System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting biometric data of a user; means for analyzing the collected biometric data to determine an emotional state of the user; means for obtaining and analyzing market data; means for correcting an emotional bias of the user; means for generating an investment strategy based on the corrected data; and means for notifying the user of the generated investment strategy.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] Individual investors and financial analysts are susceptible to emotional bias in their investment decisions, which can result in irrational investment decisions. It is also difficult to comprehensively analyze vast amounts of market data and news to formulate accurate investment strategies. This makes it difficult to formulate effective and rational investment strategies. This invention aims to solve the above problem by accurately analyzing users' emotions and providing investment strategies that eliminate emotional bias. [Means for solving the problem]

[0005] This invention solves the above-mentioned problems by providing a system that includes a means for collecting a user's biometric data, a means for analyzing the collected biometric data to identify the user's emotional state, a means for acquiring and analyzing market data, a means for correcting the user's emotional bias, a means for generating an investment strategy based on the corrected data, and a means for notifying the user of the generated investment strategy. The user's biometric data includes electroencephalogram data, and the market data includes news data and financial data. This makes it possible to provide the user with a rational investment strategy that is free from emotional bias and support appropriate investment decisions.

[0006] "Biometric data" refers to data collected from the user's body, and specifically includes electroencephalogram data.

[0007] "Electroencephalogram data" is signal data that reflects the user's brain activity, and is data for analyzing emotional states and reactions.

[0008] An "emotional state" refers to an emotion such as excitement, stress, or relaxation felt by a user, and is determined from collected biometric data.

[0009] "Market Data" means data relating to financial markets and includes, among other things, news data and financial data.

[0010] "News data" is data about the latest events and information related to financial markets.

[0011] "Financial data" refers to data relating to financial products such as stock prices, bond prices, and exchange rates.

[0012] "Emotional bias" refers to the influence that a user's emotional state has on investment decisions, and is a factor that hinders rational decision-making.

[0013] "Correction" is a process that removes or reduces the user's emotional bias and leads to rational investment decisions.

[0014] An "investment strategy" is a plan or policy for a user to conduct trading, which is generated based on the results of an analysis of market data and biometric data. [Brief explanation of the drawings]

[0015] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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, a 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), and an APU (Accelerated Processing Unit).

[0019] 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.

[0020] 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.

[0021] 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), Bluetooth (registered trademark), etc.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This invention is a system that collects and analyzes biometric data from users to propose rational investment strategies. The program and processing of this system will be described in detail below.

[0037] System configuration

[0038] This system consists of a user, a terminal, and a server. The user wears an EEG sensor and browses financial information and news articles. The terminal collects the user's EEG data and facial expression data and sends it to the server. The server analyzes the received data, generates a rational investment strategy that eliminates the user's emotional bias, and notifies the user again via the terminal.

[0039] Program processing

[0040] The program processing in this system will be explained below.

[0041] 1. Data Collection:

[0042] The user wears an EEG sensor and logs in to the terminal.

[0043] The device collects the user's brain wave data, facial expression data, and voice data in real time.

[0044] The device also captures the news and financial data the user is reading.

[0045] 2. Data transmission:

[0046] The terminal encrypts the collected brain wave data, facial expression data, and voice data and sends it to a server.

[0047] The terminal also transmits market data (news data and financial data) to the server.

[0048] 3. Data Analysis:

[0049] The server analyzes the received brainwave data to identify the user's emotional state (e.g., excitement, stress, relaxation).

[0050] The server also analyzes facial expression and voice data to provide a detailed assessment of the user's emotional response.

[0051] The server simultaneously analyzes market data to assess market trends and the performance of specific assets.

[0052] 4. Eliminate bias:

[0053] The server identifies the user's emotional bias based on the analysis results and corrects the impact of this bias on investment decisions.

[0054] The server generates a rational investment strategy using the corrected data.

[0055] 5. Investment strategy generation and proposal:

[0056] The server uses multiple AI models to generate optimal investment strategies from the collected and analyzed data.

[0057] The server transmits the generated investment strategy to the terminal.

[0058] The terminal notifies the user of the received investment strategy and displays specific investment proposals on the screen.

[0059] Specific examples

[0060] The following are specific usage scenarios.

[0061] User logs in to a device and wears an EEG sensor. User begins reading a news article about stock X.

[0062] The device collects the user's brain wave data, facial expression data, and voice data in real time, and also captures the content of the article.

[0063] The terminal encrypts the collected data and sends it to the server.

[0064] The server analyzes the received data and determines that the user is excited about stock X. At the same time, it analyzes the impact the article had on the user.

[0065] The server considers the user's excitement as an emotional bias and performs a correction process to eliminate this bias. Based on the corrected data, a rational investment strategy is generated.

[0066] The server generates a strategy recommending investment in stock Y, for example, and transmits it to the terminal.

[0067] The terminal displays the investment proposal on the terminal screen for the user, who then reviews the proposal and decides whether to accept it or not.

[0068] The device sends the user's feedback to the server and uses it as learning data for future suggestions.

[0069] The above is a specific embodiment for carrying out the present invention. The present system improves the accuracy and efficiency of investment decisions by providing a rational investment strategy that eliminates the emotional bias of users.

[0070] The processing flow will be explained below.

[0071] Step 1:

[0072] The user wears the EEG sensor and logs in to the device. The device authenticates the login information and checks whether the EEG sensor is working properly.

[0073] Step 2:

[0074] The device collects the user's brainwave data in real time. While the user is browsing stock information or news articles, the brainwave sensor captures the user's brainwave data and transmits it to the device.

[0075] Step 3:

[0076] The device collects facial expression data and voice data of the user in parallel using a camera and microphone, thereby capturing the user's emotional state from multiple angles.

[0077] Step 4:

[0078] The device encrypts the collected brainwave, facial expression, and voice data and compiles them into packets for data transmission. At the same time, it also collects market data (news data and financial data).

[0079] Step 5:

[0080] The terminal transmits the encrypted data packets to the server, which receives the transmitted data packets and performs decompression and decryption.

[0081] Step 6:

[0082] The server applies machine learning algorithms to analyze the received EEG data, identifying the user's emotional state (e.g., excited, stressed, relaxed) and labeling each data point.

[0083] Step 7:

[0084] The server uses facial and voice data to evaluate the user's emotional responses in detail, and combines this information with the analysis of the EEG data.

[0085] Step 8:

[0086] The server simultaneously analyzes market data (news data and financial data) to assess market trends, the performance of specific assets and key market drivers.

[0087] Step 9:

[0088] The server identifies the user's emotional bias based on the analysis results. For example, if the user is excited about a particular stock, it adjusts the evaluation of that stock.

[0089] Step 10:

[0090] The server performs a correction process taking into account the identified emotional bias, thereby eliminating the influence of the emotional bias on investment decisions.

[0091] Step 11:

[0092] The server uses the corrected data to generate rational investment strategies using AI models, which are customized based on market data and user sentiment data.

[0093] Step 12:

[0094] The server transmits the generated investment strategy to the terminal, which analyzes the received investment strategy and displays it in a format that is easy for the user to understand.

[0095] Step 13:

[0096] The terminal displays specific investment proposals on the screen, and the user reviews the proposed investment strategies and decides whether to accept them.

[0097] Step 14:

[0098] The device collects user feedback and sends information about whether the suggestion was accepted to the server, which uses this feedback as learning data to improve the accuracy of future suggestions.

[0099] The above is the specific flow of the program's processing. This system eliminates the user's emotional bias and provides rational investment strategies, thereby improving the accuracy and efficiency of investment decisions.

[0100] Example 1

[0101] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0102] Conventional investment support systems have the problem of proposing investment strategies without properly correcting for users' emotional biases, which can lead to irrational investment decisions that are dependent on the emotions of individual users. Additionally, there is also the issue of low reliability of the investment strategies provided due to the insufficient diversity of collected data and the insufficient accuracy of analysis.

[0103] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0104] In this invention, the server includes a means for analyzing biometric data received from the terminal to identify the user's emotional state, a means for acquiring market data and analyzing it using a text analysis algorithm, a means for correcting the user's emotional bias using a machine learning model, and a means for generating an investment strategy using an AI model based on the corrected data, thereby making it possible to provide a rational and reliable investment strategy that eliminates the user's emotional bias.

[0105] "Biometric data" refers to data obtained from the user's body, and in the present invention mainly includes electroencephalogram data, facial expression data, and voice data.

[0106] A "terminal" is an electronic device worn by a user, such as an EEG sensor, that collects and transmits data. Examples include smartphones and tablets.

[0107] The "server" is a central device that receives, analyzes, and processes data sent from the terminals. This device is responsible for large-scale data analysis and the generation of investment strategies.

[0108] "Emotional state" refers to the user's mental and emotional state based on biometric data analysis, and primarily includes states such as excitement, stress, and relaxation.

[0109] A "text analysis algorithm" is an algorithm that uses natural language processing techniques to analyze text data, allowing useful information to be extracted from market data.

[0110] A "machine learning model" refers to an algorithm that learns from past data and predicts or classifies future data. In this invention, it is used to correct emotional bias.

[0111] "AI model" means a model that uses artificial intelligence technology to learn patterns from data and generate investment strategies, including reinforcement learning models and deep learning models.

[0112] An "investment strategy" indicates specific investment policies and advice to users that are generated based on collected and analyzed data.

[0113] "Market Data" means data related to financial markets, including news data and financial data, which is analyzed to provide users with appropriate investment strategies.

[0114] The present invention relates to a system that collects and analyzes biometric data from users to propose rational investment strategies. The system is composed of a user, a terminal, and a server.

[0115] Hardware and software used

[0116] User: The user wears an EEG sensor, a camera, and a microphone and logs in to the device. The EEG sensor acquires the user's brainwave data, and the camera and microphone collect facial expression and voice data.

[0117] Device: A device is an electronic device such as a smartphone or tablet that processes biometric data collected from the user and sends it to a server. The device is equipped with EEG analysis software, camera analysis software (e.g., OpenCV, Dlib), and acoustic analysis software.

[0118] Server: The server contains databases, text analysis algorithms (e.g., natural language processing models), machine learning models, and generative AI models (e.g., reinforcement learning models, deep learning models).

[0119] System Operation

[0120] Data collection

[0121] Users wear an EEG sensor and log in to the device. As they begin browsing news articles or financial information, the device collects real-time EEG, facial expression, and voice data. This collected data is used to analyze the user's emotional state.

[0122] Data transmission

[0123] The device encrypts the collected data using the AES encryption algorithm and transmits it to the server using the HTTPS protocol, ensuring confidentiality and integrity of the data during transmission.

[0124] Data analysis and investment strategy generation

[0125] The server stores the received data in a database. EEG data is analyzed using EEG analysis software to identify the user's emotional state. Similarly, facial expression data is analyzed using computer vision techniques (e.g., OpenCV, Dlib), and voice data is analyzed using acoustic analysis tools. Market data is also analyzed using text analysis algorithms. Based on the results of these analyses, the server uses machine learning models to correct for emotional bias and generative AI models to generate rational investment strategies.

[0126] Investment Strategy Notification

[0127] The generated investment strategy is encrypted and sent back to the terminal. The terminal notifies the user and displays specific investment proposals on the screen. The user reviews the proposals and decides whether to accept them or not. This feedback is also sent to the server and used to improve the accuracy of future proposals.

[0128] Specific examples

[0129] When a user wears an EEG sensor while browsing the daily news, the device collects biometric data in real time. The data is encrypted and sent to a server for analysis of the user's emotional state and correction of emotional bias. For example, the server may detect that the user is excited about a particular stock, correct for this bias, and generate a rational investment strategy. The generated strategy is then proposed to the user via the device, and the user can confirm the proposal.

[0130] Prompt Sentence Examples

[0131] The following prompt statements can be used:

[0132] "After analyzing the user's brainwave data and excitement level, how can we eliminate bias and suggest appropriate investment strategies?"

[0133] "Describe how you would design a system that analyzes users' emotional reactions while browsing financial news and generates rational investment strategies."

[0134] The above is a specific embodiment for carrying out the present invention. The present system improves the accuracy and efficiency of investment decisions by providing a rational investment strategy that eliminates the emotional bias of users.

[0135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0136] Step 1: Data collection

[0137] The user wears an EEG sensor and logs in to the device. At this time, the device launches a dedicated application that collects the user's brainwave data, facial expression data, and voice data in real time. EEG data is obtained from the EEG sensor, facial expression data is collected using a camera, and voice data is collected through a microphone. At the same time, the device also uses screen capture software to capture the news articles and financial information the user is viewing. The input is the user's biometric data and browsing data, and the output is the collected biometric data and browsing data.

[0138] Step 2: Send data

[0139] The device encrypts the EEG data, facial expression data, voice data, news articles, and financial information collected in step 1 using the AES encryption algorithm. The encrypted data is sent to the server using the HTTPS protocol. The input is the collected biometric data and browsing data, and the output is the encrypted biometric data and browsing data.

[0140] Step 3: Data analysis

[0141] The server receives the encrypted data received in step 2 and stores it in a database. The received EEG data is then analyzed using EEG analysis software to identify the user's emotional state (excited, stressed, relaxed, etc.). For facial expression data, computer vision techniques (e.g., OpenCV, Dlib) are used to detect facial feature points and identify the user's emotions from their facial expressions. For voice data, acoustic analysis tools are used to analyze the emotional tone of the user's voice. For market data, text analysis algorithms (e.g., natural language processing models) are used to analyze news and financial information and evaluate market trends. The input is encrypted biometric data and browsing data, and the output is the analysis of the user's emotional state and market trends.

[0142] Step 4: Eliminate bias

[0143] The server uses a machine learning model to correct the emotional bias based on the user's emotional state and market data identified in step 3. This correction process reduces the impact of the user's emotional state on investment decisions and converts them into rational data. The input is the analyzed emotional state and market data, and the output is the corrected data.

[0144] Step 5: Generate an investment strategy

[0145] The server uses the corrected data from step 4 to generate an optimal investment strategy using a generative AI model (e.g., a reinforcement learning model or a deep learning model). This investment strategy eliminates the user's emotional bias and presents the most rational and advantageous investment policy. The input is the corrected data, and the output is the generated investment strategy.

[0146] Step 6: Investment strategy communication and feedback

[0147] The server encrypts the generated investment strategy and sends it back to the terminal. The terminal notifies the user of the received investment strategy and displays a specific investment proposal on the terminal screen. The user reviews the proposal and decides whether to accept the investment strategy. The user's decision is sent from the terminal to the server as feedback, and the server uses this feedback as learning data for future proposals. The input is the generated investment strategy, and the output is the investment proposal to the user and the user's feedback.

[0148] The above is the specific processing flow of the program of this system.

[0149] (Application example 1)

[0150] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0151] In recent years, with the spread of e-commerce, users have access to a wide variety of product information and make purchasing decisions. However, users' emotional biases can influence purchasing decisions, leading to impulsive purchases and irrational choices. This has led to a growing need for systems that support users in making more rational and objective purchasing decisions. While current technology makes suggestions based on users' text information and past purchase history, there are no systems that take into account their emotional state in real time.

[0152] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0153] In this invention, the server includes means for collecting biometric data of a user, means for analyzing the collected biometric data to identify the user's emotional state, means for acquiring and analyzing market data, means for correcting the user's emotional bias, means for generating an investment strategy based on the corrected data, means for notifying the user of the generated investment strategy, means for generating rational purchase proposals based on the collected biometric data of the user and market data to support purchasing decisions in e-commerce, and means for notifying the user of the generated purchase proposals, thereby eliminating the user's emotional bias and supporting rational and objective purchasing decisions.

[0154] "User's biometric data" refers to data obtained from the user's body, and includes brain wave data, facial expression data, voice data, and the like.

[0155] "Electroencephalogram data" refers to measurements of a user's brain's electrical activity that are used to identify the user's emotional state and mental responses.

[0156] "Market Data" refers to data that includes information about a particular market or product, such as news data, financial data, product information, and reviews.

[0157] "Emotional bias" refers to the influence that a user's emotional state has on purchasing and investment decisions, and correcting this influence supports rational decision-making.

[0158] "Rational purchase suggestions" refer to suggestions that recommend the purchase of products or services that are most suitable for the user, while eliminating the user's emotional bias.

[0159] "Means for notifying" refers to a method or device for communicating the investment strategy or purchase proposal generated by the system to the user, including a smartphone or computer display, audio output, etc.

[0160] "Electronic commerce" refers to a form of transaction in which goods are purchased or services are applied for via the Internet, and is also known as online shopping.

[0161] "Investment Strategy" refers to an investment policy or plan recommended to a user based on market data and biometric data, which is intended to be rational and efficient.

[0162] This invention is a system that collects and analyzes users' biometric data to make rational purchasing suggestions. Specifically, it takes into account the user's emotional state when shopping online, preventing impulsive purchases and supporting rational purchasing decisions.

[0163] System configuration

[0164] This system consists of a user, a device, and a server. The user uses a device equipped with an EEG sensor and a camera. The device collects the user's EEG data and facial expression data and transmits this data to the server. The server analyzes the received data, eliminates the user's emotional bias, and generates rational purchase suggestions, which are then sent to the user via the device.

[0165] Program processing

[0166] 1. Data Collection:

[0167] Users wear an EEG sensor and log in to the device, which collects their EEG, facial expression, and voice data in real time, including EEG acquisition using the BrainFlow library and facial expression analysis using the DeepFace library.

[0168] 2. Data transmission:

[0169] The device encrypts the collected data and sends it to a cloud server.

[0170] 3. Data Analysis:

[0171] The server analyzes the EEG data and facial expression data to identify the user's emotional state, and also analyzes market data such as product information and reviews to evaluate the product's quality and reputation.

[0172] 4. Eliminate bias:

[0173] The server identifies the user's emotional bias based on the analysis results and corrects the data to eliminate this bias.

[0174] 5. Purchase offer generation and notification:

[0175] The server generates rational purchase suggestions based on the corrected data. The suggestions are then sent back to the device and notified to the user. The suggestions include specific purchase reasons and explanations to eliminate emotional bias.

[0176] Specific examples

[0177] For example, if a user is about to purchase an expensive smartphone, and news articles and reviews are making them excited, the system will interpret the user's excitement as an emotional bias and compensate for it. As a result, it will make suggestions to help the user calmly consider whether they really need the product or whether there are more suitable options. In this way, it will prevent impulsive purchases and support rational purchasing decisions.

[0178] Prompt Sentence Examples

[0179] "You are currently in an excited state. We recommend that you re-read the reviews for this product and think things through."

[0180] "Based on your emotional state, this product may be worth reconsidering. Consider other options."

[0181] This invention makes it possible to eliminate emotional biases of users in e-commerce transactions and support rational purchasing decisions.

[0182] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0183] Step 1:

[0184] The user wears an EEG sensor and logs in to the device.

[0185] Input: None

[0186] Output: Login status and EEG sensor preparation

[0187] Specific operation: The user wears the EEG sensor on their head and logs in to an application on the device. Once logged in, the EEG sensor prepares to collect the user's EEG data in real time.

[0188] Step 2:

[0189] The device collects the user's brain wave data, facial expression data, and voice data in real time.

[0190] Input: User's brainwaves, facial image, and voice

[0191] Output: Complete set of biometric data (brain waves, facial expressions, voice)

[0192] Specific operation: EEG data is collected using the BrainFlow library, facial images are analyzed using the DeepFace library, facial expression data is obtained, and audio data is collected through the microphone.

[0193] Step 3:

[0194] The brain wave data, facial expression data, and voice data collected by the device are encrypted and sent to a cloud server.

[0195] Input: Complete set of biometric data (brain waves, facial expressions, voice)

[0196] Output: Encrypted data packet

[0197] Specific operation: The device uses an appropriate encryption algorithm to encrypt the collected data, and then sends the encrypted data packet to the cloud server as an HTTP POST request.

[0198] Step 4:

[0199] The server analyzes the received brain wave data and facial expression data to identify the user's emotional state.

[0200] Input: Encrypted data packet

[0201] Output: Emotional state (excited, stressed, relaxed, etc.)

[0202] What it does: The server decrypts the encrypted data and analyzes the EEG and facial expression data, using machine learning models to identify emotional states (e.g., excited, stressed, relaxed).

[0203] Step 5:

[0204] The server collects and analyzes market data and product review data.

[0205] Input: Market data (product information, reviews, etc.)

[0206] Output: Product quality rating and reputation

[0207] How it works: The server retrieves the latest market data and product reviews through external APIs and databases, and uses text analysis and natural language processing (NLP) techniques to evaluate the quality and reputation of products.

[0208] Step 6:

[0209] The server processes the data to identify the user's emotional bias and correct for this bias.

[0210] Input: Emotional state, product quality rating

[0211] Output: Corrected data

[0212] What it does: The server identifies the user's emotional state as an emotional bias and applies a correction algorithm to remove this bias, resulting in rational data.

[0213] Step 7:

[0214] The server generates a rational purchase proposal based on the corrected data and transmits it to the terminal.

[0215] Input: Corrected data

[0216] Output: Purchase proposal (including proposal details and reasons)

[0217] What it does: The server uses the generative AI model to generate rational purchase recommendations from the corrected data, including specific purchase reasons and explanations to eliminate emotional bias. The generated recommendations are sent to the device as an HTTP response.

[0218] Step 8:

[0219] The terminal notifies the user of the purchase offer and the reason for it.

[0220] Input: Purchase Offer

[0221] Output: Display and notification of proposal content

[0222] Specific operation: The device notifies the user of the received purchase suggestion visually or audibly. The suggestion and its reasons are displayed on the screen, and the user confirms it. At this time, a prompt message such as "You are currently in an excited state. We recommend that you review the reviews of this product and think about it calmly" is displayed.

[0223] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0224] This invention is a system that collects and analyzes a user's biometric data to identify the user's emotional state and proposes an investment strategy that corrects for emotional bias. The system includes an emotion engine that recognizes the user's emotions.

[0225] System configuration

[0226] The system consists of a user, a terminal, a server, and an emotion engine. The user wears an EEG sensor and browses financial information and news articles. The terminal collects the user's EEG and facial expression data and sends it to the server. The server analyzes the received data, generates a rational investment strategy that eliminates the user's emotional bias, and notifies the user again via the terminal. The emotion engine analyzes the user's biometric data to recognize their emotional state and correct it as necessary.

[0227] Program processing

[0228] The processing of the program in this system will be explained in detail below.

[0229] 1. Data Collection:

[0230] The user wears an EEG sensor and logs in to the device.

[0231] The device collects the user's brainwave data, facial expression data, and voice data in real time while the user is browsing stock information or news articles.

[0232] 2. Data transmission:

[0233] The device collects brainwave, facial expression, and voice data, encrypts it, and compiles it into data packets. At the same time, it also collects market data (news data and financial data).

[0234] The terminal transmits the encrypted data packets to the server, which receives the transmitted data packets and performs decompression and decryption.

[0235] 3. Data Analysis:

[0236] The server applies machine learning algorithms to analyze the received EEG data, identifying the user's emotional state (e.g., excited, stressed, relaxed) and labeling each data point.

[0237] The server also analyzes facial and voice data to assess the user's emotional response in detail, and this information is then integrated with the analysis of the EEG data.

[0238] 4. How the Emotion Engine Works:

[0239] The emotion engine integrates and analyzes the user's brain wave data, facial expression data, and voice data to recognize the user's emotional state in real time.

[0240] The emotion engine executes algorithms to detect and adjust or correct for the user's emotional biases.

[0241] 5. Eliminate bias:

[0242] The server and the emotion engine work together to identify the user's emotional bias based on the analysis results. For example, if the user is excited about a particular stock, the server recognizes this bias and adjusts its influence.

[0243] The server takes the identified emotional bias into consideration and performs correction processing, thereby eliminating the influence of emotional bias on investment decisions.

[0244] 6. Investment strategy generation and proposal:

[0245] The server runs the AI ​​model using the corrected data to generate a rational investment strategy, which is customized based on market data and user sentiment data.

[0246] The server transmits the generated investment strategy to the terminal, which analyzes the received investment strategy and displays it in a format that is easy for the user to understand.

[0247] 7. Notification of Investment Proposal:

[0248] The terminal displays specific investment proposals on the screen, and the user reviews the proposed investment strategies and decides whether to accept them.

[0249] The device collects user feedback and sends information about whether the suggestion was accepted to the server, which uses this feedback as learning data to improve the accuracy of future suggestions.

[0250] Specific examples

[0251] The following are specific usage scenarios.

[0252] The user logs in to the device and wears an EEG sensor. While browsing stock information, the device collects the user's EEG data, facial expression data, and voice data.

[0253] The data collected by the device is encrypted and sent to the server.

[0254] The server receives, decompresses, and decrypts the data, and the emotion engine analyzes the received data to determine the user's emotional state.

[0255] The emotion engine detects and corrects the user's emotional bias.

[0256] The server runs an AI model based on the corrected data to generate an optimal investment strategy.

[0257] The server sends the generated investment strategy to the terminal, which displays the proposal to the user.

[0258] The user reviews the proposal and decides whether to accept it.

[0259] The device sends the user's feedback to the server, and based on that feedback, the accuracy of future suggestions is improved.

[0260] This system improves the accuracy and efficiency of investment decisions by eliminating users' emotional biases and providing rational investment strategies. In addition, the introduction of an emotion engine corrects users' emotional state in real time, enabling more appropriate investment decisions.

[0261] The processing flow will be explained below.

[0262] Step 1:

[0263] The user wears the EEG sensor and logs in to the device. The device authenticates the user's login information and checks whether the EEG sensor is working properly.

[0264] Step 2:

[0265] The device collects the user's brainwave data in real time. While the user is browsing stock information or news articles, the brainwave sensor captures the data and transmits it to the device.

[0266] Step 3:

[0267] The device collects facial expression data and voice data of the user in parallel using a camera and microphone, thereby capturing the user's emotional state from multiple angles.

[0268] Step 4:

[0269] The device encrypts the collected brainwave, facial expression, and voice data and compiles them into packets for data transmission. At the same time, it also collects market data (news data and financial data).

[0270] Step 5:

[0271] The terminal transmits the encrypted data packets to the server, which receives the transmitted data packets and performs decompression and decryption.

[0272] Step 6:

[0273] The server applies machine learning algorithms to analyze the received EEG data, identifying the user's emotional state (e.g., excited, stressed, relaxed) and labeling each data point.

[0274] Step 7:

[0275] The server uses facial and voice data to evaluate the user's emotional responses in detail, and combines this information with the analysis of the EEG data.

[0276] Step 8:

[0277] The emotion engine integrates and analyzes the user's brainwave data, facial expression data, and voice data to recognize the user's emotional state in real time, thereby revealing the user's specific emotions.

[0278] Step 9:

[0279] The emotion engine detects users' emotional biases and executes algorithms to correct them in real time. For example, if a user is overly excited about a particular stock, the engine will correct the bias to mitigate it.

[0280] Step 10:

[0281] The server uses the corrected data to generate rational investment strategies using AI models, which are customized based on market data and user sentiment data.

[0282] Step 11:

[0283] The server transmits the generated investment strategy to the terminal, which analyzes the received investment strategy and displays it in a format that is easy for the user to understand.

[0284] Step 12:

[0285] The terminal displays specific investment proposals on the screen, and the user can review the proposed investment strategies and decide whether to accept them.

[0286] Step 13:

[0287] The device collects user feedback and sends information about whether the suggestion was accepted to the server, which uses this feedback as learning data to improve the accuracy of future suggestions.

[0288] This concrete step allows the system to eliminate users' emotional biases and provide more rational and effective investment strategies. Furthermore, the introduction of an emotion engine allows users' emotional state to be corrected in real time and reflected immediately in investments, enabling more appropriate investment decisions.

[0289] Example 2

[0290] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0291] Users' emotional biases in investment decisions often hinder rational decision-making and ultimately reduce investment performance. There is no technology that can identify in real time how such emotional biases affect users' investment decisions and correct them so that they do not affect their investment strategies. Therefore, this invention proposes a system that provides rational investment strategies by detecting and correcting users' emotional biases.

[0292] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0293] In this invention, the server includes means for collecting biometric data of users, means for encrypting and transmitting the collected biometric data in real time, and means for decrypting and analyzing the received data, thereby making it possible to provide rational investment strategies by detecting and correcting the emotional bias of users.

[0294] "User's biometric data" refers to physiological data acquired from the user, and specifically includes electroencephalogram data, facial expression data, and voice data.

[0295] "Means for real-time encryption and transmission" refers to a mechanism for instantly encrypting collected biometric data and securely transmitting it to a server via a network.

[0296] "Means for decrypting and analyzing data" refers to a method for decrypting encrypted data sent to the server and then analyzing the content of the data using machine learning algorithms or analytical tools.

[0297] "Means for identifying emotional state" refers to algorithms or techniques for identifying a user's emotional state (e.g., excited, stressed, relaxed, etc.) from the analyzed biometric data.

[0298] "Means for detecting and correcting emotional bias" refers to a technology that detects emotional bias that influences a user's decision-making based on an identified emotional state and makes adjustments to mitigate or eliminate its influence.

[0299] The "means for generating rational investment strategies" refers to a method that uses artificial intelligence and algorithms to suggest optimal investment actions based on corrected sentiment data and market data.

[0300] The "means for notifying the generated investment strategy" is a system or interface for directly communicating the investment strategy generated by the server to the user.

[0301] This invention is a system that collects and analyzes a user's biometric data to identify the user's emotional state and proposes an investment strategy that corrects for emotional bias. The system includes an emotion engine that recognizes the user's emotions.

[0302] System configuration

[0303] The system consists of a user, a terminal, a server, and an emotion engine. The user wears an EEG sensor and browses financial information and news articles. The terminal collects the user's EEG and facial expression data and sends it to the server. The server analyzes the received data, generates a rational investment strategy that eliminates the user's emotional bias, and notifies the user again via the terminal. The emotion engine analyzes the user's biometric data to recognize their emotional state and correct it as necessary.

[0304] Hardware and software used

[0305] Brainwave sensor: A device for collecting the user's brainwave data, obtaining data in real time.

[0306] Device: The user logs in and receives data from the EEG sensor. The device has a built-in camera and microphone, and also collects facial expression and voice data.

[0307] Server: A system for processing and analyzing data. Frameworks such as TensorFlow are used to execute machine learning algorithms.

[0308] Emotion engine: Software with built-in algorithms to recognize and correct emotional states.

[0309] Data collection and transmission

[0310] Data collection begins when the user wears the EEG sensor and logs in to the device. The device collects the user's EEG data, facial expression data, and voice data in real time, encrypts this data using the AES-256 encryption algorithm, and then transmits the data to the server using the HTTPs protocol.

[0311] Data analysis and the emotional engine

[0312] The server receives the transmitted data packets and decrypts them using the AES-256 algorithm. The decrypted data is then analyzed using machine learning algorithms such as TensorFlow to identify the user's emotional state—for example, "excited," "stressed," or "relaxed."

[0313] The emotion engine integrates and analyzes the received brainwave, facial expression, and voice data, and uses machine learning algorithms such as neural networks to detect the user's emotional bias and correct its influence.

[0314] Investment strategy generation and notification

[0315] Based on the corrected data, the server generates an investment strategy using a deep learning model, random forest, or other methods. This investment strategy is customized for each user. The generated investment strategy is encrypted and sent back to the terminal. The terminal decrypts the received data and displays the proposed investment strategy to the user in an easy-to-understand format. The user can review the proposed investment strategy and decide whether to accept it.

[0316] Specific examples

[0317] The following are specific usage scenarios for this system.

[0318] The user logs in to the device and wears an EEG sensor. While browsing stock information, the device collects the user's EEG data, facial expression data, and voice data.

[0319] The data collected by the device is encrypted and sent to the server.

[0320] The server receives, decompresses, and decrypts the data, and the emotion engine analyzes the received data to determine the user's emotional state.

[0321] The emotion engine detects and corrects the user's emotional bias.

[0322] The server runs an AI model based on the corrected data to generate an optimal investment strategy.

[0323] The server sends the generated investment strategy to the terminal, which displays the proposal to the user.

[0324] The user reviews the proposal and decides whether to accept it.

[0325] The device sends the user's feedback to the server, and based on that feedback, the accuracy of future suggestions is improved.

[0326] Prompt Sentence Examples

[0327] "Please describe a system that uses a user's biometric data to suggest investment strategies that eliminate emotional bias. Please include the specific processing steps of this system, the hardware and software used, and examples."

[0328] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0329] Step 1: Data collection

[0330] Input: User wears EEG sensor and logs into device. User browses financial information and news articles.

[0331] Processing: The device collects the user's brainwave data, facial expression data, and voice data in real time, using the device's built-in camera and microphone.

[0332] Output: A biometric data packet is generated containing the collected EEG, facial expression, and audio data.

[0333] Step 2: Send data

[0334] Input: The biometric data packet collected in step 1.

[0335] Processing: The terminal encrypts the data packet using the AES-256 encryption algorithm, and then sends the encrypted data packet to the server using the HTTPs protocol.

[0336] Output: The encrypted data packet is sent to the server.

[0337] Step 3: Data Decryption and Analysis

[0338] Input: The encrypted data packet sent in step 2.

[0339] Processing: The server decrypts the data using an AES-256 encryption algorithm. It then uses a machine learning framework (e.g., TensorFlow) to identify the user's emotional state from EEG, facial expression, and voice data. At each step, the data is classified with labels such as "excited," "stressed," and "relaxed."

[0340] Output: Identified emotional state labels and analysis results are generated.

[0341] Step 4: Emotion Engine in Action

[0342] Input: Emotional state labels and analysis results generated in step 3.

[0343] Processing: The emotion engine integrates and analyzes the user's brainwave data, facial expression data, and voice data. It uses machine learning algorithms (e.g., neural networks) to detect emotional bias and corrects it using specific algorithms.

[0344] Output: Corrected emotion data and bias correction results are generated.

[0345] Step 5: Generate an investment strategy

[0346] Input: Sentiment data and market data (e.g., news data, financial data) calibrated in step 4.

[0347] Processing: The server generates rational investment strategies using AI models such as deep learning models and random forests. The models integrate the corrected sentiment data with market data to propose optimal investment strategies for each user.

[0348] Output: A rational investment strategy is generated.

[0349] Step 6: Communicate your investment strategy

[0350] Input: The rational investment strategy generated in step 5.

[0351] Processing: The server encrypts the generated investment strategy and sends it to the terminal, which decrypts the received data and displays it to the user.

[0352] Output: A concrete investment proposal is generated that is displayed to the user.

[0353] Step 7: User feedback

[0354] Input: The investment proposal displayed on the user's terminal.

[0355] Processing: The user checks the proposal and decides whether to accept it. The device collects the user's feedback (information on whether or not they accepted it) and sends it to the server.

[0356] Output: User feedback data is sent to the server and used as training data to improve the accuracy of future suggestions.

[0357] (Application example 2)

[0358] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0359] Conventional systems may not be able to make rational decisions when a user's emotional state influences financial decisions and investment strategies. Furthermore, in the security field, systems may not be able to propose appropriate security measures because they do not accurately reflect the user's emotional state, such as stress or anxiety. Therefore, the present invention aims to provide a system that analyzes a user's biometric data and corrects emotional biases to propose rational measures to the user.

[0360] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0361] In this invention, the server includes means for collecting biometric data of the user, means for analyzing the collected biometric data to identify the user's emotional state, and means for correcting the user's emotional bias, thereby enabling rational countermeasures to be proposed based on the user's emotional state.

[0362] "Biometric data of the user" refers to data indicating the physiological state of the user, such as electroencephalogram data, facial expression data, and voice data obtained from the user.

[0363] The "means of analyzing and identifying the user's emotional state" refers to a method of identifying the user's emotional state (stress, excitement, relaxation, etc.) based on collected biometric data using machine learning and statistical methods.

[0364] "Market data" refers to data that shows trends in financial markets, and includes news data and financial data.

[0365] The "means for correcting emotional bias" is a processing method for eliminating or adjusting the user's emotional bias based on the identified emotional state, and assisting the user in making more rational decisions.

[0366] The "generated measures" are specific action suggestions, such as investment strategies and security countermeasures, provided to users based on data after correcting for emotional bias.

[0367] "Means for notifying the user" refers to a method for communicating the generated countermeasures to the user and presenting them in an easy-to-understand manner, and includes a notification module or interface.

[0368] This invention is a system that collects and analyzes a user's biometric data, corrects emotional bias, and proposes rational countermeasures. The system mainly consists of a user, a terminal, a server, and an emotion engine. The following describes the details of each component and its operation.

[0369] Hardware and Software

[0370] 1. Hardware:

[0371] Smartphone

[0372] Brainwave sensor

[0373] Facial Recognition Camera

[0374] Voice recognition microphone

[0375] 2. Software:

[0376] Dedicated application

[0377] Emotion recognition algorithms (e.g., machine learning models using TensorFlow or PyTorch)

[0378] Data Encryption Module

[0379] Server analysis system

[0380] Data collection and analysis

[0381] Users collect biometric data using devices such as brainwave sensors, facial recognition cameras, and voice recognition microphones. A smartphone application collects the data in real time, encrypts it, and sends it to a server. The server analyzes the biometric data to identify the user's emotional state (e.g., stress, excitement, relaxation).

[0382] Correcting emotional bias

[0383] The emotion engine in the server executes an algorithm to correct the user's emotional bias based on the analyzed emotional state, thereby eliminating or adjusting the user's emotional bias and enabling rational decision-making.

[0384] Countermeasure generation and notification

[0385] The server then runs the AI ​​model using the corrected data to generate rational countermeasures (e.g., investment strategies or security measures). The generated countermeasures are then notified to the user via a smartphone app. The user can then review the received countermeasures and implement them as necessary.

[0386] Specific examples

[0387] For example, when conducting online banking, a user can wear an EEG sensor and a facial recognition camera and use a smartphone app. The app detects the user's emotional state, such as stress or anxiety, in real time and sends it to a server. The server analyzes the data and, if it detects that the user is feeling stressed, suggests the application of two-factor authentication. In this way, the system helps users to conduct online banking safely.

[0388] Prompt Sentence Examples

[0389] "Please give a specific example of an application that collects a user's brainwave data, facial expression data, and voice data to perform real-time emotion analysis in a security service."

[0390] This enables the system to propose rational countermeasures based on the user's emotional state, enabling the provision of safer and more effective services.

[0391] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0392] Step 1:

[0393] Data collection

[0394] Input: Real-time biometric data from the user's brainwave sensor, facial expression recognition camera, and voice recognition microphone

[0395] Specific operation: The user launches the smartphone app and wears the EEG sensor, facial recognition camera, and voice recognition microphone. The device collects biometric data from these devices in real time, resulting in EEG data, facial expression data, and voice data.

[0396] Output: Collected biometric data

[0397] Step 2:

[0398] Data Encryption and Transmission

[0399] Input: Collected biometric data

[0400] Specific operation: The device encrypts the brainwave data, facial expression data, and voice data collected. Data encryption is performed using AES encryption technology and SSL / TLS protocols. The encrypted data packets are then sent to the server.

[0401] Output: Encrypted data packet

[0402] Step 3:

[0403] Decompressing and Decrypting Data

[0404] Input: Encrypted data packet

[0405] Specific operation: The server receives the transmitted data packet and decompresses and decrypts it.

[0406] Output: Decompressed and decrypted biometric data

[0407] Step 4:

[0408] Data analysis

[0409] Input: Decompressed and decrypted biometric data

[0410] What it does: The server analyzes the biometric data, applies machine learning algorithms (e.g., using TensorFlow or PyTorch) to identify the user's emotional state (e.g., stressed, excited, relaxed), and labels each data point.

[0411] Output: Data labeled with emotional states

[0412] Step 5:

[0413] Correcting emotional bias

[0414] Input: Data labeled with emotional states

[0415] Specific operation: The server's emotion engine executes an algorithm to correct the user's emotional bias based on the labeled emotion data, thereby adjusting the user's emotional bias and supporting rational decision-making.

[0416] Output: Corrected data

[0417] Step 6:

[0418] Countermeasure generation

[0419] Input: Corrected data

[0420] Specific operation: The server runs the AI ​​model based on the corrected data, generating rational countermeasures (e.g., investment strategies or security measures).

[0421] Output: Generated rational measures

[0422] Step 7:

[0423] Countermeasure notification

[0424] Input: Generated rational measures

[0425] Specific operation: The server sends the generated countermeasures to the terminal. The terminal notifies the user of the received countermeasures and displays them in an easy-to-understand format.

[0426] Output: Notify the user of the countermeasure

[0427] 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.

[0428] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0429] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0430] [Second embodiment]

[0431] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0432] 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.

[0433] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0434] 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.

[0435] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0436] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0437] 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.

[0438] 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.

[0439] 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 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.

[0440] 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.

[0441] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0442] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0443] This invention is a system that collects and analyzes biometric data from users to propose rational investment strategies. The program and processing of this system will be described in detail below.

[0444] System configuration

[0445] This system consists of a user, a terminal, and a server. The user wears an EEG sensor and browses financial information and news articles. The terminal collects the user's EEG data and facial expression data and sends it to the server. The server analyzes the received data, generates a rational investment strategy that eliminates the user's emotional bias, and notifies the user again via the terminal.

[0446] Program processing

[0447] The program processing in this system will be explained below.

[0448] 1. Data Collection:

[0449] The user wears an EEG sensor and logs in to the terminal.

[0450] The device collects the user's brain wave data, facial expression data, and voice data in real time.

[0451] The device also captures the news and financial data the user is reading.

[0452] 2. Data transmission:

[0453] The terminal encrypts the collected brain wave data, facial expression data, and voice data and sends it to a server.

[0454] The terminal also transmits market data (news data and financial data) to the server.

[0455] 3. Data Analysis:

[0456] The server analyzes the received brainwave data to identify the user's emotional state (e.g., excitement, stress, relaxation).

[0457] The server also analyzes facial expression and voice data to provide a detailed assessment of the user's emotional response.

[0458] The server simultaneously analyzes market data to assess market trends and the performance of specific assets.

[0459] 4. Eliminate bias:

[0460] The server identifies the user's emotional bias based on the analysis results and corrects the impact of this bias on investment decisions.

[0461] The server generates a rational investment strategy using the corrected data.

[0462] 5. Investment strategy generation and proposal:

[0463] The server uses multiple AI models to generate optimal investment strategies from the collected and analyzed data.

[0464] The server transmits the generated investment strategy to the terminal.

[0465] The terminal notifies the user of the received investment strategy and displays specific investment proposals on the screen.

[0466] Specific examples

[0467] The following are specific usage scenarios.

[0468] User logs in to a device and wears an EEG sensor. User begins reading a news article about stock X.

[0469] The device collects the user's brain wave data, facial expression data, and voice data in real time, and also captures the content of the article.

[0470] The terminal encrypts the collected data and sends it to the server.

[0471] The server analyzes the received data and determines that the user is excited about stock X. At the same time, it analyzes the impact the article had on the user.

[0472] The server considers the user's excitement as an emotional bias and performs a correction process to eliminate this bias. Based on the corrected data, a rational investment strategy is generated.

[0473] The server generates a strategy recommending investment in stock Y, for example, and transmits it to the terminal.

[0474] The terminal displays the investment proposal on the terminal screen for the user, who then reviews the proposal and decides whether to accept it or not.

[0475] The device sends the user's feedback to the server and uses it as learning data for future suggestions.

[0476] The above is a specific embodiment for carrying out the present invention. The present system improves the accuracy and efficiency of investment decisions by providing a rational investment strategy that eliminates the emotional bias of users.

[0477] The processing flow will be explained below.

[0478] Step 1:

[0479] The user wears the EEG sensor and logs in to the device. The device authenticates the login information and checks whether the EEG sensor is working properly.

[0480] Step 2:

[0481] The device collects the user's brainwave data in real time. While the user is browsing stock information or news articles, the brainwave sensor captures the user's brainwave data and transmits it to the device.

[0482] Step 3:

[0483] The device collects facial expression data and voice data of the user in parallel using a camera and microphone, thereby capturing the user's emotional state from multiple angles.

[0484] Step 4:

[0485] The device encrypts the collected brainwave, facial expression, and voice data and compiles them into packets for data transmission. At the same time, it also collects market data (news data and financial data).

[0486] Step 5:

[0487] The terminal transmits the encrypted data packets to the server, which receives the transmitted data packets and performs decompression and decryption.

[0488] Step 6:

[0489] The server applies machine learning algorithms to analyze the received EEG data, identifying the user's emotional state (e.g., excited, stressed, relaxed) and labeling each data point.

[0490] Step 7:

[0491] The server uses facial and voice data to evaluate the user's emotional responses in detail, and combines this information with the analysis of the EEG data.

[0492] Step 8:

[0493] The server simultaneously analyzes market data (news data and financial data) to assess market trends, the performance of specific assets and key market drivers.

[0494] Step 9:

[0495] The server identifies the user's emotional bias based on the analysis results. For example, if the user is excited about a particular stock, it adjusts the evaluation of that stock.

[0496] Step 10:

[0497] The server performs a correction process taking into account the identified emotional bias, thereby eliminating the influence of the emotional bias on investment decisions.

[0498] Step 11:

[0499] The server uses the corrected data to generate rational investment strategies using AI models, which are customized based on market data and user sentiment data.

[0500] Step 12:

[0501] The server transmits the generated investment strategy to the terminal, which analyzes the received investment strategy and displays it in a format that is easy for the user to understand.

[0502] Step 13:

[0503] The terminal displays specific investment proposals on the screen, and the user reviews the proposed investment strategies and decides whether to accept them.

[0504] Step 14:

[0505] The device collects user feedback and sends information about whether the suggestion was accepted to the server, which uses this feedback as learning data to improve the accuracy of future suggestions.

[0506] The above is the specific flow of the program's processing. This system eliminates the user's emotional bias and provides rational investment strategies, thereby improving the accuracy and efficiency of investment decisions.

[0507] Example 1

[0508] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0509] Conventional investment support systems have the problem of proposing investment strategies without properly correcting for users' emotional biases, which can lead to irrational investment decisions that are dependent on the emotions of individual users. Additionally, there is also the issue of low reliability of the investment strategies provided due to the insufficient diversity of collected data and the insufficient accuracy of analysis.

[0510] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0511] In this invention, the server includes a means for analyzing biometric data received from the terminal to identify the user's emotional state, a means for acquiring market data and analyzing it using a text analysis algorithm, a means for correcting the user's emotional bias using a machine learning model, and a means for generating an investment strategy using an AI model based on the corrected data, thereby making it possible to provide a rational and reliable investment strategy that eliminates the user's emotional bias.

[0512] "Biometric data" refers to data obtained from the user's body, and in the present invention mainly includes electroencephalogram data, facial expression data, and voice data.

[0513] A "terminal" is an electronic device worn by a user, such as an EEG sensor, that collects and transmits data. Examples include smartphones and tablets.

[0514] The "server" is a central device that receives, analyzes, and processes data sent from the terminals. This device is responsible for large-scale data analysis and the generation of investment strategies.

[0515] "Emotional state" refers to the user's mental and emotional state based on biometric data analysis, and primarily includes states such as excitement, stress, and relaxation.

[0516] A "text analysis algorithm" is an algorithm that uses natural language processing techniques to analyze text data, allowing useful information to be extracted from market data.

[0517] A "machine learning model" refers to an algorithm that learns from past data and predicts or classifies future data. In this invention, it is used to correct emotional bias.

[0518] "AI model" means a model that uses artificial intelligence technology to learn patterns from data and generate investment strategies, including reinforcement learning models and deep learning models.

[0519] An "investment strategy" indicates specific investment policies and advice to users that are generated based on collected and analyzed data.

[0520] "Market Data" means data related to financial markets, including news data and financial data, which is analyzed to provide users with appropriate investment strategies.

[0521] The present invention relates to a system that collects and analyzes biometric data from users to propose rational investment strategies. The system is composed of a user, a terminal, and a server.

[0522] Hardware and software used

[0523] User: The user wears an EEG sensor, a camera, and a microphone and logs in to the device. The EEG sensor acquires the user's brainwave data, and the camera and microphone collect facial expression and voice data.

[0524] Device: A device is an electronic device such as a smartphone or tablet that processes biometric data collected from the user and sends it to a server. The device is equipped with EEG analysis software, camera analysis software (e.g., OpenCV, Dlib), and acoustic analysis software.

[0525] Server: The server contains databases, text analysis algorithms (e.g., natural language processing models), machine learning models, and generative AI models (e.g., reinforcement learning models, deep learning models).

[0526] System Operation

[0527] Data collection

[0528] Users wear an EEG sensor and log in to the device. As they begin browsing news articles or financial information, the device collects real-time EEG, facial expression, and voice data. This collected data is used to analyze the user's emotional state.

[0529] Data transmission

[0530] The device encrypts the collected data using the AES encryption algorithm and transmits it to the server using the HTTPS protocol, ensuring confidentiality and integrity of the data during transmission.

[0531] Data analysis and investment strategy generation

[0532] The server stores the received data in a database. EEG data is analyzed using EEG analysis software to identify the user's emotional state. Similarly, facial expression data is analyzed using computer vision techniques (e.g., OpenCV, Dlib), and voice data is analyzed using acoustic analysis tools. Market data is also analyzed using text analysis algorithms. Based on the results of these analyses, the server uses machine learning models to correct for emotional bias and generative AI models to generate rational investment strategies.

[0533] Investment Strategy Notification

[0534] The generated investment strategy is encrypted and sent back to the terminal. The terminal notifies the user and displays specific investment proposals on the screen. The user reviews the proposals and decides whether to accept them or not. This feedback is also sent to the server and used to improve the accuracy of future proposals.

[0535] Specific examples

[0536] When a user wears an EEG sensor while browsing the daily news, the device collects biometric data in real time. The data is encrypted and sent to a server for analysis of the user's emotional state and correction of emotional bias. For example, the server may detect that the user is excited about a particular stock, correct for this bias, and generate a rational investment strategy. The generated strategy is then proposed to the user via the device, and the user can confirm the proposal.

[0537] Prompt Sentence Examples

[0538] The following prompt statements can be used:

[0539] "After analyzing the user's brainwave data and excitement level, how can we eliminate bias and suggest appropriate investment strategies?"

[0540] "Describe how you would design a system that analyzes users' emotional reactions while browsing financial news and generates rational investment strategies."

[0541] The above is a specific embodiment for carrying out the present invention. The present system improves the accuracy and efficiency of investment decisions by providing a rational investment strategy that eliminates the emotional bias of users.

[0542] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0543] Step 1: Data collection

[0544] The user wears an EEG sensor and logs in to the device. At this time, the device launches a dedicated application that collects the user's brainwave data, facial expression data, and voice data in real time. EEG data is obtained from the EEG sensor, facial expression data is collected using a camera, and voice data is collected through a microphone. At the same time, the device also uses screen capture software to capture the news articles and financial information the user is viewing. The input is the user's biometric data and browsing data, and the output is the collected biometric data and browsing data.

[0545] Step 2: Send data

[0546] The device encrypts the EEG data, facial expression data, voice data, news articles, and financial information collected in step 1 using the AES encryption algorithm. The encrypted data is sent to the server using the HTTPS protocol. The input is the collected biometric data and browsing data, and the output is the encrypted biometric data and browsing data.

[0547] Step 3: Data analysis

[0548] The server receives the encrypted data received in step 2 and stores it in a database. The received EEG data is then analyzed using EEG analysis software to identify the user's emotional state (excited, stressed, relaxed, etc.). For facial expression data, computer vision techniques (e.g., OpenCV, Dlib) are used to detect facial feature points and identify the user's emotions from their facial expressions. For voice data, acoustic analysis tools are used to analyze the emotional tone of the user's voice. For market data, text analysis algorithms (e.g., natural language processing models) are used to analyze news and financial information and evaluate market trends. The input is encrypted biometric data and browsing data, and the output is the analysis of the user's emotional state and market trends.

[0549] Step 4: Eliminate bias

[0550] The server uses a machine learning model to correct the emotional bias based on the user's emotional state and market data identified in step 3. This correction process reduces the impact of the user's emotional state on investment decisions and converts them into rational data. The input is the analyzed emotional state and market data, and the output is the corrected data.

[0551] Step 5: Generate an investment strategy

[0552] The server uses the corrected data from step 4 to generate an optimal investment strategy using a generative AI model (e.g., a reinforcement learning model or a deep learning model). This investment strategy eliminates the user's emotional bias and presents the most rational and advantageous investment policy. The input is the corrected data, and the output is the generated investment strategy.

[0553] Step 6: Investment strategy communication and feedback

[0554] The server encrypts the generated investment strategy and sends it back to the terminal. The terminal notifies the user of the received investment strategy and displays a specific investment proposal on the terminal screen. The user reviews the proposal and decides whether to accept the investment strategy. The user's decision is sent from the terminal to the server as feedback, and the server uses this feedback as learning data for future proposals. The input is the generated investment strategy, and the output is the investment proposal to the user and the user's feedback.

[0555] The above is the specific processing flow of the program of this system.

[0556] (Application example 1)

[0557] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0558] In recent years, with the spread of e-commerce, users have access to a wide variety of product information and make purchasing decisions. However, users' emotional biases can influence purchasing decisions, leading to impulsive purchases and irrational choices. This has led to a growing need for systems that support users in making more rational and objective purchasing decisions. While current technology makes suggestions based on users' text information and past purchase history, there are no systems that take into account their emotional state in real time.

[0559] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0560] In this invention, the server includes means for collecting biometric data of a user, means for analyzing the collected biometric data to identify the user's emotional state, means for acquiring and analyzing market data, means for correcting the user's emotional bias, means for generating an investment strategy based on the corrected data, means for notifying the user of the generated investment strategy, means for generating rational purchase proposals based on the collected biometric data of the user and market data to support purchasing decisions in e-commerce, and means for notifying the user of the generated purchase proposals, thereby eliminating the user's emotional bias and supporting rational and objective purchasing decisions.

[0561] "User's biometric data" refers to data obtained from the user's body, and includes brain wave data, facial expression data, voice data, and the like.

[0562] "Electroencephalogram data" refers to measurements of a user's brain's electrical activity that are used to identify the user's emotional state and mental responses.

[0563] "Market Data" refers to data that includes information about a particular market or product, such as news data, financial data, product information, and reviews.

[0564] "Emotional bias" refers to the influence that a user's emotional state has on purchasing and investment decisions, and correcting this influence supports rational decision-making.

[0565] "Rational purchase suggestions" refer to suggestions that recommend the purchase of products or services that are most suitable for the user, while eliminating the user's emotional bias.

[0566] "Means for notifying" refers to a method or device for communicating the investment strategy or purchase proposal generated by the system to the user, including a smartphone or computer display, audio output, etc.

[0567] "Electronic commerce" refers to a form of transaction in which goods are purchased or services are applied for via the Internet, and is also known as online shopping.

[0568] "Investment Strategy" refers to an investment policy or plan recommended to a user based on market data and biometric data, which is intended to be rational and efficient.

[0569] This invention is a system that collects and analyzes users' biometric data to make rational purchasing suggestions. Specifically, it takes into account the user's emotional state when shopping online, preventing impulsive purchases and supporting rational purchasing decisions.

[0570] System configuration

[0571] This system consists of a user, a device, and a server. The user uses a device equipped with an EEG sensor and a camera. The device collects the user's EEG data and facial expression data and transmits this data to the server. The server analyzes the received data, eliminates the user's emotional bias, and generates rational purchase suggestions, which are then sent to the user via the device.

[0572] Program processing

[0573] 1. Data Collection:

[0574] Users wear an EEG sensor and log in to the device, which collects their EEG, facial expression, and voice data in real time, including EEG acquisition using the BrainFlow library and facial expression analysis using the DeepFace library.

[0575] 2. Data transmission:

[0576] The device encrypts the collected data and sends it to a cloud server.

[0577] 3. Data Analysis:

[0578] The server analyzes the EEG data and facial expression data to identify the user's emotional state, and also analyzes market data such as product information and reviews to evaluate the product's quality and reputation.

[0579] 4. Eliminate bias:

[0580] The server identifies the user's emotional bias based on the analysis results and corrects the data to eliminate this bias.

[0581] 5. Purchase offer generation and notification:

[0582] The server generates rational purchase suggestions based on the corrected data. The suggestions are then sent back to the device and notified to the user. The suggestions include specific purchase reasons and explanations to eliminate emotional bias.

[0583] Specific examples

[0584] For example, if a user is about to purchase an expensive smartphone, and news articles and reviews are making them excited, the system will interpret the user's excitement as an emotional bias and compensate for it. As a result, it will make suggestions to help the user calmly consider whether they really need the product or whether there are more suitable options. In this way, it will prevent impulsive purchases and support rational purchasing decisions.

[0585] Prompt Sentence Examples

[0586] "You are currently in an excited state. We recommend that you re-read the reviews for this product and think things through."

[0587] "Based on your emotional state, this product may be worth reconsidering. Consider other options."

[0588] This invention makes it possible to eliminate emotional biases of users in e-commerce transactions and support rational purchasing decisions.

[0589] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0590] Step 1:

[0591] The user wears an EEG sensor and logs in to the device.

[0592] Input: None

[0593] Output: Login status and EEG sensor preparation

[0594] Specific operation: The user wears the EEG sensor on their head and logs in to an application on the device. Once logged in, the EEG sensor prepares to collect the user's EEG data in real time.

[0595] Step 2:

[0596] The device collects the user's brain wave data, facial expression data, and voice data in real time.

[0597] Input: User's brainwaves, facial image, and voice

[0598] Output: Complete set of biometric data (brain waves, facial expressions, voice)

[0599] Specific operation: EEG data is collected using the BrainFlow library, facial images are analyzed using the DeepFace library, facial expression data is obtained, and audio data is collected through the microphone.

[0600] Step 3:

[0601] The brain wave data, facial expression data, and voice data collected by the device are encrypted and sent to a cloud server.

[0602] Input: Complete set of biometric data (brain waves, facial expressions, voice)

[0603] Output: Encrypted data packet

[0604] Specific operation: The device uses an appropriate encryption algorithm to encrypt the collected data, and then sends the encrypted data packet to the cloud server as an HTTP POST request.

[0605] Step 4:

[0606] The server analyzes the received brain wave data and facial expression data to identify the user's emotional state.

[0607] Input: Encrypted data packet

[0608] Output: Emotional state (excited, stressed, relaxed, etc.)

[0609] What it does: The server decrypts the encrypted data and analyzes the EEG and facial expression data, using machine learning models to identify emotional states (e.g., excited, stressed, relaxed).

[0610] Step 5:

[0611] The server collects and analyzes market data and product review data.

[0612] Input: Market data (product information, reviews, etc.)

[0613] Output: Product quality rating and reputation

[0614] How it works: The server retrieves the latest market data and product reviews through external APIs and databases, and uses text analysis and natural language processing (NLP) techniques to evaluate the quality and reputation of products.

[0615] Step 6:

[0616] The server processes the data to identify the user's emotional bias and correct for this bias.

[0617] Input: Emotional state, product quality rating

[0618] Output: Corrected data

[0619] What it does: The server identifies the user's emotional state as an emotional bias and applies a correction algorithm to remove this bias, resulting in rational data.

[0620] Step 7:

[0621] The server generates a rational purchase proposal based on the corrected data and transmits it to the terminal.

[0622] Input: Corrected data

[0623] Output: Purchase proposal (including proposal details and reasons)

[0624] What it does: The server uses the generative AI model to generate rational purchase recommendations from the corrected data, including specific purchase reasons and explanations to eliminate emotional bias. The generated recommendations are sent to the device as an HTTP response.

[0625] Step 8:

[0626] The terminal notifies the user of the purchase offer and the reason for it.

[0627] Input: Purchase Offer

[0628] Output: Display and notification of proposal content

[0629] Specific operation: The device notifies the user of the received purchase suggestion visually or audibly. The suggestion and its reasons are displayed on the screen, and the user confirms it. At this time, a prompt message such as "You are currently in an excited state. We recommend that you review the reviews of this product and think about it calmly" is displayed.

[0630] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0631] This invention is a system that collects and analyzes a user's biometric data to identify the user's emotional state and proposes an investment strategy that corrects for emotional bias. The system includes an emotion engine that recognizes the user's emotions.

[0632] System configuration

[0633] The system consists of a user, a terminal, a server, and an emotion engine. The user wears an EEG sensor and browses financial information and news articles. The terminal collects the user's EEG and facial expression data and sends it to the server. The server analyzes the received data, generates a rational investment strategy that eliminates the user's emotional bias, and notifies the user again via the terminal. The emotion engine analyzes the user's biometric data to recognize their emotional state and correct it as necessary.

[0634] Program processing

[0635] The processing of the program in this system will be explained in detail below.

[0636] 1. Data Collection:

[0637] The user wears an EEG sensor and logs in to the device.

[0638] The device collects the user's brainwave data, facial expression data, and voice data in real time while the user is browsing stock information or news articles.

[0639] 2. Data transmission:

[0640] The device collects brainwave, facial expression, and voice data, encrypts it, and compiles it into data packets. At the same time, it also collects market data (news data and financial data).

[0641] The terminal transmits the encrypted data packets to the server, which receives the transmitted data packets and performs decompression and decryption.

[0642] 3. Data Analysis:

[0643] The server applies machine learning algorithms to analyze the received EEG data, identifying the user's emotional state (e.g., excited, stressed, relaxed) and labeling each data point.

[0644] The server also analyzes facial and voice data to assess the user's emotional response in detail, and this information is then integrated with the analysis of the EEG data.

[0645] 4. How the Emotion Engine Works:

[0646] The emotion engine integrates and analyzes the user's brain wave data, facial expression data, and voice data to recognize the user's emotional state in real time.

[0647] The emotion engine executes algorithms to detect and adjust or correct for the user's emotional biases.

[0648] 5. Eliminate bias:

[0649] The server and the emotion engine work together to identify the user's emotional bias based on the analysis results. For example, if the user is excited about a particular stock, the server recognizes this bias and adjusts its influence.

[0650] The server takes the identified emotional bias into consideration and performs correction processing, thereby eliminating the influence of emotional bias on investment decisions.

[0651] 6. Investment strategy generation and proposal:

[0652] The server runs the AI ​​model using the corrected data to generate a rational investment strategy, which is customized based on market data and user sentiment data.

[0653] The server transmits the generated investment strategy to the terminal, which analyzes the received investment strategy and displays it in a format that is easy for the user to understand.

[0654] 7. Notification of Investment Proposal:

[0655] The terminal displays specific investment proposals on the screen, and the user reviews the proposed investment strategies and decides whether to accept them.

[0656] The device collects user feedback and sends information about whether the suggestion was accepted to the server, which uses this feedback as learning data to improve the accuracy of future suggestions.

[0657] Specific examples

[0658] The following are specific usage scenarios.

[0659] The user logs in to the device and wears an EEG sensor. While browsing stock information, the device collects the user's EEG data, facial expression data, and voice data.

[0660] The data collected by the device is encrypted and sent to the server.

[0661] The server receives, decompresses, and decrypts the data, and the emotion engine analyzes the received data to determine the user's emotional state.

[0662] The emotion engine detects and corrects the user's emotional bias.

[0663] The server runs an AI model based on the corrected data to generate an optimal investment strategy.

[0664] The server sends the generated investment strategy to the terminal, which displays the proposal to the user.

[0665] The user reviews the proposal and decides whether to accept it.

[0666] The device sends the user's feedback to the server, and based on that feedback, the accuracy of future suggestions is improved.

[0667] This system improves the accuracy and efficiency of investment decisions by eliminating users' emotional biases and providing rational investment strategies. In addition, the introduction of an emotion engine corrects users' emotional state in real time, enabling more appropriate investment decisions.

[0668] The processing flow will be explained below.

[0669] Step 1:

[0670] The user wears the EEG sensor and logs in to the device. The device authenticates the user's login information and checks whether the EEG sensor is working properly.

[0671] Step 2:

[0672] The device collects the user's brainwave data in real time. While the user is browsing stock information or news articles, the brainwave sensor captures the data and transmits it to the device.

[0673] Step 3:

[0674] The device collects facial expression data and voice data of the user in parallel using a camera and microphone, thereby capturing the user's emotional state from multiple angles.

[0675] Step 4:

[0676] The device encrypts the collected brainwave, facial expression, and voice data and compiles them into packets for data transmission. At the same time, it also collects market data (news data and financial data).

[0677] Step 5:

[0678] The terminal transmits the encrypted data packets to the server, which receives the transmitted data packets and performs decompression and decryption.

[0679] Step 6:

[0680] The server applies machine learning algorithms to analyze the received EEG data, identifying the user's emotional state (e.g., excited, stressed, relaxed) and labeling each data point.

[0681] Step 7:

[0682] The server uses facial and voice data to evaluate the user's emotional responses in detail, and combines this information with the analysis of the EEG data.

[0683] Step 8:

[0684] The emotion engine integrates and analyzes the user's brainwave data, facial expression data, and voice data to recognize the user's emotional state in real time, thereby revealing the user's specific emotions.

[0685] Step 9:

[0686] The emotion engine detects users' emotional biases and executes algorithms to correct them in real time. For example, if a user is overly excited about a particular stock, the engine will correct the bias to mitigate it.

[0687] Step 10:

[0688] The server uses the corrected data to generate rational investment strategies using AI models, which are customized based on market data and user sentiment data.

[0689] Step 11:

[0690] The server transmits the generated investment strategy to the terminal, which analyzes the received investment strategy and displays it in a format that is easy for the user to understand.

[0691] Step 12:

[0692] The terminal displays specific investment proposals on the screen, and the user can review the proposed investment strategies and decide whether to accept them.

[0693] Step 13:

[0694] The device collects user feedback and sends information about whether the suggestion was accepted to the server, which uses this feedback as learning data to improve the accuracy of future suggestions.

[0695] This concrete step allows the system to eliminate users' emotional biases and provide more rational and effective investment strategies. Furthermore, the introduction of an emotion engine allows users' emotional state to be corrected in real time and reflected immediately in investments, enabling more appropriate investment decisions.

[0696] Example 2

[0697] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0698] Users' emotional biases in investment decisions often hinder rational decision-making and ultimately reduce investment performance. There is no technology that can identify in real time how such emotional biases affect users' investment decisions and correct them so that they do not affect their investment strategies. Therefore, this invention proposes a system that provides rational investment strategies by detecting and correcting users' emotional biases.

[0699] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0700] In this invention, the server includes means for collecting biometric data of users, means for encrypting and transmitting the collected biometric data in real time, and means for decrypting and analyzing the received data, thereby making it possible to provide rational investment strategies by detecting and correcting the emotional bias of users.

[0701] "User's biometric data" refers to physiological data acquired from the user, and specifically includes electroencephalogram data, facial expression data, and voice data.

[0702] "Means for real-time encryption and transmission" refers to a mechanism for instantly encrypting collected biometric data and securely transmitting it to a server via a network.

[0703] "Means for decrypting and analyzing data" refers to a method for decrypting encrypted data sent to the server and then analyzing the content of the data using machine learning algorithms or analytical tools.

[0704] "Means for identifying emotional state" refers to algorithms or techniques for identifying a user's emotional state (e.g., excited, stressed, relaxed, etc.) from the analyzed biometric data.

[0705] "Means for detecting and correcting emotional bias" refers to a technology that detects emotional bias that influences a user's decision-making based on an identified emotional state and makes adjustments to mitigate or eliminate its influence.

[0706] The "means for generating rational investment strategies" refers to a method that uses artificial intelligence and algorithms to suggest optimal investment actions based on corrected sentiment data and market data.

[0707] The "means for notifying the generated investment strategy" is a system or interface for directly communicating the investment strategy generated by the server to the user.

[0708] This invention is a system that collects and analyzes a user's biometric data to identify the user's emotional state and proposes an investment strategy that corrects for emotional bias. The system includes an emotion engine that recognizes the user's emotions.

[0709] System configuration

[0710] The system consists of a user, a terminal, a server, and an emotion engine. The user wears an EEG sensor and browses financial information and news articles. The terminal collects the user's EEG and facial expression data and sends it to the server. The server analyzes the received data, generates a rational investment strategy that eliminates the user's emotional bias, and notifies the user again via the terminal. The emotion engine analyzes the user's biometric data to recognize their emotional state and correct it as necessary.

[0711] Hardware and software used

[0712] Brainwave sensor: A device for collecting the user's brainwave data, obtaining data in real time.

[0713] Device: The user logs in and receives data from the EEG sensor. The device has a built-in camera and microphone, and also collects facial expression and voice data.

[0714] Server: A system for processing and analyzing data. Frameworks such as TensorFlow are used to execute machine learning algorithms.

[0715] Emotion engine: Software with built-in algorithms to recognize and correct emotional states.

[0716] Data collection and transmission

[0717] Data collection begins when the user wears the EEG sensor and logs in to the device. The device collects the user's EEG data, facial expression data, and voice data in real time, encrypts this data using the AES-256 encryption algorithm, and then transmits the data to the server using the HTTPs protocol.

[0718] Data analysis and the emotional engine

[0719] The server receives the transmitted data packets and decrypts them using the AES-256 algorithm. The decrypted data is then analyzed using machine learning algorithms such as TensorFlow to identify the user's emotional state—for example, "excited," "stressed," or "relaxed."

[0720] The emotion engine integrates and analyzes the received brainwave, facial expression, and voice data, and uses machine learning algorithms such as neural networks to detect the user's emotional bias and correct its influence.

[0721] Investment strategy generation and notification

[0722] Based on the corrected data, the server generates an investment strategy using a deep learning model, random forest, or other methods. This investment strategy is customized for each user. The generated investment strategy is encrypted and sent back to the terminal. The terminal decrypts the received data and displays the proposed investment strategy to the user in an easy-to-understand format. The user can review the proposed investment strategy and decide whether to accept it.

[0723] Specific examples

[0724] The following are specific usage scenarios for this system.

[0725] The user logs in to the device and wears an EEG sensor. While browsing stock information, the device collects the user's EEG data, facial expression data, and voice data.

[0726] The data collected by the device is encrypted and sent to the server.

[0727] The server receives, decompresses, and decrypts the data, and the emotion engine analyzes the received data to determine the user's emotional state.

[0728] The emotion engine detects and corrects the user's emotional bias.

[0729] The server runs an AI model based on the corrected data to generate an optimal investment strategy.

[0730] The server sends the generated investment strategy to the terminal, which displays the proposal to the user.

[0731] The user reviews the proposal and decides whether to accept it.

[0732] The device sends the user's feedback to the server, and based on that feedback, the accuracy of future suggestions is improved.

[0733] Prompt Sentence Examples

[0734] "Please describe a system that uses a user's biometric data to suggest investment strategies that eliminate emotional bias. Please include the specific processing steps of this system, the hardware and software used, and examples."

[0735] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0736] Step 1: Data collection

[0737] Input: User wears EEG sensor and logs into device. User browses financial information and news articles.

[0738] Processing: The device collects the user's brainwave data, facial expression data, and voice data in real time, using the device's built-in camera and microphone.

[0739] Output: A biometric data packet is generated containing the collected EEG, facial expression, and audio data.

[0740] Step 2: Send data

[0741] Input: The biometric data packet collected in step 1.

[0742] Processing: The terminal encrypts the data packet using the AES-256 encryption algorithm, and then sends the encrypted data packet to the server using the HTTPs protocol.

[0743] Output: The encrypted data packet is sent to the server.

[0744] Step 3: Data Decryption and Analysis

[0745] Input: The encrypted data packet sent in step 2.

[0746] Processing: The server decrypts the data using an AES-256 encryption algorithm. It then uses a machine learning framework (e.g., TensorFlow) to identify the user's emotional state from EEG, facial expression, and voice data. At each step, the data is classified with labels such as "excited," "stressed," and "relaxed."

[0747] Output: Identified emotional state labels and analysis results are generated.

[0748] Step 4: Emotion Engine in Action

[0749] Input: Emotional state labels and analysis results generated in step 3.

[0750] Processing: The emotion engine integrates and analyzes the user's brainwave data, facial expression data, and voice data. It uses machine learning algorithms (e.g., neural networks) to detect emotional bias and corrects it using specific algorithms.

[0751] Output: Corrected emotion data and bias correction results are generated.

[0752] Step 5: Generate an investment strategy

[0753] Input: Sentiment data and market data (e.g., news data, financial data) calibrated in step 4.

[0754] Processing: The server generates rational investment strategies using AI models such as deep learning models and random forests. The models integrate the corrected sentiment data with market data to propose optimal investment strategies for each user.

[0755] Output: A rational investment strategy is generated.

[0756] Step 6: Communicate your investment strategy

[0757] Input: The rational investment strategy generated in step 5.

[0758] Processing: The server encrypts the generated investment strategy and sends it to the terminal, which decrypts the received data and displays it to the user.

[0759] Output: A concrete investment proposal is generated that is displayed to the user.

[0760] Step 7: User feedback

[0761] Input: The investment proposal displayed on the user's terminal.

[0762] Processing: The user checks the proposal and decides whether to accept it. The device collects the user's feedback (information on whether or not they accepted it) and sends it to the server.

[0763] Output: User feedback data is sent to the server and used as training data to improve the accuracy of future suggestions.

[0764] (Application example 2)

[0765] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0766] Conventional systems may not be able to make rational decisions when a user's emotional state influences financial decisions and investment strategies. Furthermore, in the security field, systems may not be able to propose appropriate security measures because they do not accurately reflect the user's emotional state, such as stress or anxiety. Therefore, the present invention aims to provide a system that analyzes a user's biometric data and corrects emotional biases to propose rational measures to the user.

[0767] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0768] In this invention, the server includes means for collecting biometric data of the user, means for analyzing the collected biometric data to identify the user's emotional state, and means for correcting the user's emotional bias, thereby enabling rational countermeasures to be proposed based on the user's emotional state.

[0769] "Biometric data of the user" refers to data indicating the physiological state of the user, such as electroencephalogram data, facial expression data, and voice data obtained from the user.

[0770] The "means of analyzing and identifying the user's emotional state" refers to a method of identifying the user's emotional state (stress, excitement, relaxation, etc.) based on collected biometric data using machine learning and statistical methods.

[0771] "Market data" refers to data that shows trends in financial markets, and includes news data and financial data.

[0772] The "means for correcting emotional bias" is a processing method for eliminating or adjusting the user's emotional bias based on the identified emotional state, and assisting the user in making more rational decisions.

[0773] The "generated measures" are specific action suggestions, such as investment strategies and security countermeasures, provided to users based on data after correcting for emotional bias.

[0774] "Means for notifying the user" refers to a method for communicating the generated countermeasures to the user and presenting them in an easy-to-understand manner, and includes a notification module or interface.

[0775] This invention is a system that collects and analyzes a user's biometric data, corrects emotional bias, and proposes rational countermeasures. The system mainly consists of a user, a terminal, a server, and an emotion engine. The following describes the details of each component and its operation.

[0776] Hardware and Software

[0777] 1. Hardware:

[0778] Smartphone

[0779] Brainwave sensor

[0780] Facial Recognition Camera

[0781] Voice recognition microphone

[0782] 2. Software:

[0783] Dedicated application

[0784] Emotion recognition algorithms (e.g., machine learning models using TensorFlow or PyTorch)

[0785] Data Encryption Module

[0786] Server analysis system

[0787] Data collection and analysis

[0788] Users collect biometric data using devices such as brainwave sensors, facial recognition cameras, and voice recognition microphones. A smartphone application collects the data in real time, encrypts it, and sends it to a server. The server analyzes the biometric data to identify the user's emotional state (e.g., stress, excitement, relaxation).

[0789] Correcting emotional bias

[0790] The emotion engine in the server executes an algorithm to correct the user's emotional bias based on the analyzed emotional state, thereby eliminating or adjusting the user's emotional bias and enabling rational decision-making.

[0791] Countermeasure generation and notification

[0792] The server then runs the AI ​​model using the corrected data to generate rational countermeasures (e.g., investment strategies or security measures). The generated countermeasures are then notified to the user via a smartphone app. The user can then review the received countermeasures and implement them as necessary.

[0793] Specific examples

[0794] For example, when conducting online banking, a user can wear an EEG sensor and a facial recognition camera and use a smartphone app. The app detects the user's emotional state, such as stress or anxiety, in real time and sends it to a server. The server analyzes the data and, if it detects that the user is feeling stressed, suggests the application of two-factor authentication. In this way, the system helps users to conduct online banking safely.

[0795] Prompt Sentence Examples

[0796] "Please give a specific example of an application that collects a user's brainwave data, facial expression data, and voice data to perform real-time emotion analysis in a security service."

[0797] This enables the system to propose rational countermeasures based on the user's emotional state, enabling the provision of safer and more effective services.

[0798] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0799] Step 1:

[0800] Data collection

[0801] Input: Real-time biometric data from the user's brainwave sensor, facial expression recognition camera, and voice recognition microphone

[0802] Specific operation: The user launches the smartphone app and wears the EEG sensor, facial recognition camera, and voice recognition microphone. The device collects biometric data from these devices in real time, resulting in EEG data, facial expression data, and voice data.

[0803] Output: Collected biometric data

[0804] Step 2:

[0805] Data Encryption and Transmission

[0806] Input: Collected biometric data

[0807] Specific operation: The device encrypts the brainwave data, facial expression data, and voice data collected. Data encryption is performed using AES encryption technology and SSL / TLS protocols. The encrypted data packets are then sent to the server.

[0808] Output: Encrypted data packet

[0809] Step 3:

[0810] Decompressing and Decrypting Data

[0811] Input: Encrypted data packet

[0812] Specific operation: The server receives the transmitted data packet and decompresses and decrypts it.

[0813] Output: Decompressed and decrypted biometric data

[0814] Step 4:

[0815] Data analysis

[0816] Input: Decompressed and decrypted biometric data

[0817] What it does: The server analyzes the biometric data, applies machine learning algorithms (e.g., using TensorFlow or PyTorch) to identify the user's emotional state (e.g., stressed, excited, relaxed), and labels each data point.

[0818] Output: Data labeled with emotional states

[0819] Step 5:

[0820] Correcting emotional bias

[0821] Input: Data labeled with emotional states

[0822] Specific operation: The server's emotion engine executes an algorithm to correct the user's emotional bias based on the labeled emotion data, thereby adjusting the user's emotional bias and supporting rational decision-making.

[0823] Output: Corrected data

[0824] Step 6:

[0825] Countermeasure generation

[0826] Input: Corrected data

[0827] Specific operation: The server runs the AI ​​model based on the corrected data, generating rational countermeasures (e.g., investment strategies or security measures).

[0828] Output: Generated rational measures

[0829] Step 7:

[0830] Countermeasure notification

[0831] Input: Generated rational measures

[0832] Specific operation: The server sends the generated countermeasures to the terminal. The terminal notifies the user of the received countermeasures and displays them in an easy-to-understand format.

[0833] Output: Notify the user of the countermeasure

[0834] 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.

[0835] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0836] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0837] [Third embodiment]

[0838] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0839] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0840] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0841] 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.

[0842] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0843] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0844] 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.

[0845] 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.

[0846] 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 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.

[0847] 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.

[0848] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0849] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0850] This invention is a system that collects and analyzes biometric data from users to propose rational investment strategies. The program and processing of this system will be described in detail below.

[0851] System configuration

[0852] This system consists of a user, a terminal, and a server. The user wears an EEG sensor and browses financial information and news articles. The terminal collects the user's EEG data and facial expression data and sends it to the server. The server analyzes the received data, generates a rational investment strategy that eliminates the user's emotional bias, and notifies the user again via the terminal.

[0853] Program processing

[0854] The program processing in this system will be explained below.

[0855] 1. Data Collection:

[0856] The user wears an EEG sensor and logs in to the terminal.

[0857] The device collects the user's brain wave data, facial expression data, and voice data in real time.

[0858] The device also captures the news and financial data the user is reading.

[0859] 2. Data transmission:

[0860] The terminal encrypts the collected brain wave data, facial expression data, and voice data and sends it to a server.

[0861] The terminal also transmits market data (news data and financial data) to the server.

[0862] 3. Data Analysis:

[0863] The server analyzes the received brainwave data to identify the user's emotional state (e.g., excitement, stress, relaxation).

[0864] The server also analyzes facial expression and voice data to provide a detailed assessment of the user's emotional response.

[0865] The server simultaneously analyzes market data to assess market trends and the performance of specific assets.

[0866] 4. Eliminate bias:

[0867] The server identifies the user's emotional bias based on the analysis results and corrects the impact of this bias on investment decisions.

[0868] The server generates a rational investment strategy using the corrected data.

[0869] 5. Investment strategy generation and proposal:

[0870] The server uses multiple AI models to generate optimal investment strategies from the collected and analyzed data.

[0871] The server transmits the generated investment strategy to the terminal.

[0872] The terminal notifies the user of the received investment strategy and displays specific investment proposals on the screen.

[0873] Specific examples

[0874] The following are specific usage scenarios.

[0875] User logs in to a device and wears an EEG sensor. User begins reading a news article about stock X.

[0876] The device collects the user's brain wave data, facial expression data, and voice data in real time, and also captures the content of the article.

[0877] The terminal encrypts the collected data and sends it to the server.

[0878] The server analyzes the received data and determines that the user is excited about stock X. At the same time, it analyzes the impact the article had on the user.

[0879] The server considers the user's excitement as an emotional bias and performs a correction process to eliminate this bias. Based on the corrected data, a rational investment strategy is generated.

[0880] The server generates a strategy recommending investment in stock Y, for example, and transmits it to the terminal.

[0881] The terminal displays the investment proposal on the terminal screen for the user, who then reviews the proposal and decides whether to accept it or not.

[0882] The device sends the user's feedback to the server and uses it as learning data for future suggestions.

[0883] The above is a specific embodiment for carrying out the present invention. The present system improves the accuracy and efficiency of investment decisions by providing a rational investment strategy that eliminates the emotional bias of users.

[0884] The processing flow will be explained below.

[0885] Step 1:

[0886] The user wears the EEG sensor and logs in to the device. The device authenticates the login information and checks whether the EEG sensor is working properly.

[0887] Step 2:

[0888] The device collects the user's brainwave data in real time. While the user is browsing stock information or news articles, the brainwave sensor captures the user's brainwave data and transmits it to the device.

[0889] Step 3:

[0890] The device collects facial expression data and voice data of the user in parallel using a camera and microphone, thereby capturing the user's emotional state from multiple angles.

[0891] Step 4:

[0892] The device encrypts the collected brainwave, facial expression, and voice data and compiles them into packets for data transmission. At the same time, it also collects market data (news data and financial data).

[0893] Step 5:

[0894] The terminal transmits the encrypted data packets to the server, which receives the transmitted data packets and performs decompression and decryption.

[0895] Step 6:

[0896] The server applies machine learning algorithms to analyze the received EEG data, identifying the user's emotional state (e.g., excited, stressed, relaxed) and labeling each data point.

[0897] Step 7:

[0898] The server uses facial and voice data to evaluate the user's emotional responses in detail, and combines this information with the analysis of the EEG data.

[0899] Step 8:

[0900] The server simultaneously analyzes market data (news data and financial data) to assess market trends, the performance of specific assets and key market drivers.

[0901] Step 9:

[0902] The server identifies the user's emotional bias based on the analysis results. For example, if the user is excited about a particular stock, it adjusts the evaluation of that stock.

[0903] Step 10:

[0904] The server performs a correction process taking into account the identified emotional bias, thereby eliminating the influence of the emotional bias on investment decisions.

[0905] Step 11:

[0906] The server uses the corrected data to generate rational investment strategies using AI models, which are customized based on market data and user sentiment data.

[0907] Step 12:

[0908] The server transmits the generated investment strategy to the terminal, which analyzes the received investment strategy and displays it in a format that is easy for the user to understand.

[0909] Step 13:

[0910] The terminal displays specific investment proposals on the screen, and the user reviews the proposed investment strategies and decides whether to accept them.

[0911] Step 14:

[0912] The device collects user feedback and sends information about whether the suggestion was accepted to the server, which uses this feedback as learning data to improve the accuracy of future suggestions.

[0913] The above is the specific flow of the program's processing. This system eliminates the user's emotional bias and provides rational investment strategies, thereby improving the accuracy and efficiency of investment decisions.

[0914] Example 1

[0915] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0916] Conventional investment support systems have the problem of proposing investment strategies without properly correcting for users' emotional biases, which can lead to irrational investment decisions that are dependent on the emotions of individual users. Additionally, there is also the issue of low reliability of the investment strategies provided due to the insufficient diversity of collected data and the insufficient accuracy of analysis.

[0917] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0918] In this invention, the server includes a means for analyzing biometric data received from the terminal to identify the user's emotional state, a means for acquiring market data and analyzing it using a text analysis algorithm, a means for correcting the user's emotional bias using a machine learning model, and a means for generating an investment strategy using an AI model based on the corrected data, thereby making it possible to provide a rational and reliable investment strategy that eliminates the user's emotional bias.

[0919] "Biometric data" refers to data obtained from the user's body, and in the present invention mainly includes electroencephalogram data, facial expression data, and voice data.

[0920] A "terminal" is an electronic device worn by a user, such as an EEG sensor, that collects and transmits data. Examples include smartphones and tablets.

[0921] The "server" is a central device that receives, analyzes, and processes data sent from the terminals. This device is responsible for large-scale data analysis and the generation of investment strategies.

[0922] "Emotional state" refers to the user's mental and emotional state based on biometric data analysis, and primarily includes states such as excitement, stress, and relaxation.

[0923] A "text analysis algorithm" is an algorithm that uses natural language processing techniques to analyze text data, allowing useful information to be extracted from market data.

[0924] A "machine learning model" refers to an algorithm that learns from past data and predicts or classifies future data. In this invention, it is used to correct emotional bias.

[0925] "AI model" means a model that uses artificial intelligence technology to learn patterns from data and generate investment strategies, including reinforcement learning models and deep learning models.

[0926] An "investment strategy" indicates specific investment policies and advice to users that are generated based on collected and analyzed data.

[0927] "Market Data" means data related to financial markets, including news data and financial data, which is analyzed to provide users with appropriate investment strategies.

[0928] The present invention relates to a system that collects and analyzes biometric data from users to propose rational investment strategies. The system is composed of a user, a terminal, and a server.

[0929] Hardware and software used

[0930] User: The user wears an EEG sensor, a camera, and a microphone and logs in to the device. The EEG sensor acquires the user's brainwave data, and the camera and microphone collect facial expression and voice data.

[0931] Device: A device is an electronic device such as a smartphone or tablet that processes biometric data collected from the user and sends it to a server. The device is equipped with EEG analysis software, camera analysis software (e.g., OpenCV, Dlib), and acoustic analysis software.

[0932] Server: The server contains databases, text analysis algorithms (e.g., natural language processing models), machine learning models, and generative AI models (e.g., reinforcement learning models, deep learning models).

[0933] System Operation

[0934] Data collection

[0935] Users wear an EEG sensor and log in to the device. As they begin browsing news articles or financial information, the device collects real-time EEG, facial expression, and voice data. This collected data is used to analyze the user's emotional state.

[0936] Data transmission

[0937] The device encrypts the collected data using the AES encryption algorithm and transmits it to the server using the HTTPS protocol, ensuring confidentiality and integrity of the data during transmission.

[0938] Data analysis and investment strategy generation

[0939] The server stores the received data in a database. EEG data is analyzed using EEG analysis software to identify the user's emotional state. Similarly, facial expression data is analyzed using computer vision techniques (e.g., OpenCV, Dlib), and voice data is analyzed using acoustic analysis tools. Market data is also analyzed using text analysis algorithms. Based on the results of these analyses, the server uses machine learning models to correct for emotional bias and generative AI models to generate rational investment strategies.

[0940] Investment Strategy Notification

[0941] The generated investment strategy is encrypted and sent back to the terminal. The terminal notifies the user and displays specific investment proposals on the screen. The user reviews the proposals and decides whether to accept them or not. This feedback is also sent to the server and used to improve the accuracy of future proposals.

[0942] Specific examples

[0943] When a user wears an EEG sensor while browsing the daily news, the device collects biometric data in real time. The data is encrypted and sent to a server for analysis of the user's emotional state and correction of emotional bias. For example, the server may detect that the user is excited about a particular stock, correct for this bias, and generate a rational investment strategy. The generated strategy is then proposed to the user via the device, and the user can confirm the proposal.

[0944] Prompt Sentence Examples

[0945] The following prompt statements can be used:

[0946] "After analyzing the user's brainwave data and excitement level, how can we eliminate bias and suggest appropriate investment strategies?"

[0947] "Describe how you would design a system that analyzes users' emotional reactions while browsing financial news and generates rational investment strategies."

[0948] The above is a specific embodiment for carrying out the present invention. The present system improves the accuracy and efficiency of investment decisions by providing a rational investment strategy that eliminates the emotional bias of users.

[0949] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0950] Step 1: Data collection

[0951] The user wears an EEG sensor and logs in to the device. At this time, the device launches a dedicated application that collects the user's brainwave data, facial expression data, and voice data in real time. EEG data is obtained from the EEG sensor, facial expression data is collected using a camera, and voice data is collected through a microphone. At the same time, the device also uses screen capture software to capture the news articles and financial information the user is viewing. The input is the user's biometric data and browsing data, and the output is the collected biometric data and browsing data.

[0952] Step 2: Send data

[0953] The device encrypts the EEG data, facial expression data, voice data, news articles, and financial information collected in step 1 using the AES encryption algorithm. The encrypted data is sent to the server using the HTTPS protocol. The input is the collected biometric data and browsing data, and the output is the encrypted biometric data and browsing data.

[0954] Step 3: Data analysis

[0955] The server receives the encrypted data received in step 2 and stores it in a database. The received EEG data is then analyzed using EEG analysis software to identify the user's emotional state (excited, stressed, relaxed, etc.). For facial expression data, computer vision techniques (e.g., OpenCV, Dlib) are used to detect facial feature points and identify the user's emotions from their facial expressions. For voice data, acoustic analysis tools are used to analyze the emotional tone of the user's voice. For market data, text analysis algorithms (e.g., natural language processing models) are used to analyze news and financial information and evaluate market trends. The input is encrypted biometric data and browsing data, and the output is the analysis of the user's emotional state and market trends.

[0956] Step 4: Eliminate bias

[0957] The server uses a machine learning model to correct the emotional bias based on the user's emotional state and market data identified in step 3. This correction process reduces the impact of the user's emotional state on investment decisions and converts them into rational data. The input is the analyzed emotional state and market data, and the output is the corrected data.

[0958] Step 5: Generate an investment strategy

[0959] The server uses the corrected data from step 4 to generate an optimal investment strategy using a generative AI model (e.g., a reinforcement learning model or a deep learning model). This investment strategy eliminates the user's emotional bias and presents the most rational and advantageous investment policy. The input is the corrected data, and the output is the generated investment strategy.

[0960] Step 6: Investment strategy communication and feedback

[0961] The server encrypts the generated investment strategy and sends it back to the terminal. The terminal notifies the user of the received investment strategy and displays a specific investment proposal on the terminal screen. The user reviews the proposal and decides whether to accept the investment strategy. The user's decision is sent from the terminal to the server as feedback, and the server uses this feedback as learning data for future proposals. The input is the generated investment strategy, and the output is the investment proposal to the user and the user's feedback.

[0962] The above is the specific processing flow of the program of this system.

[0963] (Application example 1)

[0964] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0965] In recent years, with the spread of e-commerce, users have access to a wide variety of product information and make purchasing decisions. However, users' emotional biases can influence purchasing decisions, leading to impulsive purchases and irrational choices. This has led to a growing need for systems that support users in making more rational and objective purchasing decisions. While current technology makes suggestions based on users' text information and past purchase history, there are no systems that take into account their emotional state in real time.

[0966] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0967] In this invention, the server includes means for collecting biometric data of a user, means for analyzing the collected biometric data to identify the user's emotional state, means for acquiring and analyzing market data, means for correcting the user's emotional bias, means for generating an investment strategy based on the corrected data, means for notifying the user of the generated investment strategy, means for generating rational purchase proposals based on the collected biometric data of the user and market data to support purchasing decisions in e-commerce, and means for notifying the user of the generated purchase proposals, thereby eliminating the user's emotional bias and supporting rational and objective purchasing decisions.

[0968] "User's biometric data" refers to data obtained from the user's body, and includes brain wave data, facial expression data, voice data, and the like.

[0969] "Electroencephalogram data" refers to measurements of a user's brain's electrical activity that are used to identify the user's emotional state and mental responses.

[0970] "Market Data" refers to data that includes information about a particular market or product, such as news data, financial data, product information, and reviews.

[0971] "Emotional bias" refers to the influence that a user's emotional state has on purchasing and investment decisions, and correcting this influence supports rational decision-making.

[0972] "Rational purchase suggestions" refer to suggestions that recommend the purchase of products or services that are most suitable for the user, while eliminating the user's emotional bias.

[0973] "Means for notifying" refers to a method or device for communicating the investment strategy or purchase proposal generated by the system to the user, including a smartphone or computer display, audio output, etc.

[0974] "Electronic commerce" refers to a form of transaction in which goods are purchased or services are applied for via the Internet, and is also known as online shopping.

[0975] "Investment Strategy" refers to an investment policy or plan recommended to a user based on market data and biometric data, which is intended to be rational and efficient.

[0976] This invention is a system that collects and analyzes users' biometric data to make rational purchasing suggestions. Specifically, it takes into account the user's emotional state when shopping online, preventing impulsive purchases and supporting rational purchasing decisions.

[0977] System configuration

[0978] This system consists of a user, a device, and a server. The user uses a device equipped with an EEG sensor and a camera. The device collects the user's EEG data and facial expression data and transmits this data to the server. The server analyzes the received data, eliminates the user's emotional bias, and generates rational purchase suggestions, which are then sent to the user via the device.

[0979] Program processing

[0980] 1. Data Collection:

[0981] Users wear an EEG sensor and log in to the device, which collects their EEG, facial expression, and voice data in real time, including EEG acquisition using the BrainFlow library and facial expression analysis using the DeepFace library.

[0982] 2. Data transmission:

[0983] The device encrypts the collected data and sends it to a cloud server.

[0984] 3. Data Analysis:

[0985] The server analyzes the EEG data and facial expression data to identify the user's emotional state, and also analyzes market data such as product information and reviews to evaluate the product's quality and reputation.

[0986] 4. Eliminate bias:

[0987] The server identifies the user's emotional bias based on the analysis results and corrects the data to eliminate this bias.

[0988] 5. Purchase offer generation and notification:

[0989] The server generates rational purchase suggestions based on the corrected data. The suggestions are then sent back to the device and notified to the user. The suggestions include specific purchase reasons and explanations to eliminate emotional bias.

[0990] Specific examples

[0991] For example, if a user is about to purchase an expensive smartphone, and news articles and reviews are making them excited, the system will interpret the user's excitement as an emotional bias and compensate for it. As a result, it will make suggestions to help the user calmly consider whether they really need the product or whether there are more suitable options. In this way, it will prevent impulsive purchases and support rational purchasing decisions.

[0992] Prompt Sentence Examples

[0993] "You are currently in an excited state. We recommend that you re-read the reviews for this product and think things through."

[0994] "Based on your emotional state, this product may be worth reconsidering. Consider other options."

[0995] This invention makes it possible to eliminate emotional biases of users in e-commerce transactions and support rational purchasing decisions.

[0996] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0997] Step 1:

[0998] The user wears an EEG sensor and logs in to the device.

[0999] Input: None

[1000] Output: Login status and EEG sensor preparation

[1001] Specific operation: The user wears the EEG sensor on their head and logs in to an application on the device. Once logged in, the EEG sensor prepares to collect the user's EEG data in real time.

[1002] Step 2:

[1003] The device collects the user's brain wave data, facial expression data, and voice data in real time.

[1004] Input: User's brainwaves, facial image, and voice

[1005] Output: Complete set of biometric data (brain waves, facial expressions, voice)

[1006] Specific operation: EEG data is collected using the BrainFlow library, facial images are analyzed using the DeepFace library, facial expression data is obtained, and audio data is collected through the microphone.

[1007] Step 3:

[1008] The brain wave data, facial expression data, and voice data collected by the device are encrypted and sent to a cloud server.

[1009] Input: Complete set of biometric data (brain waves, facial expressions, voice)

[1010] Output: Encrypted data packet

[1011] Specific operation: The device uses an appropriate encryption algorithm to encrypt the collected data, and then sends the encrypted data packet to the cloud server as an HTTP POST request.

[1012] Step 4:

[1013] The server analyzes the received brain wave data and facial expression data to identify the user's emotional state.

[1014] Input: Encrypted data packet

[1015] Output: Emotional state (excited, stressed, relaxed, etc.)

[1016] What it does: The server decrypts the encrypted data and analyzes the EEG and facial expression data, using machine learning models to identify emotional states (e.g., excited, stressed, relaxed).

[1017] Step 5:

[1018] The server collects and analyzes market data and product review data.

[1019] Input: Market data (product information, reviews, etc.)

[1020] Output: Product quality rating and reputation

[1021] How it works: The server retrieves the latest market data and product reviews through external APIs and databases, and uses text analysis and natural language processing (NLP) techniques to evaluate the quality and reputation of products.

[1022] Step 6:

[1023] The server processes the data to identify the user's emotional bias and correct for this bias.

[1024] Input: Emotional state, product quality rating

[1025] Output: Corrected data

[1026] What it does: The server identifies the user's emotional state as an emotional bias and applies a correction algorithm to remove this bias, resulting in rational data.

[1027] Step 7:

[1028] The server generates a rational purchase proposal based on the corrected data and transmits it to the terminal.

[1029] Input: Corrected data

[1030] Output: Purchase proposal (including proposal details and reasons)

[1031] What it does: The server uses the generative AI model to generate rational purchase recommendations from the corrected data, including specific purchase reasons and explanations to eliminate emotional bias. The generated recommendations are sent to the device as an HTTP response.

[1032] Step 8:

[1033] The terminal notifies the user of the purchase offer and the reason for it.

[1034] Input: Purchase Offer

[1035] Output: Display and notification of proposal content

[1036] Specific operation: The device notifies the user of the received purchase suggestion visually or audibly. The suggestion and its reasons are displayed on the screen, and the user confirms it. At this time, a prompt message such as "You are currently in an excited state. We recommend that you review the reviews of this product and think about it calmly" is displayed.

[1037] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1038] This invention is a system that collects and analyzes a user's biometric data to identify the user's emotional state and proposes an investment strategy that corrects for emotional bias. The system includes an emotion engine that recognizes the user's emotions.

[1039] System configuration

[1040] The system consists of a user, a terminal, a server, and an emotion engine. The user wears an EEG sensor and browses financial information and news articles. The terminal collects the user's EEG and facial expression data and sends it to the server. The server analyzes the received data, generates a rational investment strategy that eliminates the user's emotional bias, and notifies the user again via the terminal. The emotion engine analyzes the user's biometric data to recognize their emotional state and correct it as necessary.

[1041] Program processing

[1042] The processing of the program in this system will be explained in detail below.

[1043] 1. Data Collection:

[1044] The user wears an EEG sensor and logs in to the device.

[1045] The device collects the user's brainwave data, facial expression data, and voice data in real time while the user is browsing stock information or news articles.

[1046] 2. Data transmission:

[1047] The device collects brainwave, facial expression, and voice data, encrypts it, and compiles it into data packets. At the same time, it also collects market data (news data and financial data).

[1048] The terminal transmits the encrypted data packets to the server, which receives the transmitted data packets and performs decompression and decryption.

[1049] 3. Data Analysis:

[1050] The server applies machine learning algorithms to analyze the received EEG data, identifying the user's emotional state (e.g., excited, stressed, relaxed) and labeling each data point.

[1051] The server also analyzes facial and voice data to assess the user's emotional response in detail, and this information is then integrated with the analysis of the EEG data.

[1052] 4. How the Emotion Engine Works:

[1053] The emotion engine integrates and analyzes the user's brain wave data, facial expression data, and voice data to recognize the user's emotional state in real time.

[1054] The emotion engine executes algorithms to detect and adjust or correct for the user's emotional biases.

[1055] 5. Eliminate bias:

[1056] The server and the emotion engine work together to identify the user's emotional bias based on the analysis results. For example, if the user is excited about a particular stock, the server recognizes this bias and adjusts its influence.

[1057] The server takes the identified emotional bias into consideration and performs correction processing, thereby eliminating the influence of emotional bias on investment decisions.

[1058] 6. Investment strategy generation and proposal:

[1059] The server runs the AI ​​model using the corrected data to generate a rational investment strategy, which is customized based on market data and user sentiment data.

[1060] The server transmits the generated investment strategy to the terminal, which analyzes the received investment strategy and displays it in a format that is easy for the user to understand.

[1061] 7. Notification of Investment Proposal:

[1062] The terminal displays specific investment proposals on the screen, and the user reviews the proposed investment strategies and decides whether to accept them.

[1063] The device collects user feedback and sends information about whether the suggestion was accepted to the server, which uses this feedback as learning data to improve the accuracy of future suggestions.

[1064] Specific examples

[1065] The following are specific usage scenarios.

[1066] The user logs in to the device and wears an EEG sensor. While browsing stock information, the device collects the user's EEG data, facial expression data, and voice data.

[1067] The data collected by the device is encrypted and sent to the server.

[1068] The server receives, decompresses, and decrypts the data, and the emotion engine analyzes the received data to determine the user's emotional state.

[1069] The emotion engine detects and corrects the user's emotional bias.

[1070] The server runs an AI model based on the corrected data to generate an optimal investment strategy.

[1071] The server sends the generated investment strategy to the terminal, which displays the proposal to the user.

[1072] The user reviews the proposal and decides whether to accept it.

[1073] The device sends the user's feedback to the server, and based on that feedback, the accuracy of future suggestions is improved.

[1074] This system improves the accuracy and efficiency of investment decisions by eliminating users' emotional biases and providing rational investment strategies. In addition, the introduction of an emotion engine corrects users' emotional state in real time, enabling more appropriate investment decisions.

[1075] The processing flow will be explained below.

[1076] Step 1:

[1077] The user wears the EEG sensor and logs in to the device. The device authenticates the user's login information and checks whether the EEG sensor is working properly.

[1078] Step 2:

[1079] The device collects the user's brainwave data in real time. While the user is browsing stock information or news articles, the brainwave sensor captures the data and transmits it to the device.

[1080] Step 3:

[1081] The device collects facial expression data and voice data of the user in parallel using a camera and microphone, thereby capturing the user's emotional state from multiple angles.

[1082] Step 4:

[1083] The device encrypts the collected brainwave, facial expression, and voice data and compiles them into packets for data transmission. At the same time, it also collects market data (news data and financial data).

[1084] Step 5:

[1085] The terminal transmits the encrypted data packets to the server, which receives the transmitted data packets and performs decompression and decryption.

[1086] Step 6:

[1087] The server applies machine learning algorithms to analyze the received EEG data, identifying the user's emotional state (e.g., excited, stressed, relaxed) and labeling each data point.

[1088] Step 7:

[1089] The server uses facial and voice data to evaluate the user's emotional responses in detail, and combines this information with the analysis of the EEG data.

[1090] Step 8:

[1091] The emotion engine integrates and analyzes the user's brainwave data, facial expression data, and voice data to recognize the user's emotional state in real time, thereby revealing the user's specific emotions.

[1092] Step 9:

[1093] The emotion engine detects users' emotional biases and executes algorithms to correct them in real time. For example, if a user is overly excited about a particular stock, the engine will correct the bias to mitigate it.

[1094] Step 10:

[1095] The server uses the corrected data to generate rational investment strategies using AI models, which are customized based on market data and user sentiment data.

[1096] Step 11:

[1097] The server transmits the generated investment strategy to the terminal, which analyzes the received investment strategy and displays it in a format that is easy for the user to understand.

[1098] Step 12:

[1099] The terminal displays specific investment proposals on the screen, and the user can review the proposed investment strategies and decide whether to accept them.

[1100] Step 13:

[1101] The device collects user feedback and sends information about whether the suggestion was accepted to the server, which uses this feedback as learning data to improve the accuracy of future suggestions.

[1102] This concrete step allows the system to eliminate users' emotional biases and provide more rational and effective investment strategies. Furthermore, the introduction of an emotion engine allows users' emotional state to be corrected in real time and reflected immediately in investments, enabling more appropriate investment decisions.

[1103] Example 2

[1104] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1105] Users' emotional biases in investment decisions often hinder rational decision-making and ultimately reduce investment performance. There is no technology that can identify in real time how such emotional biases affect users' investment decisions and correct them so that they do not affect their investment strategies. Therefore, this invention proposes a system that provides rational investment strategies by detecting and correcting users' emotional biases.

[1106] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1107] In this invention, the server includes means for collecting biometric data of users, means for encrypting and transmitting the collected biometric data in real time, and means for decrypting and analyzing the received data, thereby making it possible to provide rational investment strategies by detecting and correcting the emotional bias of users.

[1108] "User's biometric data" refers to physiological data acquired from the user, and specifically includes electroencephalogram data, facial expression data, and voice data.

[1109] "Means for real-time encryption and transmission" refers to a mechanism for instantly encrypting collected biometric data and securely transmitting it to a server via a network.

[1110] "Means for decrypting and analyzing data" refers to a method for decrypting encrypted data sent to the server and then analyzing the content of the data using machine learning algorithms or analytical tools.

[1111] "Means for identifying emotional state" refers to algorithms or techniques for identifying a user's emotional state (e.g., excited, stressed, relaxed, etc.) from the analyzed biometric data.

[1112] "Means for detecting and correcting emotional bias" refers to a technology that detects emotional bias that influences a user's decision-making based on an identified emotional state and makes adjustments to mitigate or eliminate its influence.

[1113] The "means for generating rational investment strategies" refers to a method that uses artificial intelligence and algorithms to suggest optimal investment actions based on corrected sentiment data and market data.

[1114] The "means for notifying the generated investment strategy" is a system or interface for directly communicating the investment strategy generated by the server to the user.

[1115] This invention is a system that collects and analyzes a user's biometric data to identify the user's emotional state and proposes an investment strategy that corrects for emotional bias. The system includes an emotion engine that recognizes the user's emotions.

[1116] System configuration

[1117] The system consists of a user, a terminal, a server, and an emotion engine. The user wears an EEG sensor and browses financial information and news articles. The terminal collects the user's EEG and facial expression data and sends it to the server. The server analyzes the received data, generates a rational investment strategy that eliminates the user's emotional bias, and notifies the user again via the terminal. The emotion engine analyzes the user's biometric data to recognize their emotional state and correct it as necessary.

[1118] Hardware and software used

[1119] Brainwave sensor: A device for collecting the user's brainwave data, obtaining data in real time.

[1120] Device: The user logs in and receives data from the EEG sensor. The device has a built-in camera and microphone, and also collects facial expression and voice data.

[1121] Server: A system for processing and analyzing data. Frameworks such as TensorFlow are used to execute machine learning algorithms.

[1122] Emotion engine: Software with built-in algorithms to recognize and correct emotional states.

[1123] Data collection and transmission

[1124] Data collection begins when the user wears the EEG sensor and logs in to the device. The device collects the user's EEG data, facial expression data, and voice data in real time, encrypts this data using the AES-256 encryption algorithm, and then transmits the data to the server using the HTTPs protocol.

[1125] Data analysis and the emotional engine

[1126] The server receives the transmitted data packets and decrypts them using the AES-256 algorithm. The decrypted data is then analyzed using machine learning algorithms such as TensorFlow to identify the user's emotional state—for example, "excited," "stressed," or "relaxed."

[1127] The emotion engine integrates and analyzes the received brainwave, facial expression, and voice data, and uses machine learning algorithms such as neural networks to detect the user's emotional bias and correct its influence.

[1128] Investment strategy generation and notification

[1129] Based on the corrected data, the server generates an investment strategy using a deep learning model, random forest, or other methods. This investment strategy is customized for each user. The generated investment strategy is encrypted and sent back to the terminal. The terminal decrypts the received data and displays the proposed investment strategy to the user in an easy-to-understand format. The user can review the proposed investment strategy and decide whether to accept it.

[1130] Specific examples

[1131] The following are specific usage scenarios for this system.

[1132] The user logs in to the device and wears an EEG sensor. While browsing stock information, the device collects the user's EEG data, facial expression data, and voice data.

[1133] The data collected by the device is encrypted and sent to the server.

[1134] The server receives, decompresses, and decrypts the data, and the emotion engine analyzes the received data to determine the user's emotional state.

[1135] The emotion engine detects and corrects the user's emotional bias.

[1136] The server runs an AI model based on the corrected data to generate an optimal investment strategy.

[1137] The server sends the generated investment strategy to the terminal, which displays the proposal to the user.

[1138] The user reviews the proposal and decides whether to accept it.

[1139] The device sends the user's feedback to the server, and based on that feedback, the accuracy of future suggestions is improved.

[1140] Prompt Sentence Examples

[1141] "Please describe a system that uses a user's biometric data to suggest investment strategies that eliminate emotional bias. Please include the specific processing steps of this system, the hardware and software used, and examples."

[1142] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1143] Step 1: Data collection

[1144] Input: User wears EEG sensor and logs into device. User browses financial information and news articles.

[1145] Processing: The device collects the user's brainwave data, facial expression data, and voice data in real time, using the device's built-in camera and microphone.

[1146] Output: A biometric data packet is generated containing the collected EEG, facial expression, and audio data.

[1147] Step 2: Send data

[1148] Input: The biometric data packet collected in step 1.

[1149] Processing: The terminal encrypts the data packet using the AES-256 encryption algorithm, and then sends the encrypted data packet to the server using the HTTPs protocol.

[1150] Output: The encrypted data packet is sent to the server.

[1151] Step 3: Data Decryption and Analysis

[1152] Input: The encrypted data packet sent in step 2.

[1153] Processing: The server decrypts the data using an AES-256 encryption algorithm. It then uses a machine learning framework (e.g., TensorFlow) to identify the user's emotional state from EEG, facial expression, and voice data. At each step, the data is classified with labels such as "excited," "stressed," and "relaxed."

[1154] Output: Identified emotional state labels and analysis results are generated.

[1155] Step 4: Emotion Engine in Action

[1156] Input: Emotional state labels and analysis results generated in step 3.

[1157] Processing: The emotion engine integrates and analyzes the user's brainwave data, facial expression data, and voice data. It uses machine learning algorithms (e.g., neural networks) to detect emotional bias and corrects it using specific algorithms.

[1158] Output: Corrected emotion data and bias correction results are generated.

[1159] Step 5: Generate an investment strategy

[1160] Input: Sentiment data and market data (e.g., news data, financial data) calibrated in step 4.

[1161] Processing: The server generates rational investment strategies using AI models such as deep learning models and random forests. The models integrate the corrected sentiment data with market data to propose optimal investment strategies for each user.

[1162] Output: A rational investment strategy is generated.

[1163] Step 6: Communicate your investment strategy

[1164] Input: The rational investment strategy generated in step 5.

[1165] Processing: The server encrypts the generated investment strategy and sends it to the terminal, which decrypts the received data and displays it to the user.

[1166] Output: A concrete investment proposal is generated that is displayed to the user.

[1167] Step 7: User feedback

[1168] Input: The investment proposal displayed on the user's terminal.

[1169] Processing: The user checks the proposal and decides whether to accept it. The device collects the user's feedback (information on whether or not they accepted it) and sends it to the server.

[1170] Output: User feedback data is sent to the server and used as training data to improve the accuracy of future suggestions.

[1171] (Application example 2)

[1172] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1173] Conventional systems may not be able to make rational decisions when a user's emotional state influences financial decisions and investment strategies. Furthermore, in the security field, systems may not be able to propose appropriate security measures because they do not accurately reflect the user's emotional state, such as stress or anxiety. Therefore, the present invention aims to provide a system that analyzes a user's biometric data and corrects emotional biases to propose rational measures to the user.

[1174] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1175] In this invention, the server includes means for collecting biometric data of the user, means for analyzing the collected biometric data to identify the user's emotional state, and means for correcting the user's emotional bias, thereby enabling rational countermeasures to be proposed based on the user's emotional state.

[1176] "Biometric data of the user" refers to data indicating the physiological state of the user, such as electroencephalogram data, facial expression data, and voice data obtained from the user.

[1177] The "means of analyzing and identifying the user's emotional state" refers to a method of identifying the user's emotional state (stress, excitement, relaxation, etc.) based on collected biometric data using machine learning and statistical methods.

[1178] "Market data" refers to data that shows trends in financial markets, and includes news data and financial data.

[1179] The "means for correcting emotional bias" is a processing method for eliminating or adjusting the user's emotional bias based on the identified emotional state, and assisting the user in making more rational decisions.

[1180] The "generated measures" are specific action suggestions, such as investment strategies and security countermeasures, provided to users based on data after correcting for emotional bias.

[1181] "Means for notifying the user" refers to a method for communicating the generated countermeasures to the user and presenting them in an easy-to-understand manner, and includes a notification module or interface.

[1182] This invention is a system that collects and analyzes a user's biometric data, corrects emotional bias, and proposes rational countermeasures. The system mainly consists of a user, a terminal, a server, and an emotion engine. The following describes the details of each component and its operation.

[1183] Hardware and Software

[1184] 1. Hardware:

[1185] Smartphone

[1186] Brainwave sensor

[1187] Facial Recognition Camera

[1188] Voice recognition microphone

[1189] 2. Software:

[1190] Dedicated application

[1191] Emotion recognition algorithms (e.g., machine learning models using TensorFlow or PyTorch)

[1192] Data Encryption Module

[1193] Server analysis system

[1194] Data collection and analysis

[1195] Users collect biometric data using devices such as brainwave sensors, facial recognition cameras, and voice recognition microphones. A smartphone application collects the data in real time, encrypts it, and sends it to a server. The server analyzes the biometric data to identify the user's emotional state (e.g., stress, excitement, relaxation).

[1196] Correcting emotional bias

[1197] The emotion engine in the server executes an algorithm to correct the user's emotional bias based on the analyzed emotional state, thereby eliminating or adjusting the user's emotional bias and enabling rational decision-making.

[1198] Countermeasure generation and notification

[1199] The server then runs the AI ​​model using the corrected data to generate rational countermeasures (e.g., investment strategies or security measures). The generated countermeasures are then notified to the user via a smartphone app. The user can then review the received countermeasures and implement them as necessary.

[1200] Specific examples

[1201] For example, when conducting online banking, a user can wear an EEG sensor and a facial recognition camera and use a smartphone app. The app detects the user's emotional state, such as stress or anxiety, in real time and sends it to a server. The server analyzes the data and, if it detects that the user is feeling stressed, suggests the application of two-factor authentication. In this way, the system helps users to conduct online banking safely.

[1202] Prompt Sentence Examples

[1203] "Please give a specific example of an application that collects a user's brainwave data, facial expression data, and voice data to perform real-time emotion analysis in a security service."

[1204] This enables the system to propose rational countermeasures based on the user's emotional state, enabling the provision of safer and more effective services.

[1205] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1206] Step 1:

[1207] Data collection

[1208] Input: Real-time biometric data from the user's brainwave sensor, facial expression recognition camera, and voice recognition microphone

[1209] Specific operation: The user launches the smartphone app and wears the EEG sensor, facial recognition camera, and voice recognition microphone. The device collects biometric data from these devices in real time, resulting in EEG data, facial expression data, and voice data.

[1210] Output: Collected biometric data

[1211] Step 2:

[1212] Data Encryption and Transmission

[1213] Input: Collected biometric data

[1214] Specific operation: The device encrypts the brainwave data, facial expression data, and voice data collected. Data encryption is performed using AES encryption technology and SSL / TLS protocols. The encrypted data packets are then sent to the server.

[1215] Output: Encrypted data packet

[1216] Step 3:

[1217] Decompressing and Decrypting Data

[1218] Input: Encrypted data packet

[1219] Specific operation: The server receives the transmitted data packet and decompresses and decrypts it.

[1220] Output: Decompressed and decrypted biometric data

[1221] Step 4:

[1222] Data analysis

[1223] Input: Decompressed and decrypted biometric data

[1224] What it does: The server analyzes the biometric data, applies machine learning algorithms (e.g., using TensorFlow or PyTorch) to identify the user's emotional state (e.g., stressed, excited, relaxed), and labels each data point.

[1225] Output: Data labeled with emotional states

[1226] Step 5:

[1227] Correcting emotional bias

[1228] Input: Data labeled with emotional states

[1229] Specific operation: The server's emotion engine executes an algorithm to correct the user's emotional bias based on the labeled emotion data, thereby adjusting the user's emotional bias and supporting rational decision-making.

[1230] Output: Corrected data

[1231] Step 6:

[1232] Countermeasure generation

[1233] Input: Corrected data

[1234] Specific operation: The server runs the AI ​​model based on the corrected data, generating rational countermeasures (e.g., investment strategies or security measures).

[1235] Output: Generated rational measures

[1236] Step 7:

[1237] Countermeasure notification

[1238] Input: Generated rational measures

[1239] Specific operation: The server sends the generated countermeasures to the terminal. The terminal notifies the user of the received countermeasures and displays them in an easy-to-understand format.

[1240] Output: Notify the user of the countermeasure

[1241] 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.

[1242] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1243] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1244] [Fourth embodiment]

[1245] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1246] 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.

[1247] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1248] 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.

[1249] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1250] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1251] 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.

[1252] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1253] 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.

[1254] 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 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.

[1255] 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.

[1256] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1257] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1258] This invention is a system that collects and analyzes biometric data from users to propose rational investment strategies. The program and processing of this system will be described in detail below.

[1259] System configuration

[1260] This system consists of a user, a terminal, and a server. The user wears an EEG sensor and browses financial information and news articles. The terminal collects the user's EEG data and facial expression data and sends it to the server. The server analyzes the received data, generates a rational investment strategy that eliminates the user's emotional bias, and notifies the user again via the terminal.

[1261] Program processing

[1262] The program processing in this system will be explained below.

[1263] 1. Data Collection:

[1264] The user wears an EEG sensor and logs in to the terminal.

[1265] The device collects the user's brain wave data, facial expression data, and voice data in real time.

[1266] The device also captures the news and financial data the user is reading.

[1267] 2. Data transmission:

[1268] The terminal encrypts the collected brain wave data, facial expression data, and voice data and sends it to a server.

[1269] The terminal also transmits market data (news data and financial data) to the server.

[1270] 3. Data Analysis:

[1271] The server analyzes the received brainwave data to identify the user's emotional state (e.g., excitement, stress, relaxation).

[1272] The server also analyzes facial expression and voice data to provide a detailed assessment of the user's emotional response.

[1273] The server simultaneously analyzes market data to assess market trends and the performance of specific assets.

[1274] 4. Eliminate bias:

[1275] The server identifies the user's emotional bias based on the analysis results and corrects the impact of this bias on investment decisions.

[1276] The server generates a rational investment strategy using the corrected data.

[1277] 5. Investment strategy generation and proposal:

[1278] The server uses multiple AI models to generate optimal investment strategies from the collected and analyzed data.

[1279] The server transmits the generated investment strategy to the terminal.

[1280] The terminal notifies the user of the received investment strategy and displays specific investment proposals on the screen.

[1281] Specific examples

[1282] The following are specific usage scenarios.

[1283] User logs in to a device and wears an EEG sensor. User begins reading a news article about stock X.

[1284] The device collects the user's brain wave data, facial expression data, and voice data in real time, and also captures the content of the article.

[1285] The terminal encrypts the collected data and sends it to the server.

[1286] The server analyzes the received data and determines that the user is excited about stock X. At the same time, it analyzes the impact the article had on the user.

[1287] The server considers the user's excitement as an emotional bias and performs a correction process to eliminate this bias. Based on the corrected data, a rational investment strategy is generated.

[1288] The server generates a strategy recommending investment in stock Y, for example, and transmits it to the terminal.

[1289] The terminal displays the investment proposal on the terminal screen for the user, who then reviews the proposal and decides whether to accept it or not.

[1290] The device sends the user's feedback to the server and uses it as learning data for future suggestions.

[1291] The above is a specific embodiment for carrying out the present invention. The present system improves the accuracy and efficiency of investment decisions by providing a rational investment strategy that eliminates the emotional bias of users.

[1292] The processing flow will be explained below.

[1293] Step 1:

[1294] The user wears the EEG sensor and logs in to the device. The device authenticates the login information and checks whether the EEG sensor is working properly.

[1295] Step 2:

[1296] The device collects the user's brainwave data in real time. While the user is browsing stock information or news articles, the brainwave sensor captures the user's brainwave data and transmits it to the device.

[1297] Step 3:

[1298] The device collects facial expression data and voice data of the user in parallel using a camera and microphone, thereby capturing the user's emotional state from multiple angles.

[1299] Step 4:

[1300] The device encrypts the collected brainwave, facial expression, and voice data and compiles them into packets for data transmission. At the same time, it also collects market data (news data and financial data).

[1301] Step 5:

[1302] The terminal transmits the encrypted data packets to the server, which receives the transmitted data packets and performs decompression and decryption.

[1303] Step 6:

[1304] The server applies machine learning algorithms to analyze the received EEG data, identifying the user's emotional state (e.g., excited, stressed, relaxed) and labeling each data point.

[1305] Step 7:

[1306] The server uses facial and voice data to evaluate the user's emotional responses in detail, and combines this information with the analysis of the EEG data.

[1307] Step 8:

[1308] The server simultaneously analyzes market data (news data and financial data) to assess market trends, the performance of specific assets and key market drivers.

[1309] Step 9:

[1310] The server identifies the user's emotional bias based on the analysis results. For example, if the user is excited about a particular stock, it adjusts the evaluation of that stock.

[1311] Step 10:

[1312] The server performs a correction process taking into account the identified emotional bias, thereby eliminating the influence of the emotional bias on investment decisions.

[1313] Step 11:

[1314] The server uses the corrected data to generate rational investment strategies using AI models, which are customized based on market data and user sentiment data.

[1315] Step 12:

[1316] The server transmits the generated investment strategy to the terminal, which analyzes the received investment strategy and displays it in a format that is easy for the user to understand.

[1317] Step 13:

[1318] The terminal displays specific investment proposals on the screen, and the user reviews the proposed investment strategies and decides whether to accept them.

[1319] Step 14:

[1320] The device collects user feedback and sends information about whether the suggestion was accepted to the server, which uses this feedback as learning data to improve the accuracy of future suggestions.

[1321] The above is the specific flow of the program's processing. This system eliminates the user's emotional bias and provides rational investment strategies, thereby improving the accuracy and efficiency of investment decisions.

[1322] Example 1

[1323] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1324] Conventional investment support systems have the problem of proposing investment strategies without properly correcting for users' emotional biases, which can lead to irrational investment decisions that are dependent on the emotions of individual users. Additionally, there is also the issue of low reliability of the investment strategies provided due to the insufficient diversity of collected data and the insufficient accuracy of analysis.

[1325] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1326] In this invention, the server includes a means for analyzing biometric data received from the terminal to identify the user's emotional state, a means for acquiring market data and analyzing it using a text analysis algorithm, a means for correcting the user's emotional bias using a machine learning model, and a means for generating an investment strategy using an AI model based on the corrected data, thereby making it possible to provide a rational and reliable investment strategy that eliminates the user's emotional bias.

[1327] "Biometric data" refers to data obtained from the user's body, and in the present invention mainly includes electroencephalogram data, facial expression data, and voice data.

[1328] A "terminal" is an electronic device worn by a user, such as an EEG sensor, that collects and transmits data. Examples include smartphones and tablets.

[1329] The "server" is a central device that receives, analyzes, and processes data sent from the terminals. This device is responsible for large-scale data analysis and the generation of investment strategies.

[1330] "Emotional state" refers to the user's mental and emotional state based on biometric data analysis, and primarily includes states such as excitement, stress, and relaxation.

[1331] A "text analysis algorithm" is an algorithm that uses natural language processing techniques to analyze text data, allowing useful information to be extracted from market data.

[1332] A "machine learning model" refers to an algorithm that learns from past data and predicts or classifies future data. In this invention, it is used to correct emotional bias.

[1333] "AI model" means a model that uses artificial intelligence technology to learn patterns from data and generate investment strategies, including reinforcement learning models and deep learning models.

[1334] An "investment strategy" indicates specific investment policies and advice to users that are generated based on collected and analyzed data.

[1335] "Market Data" means data related to financial markets, including news data and financial data, which is analyzed to provide users with appropriate investment strategies.

[1336] The present invention relates to a system that collects and analyzes biometric data from users to propose rational investment strategies. The system is composed of a user, a terminal, and a server.

[1337] Hardware and software used

[1338] User: The user wears an EEG sensor, a camera, and a microphone and logs in to the device. The EEG sensor acquires the user's brainwave data, and the camera and microphone collect facial expression and voice data.

[1339] Device: A device is an electronic device such as a smartphone or tablet that processes biometric data collected from the user and sends it to a server. The device is equipped with EEG analysis software, camera analysis software (e.g., OpenCV, Dlib), and acoustic analysis software.

[1340] Server: The server contains databases, text analysis algorithms (e.g., natural language processing models), machine learning models, and generative AI models (e.g., reinforcement learning models, deep learning models).

[1341] System Operation

[1342] Data collection

[1343] Users wear an EEG sensor and log in to the device. As they begin browsing news articles or financial information, the device collects real-time EEG, facial expression, and voice data. This collected data is used to analyze the user's emotional state.

[1344] Data transmission

[1345] The device encrypts the collected data using the AES encryption algorithm and transmits it to the server using the HTTPS protocol, ensuring confidentiality and integrity of the data during transmission.

[1346] Data analysis and investment strategy generation

[1347] The server stores the received data in a database. EEG data is analyzed using EEG analysis software to identify the user's emotional state. Similarly, facial expression data is analyzed using computer vision techniques (e.g., OpenCV, Dlib), and voice data is analyzed using acoustic analysis tools. Market data is also analyzed using text analysis algorithms. Based on the results of these analyses, the server uses machine learning models to correct for emotional bias and generative AI models to generate rational investment strategies.

[1348] Investment Strategy Notification

[1349] The generated investment strategy is encrypted and sent back to the terminal. The terminal notifies the user and displays specific investment proposals on the screen. The user reviews the proposals and decides whether to accept them or not. This feedback is also sent to the server and used to improve the accuracy of future proposals.

[1350] Specific examples

[1351] When a user wears an EEG sensor while browsing the daily news, the device collects biometric data in real time. The data is encrypted and sent to a server for analysis of the user's emotional state and correction of emotional bias. For example, the server may detect that the user is excited about a particular stock, correct for this bias, and generate a rational investment strategy. The generated strategy is then proposed to the user via the device, and the user can confirm the proposal.

[1352] Prompt Sentence Examples

[1353] The following prompt statements can be used:

[1354] "After analyzing the user's brainwave data and excitement level, how can we eliminate bias and suggest appropriate investment strategies?"

[1355] "Describe how you would design a system that analyzes users' emotional reactions while browsing financial news and generates rational investment strategies."

[1356] The above is a specific embodiment for carrying out the present invention. The present system improves the accuracy and efficiency of investment decisions by providing a rational investment strategy that eliminates the emotional bias of users.

[1357] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1358] Step 1: Data collection

[1359] The user wears an EEG sensor and logs in to the device. At this time, the device launches a dedicated application that collects the user's brainwave data, facial expression data, and voice data in real time. EEG data is obtained from the EEG sensor, facial expression data is collected using a camera, and voice data is collected through a microphone. At the same time, the device also uses screen capture software to capture the news articles and financial information the user is viewing. The input is the user's biometric data and browsing data, and the output is the collected biometric data and browsing data.

[1360] Step 2: Send data

[1361] The device encrypts the EEG data, facial expression data, voice data, news articles, and financial information collected in step 1 using the AES encryption algorithm. The encrypted data is sent to the server using the HTTPS protocol. The input is the collected biometric data and browsing data, and the output is the encrypted biometric data and browsing data.

[1362] Step 3: Data analysis

[1363] The server receives the encrypted data received in step 2 and stores it in a database. The received EEG data is then analyzed using EEG analysis software to identify the user's emotional state (excited, stressed, relaxed, etc.). For facial expression data, computer vision techniques (e.g., OpenCV, Dlib) are used to detect facial feature points and identify the user's emotions from their facial expressions. For voice data, acoustic analysis tools are used to analyze the emotional tone of the user's voice. For market data, text analysis algorithms (e.g., natural language processing models) are used to analyze news and financial information and evaluate market trends. The input is encrypted biometric data and browsing data, and the output is the analysis of the user's emotional state and market trends.

[1364] Step 4: Eliminate bias

[1365] The server uses a machine learning model to correct the emotional bias based on the user's emotional state and market data identified in step 3. This correction process reduces the impact of the user's emotional state on investment decisions and converts them into rational data. The input is the analyzed emotional state and market data, and the output is the corrected data.

[1366] Step 5: Generate an investment strategy

[1367] The server uses the corrected data from step 4 to generate an optimal investment strategy using a generative AI model (e.g., a reinforcement learning model or a deep learning model). This investment strategy eliminates the user's emotional bias and presents the most rational and advantageous investment policy. The input is the corrected data, and the output is the generated investment strategy.

[1368] Step 6: Investment strategy communication and feedback

[1369] The server encrypts the generated investment strategy and sends it back to the terminal. The terminal notifies the user of the received investment strategy and displays a specific investment proposal on the terminal screen. The user reviews the proposal and decides whether to accept the investment strategy. The user's decision is sent from the terminal to the server as feedback, and the server uses this feedback as learning data for future proposals. The input is the generated investment strategy, and the output is the investment proposal to the user and the user's feedback.

[1370] The above is the specific processing flow of the program of this system.

[1371] (Application example 1)

[1372] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1373] In recent years, with the spread of e-commerce, users have access to a wide variety of product information and make purchasing decisions. However, users' emotional biases can influence purchasing decisions, leading to impulsive purchases and irrational choices. This has led to a growing need for systems that support users in making more rational and objective purchasing decisions. While current technology makes suggestions based on users' text information and past purchase history, there are no systems that take into account their emotional state in real time.

[1374] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1375] In this invention, the server includes means for collecting biometric data of a user, means for analyzing the collected biometric data to identify the user's emotional state, means for acquiring and analyzing market data, means for correcting the user's emotional bias, means for generating an investment strategy based on the corrected data, means for notifying the user of the generated investment strategy, means for generating rational purchase proposals based on the collected biometric data of the user and market data to support purchasing decisions in e-commerce, and means for notifying the user of the generated purchase proposals, thereby eliminating the user's emotional bias and supporting rational and objective purchasing decisions.

[1376] "User's biometric data" refers to data obtained from the user's body, and includes brain wave data, facial expression data, voice data, and the like.

[1377] "Electroencephalogram data" refers to measurements of a user's brain's electrical activity that are used to identify the user's emotional state and mental responses.

[1378] "Market Data" refers to data that includes information about a particular market or product, such as news data, financial data, product information, and reviews.

[1379] "Emotional bias" refers to the influence that a user's emotional state has on purchasing and investment decisions, and correcting this influence supports rational decision-making.

[1380] "Rational purchase suggestions" refer to suggestions that recommend the purchase of products or services that are most suitable for the user, while eliminating the user's emotional bias.

[1381] "Means for notifying" refers to a method or device for communicating the investment strategy or purchase proposal generated by the system to the user, including a smartphone or computer display, audio output, etc.

[1382] "Electronic commerce" refers to a form of transaction in which goods are purchased or services are applied for via the Internet, and is also known as online shopping.

[1383] "Investment Strategy" refers to an investment policy or plan recommended to a user based on market data and biometric data, which is intended to be rational and efficient.

[1384] This invention is a system that collects and analyzes users' biometric data to make rational purchasing suggestions. Specifically, it takes into account the user's emotional state when shopping online, preventing impulsive purchases and supporting rational purchasing decisions.

[1385] System configuration

[1386] This system consists of a user, a device, and a server. The user uses a device equipped with an EEG sensor and a camera. The device collects the user's EEG data and facial expression data and transmits this data to the server. The server analyzes the received data, eliminates the user's emotional bias, and generates rational purchase suggestions, which are then sent to the user via the device.

[1387] Program processing

[1388] 1. Data Collection:

[1389] Users wear an EEG sensor and log in to the device, which collects their EEG, facial expression, and voice data in real time, including EEG acquisition using the BrainFlow library and facial expression analysis using the DeepFace library.

[1390] 2. Data transmission:

[1391] The device encrypts the collected data and sends it to a cloud server.

[1392] 3. Data Analysis:

[1393] The server analyzes the EEG data and facial expression data to identify the user's emotional state, and also analyzes market data such as product information and reviews to evaluate the product's quality and reputation.

[1394] 4. Eliminate bias:

[1395] The server identifies the user's emotional bias based on the analysis results and corrects the data to eliminate this bias.

[1396] 5. Purchase offer generation and notification:

[1397] The server generates rational purchase suggestions based on the corrected data. The suggestions are then sent back to the device and notified to the user. The suggestions include specific purchase reasons and explanations to eliminate emotional bias.

[1398] Specific examples

[1399] For example, if a user is about to purchase an expensive smartphone, and news articles and reviews are making them excited, the system will interpret the user's excitement as an emotional bias and compensate for it. As a result, it will make suggestions to help the user calmly consider whether they really need the product or whether there are more suitable options. In this way, it will prevent impulsive purchases and support rational purchasing decisions.

[1400] Prompt Sentence Examples

[1401] "You are currently in an excited state. We recommend that you re-read the reviews for this product and think things through."

[1402] "Based on your emotional state, this product may be worth reconsidering. Consider other options."

[1403] This invention makes it possible to eliminate emotional biases of users in e-commerce transactions and support rational purchasing decisions.

[1404] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1405] Step 1:

[1406] The user wears an EEG sensor and logs in to the device.

[1407] Input: None

[1408] Output: Login status and EEG sensor preparation

[1409] Specific operation: The user wears the EEG sensor on their head and logs in to an application on the device. Once logged in, the EEG sensor prepares to collect the user's EEG data in real time.

[1410] Step 2:

[1411] The device collects the user's brain wave data, facial expression data, and voice data in real time.

[1412] Input: User's brainwaves, facial image, and voice

[1413] Output: Complete set of biometric data (brain waves, facial expressions, voice)

[1414] Specific operation: EEG data is collected using the BrainFlow library, facial images are analyzed using the DeepFace library, facial expression data is obtained, and audio data is collected through the microphone.

[1415] Step 3:

[1416] The brain wave data, facial expression data, and voice data collected by the device are encrypted and sent to a cloud server.

[1417] Input: Complete set of biometric data (brain waves, facial expressions, voice)

[1418] Output: Encrypted data packet

[1419] Specific operation: The device uses an appropriate encryption algorithm to encrypt the collected data, and then sends the encrypted data packet to the cloud server as an HTTP POST request.

[1420] Step 4:

[1421] The server analyzes the received brain wave data and facial expression data to identify the user's emotional state.

[1422] Input: Encrypted data packet

[1423] Output: Emotional state (excited, stressed, relaxed, etc.)

[1424] What it does: The server decrypts the encrypted data and analyzes the EEG and facial expression data, using machine learning models to identify emotional states (e.g., excited, stressed, relaxed).

[1425] Step 5:

[1426] The server collects and analyzes market data and product review data.

[1427] Input: Market data (product information, reviews, etc.)

[1428] Output: Product quality rating and reputation

[1429] How it works: The server retrieves the latest market data and product reviews through external APIs and databases, and uses text analysis and natural language processing (NLP) techniques to evaluate the quality and reputation of products.

[1430] Step 6:

[1431] The server processes the data to identify the user's emotional bias and correct for this bias.

[1432] Input: Emotional state, product quality rating

[1433] Output: Corrected data

[1434] What it does: The server identifies the user's emotional state as an emotional bias and applies a correction algorithm to remove this bias, resulting in rational data.

[1435] Step 7:

[1436] The server generates a rational purchase proposal based on the corrected data and transmits it to the terminal.

[1437] Input: Corrected data

[1438] Output: Purchase proposal (including proposal details and reasons)

[1439] What it does: The server uses the generative AI model to generate rational purchase recommendations from the corrected data, including specific purchase reasons and explanations to eliminate emotional bias. The generated recommendations are sent to the device as an HTTP response.

[1440] Step 8:

[1441] The terminal notifies the user of the purchase offer and the reason for it.

[1442] Input: Purchase Offer

[1443] Output: Display and notification of proposal content

[1444] Specific operation: The device notifies the user of the received purchase suggestion visually or audibly. The suggestion and its reasons are displayed on the screen, and the user confirms it. At this time, a prompt message such as "You are currently in an excited state. We recommend that you review the reviews of this product and think about it calmly" is displayed.

[1445] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1446] This invention is a system that collects and analyzes a user's biometric data to identify the user's emotional state and proposes an investment strategy that corrects for emotional bias. The system includes an emotion engine that recognizes the user's emotions.

[1447] System configuration

[1448] The system consists of a user, a terminal, a server, and an emotion engine. The user wears an EEG sensor and browses financial information and news articles. The terminal collects the user's EEG and facial expression data and sends it to the server. The server analyzes the received data, generates a rational investment strategy that eliminates the user's emotional bias, and notifies the user again via the terminal. The emotion engine analyzes the user's biometric data to recognize their emotional state and correct it as necessary.

[1449] Program processing

[1450] The processing of the program in this system will be explained in detail below.

[1451] 1. Data Collection:

[1452] The user wears an EEG sensor and logs in to the device.

[1453] The device collects the user's brainwave data, facial expression data, and voice data in real time while the user is browsing stock information or news articles.

[1454] 2. Data transmission:

[1455] The device collects brainwave, facial expression, and voice data, encrypts it, and compiles it into data packets. At the same time, it also collects market data (news data and financial data).

[1456] The terminal transmits the encrypted data packets to the server, which receives the transmitted data packets and performs decompression and decryption.

[1457] 3. Data Analysis:

[1458] The server applies machine learning algorithms to analyze the received EEG data, identifying the user's emotional state (e.g., excited, stressed, relaxed) and labeling each data point.

[1459] The server also analyzes facial and voice data to assess the user's emotional response in detail, and this information is then integrated with the analysis of the EEG data.

[1460] 4. How the Emotion Engine Works:

[1461] The emotion engine integrates and analyzes the user's brain wave data, facial expression data, and voice data to recognize the user's emotional state in real time.

[1462] The emotion engine executes algorithms to detect and adjust or correct for the user's emotional biases.

[1463] 5. Eliminate bias:

[1464] The server and the emotion engine work together to identify the user's emotional bias based on the analysis results. For example, if the user is excited about a particular stock, the server recognizes this bias and adjusts its influence.

[1465] The server takes the identified emotional bias into consideration and performs correction processing, thereby eliminating the influence of emotional bias on investment decisions.

[1466] 6. Investment strategy generation and proposal:

[1467] The server runs the AI ​​model using the corrected data to generate a rational investment strategy, which is customized based on market data and user sentiment data.

[1468] The server transmits the generated investment strategy to the terminal, which analyzes the received investment strategy and displays it in a format that is easy for the user to understand.

[1469] 7. Notification of Investment Proposal:

[1470] The terminal displays specific investment proposals on the screen, and the user reviews the proposed investment strategies and decides whether to accept them.

[1471] The device collects user feedback and sends information about whether the suggestion was accepted to the server, which uses this feedback as learning data to improve the accuracy of future suggestions.

[1472] Specific examples

[1473] The following are specific usage scenarios.

[1474] The user logs in to the device and wears an EEG sensor. While browsing stock information, the device collects the user's EEG data, facial expression data, and voice data.

[1475] The data collected by the device is encrypted and sent to the server.

[1476] The server receives, decompresses, and decrypts the data, and the emotion engine analyzes the received data to determine the user's emotional state.

[1477] The emotion engine detects and corrects the user's emotional bias.

[1478] The server runs an AI model based on the corrected data to generate an optimal investment strategy.

[1479] The server sends the generated investment strategy to the terminal, which displays the proposal to the user.

[1480] The user reviews the proposal and decides whether to accept it.

[1481] The device sends the user's feedback to the server, and based on that feedback, the accuracy of future suggestions is improved.

[1482] This system improves the accuracy and efficiency of investment decisions by eliminating users' emotional biases and providing rational investment strategies. In addition, the introduction of an emotion engine corrects users' emotional state in real time, enabling more appropriate investment decisions.

[1483] The processing flow will be explained below.

[1484] Step 1:

[1485] The user wears the EEG sensor and logs in to the device. The device authenticates the user's login information and checks whether the EEG sensor is working properly.

[1486] Step 2:

[1487] The device collects the user's brainwave data in real time. While the user is browsing stock information or news articles, the brainwave sensor captures the data and transmits it to the device.

[1488] Step 3:

[1489] The device collects facial expression data and voice data of the user in parallel using a camera and microphone, thereby capturing the user's emotional state from multiple angles.

[1490] Step 4:

[1491] The device encrypts the collected brainwave, facial expression, and voice data and compiles them into packets for data transmission. At the same time, it also collects market data (news data and financial data).

[1492] Step 5:

[1493] The terminal transmits the encrypted data packets to the server, which receives the transmitted data packets and performs decompression and decryption.

[1494] Step 6:

[1495] The server applies machine learning algorithms to analyze the received EEG data, identifying the user's emotional state (e.g., excited, stressed, relaxed) and labeling each data point.

[1496] Step 7:

[1497] The server uses facial and voice data to evaluate the user's emotional responses in detail, and combines this information with the analysis of the EEG data.

[1498] Step 8:

[1499] The emotion engine integrates and analyzes the user's brainwave data, facial expression data, and voice data to recognize the user's emotional state in real time, thereby revealing the user's specific emotions.

[1500] Step 9:

[1501] The emotion engine detects users' emotional biases and executes algorithms to correct them in real time. For example, if a user is overly excited about a particular stock, the engine will correct the bias to mitigate it.

[1502] Step 10:

[1503] The server uses the corrected data to generate rational investment strategies using AI models, which are customized based on market data and user sentiment data.

[1504] Step 11:

[1505] The server transmits the generated investment strategy to the terminal, which analyzes the received investment strategy and displays it in a format that is easy for the user to understand.

[1506] Step 12:

[1507] The terminal displays specific investment proposals on the screen, and the user can review the proposed investment strategies and decide whether to accept them.

[1508] Step 13:

[1509] The device collects user feedback and sends information about whether the suggestion was accepted to the server, which uses this feedback as learning data to improve the accuracy of future suggestions.

[1510] This concrete step allows the system to eliminate users' emotional biases and provide more rational and effective investment strategies. Furthermore, the introduction of an emotion engine allows users' emotional state to be corrected in real time and reflected immediately in investments, enabling more appropriate investment decisions.

[1511] Example 2

[1512] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1513] Users' emotional biases in investment decisions often hinder rational decision-making and ultimately reduce investment performance. There is no technology that can identify in real time how such emotional biases affect users' investment decisions and correct them so that they do not affect their investment strategies. Therefore, this invention proposes a system that provides rational investment strategies by detecting and correcting users' emotional biases.

[1514] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1515] In this invention, the server includes means for collecting biometric data of users, means for encrypting and transmitting the collected biometric data in real time, and means for decrypting and analyzing the received data, thereby making it possible to provide rational investment strategies by detecting and correcting the emotional bias of users.

[1516] "User's biometric data" refers to physiological data acquired from the user, and specifically includes electroencephalogram data, facial expression data, and voice data.

[1517] "Means for real-time encryption and transmission" refers to a mechanism for instantly encrypting collected biometric data and securely transmitting it to a server via a network.

[1518] "Means for decrypting and analyzing data" refers to a method for decrypting encrypted data sent to the server and then analyzing the content of the data using machine learning algorithms or analytical tools.

[1519] "Means for identifying emotional state" refers to algorithms or techniques for identifying a user's emotional state (e.g., excited, stressed, relaxed, etc.) from the analyzed biometric data.

[1520] "Means for detecting and correcting emotional bias" refers to a technology that detects emotional bias that influences a user's decision-making based on an identified emotional state and makes adjustments to mitigate or eliminate its influence.

[1521] The "means for generating rational investment strategies" refers to a method that uses artificial intelligence and algorithms to suggest optimal investment actions based on corrected sentiment data and market data.

[1522] The "means for notifying the generated investment strategy" is a system or interface for directly communicating the investment strategy generated by the server to the user.

[1523] This invention is a system that collects and analyzes a user's biometric data to identify the user's emotional state and proposes an investment strategy that corrects for emotional bias. The system includes an emotion engine that recognizes the user's emotions.

[1524] System configuration

[1525] The system consists of a user, a terminal, a server, and an emotion engine. The user wears an EEG sensor and browses financial information and news articles. The terminal collects the user's EEG and facial expression data and sends it to the server. The server analyzes the received data, generates a rational investment strategy that eliminates the user's emotional bias, and notifies the user again via the terminal. The emotion engine analyzes the user's biometric data to recognize their emotional state and correct it as necessary.

[1526] Hardware and software used

[1527] Brainwave sensor: A device for collecting the user's brainwave data, obtaining data in real time.

[1528] Device: The user logs in and receives data from the EEG sensor. The device has a built-in camera and microphone, and also collects facial expression and voice data.

[1529] Server: A system for processing and analyzing data. Frameworks such as TensorFlow are used to execute machine learning algorithms.

[1530] Emotion engine: Software with built-in algorithms to recognize and correct emotional states.

[1531] Data collection and transmission

[1532] Data collection begins when the user wears the EEG sensor and logs in to the device. The device collects the user's EEG data, facial expression data, and voice data in real time, encrypts this data using the AES-256 encryption algorithm, and then transmits the data to the server using the HTTPs protocol.

[1533] Data analysis and the emotional engine

[1534] The server receives the transmitted data packets and decrypts them using the AES-256 algorithm. The decrypted data is then analyzed using machine learning algorithms such as TensorFlow to identify the user's emotional state—for example, "excited," "stressed," or "relaxed."

[1535] The emotion engine integrates and analyzes the received brainwave, facial expression, and voice data, and uses machine learning algorithms such as neural networks to detect the user's emotional bias and correct its influence.

[1536] Investment strategy generation and notification

[1537] Based on the corrected data, the server generates an investment strategy using a deep learning model, random forest, or other methods. This investment strategy is customized for each user. The generated investment strategy is encrypted and sent back to the terminal. The terminal decrypts the received data and displays the proposed investment strategy to the user in an easy-to-understand format. The user can review the proposed investment strategy and decide whether to accept it.

[1538] Specific examples

[1539] The following are specific usage scenarios for this system.

[1540] The user logs in to the device and wears an EEG sensor. While browsing stock information, the device collects the user's EEG data, facial expression data, and voice data.

[1541] The data collected by the device is encrypted and sent to the server.

[1542] The server receives, decompresses, and decrypts the data, and the emotion engine analyzes the received data to determine the user's emotional state.

[1543] The emotion engine detects and corrects the user's emotional bias.

[1544] The server runs an AI model based on the corrected data to generate an optimal investment strategy.

[1545] The server sends the generated investment strategy to the terminal, which displays the proposal to the user.

[1546] The user reviews the proposal and decides whether to accept it.

[1547] The device sends the user's feedback to the server, and based on that feedback, the accuracy of future suggestions is improved.

[1548] Prompt Sentence Examples

[1549] "Please describe a system that uses a user's biometric data to suggest investment strategies that eliminate emotional bias. Please include the specific processing steps of this system, the hardware and software used, and examples."

[1550] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1551] Step 1: Data collection

[1552] Input: User wears EEG sensor and logs into device. User browses financial information and news articles.

[1553] Processing: The device collects the user's brainwave data, facial expression data, and voice data in real time, using the device's built-in camera and microphone.

[1554] Output: A biometric data packet is generated containing the collected EEG, facial expression, and audio data.

[1555] Step 2: Send data

[1556] Input: The biometric data packet collected in step 1.

[1557] Processing: The terminal encrypts the data packet using the AES-256 encryption algorithm, and then sends the encrypted data packet to the server using the HTTPs protocol.

[1558] Output: The encrypted data packet is sent to the server.

[1559] Step 3: Data Decryption and Analysis

[1560] Input: The encrypted data packet sent in step 2.

[1561] Processing: The server decrypts the data using an AES-256 encryption algorithm. It then uses a machine learning framework (e.g., TensorFlow) to identify the user's emotional state from EEG, facial expression, and voice data. At each step, the data is classified with labels such as "excited," "stressed," and "relaxed."

[1562] Output: Identified emotional state labels and analysis results are generated.

[1563] Step 4: Emotion Engine in Action

[1564] Input: Emotional state labels and analysis results generated in step 3.

[1565] Processing: The emotion engine integrates and analyzes the user's brainwave data, facial expression data, and voice data, and uses machine learning algorithms (e.g., neural networks) to detect emotional bias and correct it using specific algorithms.

[1566] Output: Corrected emotion data and bias correction results are generated.

[1567] Step 5: Generate an investment strategy

[1568] Input: Sentiment data and market data (e.g., news data, financial data) calibrated in step 4.

[1569] Processing: The server generates rational investment strategies using AI models such as deep learning models and random forests. The models integrate the corrected sentiment data with market data to propose optimal investment strategies for each user.

[1570] Output: A rational investment strategy is generated.

[1571] Step 6: Communicate your investment strategy

[1572] Input: The rational investment strategy generated in step 5.

[1573] Processing: The server encrypts the generated investment strategy and sends it to the terminal, which decrypts the received data and displays it to the user.

[1574] Output: A concrete investment proposal is generated that is displayed to the user.

[1575] Step 7: User feedback

[1576] Input: The investment proposal displayed on the user's terminal.

[1577] Processing: The user checks the proposal and decides whether to accept it. The device collects the user's feedback (information on whether or not they accepted it) and sends it to the server.

[1578] Output: User feedback data is sent to the server and used as training data to improve the accuracy of future suggestions.

[1579] (Application example 2)

[1580] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1581] Conventional systems may not be able to make rational decisions when a user's emotional state influences financial decisions and investment strategies. Furthermore, in the security field, systems may not be able to propose appropriate security measures because they do not accurately reflect the user's emotional state, such as stress or anxiety. Therefore, the present invention aims to provide a system that analyzes a user's biometric data and corrects emotional biases to propose rational measures to the user.

[1582] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1583] In this invention, the server includes means for collecting biometric data of the user, means for analyzing the collected biometric data to identify the user's emotional state, and means for correcting the user's emotional bias, thereby enabling rational countermeasures to be proposed based on the user's emotional state.

[1584] "Biometric data of the user" refers to data indicating the physiological state of the user, such as electroencephalogram data, facial expression data, and voice data obtained from the user.

[1585] The "means of analyzing and identifying the user's emotional state" refers to a method of identifying the user's emotional state (stress, excitement, relaxation, etc.) based on collected biometric data using machine learning and statistical methods.

[1586] "Market data" refers to data that shows trends in financial markets, and includes news data and financial data.

[1587] The "means for correcting emotional bias" is a processing method for eliminating or adjusting the user's emotional bias based on the identified emotional state, and assisting the user in making more rational decisions.

[1588] The "generated measures" are specific action suggestions, such as investment strategies and security countermeasures, provided to users based on data after correcting for emotional bias.

[1589] "Means for notifying the user" refers to a method for communicating the generated countermeasures to the user and presenting them in an easy-to-understand manner, and includes a notification module or interface.

[1590] This invention is a system that collects and analyzes a user's biometric data, corrects emotional bias, and proposes rational countermeasures. The system mainly consists of a user, a terminal, a server, and an emotion engine. The following describes the details of each component and its operation.

[1591] Hardware and Software

[1592] 1. Hardware:

[1593] Smartphone

[1594] Brainwave sensor

[1595] Facial Recognition Camera

[1596] Voice recognition microphone

[1597] 2. Software:

[1598] Dedicated application

[1599] Emotion recognition algorithms (e.g., machine learning models using TensorFlow or PyTorch)

[1600] Data Encryption Module

[1601] Server analysis system

[1602] Data collection and analysis

[1603] Users collect biometric data using devices such as brainwave sensors, facial recognition cameras, and voice recognition microphones. A smartphone application collects the data in real time, encrypts it, and sends it to a server. The server analyzes the biometric data to identify the user's emotional state (e.g., stress, excitement, relaxation).

[1604] Correcting emotional bias

[1605] The emotion engine in the server executes an algorithm to correct the user's emotional bias based on the analyzed emotional state, thereby eliminating or adjusting the user's emotional bias and enabling rational decision-making.

[1606] Countermeasure generation and notification

[1607] The server then runs the AI ​​model using the corrected data to generate rational countermeasures (e.g., investment strategies or security measures). The generated countermeasures are then notified to the user via a smartphone app. The user can then review the received countermeasures and implement them as necessary.

[1608] Specific examples

[1609] For example, when conducting online banking, a user can wear an EEG sensor and a facial recognition camera and use a smartphone app. The app detects the user's emotional state, such as stress or anxiety, in real time and sends it to a server. The server analyzes the data and, if it detects that the user is feeling stressed, suggests the application of two-factor authentication. In this way, the system helps users to conduct online banking safely.

[1610] Prompt Sentence Examples

[1611] "Please give a specific example of an application that collects a user's brainwave data, facial expression data, and voice data to perform real-time emotion analysis in a security service."

[1612] This enables the system to propose rational countermeasures based on the user's emotional state, enabling the provision of safer and more effective services.

[1613] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1614] Step 1:

[1615] Data collection

[1616] Input: Real-time biometric data from the user's brainwave sensor, facial expression recognition camera, and voice recognition microphone

[1617] Specific operation: The user launches the smartphone app and wears the EEG sensor, facial recognition camera, and voice recognition microphone. The device collects biometric data from these devices in real time, resulting in EEG data, facial expression data, and voice data.

[1618] Output: Collected biometric data

[1619] Step 2:

[1620] Data Encryption and Transmission

[1621] Input: Collected biometric data

[1622] Specific operation: The device encrypts the brainwave data, facial expression data, and voice data collected. Data encryption is performed using AES encryption technology and SSL / TLS protocols. The encrypted data packets are then sent to the server.

[1623] Output: Encrypted data packet

[1624] Step 3:

[1625] Decompressing and Decrypting Data

[1626] Input: Encrypted data packet

[1627] Specific operation: The server receives the transmitted data packet and decompresses and decrypts it.

[1628] Output: Decompressed and decrypted biometric data

[1629] Step 4:

[1630] Data analysis

[1631] Input: Decompressed and decrypted biometric data

[1632] What it does: The server analyzes the biometric data, applies machine learning algorithms (e.g., using TensorFlow or PyTorch) to identify the user's emotional state (e.g., stressed, excited, relaxed), and labels each data point.

[1633] Output: Data labeled with emotional states

[1634] Step 5:

[1635] Correcting emotional bias

[1636] Input: Data labeled with emotional states

[1637] Specific operation: The server's emotion engine executes an algorithm to correct the user's emotional bias based on the labeled emotion data, thereby adjusting the user's emotional bias and supporting rational decision-making.

[1638] Output: Corrected data

[1639] Step 6:

[1640] Countermeasure generation

[1641] Input: Corrected data

[1642] Specific operation: The server runs the AI ​​model based on the corrected data, generating rational countermeasures (e.g., investment strategies or security measures).

[1643] Output: Generated rational measures

[1644] Step 7:

[1645] Countermeasure notification

[1646] Input: Generated rational measures

[1647] Specific operation: The server sends the generated countermeasures to the terminal. The terminal notifies the user of the received countermeasures and displays them in an easy-to-understand format.

[1648] Output: Notify the user of the countermeasure

[1649] 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.

[1650] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1651] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1652] 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.

[1653] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1654] 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.

[1655] 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).

[1656] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, 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.

[1657] 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."

[1658] 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.

[1659] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1660] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1661] 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.

[1662] 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.

[1663] 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.

[1664] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1665] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1666] 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.

[1667] 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.

[1668] 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.

[1669] 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.

[1670] The following is further disclosed regarding the above embodiment.

[1671] (Claim 1)

[1672] means for collecting biometric data of a user;

[1673] means for analyzing the collected biometric data to identify the user's emotional state;

[1674] a means of obtaining and analyzing market data;

[1675] A means for correcting a user's emotional bias;

[1676] A means for generating an investment strategy based on the corrected data;

[1677] means for notifying a user of the generated investment strategy;

[1678] A system including:

[1679] (Claim 2)

[1680] 2. The system of claim 1, wherein the biometric data of the user includes electroencephalogram data.

[1681] (Claim 3)

[1682] 10. The system of claim 1, wherein the market data includes news data and financial data.

[1683] "Example 1"

[1684] (Claim 1)

[1685] means for collecting biometric data of a user;

[1686] a means for transmitting the collected biometric data to a terminal in real time;

[1687] means for encrypting the collected biometric data and transmitting it to a server;

[1688] means for analyzing biometric data received from the terminal and identifying the emotional state of the user;

[1689] a means for acquiring and analyzing market data using text analysis algorithms;

[1690] A means to correct user emotional biases using machine learning models,

[1691] A means for generating an investment strategy using an AI model based on the corrected data;

[1692] means for transmitting the generated investment strategy to a terminal and notifying the user;

[1693] A system including:

[1694] (Claim 2)

[1695] 2. The system according to claim 1, wherein the biometric data of the user includes electroencephalogram data, facial expression data, and voice data.

[1696] (Claim 3)

[1697] 10. The system of claim 1, wherein the market data includes news data and financial data.

[1698] "Application Example 1"

[1699] (Claim 1)

[1700] means for collecting biometric data of a user;

[1701] means for analyzing the collected biometric data to identify the user's emotional state;

[1702] a means of obtaining and analyzing market data;

[1703] A means for correcting a user's emotional bias;

[1704] A means for generating an investment strategy based on the corrected data;

[1705] means for notifying a user of the generated investment strategy;

[1706] A means for generating rational purchase suggestions based on the collected biometric data and market data of a user to support purchase decisions in e-commerce transactions;

[1707] means for notifying the user of the generated purchase offer;

[1708] A system including:

[1709] (Claim 2)

[1710] 2. The system of claim 1, wherein the biometric data of the user includes electroencephalogram data.

[1711] (Claim 3)

[1712] 10. The system of claim 1, wherein the market data includes news data and financial data.

[1713] "Example 2: Combining Emotion Engines"

[1714] (Claim 1)

[1715] means for collecting biometric data of a user;

[1716] A means for encrypting and transmitting the collected biometric data in real time; and

[1717] means for decoding and analyzing the received data;

[1718] means for determining an emotional state of a user based on biometric data;

[1719] means for detecting and correcting a user's emotional bias;

[1720] A means for generating a rational investment strategy using the corrected data;

[1721] means for notifying a user of the generated investment strategy;

[1722] A system including:

[1723] (Claim 2)

[1724] 2. The system of claim 1, wherein the collected biometric data includes electroencephalogram data.

[1725] (Claim 3)

[1726] 10. The system according to claim 1, wherein news data and financial data are acquired and analyzed as market data.

[1727] "Application example 2 when combining emotion engines"

[1728] (Claim 1)

[1729] means for collecting biometric data of a user;

[1730] means for analyzing the collected biometric data to identify the user's emotional state;

[1731] a means of obtaining and analyzing market data;

[1732] A means for correcting a user's emotional bias;

[1733] A means of proposing appropriate measures based on the corrected data; and

[1734] means for notifying a user of the generated countermeasure;

[1735] A system including:

[1736] (Claim 2)

[1737] 2. The system of claim 1, wherein the biometric data of the user includes electroencephalogram data.

[1738] (Claim 3)

[1739] 10. The system of claim 1, wherein the market data includes news data and financial data. [Explanation of symbols]

[1740] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for collecting biometric data of a user; means for analyzing the collected biometric data to identify the user's emotional state; a means of obtaining and analyzing market data; A means for correcting a user's emotional bias; A means for generating an investment strategy based on the corrected data; means for notifying a user of the generated investment strategy; A system including:

2. The system according to claim 1 , wherein the biometric data of the user includes electroencephalogram data.

3. 10. The system of claim 1, wherein the market data includes news data and financial data.

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