI. For example, users with healthy lifestyles and those who actively contribute to society tend to have higher stock prices. On the other hand, users with unhealthy lifestyles and negative behaviors may have lower stock prices. This allows users to see in real time how their behavior affects stock prices. Furthermore, the personal stock market system uses a generation AI to predict future stock prices based on past data and calculates the user's credit index based on the prediction. Users can then make investments based on this credit index. For example, by investing in users whose stock prices are predicted to rise in the future, a return can be obtained. This allows the personal stock market system to collect and analyze users' behavioral data, display stock prices, calculate a credit index, and make investments. This allows the personal stock market system to collect and analyze users' behavioral data, display stock prices, calculate a credit index, and make investments. For example, by users being aware of how their actions affect stock prices, they will adopt better lifestyle habits and behavior. Also, by referring to the actions and credit indexes of other users, it can be an opportunity to reconsider one's own behavior. This is expected to lead to improvements in the lifestyle habits and behavior of society as a whole.

[0075] The individual stock market system according to the embodiment includes a collection unit, an analysis unit, a stock price display unit, a credit index calculation unit, and an investment interface unit. The collection unit collects user behavioral data. The user behavioral data includes, but is not limited to, location information, purchase history, and browsing history. The collection unit collects data from, for example, the user's smartphone or wearable device. The collection unit can also anonymize and encrypt the collected data. For example, the collection unit can delete personal information and mask the data. The collection unit can also encrypt the collected data using an encryption algorithm such as AES (Advanced Encryption Standard). The analysis unit uses a generation AI to analyze the data collected by the collection unit. The analysis can be performed using, for example, a machine learning algorithm, but is not limited to, an example. For example, the analysis unit can analyze the data using deep learning. The analysis unit can also analyze the data using a support vector machine. The analysis unit can also use the generation AI to extract patterns from the data and identify user behavioral patterns. The stock price display unit displays the user's stock price based on the data analyzed by the analysis unit. The stock price display includes, for example, a real-time graph display and a notification function, but is not limited to these examples. For example, the stock price display unit displays the user's stock price in a graph in real time. The stock price display unit can also include a function for notifying the user of stock price fluctuations. The stock price display unit can also display the user's stock price in text format. The credit index calculation unit calculates the user's credit index based on the data obtained by the analysis unit. The credit index is calculated using, for example, a prediction model based on past data, but is not limited to these examples. For example, the credit index calculation unit calculates the credit index using a regression model. The credit index calculation unit can also calculate the credit index using time series analysis. The credit index calculation unit can also calculate the user's credit index using a generation AI. The investment interface unit makes an investment based on the credit index calculated by the credit index calculation unit.The investment interface may, for example, include an investment interface and an investment history management function via an application, but is not limited to such examples. For example, the investment interface unit provides an interface for a user to make investments via an application. The investment interface unit may also include a function for managing the user's investment history. The investment interface unit may also include a function for displaying the user's investment status in real time. As a result, the personal stock market system according to the embodiment can collect and analyze user behavioral data, display stock prices, calculate a credit index, and make investments. For example, by being aware of how their own behavior affects stock prices, users will adopt better lifestyle habits and behavior. Furthermore, referring to the behavior and credit index of other users can provide an opportunity to reconsider their own behavior. This is expected to lead to improvements in the lifestyle habits and behavior of society as a whole.

[0076] The collection unit can collect data from the user's smartphone or wearable device. For example, the collection unit collects location information and app usage history from the user's smartphone. The collection unit can also collect heart rate and step count data from the user's wearable device. For example, the collection unit collects heart rate data from a smartwatch to understand the user's health condition. The collection unit can also collect step count data from a fitness tracker to analyze the user's exercise habits. In this way, by collecting data from the user's smartphone or wearable device, more detailed behavioral data can be obtained. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input location information acquired from the smartphone to a generation AI and have the generation AI analyze the location information.

[0077] The analysis unit can analyze data using a machine learning algorithm. The analysis unit analyzes data using, for example, deep learning. For example, the analysis unit learns large amounts of data and performs advanced pattern recognition. The analysis unit can also analyze data using a support vector machine. For example, the analysis unit performs data classification and regression analysis. The analysis unit can also use a generation AI to extract patterns from the data and reveal user behavior patterns. For example, the analysis unit can input data into the generation AI and have the generation AI analyze the behavior patterns. In this way, the use of a machine learning algorithm improves the accuracy of data analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0078] The collection unit can anonymize and encrypt the collected data. For example, the collection unit deletes personal information and masks the data. For example, the collection unit anonymizes the data by deleting personal information such as the user's name and address. The collection unit can also encrypt the collected data using an encryption algorithm such as AES (Advanced Encryption Standard). For example, the collection unit encrypts the data to prevent unauthorized access by third parties. This protects the user's privacy by anonymizing and encrypting the data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the collected data into a generation AI and have the generation AI anonymize and encrypt the data.

[0079] The stock price display unit may have a real-time graph display or notification function. The stock price display unit, for example, displays a graph of the user's stock price in real time. For example, the stock price display unit displays fluctuations in the user's stock price in real time, allowing the user to immediately understand the fluctuations in the stock price. The stock price display unit may also have a function of notifying the user of stock price fluctuations. For example, the stock price display unit notifies the user when the user's stock price exceeds a certain threshold. The stock price display unit may also display the user's stock price in text format. For example, the stock price display unit displays the user's stock price in text, allowing the user to check the details of the stock price. This allows the user to immediately understand fluctuations in the stock price through the real-time graph display or notification function. Some or all of the above-mentioned processing in the stock price display unit may be performed, for example, using AI, or may be performed without using AI. For example, the stock price display unit may display stock prices graphically using a generation AI.

[0080] The credit index calculation unit can calculate the credit index using a prediction model based on past data. The credit index calculation unit can calculate the credit index using, for example, a regression model. For example, the credit index calculation unit performs regression analysis based on past data to calculate the credit index. The credit index calculation unit can also calculate the credit index using time series analysis. For example, the credit index calculation unit analyzes time series data and predicts a future credit index. The credit index calculation unit can also calculate the user's credit index using a generation AI. For example, the credit index calculation unit can input data to the generation AI and cause the generation AI to calculate the credit index. As a result, by using a prediction model based on past data, the calculation accuracy of the credit index is improved. Some or all of the above-mentioned processing in the credit index calculation unit can be performed, for example, using AI or without using AI.

[0081] The investment interface unit may have an investment interface and investment history management function through an application. The investment interface unit, for example, provides an interface through which a user makes an investment through an application. For example, the investment interface unit provides an intuitive user interface so that the user can easily make an investment. The investment interface unit may also have a function to manage the user's investment history. For example, the investment interface unit stores the user's past investment history so that the user can check it at any time. The investment interface unit may also have a function to display the user's investment status in real time. For example, the investment interface unit displays the user's current investment status in graphs or text so that the user can understand the progress of their investments. This allows the user to easily make and manage their investments through the investment interface and investment history management function through the application. Some or all of the above-mentioned processing in the investment interface unit may be performed, for example, using AI, or may be performed without using AI. For example, the investment interface unit may display the investment interface and manage the investment history using a generation AI.

[0082] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the user's burden. For example, the collection unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. For example, the collection unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the collection unit can temporarily stop data collection and resume it later. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate emotions.

[0083] The collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, the collection unit prioritizes collecting data from devices that the user has frequently used in the past. For example, the collection unit collects data from the user's smartphone or wearable device. The collection unit can also analyze the user's past behavioral patterns and select the most efficient timing for collecting data. For example, the collection unit determines the optimal timing for collecting data based on the user's past behavioral data. The collection unit can also select the optimal data collection method (audio, text, image, etc.) based on the user's past data collection history. For example, the collection unit analyzes the user's past data collection history and determines the optimal data collection method. In this way, the optimal data collection method can be selected by analyzing the user's past behavioral data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavioral data into a generation AI and cause the generation AI to select the optimal data collection method.

[0084] The collection unit can filter data based on the user's current activity status and areas of interest when collecting data. For example, when the user is exercising, the collection unit prioritizes collecting data related to exercise. For example, the collection unit collects the user's exercise data and prioritizes analyzing the exercise-related data. Furthermore, when the user is working, the collection unit can prioritize collecting data related to work. For example, the collection unit collects data related to the user's work and prioritizes analyzing the work-related data. Furthermore, when the user is immersed in a hobby, the collection unit can prioritize collecting data related to the hobby. For example, the collection unit collects data related to the user's hobby and prioritizes analyzing the hobby-related data. This allows highly relevant data to be collected by filtering data based on the user's current activity status and areas of interest. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can cause a generation AI to filter data based on the user's current activity status and areas of interest.

[0085] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit prioritizes collecting voice data. For example, the collection unit collects the user's voice data and analyzes the data based on the voice input. Furthermore, when the user uses text input, the collection unit can also prioritize collecting text data. For example, the collection unit collects the user's text data and analyzes the data based on the text input. Furthermore, when the user uses image input, the collection unit can also prioritize collecting image data. For example, the collection unit collects the user's image data and analyzes the data based on the image input. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause a generation AI to select the optimal collection means depending on the user's input method.

[0086] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting data related to stress. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the collection unit can prioritize collecting data related to relaxation. For example, the collection unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is excited, the collection unit can prioritize collecting data related to excitement. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. Thus, by prioritizing data based on the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate emotions.

[0087] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. For example, the collection unit collects the user's location information and prioritizes analyzing data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the user's travel destination. For example, the collection unit collects the user's location information and prioritizes analyzing data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to the user's home. For example, the collection unit collects the user's location information and prioritizes analyzing data related to the user's home. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's location information to the generation AI and cause the generation AI to collect highly relevant data.

[0088] During data collection, the collection unit can analyze the user's social media activities and collect related data. The collection unit, for example, collects data related to places where the user has checked in on social media. For example, the collection unit analyzes the user's social media activities and prioritizes analyzing data related to the checked-in places. The collection unit can also analyze the content of the user's social media posts and collect related data. For example, the collection unit analyzes the content of the user's posts and prioritizes analyzing related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the activities of the user's friends and prioritizes analyzing related data. In this way, related data can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activities into a generation AI and cause the generation AI to collect related data.

[0089] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, adjusts the frequency of data collection based on feedback provided by the user in the past. For example, the collection unit analyzes the user's feedback and optimizes the frequency of data collection. The collection unit can also select the type of data to collect by referring to the user's past feedback. For example, the collection unit determines the type of data to collect based on the user's feedback. The collection unit can also adjust the timing of data collection by reflecting the user's past feedback. For example, the collection unit determines the optimal timing for data collection based on the user's feedback. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's feedback to a generation AI and cause the generation AI to customize the collection method.

[0090] The analysis unit can estimate the user's emotions and adjust the data analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize stress-related data during analysis. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the analysis unit can prioritize relaxation-related data during analysis. For example, the analysis unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is excited, the analysis unit can prioritize excitement-related data during analysis. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. This improves the accuracy of the analysis by adjusting the data analysis algorithm based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate emotions.

[0091] During data analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the user's behavioral pattern. For example, if the user's behavioral pattern is consistent, the analysis unit performs a detailed analysis. For example, the analysis unit analyzes the user's behavioral pattern in detail and extracts important data. Furthermore, if the user's behavioral pattern fluctuates, the analysis unit can perform a simplified analysis. For example, the analysis unit analyzes the user's behavioral pattern in a simplified manner and extracts basic data. Furthermore, if the user's behavioral pattern matches a specific condition, the analysis unit can adjust the level of detail of the analysis based on the condition. For example, if the user's behavioral pattern matches a specific condition, the analysis unit performs a detailed analysis based on the condition. This enables efficient data analysis by adjusting the level of detail of the analysis based on the importance of the user's behavioral pattern. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's behavioral pattern into a generation AI and have the generation AI adjust the level of detail of the analysis.

[0092] When analyzing data, the analysis unit can apply different analysis algorithms depending on the user's category. For example, if the user belongs to a health category, the analysis unit applies a health-related analysis algorithm. For example, the analysis unit analyzes the user's health data and evaluates the user's health condition. Furthermore, if the user belongs to a financial category, the analysis unit can also apply a finance-related analysis algorithm. For example, the analysis unit analyzes the user's financial data and evaluates financial risk. Furthermore, if the user belongs to an entertainment category, the analysis unit can also apply an entertainment-related analysis algorithm. For example, the analysis unit analyzes the user's entertainment data and evaluates entertainment preferences. By applying different analysis algorithms depending on the user's category, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause a generation AI to apply different analysis algorithms depending on the user's category.

[0093] When analyzing data, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, improves the accuracy of the current analysis based on the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and reflects them in the current analysis. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit adjusts the parameters of the analysis algorithm based on the user's past analysis results. The analysis unit can also reduce analysis errors by using the user's past analysis results. For example, the analysis unit minimizes analysis errors based on the user's past analysis results. This improves the accuracy of the current analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0094] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit provides a simple, highly visible display method. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, the analysis unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating display method. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This improves visibility by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate emotions.

[0095] During data analysis, the analysis unit can determine analysis priorities based on the user's behavioral history. For example, the analysis unit prioritizes analysis of the most important data from the user's behavioral history. For example, the analysis unit analyzes the user's behavioral history and prioritizes analysis of important data. The analysis unit can also dynamically adjust analysis priorities based on the user's behavioral history. For example, the analysis unit adjusts analysis priorities in real time based on the user's behavioral history. The analysis unit can also determine the order of analysis with reference to the user's behavioral history. For example, the analysis unit optimizes the order of analysis based on the user's behavioral history. This allows important data to be analyzed preferentially by determining analysis priorities based on the user's behavioral history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's behavioral history into a generation AI and have the generation AI determine the analysis priorities.

[0096] The analysis unit can adjust the order of analysis based on the user's relevance during data analysis. For example, the analysis unit prioritizes analysis of data with high relevance to the user. For example, the analysis unit evaluates the user's relevance and prioritizes analysis of highly relevant data. The analysis unit can also dynamically adjust the order of analysis based on the user's relevance. For example, the analysis unit evaluates the user's relevance in real time and adjusts the order of analysis. The analysis unit can also determine the priority of analysis taking the user's relevance into consideration. For example, the analysis unit optimizes the priority of analysis based on the user's relevance. This enables efficient data analysis by adjusting the order of analysis based on the user's relevance. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's relevance to a generation AI and cause the generation AI to adjust the order of analysis.

[0097] During data analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user's level of expertise is high, the analysis unit provides analysis results that use a lot of technical terms. For example, the analysis unit evaluates the user's level of expertise and provides analysis results that use a lot of technical terms. Furthermore, if the user's level of expertise is low, the analysis unit can also provide analysis results that avoid technical terms. For example, the analysis unit evaluates the user's level of expertise and provides analysis results that avoid technical terms. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. For example, the analysis unit optimizes the way in which the analysis results are presented based on the user's level of expertise. This allows for the provision of analysis results that are easy to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms in the analysis.

[0098] The stock price display unit can estimate a user's emotions and adjust the stock price display method based on the estimated user emotions. For example, when a user is feeling stressed, the stock price display unit provides a simple, highly visible stock price display method. For example, the stock price display unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, when a user is relaxed, the stock price display unit can provide a stock price display method that includes detailed information. For example, the stock price display unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, when a user is excited, the stock price display unit can provide a visually stimulating stock price display method. For example, the stock price display unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This improves visibility by adjusting the stock price display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the stock price display unit may be performed using, for example, AI, or may be performed without using AI. For example, the stock price display unit may input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0099] When displaying stock prices, the stock price display unit can adjust the level of detail of the display based on the importance of the user's behavioral pattern. For example, if the user's behavioral pattern is consistent, the stock price display unit displays detailed stock prices. For example, the stock price display unit analyzes the user's behavioral pattern in detail and displays important data. Furthermore, if the user's behavioral pattern fluctuates, the stock price display unit can display simplified stock prices. For example, the stock price display unit analyzes the user's behavioral pattern in simple terms and displays basic data. Furthermore, if the user's behavioral pattern matches a specific condition, the stock price display unit can adjust the level of detail of the display based on the condition. For example, if the user's behavioral pattern matches a specific condition, the stock price display unit displays detailed stock prices based on the condition. This enables efficient stock price display by adjusting the level of detail of the display based on the importance of the user's behavioral pattern. Some or all of the above-described processing in the stock price display unit may be performed using, or without, AI. For example, the stock price display unit can input the user's behavioral pattern into a generation AI and have the generation AI adjust the level of detail of the display.

[0100] The stock price display unit can apply different display algorithms depending on the user's category when displaying stock prices. For example, if the user belongs to a health category, the stock price display unit applies a health-related stock price display algorithm. For example, the stock price display unit analyzes the user's health data and evaluates the user's health condition. Also, if the user belongs to a finance category, the stock price display unit can apply a finance-related stock price display algorithm. For example, the stock price display unit analyzes the user's financial data and evaluates financial risk. Also, if the user belongs to an entertainment category, the stock price display unit can apply an entertainment-related stock price display algorithm. For example, the stock price display unit analyzes the user's entertainment data and evaluates the user's entertainment preferences. This improves the accuracy of the display by applying different display algorithms depending on the user's category. Some or all of the above-described processing in the stock price display unit may be performed using, for example, AI, or may be performed without using AI. For example, the stock price display unit can cause a generation AI to apply different display algorithms depending on the user's category.

[0101] When displaying stock prices, the stock price display unit can improve the accuracy of the display by referring to the user's past display results. The stock price display unit, for example, improves the accuracy of the current stock price display based on the user's past display results. For example, the stock price display unit analyzes the user's past display results and reflects them in the current display. The stock price display unit can also adjust the display algorithm by referring to the user's past display results. For example, the stock price display unit adjusts the parameters of the display algorithm based on the user's past display results. The stock price display unit can also reduce display errors by using the user's past display results. For example, the stock price display unit minimizes display errors based on the user's past display results. This improves the accuracy of the current display by referring to the user's past display results. Some or all of the above-mentioned processing in the stock price display unit may be performed using, for example, AI, or may be performed without using AI. For example, the stock price display unit can input the user's past display results into a generation AI and cause the generation AI to improve the display accuracy.

[0102] The stock price display unit can estimate the user's emotions and adjust the length of the stock price display based on the estimated user emotions. For example, if the user is stressed, the stock price display unit can provide a short, concise stock price display. For example, the stock price display unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Alternatively, if the user is relaxed, the stock price display unit can provide a longer stock price display with detailed explanations. For example, the stock price display unit can record the user's voice and estimate the emotion using voice analysis technology. Alternatively, if the user is excited, the stock price display unit can provide a stock price display with visually stimulating effects. For example, the stock price display unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This improves legibility by adjusting the length of the stock price display based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the stock price display unit may be performed using, for example, AI, or may be performed without using AI. For example, the stock price display unit may input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0103] When displaying stock prices, the stock price display unit can determine display priorities based on the user's behavioral history. For example, the stock price display unit prioritizes displaying the most important stock price information based on the user's behavioral history. For example, the stock price display unit analyzes the user's behavioral history and prioritizes displaying important stock price information. The stock price display unit can also dynamically adjust display priorities based on the user's behavioral history. For example, the stock price display unit adjusts display priorities in real time based on the user's behavioral history. The stock price display unit can also determine the display order with reference to the user's behavioral history. For example, the stock price display unit optimizes the display order based on the user's behavioral history. This allows important stock price information to be prioritized by determining display priorities based on the user's behavioral history. Some or all of the above-described processing in the stock price display unit may be performed using, for example, AI, or may be performed without using AI. For example, the stock price display unit can input the user's behavioral history into a generation AI and have the generation AI determine the display priorities.

[0104] The stock price display unit can adjust the display order based on the user's relevance when displaying stock prices. For example, the stock price display unit prioritizes displaying stock price information that is highly relevant to the user. For example, the stock price display unit evaluates the user's relevance and prioritizes displaying highly relevant stock price information. The stock price display unit can also dynamically adjust the display order based on the user's relevance. For example, the stock price display unit evaluates the user's relevance in real time and adjusts the display order. The stock price display unit can also determine the display priority taking the user's relevance into consideration. For example, the stock price display unit optimizes the display priority based on the user's relevance. This enables efficient stock price display by adjusting the display order based on the user's relevance. Some or all of the above-described processing in the stock price display unit may be performed using, for example, AI, or may be performed without using AI. For example, the stock price display unit can input the user's relevance to a generation AI and have the generation AI adjust the display order.

[0105] The stock price display unit can adjust the use of technical terms in the display according to the user's level of expertise when displaying stock prices. For example, if the user's level of expertise is high, the stock price display unit provides a stock price display that uses a lot of technical terms. For example, the stock price display unit evaluates the user's level of expertise and provides a stock price display that uses a lot of technical terms. Furthermore, if the user's level of expertise is low, the stock price display unit can provide a stock price display that avoids technical terms. For example, the stock price display unit evaluates the user's level of expertise and provides a stock price display that avoids technical terms. Furthermore, the stock price display unit can adjust the way the stock price display is displayed according to the user's level of expertise. For example, the stock price display unit optimizes the way the stock price display is displayed based on the user's level of expertise. This allows for adjusting the use of technical terms in the display according to the user's level of expertise, thereby providing a stock price display that is easy to understand. Some or all of the above-described processing in the stock price display unit may be performed using, for example, AI, or may be performed without AI. For example, the stock price display unit can input the user's level of expertise into a generation AI and cause the generation AI to adjust the use of technical terms in the display.

[0106] The trust index calculation unit can estimate the user's emotion and adjust the method of calculating the trust index based on the estimated user's emotion. For example, if the user is feeling stressed, the trust index calculation unit calculates the trust index by emphasizing data related to stress. For example, the trust index calculation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Also, if the user is relaxed, the trust index calculation unit can calculate the trust index by emphasizing data related to relaxation. For example, the trust index calculation unit records the user's voice and estimates the emotion using voice analysis technology. Also, if the user is excited, the trust index calculation unit can calculate the trust index by emphasizing data related to excitement. For example, the trust index calculation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This improves the accuracy of the calculation by adjusting the method of calculating the trust index based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may 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-described processing in the trust index calculation unit may be performed using AI, for example, or may be performed without using AI. For example, the trust index calculation unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate emotions.

[0107] The trust index calculation unit can adjust the level of detail of the calculation based on the importance of the user's behavioral pattern when calculating the trust index. For example, if the user's behavioral pattern is consistent, the trust index calculation unit calculates a detailed trust index. For example, the trust index calculation unit analyzes the user's behavioral pattern in detail and calculates the trust index based on important data. Furthermore, if the user's behavioral pattern fluctuates, the trust index calculation unit can calculate a simplified trust index. For example, the trust index calculation unit analyzes the user's behavioral pattern in a simplified manner and calculates the trust index based on basic data. Furthermore, if the user's behavioral pattern matches a specific condition, the trust index calculation unit can adjust the level of detail of the calculation based on the condition. For example, if the user's behavioral pattern matches a specific condition, the trust index calculation unit calculates a detailed trust index based on the condition. This enables efficient trust index calculation by adjusting the level of detail of the calculation based on the importance of the user's behavioral pattern. Some or all of the above-mentioned processing in the trust index calculation unit may be performed using AI, for example, or without AI. For example, the credit index calculation unit can input the user's behavioral patterns into the generation AI and have the generation AI adjust the level of detail of the calculation.

[0108] The credit index calculation unit can apply different calculation algorithms depending on the user category when calculating the credit index. For example, if the user belongs to a health category, the credit index calculation unit applies a health-related credit index calculation algorithm. For example, the credit index calculation unit analyzes the user's health data and evaluates the user's health condition. Furthermore, if the user belongs to a finance category, the credit index calculation unit can also apply a finance-related credit index calculation algorithm. For example, the credit index calculation unit analyzes the user's financial data and evaluates financial risk. Furthermore, if the user belongs to an entertainment category, the credit index calculation unit can also apply an entertainment-related credit index calculation algorithm. For example, the credit index calculation unit analyzes the user's entertainment data and evaluates the user's entertainment preferences. Thus, by applying different calculation algorithms depending on the user category, the accuracy of the calculation is improved. Some or all of the above-described processing in the credit index calculation unit may be performed using, or without, AI. For example, the credit index calculation unit can cause a generation AI to apply different calculation algorithms depending on the user category.

[0109] The trust index calculation unit can improve the accuracy of the calculation when calculating the trust index by referring to the user's past calculation results. The trust index calculation unit, for example, improves the accuracy of the current trust index calculation based on the user's past calculation results. For example, the trust index calculation unit analyzes the user's past calculation results and reflects them in the current calculation. The trust index calculation unit can also adjust the calculation algorithm by referring to the user's past calculation results. For example, the trust index calculation unit adjusts the parameters of the calculation algorithm based on the user's past calculation results. The trust index calculation unit can also reduce calculation errors by using the user's past calculation results. For example, the trust index calculation unit minimizes calculation errors based on the user's past calculation results. This improves the accuracy of the current calculation by referring to the user's past calculation results. Some or all of the above-mentioned processing in the trust index calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the trust index calculation unit can input the user's past calculation results into the generation AI and cause the generation AI to improve the calculation accuracy.

[0110] The trust index calculation unit can estimate the user's emotion and determine the priority of the trust index calculation based on the estimated user's emotion. For example, if the user is feeling stressed, the trust index calculation unit can prioritize calculating a trust index related to stress. For example, the trust index calculation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Also, if the user is relaxed, the trust index calculation unit can prioritize calculating a trust index related to relaxation. For example, the trust index calculation unit can record the user's voice and estimate the emotion using voice analysis technology. Also, if the user is excited, the trust index calculation unit can prioritize calculating a trust index related to excitement. For example, the trust index calculation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. In this way, by determining the priority of the trust index calculation based on the user's emotion, important trust indexes can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may 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-described processing in the trust index calculation unit may be performed using AI, for example, or may be performed without using AI. For example, the trust index calculation unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate emotions.

[0111] The trust index calculation unit can weight the calculation based on the user's behavioral history when calculating the trust index. The trust index calculation unit, for example, calculates the trust index by weighting the most important data from the user's behavioral history. For example, the trust index calculation unit analyzes the user's behavioral history and weights important data. The trust index calculation unit can also dynamically adjust the calculation weighting based on the user's behavioral history. For example, the trust index calculation unit evaluates the user's behavioral history in real time and adjusts the weighting. The trust index calculation unit can also determine the calculation weighting with reference to the user's behavioral history. For example, the trust index calculation unit optimizes the weighting based on the user's behavioral history. In this way, by weighting the calculation based on the user's behavioral history, it is possible to calculate a trust index that emphasizes important data. Some or all of the above-described processing in the trust index calculation unit may be performed using, for example, AI, or may be performed without AI. For example, the trust index calculation unit can input the user's behavioral history to a generation AI and cause the generation AI to adjust the weighting.

[0112] The trust index calculation unit can adjust the calculation order based on the user's relevance when calculating the trust index. The trust index calculation unit, for example, prioritizes calculation of data with high user relevance. For example, the trust index calculation unit evaluates the user's relevance and prioritizes calculation of data with high relevance. The trust index calculation unit can also dynamically adjust the calculation order based on the user's relevance. For example, the trust index calculation unit evaluates the user's relevance in real time and adjusts the calculation order. The trust index calculation unit can also determine the calculation priority taking the user's relevance into consideration. For example, the trust index calculation unit optimizes the calculation priority based on the user's relevance. This enables efficient trust index calculation by adjusting the calculation order based on the user's relevance. Some or all of the above-described processing in the trust index calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the trust index calculation unit can input the user's relevance to a generation AI and cause the generation AI to adjust the calculation order.

[0113] The trust index calculation unit can adjust the use of technical terms in the calculation according to the user's level of expertise when calculating the trust index. For example, if the user's level of expertise is high, the trust index calculation unit calculates the trust index using a lot of technical terms. For example, the trust index calculation unit evaluates the user's level of expertise and calculates the trust index using a lot of technical terms. Furthermore, if the user's level of expertise is low, the trust index calculation unit can also calculate the trust index while avoiding technical terms. For example, the trust index calculation unit evaluates the user's level of expertise and calculates the trust index while avoiding technical terms. Furthermore, the trust index calculation unit can adjust the expression method for the trust index calculation according to the user's level of expertise. For example, the trust index calculation unit optimizes the expression method for the trust index calculation based on the user's level of expertise. This adjusts the use of technical terms in the calculation according to the user's level of expertise, making it possible to provide an easy-to-understand trust index. Some or all of the above-mentioned processing in the trust index calculation unit may be performed, for example, using AI or without AI. For example, the trust index calculation unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology in the calculation.

[0114] The investment interface unit can estimate a user's emotions and adjust the display method of the investment interface based on the estimated user emotions. For example, when a user is feeling stressed, the investment interface unit provides a simple, highly visible investment interface. For example, the investment interface unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, when a user is relaxed, the investment interface unit can provide an investment interface with detailed information. For example, the investment interface unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, when a user is excited, the investment interface unit can provide a visually stimulating investment interface. For example, the investment interface unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. This improves visibility by adjusting the display method of the investment interface based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the investment interface unit may be performed using, for example, AI, or may be performed without using AI. For example, the investment interface unit may input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0115] When displaying the investment interface, the investment interface unit can select the optimal display method by referring to the user's past investment history. The investment interface unit provides the optimal investment interface, for example, based on the user's past investment history. For example, the investment interface unit analyzes the user's past investment history and selects the optimal display method. The investment interface unit can also adjust the display algorithm by referring to the user's past investment history. For example, the investment interface unit adjusts the parameters of the display algorithm based on the user's past investment history. The investment interface unit can also reduce display errors by using the user's past investment history. For example, the investment interface unit minimizes display errors based on the user's past investment history. In this way, the optimal investment interface can be provided by referring to the user's past investment history. Some or all of the above-described processing in the investment interface unit may be performed using, for example, AI, or may be performed without using AI. For example, the investment interface unit can input the user's past investment history into a generation AI and have the generation AI select a display method.

[0116] When displaying the investment interface, the investment interface unit can customize the display content according to the user's current investment situation. The investment interface unit, for example, provides an optimal investment interface based on the user's current investment situation. For example, the investment interface unit analyzes the user's current investment situation and customizes the display content. The investment interface unit can also adjust the display algorithm based on the user's current investment situation. For example, the investment interface unit adjusts the parameters of the display algorithm based on the user's current investment situation. The investment interface unit can also reduce display errors using the user's current investment situation. For example, the investment interface unit minimizes display errors based on the user's current investment situation. This enables efficient investment management by customizing the display content according to the user's current investment situation. Some or all of the above-described processing in the investment interface unit may be performed using, for example, AI, or may be performed without AI. For example, the investment interface unit can input the user's current investment situation into a generation AI and have the generation AI customize the display content.

[0117] The investment interface unit can improve the display method by reflecting user feedback when displaying the investment interface. The investment interface unit, for example, improves the display method of the investment interface based on user feedback. For example, the investment interface unit analyzes user feedback and optimizes the display method. The investment interface unit can also adjust the display algorithm by referring to user feedback. For example, the investment interface unit adjusts parameters of the display algorithm based on user feedback. The investment interface unit can also reduce display errors by using user feedback. For example, the investment interface unit minimizes display errors based on user feedback. In this way, the display method of the investment interface can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the investment interface unit may be performed using AI, for example, or may be performed without using AI. For example, the investment interface unit can input user feedback into a generation AI and cause the generation AI to improve the display method.

[0118] The investment interface unit can estimate a user's emotions and adjust the operation procedures of the investment interface based on the estimated user emotions. For example, if the user is feeling stressed, the investment interface unit provides simple and intuitive operation procedures. For example, the investment interface unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the investment interface unit can provide operation procedures with more detailed information. For example, the investment interface unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is excited, the investment interface unit can provide visually stimulating operation procedures. For example, the investment interface unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. This improves operability by adjusting the operation procedures of the investment interface based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the investment interface unit may be performed using, for example, AI, or may be performed without using AI. For example, the investment interface unit may input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0119] When displaying the investment interface, the investment interface unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the investment interface unit provides a display method that matches the screen size. For example, the investment interface unit provides a display method optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the investment interface unit can also provide a display method optimized for a larger screen. For example, the investment interface unit provides a display method optimized for the tablet screen size. Furthermore, if the user is using a smartwatch, the investment interface unit can also provide a simple and highly visible display method. For example, the investment interface unit provides a display method optimized for the smartwatch screen size. This makes it possible to provide an optimal investment interface by taking into account the user's device information. Some or all of the above-described processing in the investment interface unit may be performed using AI, for example, or may be performed without using AI. For example, the investment interface unit can input the user's device information into the generation AI and have the generation AI select the display method.

[0120] When displaying the investment interface, the investment interface unit can make the display content multilingual according to the user's language setting. The investment interface unit, for example, automatically sets the language of the investment interface based on the language setting of the user's device. For example, the investment interface unit detects the language setting of the user's device and provides display content corresponding to that language. The investment interface unit can also provide a language switching function if the user uses multiple languages. For example, the investment interface unit provides an interface that allows the user to select the language they want to use. Furthermore, if the user selects a specific language, the investment interface unit can provide the investment interface in that language. For example, the investment interface unit provides display content corresponding to the language selected by the user. This improves user convenience by making the display content multilingual according to the user's language setting. Some or all of the above-described processing in the investment interface unit may be performed using, for example, AI, or may be performed without AI. For example, the investment interface unit can input the user's language setting into a generation AI and have the generation AI perform multilingual support for the display content.

[0121] The investment interface unit can customize the display content based on the user's investment goals when displaying the investment interface. The investment interface unit, for example, provides an optimal investment interface based on the user's investment goals. For example, the investment interface unit analyzes the user's investment goals and customizes the display content. The investment interface unit can also adjust the display algorithm based on the user's investment goals. For example, the investment interface unit adjusts the parameters of the display algorithm based on the user's investment goals. The investment interface unit can also reduce display errors using the user's investment goals. For example, the investment interface unit minimizes display errors based on the user's investment goals. This enables efficient investment management by customizing the display content based on the user's investment goals. Some or all of the above-described processing in the investment interface unit may be performed using, for example, AI, or may be performed without using AI. For example, the investment interface unit can input the user's investment goals into a generation AI and have the generation AI customize the display content. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, stock price display unit, credit index calculation unit, and investment interface unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect user behavior data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI. The stock price display unit can display the user's stock price using the display 40A of the smart device 14. The credit index calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the user's credit index based on the analyzed data. The investment interface unit provides an interface for the user to make investments using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, stock price display unit, credit index calculation unit, and investment interface unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect user behavior data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI. The stock price display unit can display the user's stock price using the display of the smart glasses 214. The credit index calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the user's credit index based on the analyzed data. The investment interface unit provides an interface for the user to make investments using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, stock price display unit, credit index calculation unit, and investment interface unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect user behavior data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI. The stock price display unit can display the user's stock price using the display 343 of the headset terminal 314. The credit index calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the user's credit index based on the analyzed data. The investment interface unit provides an interface for the user to make investments using the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, stock price display unit, credit index calculation unit, and investment interface unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect user behavior data using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI. The stock price display unit can display the user's stock price using the display of the robot 414. The credit index calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the user's credit index based on the analyzed data. The investment interface unit provides an interface for the user to make investments using the control unit 46A of the robot 414.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The personal stock market system can further include a sensor network for collecting user behavioral data. The sensor network is installed in the user's living environment and collects environmental data such as temperature, humidity, and light intensity. For example, sensors installed in the user's home can monitor the indoor temperature and humidity in real time and evaluate the user's comfort level. Sensors installed in offices and public facilities can also provide data for detailed understanding of user behavioral patterns. Furthermore, the sensor network can collect biometric data such as heart rate and blood pressure to monitor the user's health. This allows for more detailed collection of user behavioral data and improved analysis accuracy.

[0124] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the burden on the user. The user's facial expression can be captured with a camera and the emotion can be estimated using an emotion estimation algorithm. Also, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is in a hurry, data collection can be temporarily stopped and resumed later. In this way, the burden on the user can be reduced by adjusting the timing of data collection according to the user's emotions.

[0125] When analyzing user behavior data, the analysis unit can adjust the analysis algorithm taking into account the user's past behavior patterns. For example, if the user has repeatedly performed a specific behavior pattern in the past, the analysis algorithm can be optimized based on that pattern. Also, if the user's behavior pattern is fluctuating, the analysis algorithm can be dynamically adjusted to perform analysis based on the latest data. Furthermore, if the user's behavior pattern matches certain conditions, the level of analysis detail can be adjusted based on those conditions. In this way, by adjusting the analysis algorithm based on the user's behavior pattern, the accuracy of the analysis can be improved.

[0126] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, data related to stress is collected with priority. The user's facial expression is captured with a camera, and the emotion is estimated using an emotion estimation algorithm. Also, if the user is relaxed, data related to relaxation can be collected with priority. Furthermore, if the user is excited, data related to excitement can be collected with priority. Thus, by determining the priority of data based on the user's emotions, important data can be collected with priority.

[0127] The stock price display unit can estimate the user's emotions and adjust the stock price display method based on the estimated user emotions. For example, if the user is feeling stressed, a simple and highly visible display method is provided. The user's facial expression is captured with a camera and the emotion is estimated using an emotion estimation algorithm. Also, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is excited, a visually stimulating display method can be provided. In this way, visibility can be improved by adjusting the stock price display method based on the user's emotions.

[0128] The trust index calculation unit can estimate the user's emotion and adjust the method of calculating the trust index based on the estimated user's emotion. For example, if the user is feeling stressed, the trust index is calculated by emphasizing data related to stress. The user's facial expression is captured with a camera, and the emotion is estimated using an emotion estimation algorithm. Also, if the user is relaxed, the trust index can be calculated by emphasizing data related to relaxation. Furthermore, if the user is excited, the trust index can be calculated by emphasizing data related to excitement. In this way, by adjusting the method of calculating the trust index based on the user's emotion, the accuracy of the calculation can be improved.

[0129] The investment interface unit can estimate the user's emotions and adjust the display method of the investment interface based on the estimated user emotions. For example, if the user is feeling stressed, a simple and highly visible investment interface is provided. The user's facial expression is captured with a camera and the emotion is estimated using an emotion estimation algorithm. Also, if the user is relaxed, an investment interface including detailed information can be provided. Furthermore, if the user is excited, a visually stimulating investment interface can be provided. In this way, visibility can be improved by adjusting the display method of the investment interface based on the user's emotions.

[0130] The personal stock market system can further include a drone network for collecting user behavioral data. The drone network flies over the user's living environment and collects data from the air. For example, a drone flies around the user's home and collects environmental data and user behavioral data. The drone can also provide data to understand the user's behavioral patterns in detail while they are out. Furthermore, the drone can collect biometric data such as heart rate and body temperature to monitor the user's health. This allows user behavioral data to be collected over a wider area and improves the accuracy of analysis.

[0131] When analyzing user behavior data, the analysis unit can adjust the analysis algorithm by taking into account the user's social network data. For example, the analysis unit can analyze the behavioral patterns of the user's friends and followers to evaluate their influence on the user's behavior. It can also analyze the user's comments and posts on social networks to gain a more detailed understanding of the user's behavioral patterns. Furthermore, it can predict fluctuations in the user's behavioral patterns based on the user's social network data and dynamically adjust the analysis algorithm. By taking into account the user's social network data, the accuracy of the analysis can be improved.

[0132] The collection unit may be equipped with a privacy filtering function to protect the user's privacy when collecting the user's behavioral data. For example, the collection unit may automatically mask and anonymize the user's personal information. The collection unit may also limit the scope of data collection based on specific privacy settings when collecting the user's data. Furthermore, the collection unit may comply with the user's privacy policy and ensure transparency regarding data collection and use. This allows the behavioral data to be collected while protecting the user's privacy.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The collection unit collects user behavioral data. The user behavioral data includes, for example, location information, purchase history, browsing history, etc. The collection unit collects data from the user's smartphone or wearable device. The collection unit can also anonymize and encrypt the collected data. For example, the collection unit can delete personal information and mask the data. The collection unit can also encrypt the data using an encryption algorithm such as AES (Advanced Encryption Standard). Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit. The analysis is performed using, for example, machine learning algorithms, deep learning, or support vector machines. The analysis unit also extracts patterns from the data and identifies user behavior patterns. Step 3: The stock price display unit displays the user's stock price based on the data analyzed by the analysis unit. The stock price display has a real-time graph display and notification function. For example, the stock price display unit can have a function to display the user's stock price in a graph in real time and notify the user of stock price fluctuations. The stock price display unit can also display the user's stock price in text format. Step 4: The credit index calculation unit calculates the user's credit index based on the data obtained by the analysis unit. The credit index is calculated using, for example, a predictive model or regression model based on past data, time series analysis, or generation AI. Step 5: The investment interface unit invests based on the credit index calculated by the credit index calculation unit. The investment interface has an investment interface and an investment history management function through an application. For example, it can provide an interface for users to invest through an application, and have a function to manage the user's investment history and a function to display the investment status in real time.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0205] 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.

[0206] [Explanation of symbols]

[0207] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user behavior data; an analysis unit that analyzes the data collected by the collection unit; a stock price display unit that displays stock prices related to the user based on the data analyzed by the analysis unit; a credit index calculation unit that calculates a credit index of a user based on the data obtained by the analysis unit; an investment interface unit that makes investments based on the credit index calculated by the credit index calculation unit; A system characterized by:

2. The collecting unit Collect data from users' smartphones or wearable devices 2. The system of claim 1.

3. The analysis unit Analyze data using machine learning algorithms 2. The system of claim 1.

4. The collecting unit Anonymize and encrypt the data collected 2. The system of claim 1.

5. The stock price display unit is Real-time graph display or notification function 2. The system of claim 1.

6. The credit index calculation unit Calculates credit indices using predictive models based on historical data 2. The system of claim 1.

7. The investment interface unit Equipped with an investment interface and investment history management function via the application 2. The system of claim 1.

8. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

Images