system

Abstract

We provide the system. [Solution] A means of collecting personal life log data and storing it in a database, A means for preprocessing collected lifelog data, including imputing missing data and removing outliers, A means for extracting characteristics about an individual's behavioral patterns and hobbies based on preprocessed data, A means of generating a digital twin using extracted features and performing a simulation of future life in a virtual space, A means of analyzing simulation results and providing insights to support individual decision-making, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, due to an overabundance of information and diversification of options, it has become increasingly difficult for individuals to make optimal choices. As a result, the risk of making incorrect judgments in personal decision-making increases, which may have an adverse impact on future life events. To solve such problems, it is required to simulate the results of options in the real world in advance and support optimal judgment.

Means for Solving the Problems

[0005] This invention provides a system for collecting and preprocessing individual life log data and extracting user behavior patterns and hobby characteristics based on that data. This system uses characteristic information to generate a digital twin and simulates future choices in a virtual space, thereby supporting informed decision-making for individuals. Furthermore, by allowing users to input choices using a terminal and obtain individually customized simulation results, it encourages appropriate judgments regarding specific life events.

[0006] "Life log data" refers to data collected from digital devices regarding an individual's daily activities and physiological information.

[0007] A "database" is an information system for systematically storing and managing collected life log data.

[0008] "Preprocessing" refers to the operations performed to clean up data, such as filling in missing data points and removing outliers, as a preliminary step before data analysis.

[0009] "Feature extraction" is the process of extracting specific information about an individual's behavioral patterns and preferences from data.

[0010] A "digital twin" is a virtual model that reflects an individual's current characteristics, enabling simulations of the real world.

[0011] A "virtual space" is a digital environment that allows for simulations in a different environment from reality, reproduced on a computer.

[0012] "Simulation" is a method of recreating future choices and actions in a virtual space and analyzing the results.

[0013] "Insight" refers to new insights and useful knowledge gained from simulation results, and is information that supports decision-making. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] An embodiment for carrying out the present invention consists of the following steps.

[0036] First, after obtaining permission from the user, the server automatically collects lifelog data from the user's terminal, wearable devices, and social media. This data includes the user's movement history, fitness activities, and online activities. Next, the server preprocesses the collected data by organizing it, imputing missing data, and removing outliers. This creates a well-structured dataset suitable for analysis.

[0037] Subsequently, the server uses the pre-processed data to clarify the user's behavioral patterns and hobbies / preferences using a feature extraction algorithm. Based on the feature information obtained in this process, the server generates a digital twin unique to the user. The digital twin operates in a virtual environment that mimics the real world, recreating situations where the user needs to consider specific choices.

[0038] Users input choices related to life events such as changing jobs, moving, or relationships into the server via a terminal. Based on this input, the server sets the parameters for the simulation. According to the set parameters, the digital twin considers multiple choices and simulates actions in a virtual space. Each simulation result is recorded in detail, and the server analyzes them to predict the possible consequences of a particular choice.

[0039] Ultimately, the server visualizes and provides the results to the user's terminal to support their decision-making. The user can review the presented data and make the decision best suited to their situation. Through this mechanism, the present invention aims to strongly support individual decision-making in today's complex society.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server automatically collects life log data from the user's terminal or wearable device. This data includes location information, fitness information, and online activity, and is obtained in a secure manner.

[0043] Step 2:

[0044] The server performs preprocessing to format the collected lifelog data. This process involves imputing missing data, removing outliers, and converting the data into a format suitable for analysis.

[0045] Step 3:

[0046] The server uses pre-processed data to extract user behavior patterns, hobbies, and preferences. This process uses data mining techniques to clarify the characteristics of individual users.

[0047] Step 4:

[0048] The server generates a digital twin of the user based on the extracted feature information. The digital twin is a model that reproduces the user's real-world behavior in a virtual space.

[0049] Step 5:

[0050] Users use a terminal to input information into the server about choices and life events they want to simulate. This includes things like changing jobs or moving.

[0051] Step 6:

[0052] The server sets the simulation parameters based on the input information. This setting provides the foundation for the digital twin to perform simulations in a virtual space.

[0053] Step 7:

[0054] The server runs simulations under multiple scenarios and predicts the outcome based on each choice. It meticulously records the obtained data and analyzes the results for each scenario.

[0055] Step 8:

[0056] Based on the analyzed simulation results, the server provides users with insights via their terminals to help them make optimal choices. Users can then use this information to make their actual decisions.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] In modern society, the complexity of choices and decision-making that individuals face is increasing. Appropriate judgments are required for various life events, such as changing jobs, moving, or starting a new hobby, but it is difficult to assess the impact of each choice in advance. Therefore, predicting the consequences of future choices based on an individual's behavior and preferences, and supporting their decision-making, is a crucial challenge.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for collecting and storing information related to an individual's behavior in an information storage medium, means for preprocessing the collected behavior-related information to fill in missing information and remove abnormal values, and means for extracting characteristics related to the individual's behavioral traits and preferences based on the preprocessed information. This makes it possible to simulate future outcomes and impacts based on various choices that an individual should consider, and to support decision-making.

[0062] "Information related to individual behavior" refers to data about a user's daily life and online activities, such as their travel history, fitness activities, and online activities.

[0063] "Information storage media" refers to devices and systems for storing data, and includes databases and cloud storage.

[0064] "Preprocessing" refers to processes performed to prepare collected data into an analyzable format, such as organizing the data, imputing missing values, and removing outliers.

[0065] "Behavioral characteristics and preferences" refers to information that quantifies or categorizes an individual's behavioral patterns, hobbies, and interests.

[0066] A "virtual model" refers to a simulated data model that is recreated in a digital space based on an individual's characteristics.

[0067] A "virtual domain" refers to a simulated environment that resembles the real world, reproduced within a computer.

[0068] "Future behavior simulation" refers to a process that uses a user-specific digital data model to virtually recreate future scenarios based on various choices and evaluate their impact in advance.

[0069] "Insight" refers to useful knowledge and suggestions derived from analyzing the results of a simulation.

[0070] "Communication equipment" refers to devices used by users to input information and send and receive data to and from a server.

[0071] A "choice pattern" refers to a set of various options or scenarios that a user considers.

[0072] A "situation" refers to a hypothetical scenario generated based on a specific selection pattern, describing how the digital model behaves within that scenario.

[0073] A description of embodiments for carrying out the present invention will be provided.

[0074] In this system, the server plays a central role. First, after obtaining user permission, the server collects "personal behavior-related information" from the user's terminal, wearable devices, and social media. This information includes travel history, fitness activities, and online activity. The collected data is stored in "information storage media" such as cloud storage. For data collection, the server uses dedicated APIs and database management systems.

[0075] Next, the server "preprocesses" the collected information. In this step, libraries such as Python's Pandas are used to impute missing information and remove abnormal values. During this process, the data is prepared into a user-friendly format using statistical analysis and machine learning algorithms.

[0076] Subsequently, the server uses extraction algorithms to extract "behavioral characteristics and preferences." As part of this process, machine learning libraries such as Scikit-learn are used. This clarifies an individual's behavioral patterns as numerical values ​​and categories.

[0077] Next, based on the extracted features, the server generates a "virtual model," or digital twin. This model operates in a "virtual domain" using Unity or other 3D simulation tools, enabling the simulation of the user's future actions and choices.

[0078] The user sends the "selection patterns" they want to simulate as prompt messages to the server via their terminal. For example, they might enter a prompt such as, "Please simulate the choice of taking up yoga as a hobby." This sets the conditions for the simulation, and the server virtually reproduces various scenarios.

[0079] Ultimately, the server analyzes the simulation results and provides "insights" to support the user's decision-making. These results are visualized on the terminal as graphs and charts, allowing the user to make informed decisions based on them.

[0080] This system aims to support users in making appropriate decisions by utilizing generative AI models to proactively evaluate the future impact of various choices they face.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] After obtaining user permission, the server collects personal behavior-related information from the user's device, wearable devices, and social media. Inputs include data on movement history, fitness activities, and online activities. This data is retrieved using APIs and data collection programs and stored in cloud storage. Outputs are collected information in a structured data format.

[0084] Step 2:

[0085] The server preprocesses the collected information. The input is the raw data obtained in step 1. The data is organized using the Pandas library, and the mean or linear interpolation is used to impute missing values. Outliers are identified and removed based on the standard deviation. The output is a clean dataset prepared for analysis.

[0086] Step 3:

[0087] The server extracts features related to behavioral characteristics and preferences based on preprocessed data. The input is the clean data obtained in step 2. The server uses the Scikit-learn library to perform feature extraction based on clustering and principal component analysis. The output is numerical or categorical features related to user behavior patterns and preferences.

[0088] Step 4:

[0089] The server generates a virtual model based on the extracted features. The input is the feature data obtained in step 3. Using a 3D simulation tool such as Unity, a digital twin is created in a virtual space, constructing an environment that mimics the real world. The output is a digital twin that can be operated in the virtual space.

[0090] Step 5:

[0091] The user sends the selection patterns they want to simulate as prompts to the server via their terminal. The input is the prompt entered by the user from their terminal. An example would be a specific request such as, "Please simulate the selection of taking up yoga as a hobby." The output is the parameters necessary for the simulation, which the server receives.

[0092] Step 6:

[0093] The server performs a digital twin simulation according to the configured parameters. The input is the simulation parameters obtained in step 5. The server runs multiple scenarios in the virtual environment and records the results of each. The output is the detailed simulation results, which are data on the impact of each choice.

[0094] Step 7:

[0095] The server analyzes the simulation results and provides insights to support user decision-making. The input is the simulation results obtained in step 6. Machine learning algorithms are used to analyze the results and extract suggestions and trends for the user. The output is visualized insights on the terminal and presented to the user as graphs and charts.

[0096] (Application Example 1)

[0097] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0098] Individual consumer behavior often presents challenges in budget management and rational decision-making. This is because it's difficult to understand in real time how the goods and services being considered for purchase will impact one's individual financial situation. This problem frequently leads to overconsumption and unplanned spending, hindering the achievement of long-term financial goals. Therefore, there is a need for purchasing support tools based on individual consumption trends.

[0099] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0100] In this invention, the server includes means for collecting and storing an individual's behavioral history information in an information storage device, means for preprocessing the collected behavioral history information to fill in missing information and remove outliers, and means for generating a virtual model using the extracted characteristics and simulating future consumption behavior in a virtual space. This makes it possible to visualize the impact of an individual's consumption behavior in real time and support budget management.

[0101] "Behavioral history information" refers to data that shows a record of an individual's daily consumption-related behaviors and movements.

[0102] An "information storage device" is a digital storage medium or system used to store collected data.

[0103] "Preprocessing" refers to the procedure of preparing collected raw data by filling in missing information and removing outliers, thereby creating a format suitable for analysis.

[0104] "Consumer behavior trends" refer to a set of statistical and behavioral characteristics that indicate what kind of consumption patterns an individual typically has.

[0105] A "virtual model" is a digital model created on a computer for simulation purposes, with the aim of mimicking an individual's consumption activities.

[0106] "Simulation of future consumer behavior" is an analytical process that uses virtual models to predict an individual's future consumption patterns and financial situation.

[0107] "Visualization" refers to the visual representation of analysis results or simulation results using graphs and charts.

[0108] "Budget management" is the process of planning and adjusting spending based on financial goals set by an individual.

[0109] To realize this invention, it is necessary to configure a system in cooperation with a server, terminal, and user. The server first collects individual behavioral history information and stores it in an information storage device. Behavioral history information includes shopping history, travel routes, and online activities. This uses data acquired via devices such as smartphones and smart glasses.

[0110] Next, the server preprocesses the collected behavioral history information. Specifically, it fills in missing information using statistical inference and external databases, and removes outliers to form a dataset suitable for analysis. Data cleaning software and data analysis platforms are used for this process.

[0111] Based on the pre-processed data, the server extracts individual consumer behavior trends. Here, machine learning algorithms (e.g., clustering techniques) are applied to identify individual purchasing patterns. Machine learning libraries in Python or R are used for this process.

[0112] Using the extracted features, the server generates a virtual model and simulates future consumer behavior in a virtual space. This allows for the evaluation of the impact of specific purchasing behaviors on individual budgets and financial goals. Virtualization technology and simulation software are used in this simulation.

[0113] The user enters the items they are considering purchasing through their device. As the smart glasses scan the items, the server visualizes the impact of purchasing those items on the user's budget in real time and displays it on the device. This visualization utilizes augmented reality technology and data visualization tools.

[0114] For example, if a user is considering purchasing high-end coffee equipment, the server predicts the financial impact of that purchase based on the information scanned by the terminal. The system then uses the simulation results to help the user decide whether or not to make the purchase.

[0115] An example of a prompt is: "Please provide the steps to develop an application that analyzes in real time how a user's purchase of an expensive product impacts their monthly budget and displays the results visually."

[0116] This system enables users to consistently make appropriate consumption choices, helping them maintain long-term financial health.

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] Users generate their own behavioral history information. This is done by collecting daily consumption activities and travel history using smartphones or smart glasses. This information is recorded as time-series data and transmitted to a server.

[0120] Step 2:

[0121] The server stores the received behavioral history information in its data storage device. Since the stored data is unsuitable for analysis in its raw state, preprocessing is required.

[0122] Step 3:

[0123] The server preprocesses the behavioral history data. This process involves estimating and imputing missing information using data cleaning techniques, and detecting and removing outliers using statistical methods. As a result, a formatted dataset is obtained.

[0124] Step 4:

[0125] The server uses pre-processed data to extract individual consumer behavior trends. Here, machine learning algorithms are applied, and consumption patterns are identified through clustering. The input is formatted data, and the output is the consumption trends of each user.

[0126] Step 5:

[0127] The server generates a virtual model from extracted consumer behavior trends and runs a digital simulation. This simulation recreates future consumer behavior in a virtual space and analyzes its impact on the user's budget. The output is the simulation result.

[0128] Step 6:

[0129] The user scans the product they are considering purchasing using their device. Based on this, the server calculates the budget impact of purchasing that product in real time and displays it visually on the device. Augmented reality technology and data visualization tools are used for this.

[0130] Step 7:

[0131] Users review the simulation results visualized on their devices and make purchasing decisions. The results serve as information to help them make the best choices for achieving long-term financial goals.

[0132] In each step, a generative AI model is used to generate prompt messages, supporting necessary analysis and decision-making. This process enables users to make informed purchasing decisions.

[0133] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0134] Embodiments of the present invention include a life simulation system combined with an emotion engine. The server automatically collects life log data from the user's terminal, wearable device, online activity, etc. Next, the emotion engine is used to analyze the user's emotional state from the collected data and record the emotional state in a database. The emotion engine can evaluate emotions from voice tone, message content, behavioral history, etc.

[0135] The server integrates and preprocesses the aforementioned lifelog data and emotional data, filling in missing data and removing outliers. Next, the server extracts characteristics of the user's behavioral patterns, hobbies, and emotional states from the preprocessed data. Based on this characteristic information, the server generates a digital twin and simulates future choices and actions in the virtual space.

[0136] The user uses a terminal to input specific choices or life events they want to simulate into the server. The server sets the simulation parameters based on this information and extracted emotional characteristics. During the simulation, the emotional engine's emotional influence is reflected in the digital twin's behavior, generating multiple scenarios that take the user's emotional state into account.

[0137] The server analyzes the generated scenarios in detail and evaluates the impact each option has on the user's emotions and life. The results are then sent to the terminal in a visual format for the user to see. The user can then use the presented results to make decisions that take emotional aspects into consideration. In this form, the present invention provides support for individuals to make choices that are more emotionally satisfying.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The server collects lifelog data from the user's terminal or wearable device. This data includes movement history, heart rate, and social media message history. This allows the server to understand the user's daily activities and psychological changes.

[0141] Step 2:

[0142] The server uses an emotion engine to analyze the user's emotional state from the collected data. The emotion engine analyzes message content, voice tone, and facial recognition data to evaluate the user's emotional state.

[0143] Step 3:

[0144] The server integrates life log data and emotional data and preprocesses this data. This includes imputing missing data and removing outliers, with the goal of preparing the data in a format that is easy to analyze.

[0145] Step 4:

[0146] The server extracts user behavior patterns, hobbies, and emotional characteristics from pre-processed data. This allows for a numerical representation of individual-specific behaviors and emotions, forming the basis for digital twin generation.

[0147] Step 5:

[0148] The user sends requests to the server via their device regarding specific choices or life events they want to simulate. For example, they might input their intention to change jobs or start a new hobby.

[0149] Step 6:

[0150] The server sets the simulation parameters for the digital twin based on the user's input information and emotional characteristics. These settings reflect the user's emotional tendencies and past behavioral history.

[0151] Step 7:

[0152] The server executes multiple simulation scenarios using a digital twin, according to the configured parameters. Because an emotion engine is integrated into the simulation, each scenario progresses while considering the user's emotional responses.

[0153] Step 8:

[0154] The server analyzes the simulation results and evaluates the emotional impact each option has on the user and its effect on their life. The analysis results are sent to the terminal as visual information for the user to review.

[0155] Step 9:

[0156] Users review the simulation results presented through their devices and make decisions after considering their emotional impact. This allows users to make choices that lead to desirable outcomes.

[0157] (Example 2)

[0158] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0159] In modern society, there is a need to accurately understand the emotional impact individuals experience in their daily lives and to make more satisfying decisions. However, conventional technologies have struggled to simulate situations that take an individual's emotional state into account, resulting in insufficient evaluation of future choices that reflect emotional factors. Therefore, there is a need for decision-making support that takes emotions into account.

[0160] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0161] In this invention, the server includes means for collecting personal life log information and storing it in an information storage device, means for preprocessing the collected life log information to fill in missing information and remove outliers, and means for integrating the collected life log and emotional information using an emotion analysis function and reflecting the emotional state in the simulation. This makes it possible to support decision-making to enhance an individual's emotional satisfaction through a future life simulation that takes emotional state into account.

[0162] "Life log information" refers to data about an individual's activities and behaviors in their daily life.

[0163] "Information storage device" refers to a system or medium for storing collected data.

[0164] "Preprocessing" refers to the techniques of processing and cleaning applied to raw data, and is a process that improves the quality of the data.

[0165] "Imputing missing information" refers to filling in incomplete information in a dataset using existing data or estimates.

[0166] "Outlier removal" is the process of detecting extreme data points in a dataset based on statistical methods and removing those that may negatively impact the analysis.

[0167] "Emotional analysis function" refers to a technology or algorithm for evaluating an individual's emotional state and integrating related data.

[0168] A "virtual model" refers to a digital representation created to reproduce an individual's characteristics and perform simulations in a virtual environment.

[0169] A "virtual environment" refers to a computer-generated simulation space through which users can experience various scenarios.

[0170] "Future life simulation" refers to an attempt to predict future choices and actions within a virtual environment and analyze the results under specific conditions.

[0171] "Emotional state" is a term that describes an individual's psychological or emotional state and is a factor that influences their individual behavior and choices.

[0172] "Decision support" refers to the process of providing information and insights to help a system improve an individual's choices and make more satisfying decisions.

[0173] In implementing this invention, the system consists of a server, a terminal, and a user. The server automatically collects personal life log information from various devices and stores it in an information storage device. The devices from which data is collected include smartphones, wearable devices, and internet platforms. This data includes user behavior, message content, and voice tone.

[0174] The server preprocesses the collected data. It improves data quality by using statistical software and data analysis tools to impart missing information and remove outliers. Based on this preprocessed data, the server uses machine learning algorithms to extract features related to individual behavior patterns and preferences.

[0175] This feature information is used in combination with emotion analysis functions to evaluate the user's emotional state. The evaluated emotion data forms the basis for building a virtual model of the user. The generation of the virtual model takes place on a digital simulation platform, enabling simulations of future life.

[0176] The user uses a terminal to input the choices or events they want to simulate into the server. This input uses prompts for the generated AI model. For example, a user might send the prompt "I want to know how relationships in a new workplace affect emotions" to the server. Based on this information, the server sets the control variables for the simulation and simulates multiple situations.

[0177] The server analyzes the generated simulation results and provides visually clear results to the terminal to support the user's decision-making. This allows the user to make decisions based on their emotional state, supporting choices that enhance their emotional satisfaction.

[0178] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0179] Step 1:

[0180] The server collects user life log information. Inputs include data from smartphones and wearable devices, as well as activity history from online platforms. The server aggregates this information in an information storage device and stores it in a database. Specifically, it retrieves data from devices via APIs and updates the database periodically.

[0181] Step 2:

[0182] The server preprocesses the collected lifelog information. The input is the stored raw data, and the output is a clean, integrated dataset. The server uses statistical analysis software to fill in missing information with historical data and estimates, and to detect and remove outliers. Specifically, it makes full use of filtering algorithms and interpolation techniques.

[0183] Step 3:

[0184] The server extracts feature information from pre-processed data. The input is a clean dataset, and the output is a feature vector relating to user behavior patterns and preferences. Machine learning algorithms are used to analyze the data using clustering and classifiers. A specific example is pattern recognition based on user activity frequency and preferences.

[0185] Step 4:

[0186] The server integrates life logs and feature information collected using sentiment analysis capabilities. The input is a feature vector, and the output is a dataset containing the user's emotional state. Natural language processing techniques and speech tone analysis tools are used to estimate the user's emotions. Specifically, emotions are classified from text messages.

[0187] Step 5:

[0188] The server generates a virtual model based on extracted feature information and emotional states. The input is an integrated dataset, and the output is a user-specific digital twin. Simulations are performed within the virtual environment to model the user's future life. Specifically, 3D model generation tools and simulation engines are used.

[0189] Step 6:

[0190] The user uses a terminal to input specific choices they want to simulate into the server. The input is a prompt message for the generated AI model, and the output is the simulation control variables set by the server. Based on the prompt message, the server reflects various conditions. A concrete example of a prompt might be, "I want to analyze the impact of a new work environment on emotions."

[0191] Step 7:

[0192] The server performs simulations based on configured control variables and generates multiple scenarios. The inputs are the control variables and a digital twin, while the output is the simulation results reflecting various situations. The server utilizes emotion analysis capabilities to reflect the user's emotional state in the scenarios. Specifically, dynamic simulation software is used.

[0193] Step 8:

[0194] The server analyzes the generated simulation results and provides the user with information to support their decision-making. The input is the simulation output, and the output is a visually easy-to-understand insight. The server sends the results to the terminal to help the user make choices that enhance their emotional satisfaction. For example, the results can be displayed as graphs or charts.

[0195] (Application Example 2)

[0196] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0197] The system did not adequately present options that took into account individual emotional states, resulting in users having difficulty making emotionally satisfying decisions. In particular, insights that reflected emotions were not provided in real-world behaviors such as shopping and service selection.

[0198] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0199] In this invention, the server includes means for collecting personal life log data and storing it in a data storage device, means for preprocessing the collected life log data to complete missing data and remove outliers, and means for analyzing emotional data and presenting optimal choices based on the user's emotional state. As a result, the user receives choices that match their emotional state and can make emotionally satisfying decisions.

[0200] "Life log data" refers to digital information that shows an individual's actions and state in their daily life, recording their daily activities and changes.

[0201] A "data storage device" is a storage device for securely storing digital data, and is a hardware or software system that enables data management and access.

[0202] "Data imputation" is the process of inferring and filling in missing information to make a dataset complete.

[0203] "Outlier removal" is a technique that improves data accuracy by identifying and removing or adjusting statistically abnormal values ​​present in a dataset.

[0204] "Behavioral patterns" refer to tendencies in actions and activities that an individual repeatedly performs.

[0205] "Preferences" refer to an individual's likes and tendencies, and can mean a strong attachment to a particular activity or thing.

[0206] A "digital twin" is an accurate digital representation of an object or process in the real world, enabling simulations between reality and the virtual world.

[0207] A "virtual realm" is a digital space that mimics the real world, created using computer technology.

[0208] "Insight" refers to a deep understanding or knowledge gained through analysis and observation, providing crucial information to guide actions and decisions in specific situations.

[0209] "Emotional data" refers to information that quantitatively or qualitatively indicates an individual's emotional state, and is obtained from data such as voice tone, facial expressions, and behavioral history.

[0210] In this invention, the server collects personal lifelog data and processes it for storage in a data storage device. This includes a communication module for acquiring data from smart devices and wearable devices. The server preprocesses the collected lifelog data, fills in missing data, and removes outliers. Data processing software such as Python and TENSORFLOW® is used in this process.

[0211] The server also extracts individual behavioral patterns and preferences based on pre-processed data. This allows for the creation of a digital twin and the simulation of future life in a virtual realm. Simultaneously analyzing the simulation results and user emotional data, the server presents optimal choices. Analysis tools such as Amazon SageMaker are used in this process.

[0212] The device receives insights from the server and functions as an interface that presents the user with options tailored to their emotional state. For example, if a user is determined to be stressed, the system can recommend purchasing products that help with relaxation.

[0213] An example of a prompt using a generative AI model is, "If the customer's emotional state is 'stressed', generate a list of recommended products in the shopping guide." In this way, it is possible to help users make emotionally satisfying decisions.

[0214] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0215] Step 1:

[0216] The server acquires lifelog data from smart devices and wearable devices and stores it in a data storage device. The input here is digital information about daily activities transmitted from each device, and the output is a structured dataset. This process involves receiving and formatting data from devices and appropriately storing it in a database.

[0217] Step 2:

[0218] The server preprocesses the collected lifelog data, filling in missing data and removing outliers. The input is the stored raw data, and the output is a clean dataset with outliers removed. Data processing scripts using Python or Pandas estimate and fill in missing parts, and detect and remove outliers.

[0219] Step 3:

[0220] The server extracts individual behavioral patterns and preferences based on pre-processed data. The input is a clean dataset, and the output is feature vectors related to behavior and preferences. It uses a machine learning model to analyze the data and extract those features.

[0221] Step 4:

[0222] The server generates a digital twin using extracted features and simulates future life in a virtual realm. The input is a feature vector, and the output is the result of the digital twin simulation. It executes a simulation algorithm to generate diverse future scenarios.

[0223] Step 5:

[0224] The server analyzes the simulation results and uses sentiment data to present the optimal choices based on the user's current emotional state. The input is the simulation results and sentiment data, and the output is a list of choices to aid in the user's decision-making. This includes the operation of the sentiment engine, which evaluates the emotional state and optimizes the choices.

[0225] Step 6:

[0226] The terminal presents the user with choices and insights sent from the server. The input is the choices and insights received from the server, and the output is visually formatted information. The terminal displays the information through a user interface, making it easy for the user to understand.

[0227] Step 7:

[0228] The user makes a decision based on the information presented, taking emotional aspects into consideration. The input is the information presented from the device, and the output is the user's chosen actions and decisions. This includes making judgments based on the given information and deciding on the next course of action.

[0229] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0230] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0231] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0235] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0236] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0237] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0238] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0239] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0240] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0241] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0242] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0243] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0244] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0245] An embodiment for carrying out the present invention consists of the following steps.

[0246] First, after obtaining permission from the user, the server automatically collects lifelog data from the user's terminal, wearable devices, and social media. This data includes the user's movement history, fitness activities, and online activities. Next, the server preprocesses the collected data by organizing it, imputing missing data, and removing outliers. This creates a well-structured dataset suitable for analysis.

[0247] Subsequently, the server uses the pre-processed data to clarify the user's behavioral patterns and hobbies / preferences using a feature extraction algorithm. Based on the feature information obtained in this process, the server generates a digital twin unique to the user. The digital twin operates in a virtual environment that mimics the real world, recreating situations where the user needs to consider specific choices.

[0248] Users input choices related to life events such as changing jobs, moving, or relationships into the server via a terminal. Based on this input, the server sets the parameters for the simulation. According to the set parameters, the digital twin considers multiple choices and simulates actions in a virtual space. Each simulation result is recorded in detail, and the server analyzes them to predict the possible consequences of a particular choice.

[0249] Ultimately, the server visualizes and provides the results to the user's terminal to support their decision-making. The user can review the presented data and make the decision best suited to their situation. Through this mechanism, the present invention aims to strongly support individual decision-making in today's complex society.

[0250] The following describes the processing flow.

[0251] Step 1:

[0252] The server automatically collects life log data from the user's terminal or wearable device. This data includes location information, fitness information, and online activity, and is obtained in a secure manner.

[0253] Step 2:

[0254] The server performs preprocessing to format the collected lifelog data. This process involves imputing missing data, removing outliers, and converting the data into a format suitable for analysis.

[0255] Step 3:

[0256] The server uses pre-processed data to extract user behavior patterns, hobbies, and preferences. This process uses data mining techniques to clarify the characteristics of individual users.

[0257] Step 4:

[0258] The server generates a digital twin of the user based on the extracted feature information. The digital twin is a model that reproduces the user's real-world behavior in a virtual space.

[0259] Step 5:

[0260] Users use a terminal to input information into the server about choices and life events they want to simulate. This includes things like changing jobs or moving.

[0261] Step 6:

[0262] The server sets the simulation parameters based on the input information. This setting provides the foundation for the digital twin to perform simulations in a virtual space.

[0263] Step 7:

[0264] The server runs simulations under multiple scenarios and predicts the outcome based on each choice. It meticulously records the obtained data and analyzes the results for each scenario.

[0265] Step 8:

[0266] Based on the analyzed simulation results, the server provides users with insights via their terminals to help them make optimal choices. Users can then use this information to make their actual decisions.

[0267] (Example 1)

[0268] Next, we will describe Example 1. 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."

[0269] In modern society, the complexity of choices and decision-making that individuals face is increasing. Appropriate judgments are required for various life events, such as changing jobs, moving, or starting a new hobby, but it is difficult to assess the impact of each choice in advance. Therefore, predicting the consequences of future choices based on an individual's behavior and preferences, and supporting their decision-making, is a crucial challenge.

[0270] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0271] In this invention, the server includes means for collecting and storing information related to an individual's behavior in an information storage medium, means for preprocessing the collected behavior-related information to fill in missing information and remove abnormal values, and means for extracting characteristics related to the individual's behavioral traits and preferences based on the preprocessed information. This makes it possible to simulate future outcomes and impacts based on various choices that an individual should consider, and to support decision-making.

[0272] "Information related to individual behavior" refers to data about a user's daily life and online activities, such as their travel history, fitness activities, and online activities.

[0273] "Information storage media" refers to devices and systems for storing data, and includes databases and cloud storage.

[0274] "Preprocessing" refers to processes performed to prepare collected data into an analyzable format, such as organizing the data, imputing missing values, and removing outliers.

[0275] "Behavioral characteristics and preferences" refers to information that quantifies or categorizes an individual's behavioral patterns, hobbies, and interests.

[0276] The "virtual model" refers to a simulatable data model reproduced in the digital space based on an individual's characteristics.

[0277] The "virtual area" refers to a simulation environment similar to the real world, reproduced within a computer.

[0278] The "future action simulation" refers to a process of virtually reproducing future scenarios based on various options using a user-specific digital data model and pre-evaluating its impact.

[0279] "Insight" refers to analyzing the results of a simulation and useful findings or suggestions obtained from those results.

[0280] The "communication device" refers to a device for a user to input information and transmit and receive data with a server.

[0281] The "selection pattern" refers to a set of various options and scenarios considered by a user.

[0282] The "situation" refers to a virtual scenario generated based on a specific selection pattern and represents how the digital model behaves within it.

[0283] The embodiments for implementing the present invention will be described.

[0284] In this system, the server plays a central role. First, after obtaining the user's permission, the server collects "information related to personal behavior" from the user's terminal, wearable device, and social media. This information includes movement history, fitness activities, and online activities. The collected data is stored in an "information storage medium" such as cloud storage. For data collection, the server uses a dedicated API or a database management system.

[0285] Next, the server "preprocesses" the collected information. In this step, libraries such as the Pandas library in Python are utilized to complement missing information and remove abnormal values. In this process, the data is organized into a more user-friendly form by using statistical analysis and machine learning algorithms.

[0286] After that, the server makes full use of extraction algorithms to extract "features related to behavior characteristics and preferences". As part of this process, machine learning libraries such as Scikit-learn are used. This clarifies an individual's behavior pattern as numerical values or categories.

[0287] Subsequently, based on the extracted features, the server generates a "virtual model", that is, a digital twin. This model operates in a "virtual area" using Unity or other 3D simulation tools. It enables the simulation of a user's future actions and options.

[0288] The user sends the server, through the terminal, the "selection pattern" to be simulated as a prompt sentence. For example, a prompt such as "Please conduct a simulation on the selection of making yoga a hobby" can be input. This sets the conditions for the simulation, and the server virtually reproduces various scenarios.

[0289] Finally, the server analyzes the results of the simulation and provides "insights" to support the user's decision-making. This result is visualized as graphs or charts on the terminal, enabling the user to make information-based decisions based on it.

[0290] This system aims to utilize a generative AI model to pre-evaluate the future impacts of various options faced by users and support appropriate judgments.

[0291] The flow of the specific process in Example 1 will be described using FIG. 11.

[0292] Step 1:

[0293] After obtaining user permission, the server collects personal behavior-related information from the user's device, wearable devices, and social media. Inputs include data on movement history, fitness activities, and online activities. This data is retrieved using APIs and data collection programs and stored in cloud storage. Outputs are collected information in a structured data format.

[0294] Step 2:

[0295] The server preprocesses the collected information. The input is the raw data obtained in step 1. The data is organized using the Pandas library, and the mean or linear interpolation is used to impute missing values. Outliers are identified and removed based on the standard deviation. The output is a clean dataset prepared for analysis.

[0296] Step 3:

[0297] The server extracts features related to behavioral characteristics and preferences based on preprocessed data. The input is the clean data obtained in step 2. The server uses the Scikit-learn library to perform feature extraction based on clustering and principal component analysis. The output is numerical or categorical features related to user behavior patterns and preferences.

[0298] Step 4:

[0299] The server generates a virtual model based on the extracted features. The input is the feature data obtained in step 3. Using a 3D simulation tool such as Unity, a digital twin is created in a virtual space, constructing an environment that mimics the real world. The output is a digital twin that can be operated in the virtual space.

[0300] Step 5:

[0301] The user sends the selection pattern to be simulated as a prompt sentence to the server through the terminal. The input is the prompt sentence input by the user from the terminal. For example, it includes a specific requirement such as "Please perform a simulation on the selection of making yoga a hobby". The output is the parameters required for the simulation set, and the server receives this.

[0302] Step 6:

[0303] The server performs the simulation of the digital twin according to the set parameters. The input is the simulation parameters obtained in Step 5. The server executes multiple scenarios in the virtual environment and records the results of each. The output is the detailed simulation result and the data on the impact brought by each selection.

[0304] Step 7:

[0305] The server analyzes the simulation results and provides insights to assist the user's decision-making. The input is the simulation result obtained in Step 6. The results are analyzed using a machine learning algorithm to extract suggestions and trends for the user. The output is the insight information visualized on the terminal and presented to the user as graphs and charts.

[0306] (Application Example 1)

[0307] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0308] In an individual's consumption behavior, it is often difficult to manage the budget and make reasonable decisions. This is because it is impossible to grasp in real time how the goods and services under consideration for purchase affect the individual's financial situation. Due to this problem, in many cases, overconsumption and unplanned expenditures occur, hindering the achievement of an individual's long-term financial goals. Therefore, there is a need for a purchase support tool based on individual consumption trends.

[0309] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0310] In this invention, the server includes means for collecting and storing an individual's behavioral history information in an information storage device, means for preprocessing the collected behavioral history information to fill in missing information and remove outliers, and means for generating a virtual model using the extracted characteristics and simulating future consumption behavior in a virtual space. This makes it possible to visualize the impact of an individual's consumption behavior in real time and support budget management.

[0311] "Behavioral history information" refers to data that shows a record of an individual's daily consumption-related behaviors and movements.

[0312] An "information storage device" is a digital storage medium or system used to store collected data.

[0313] "Preprocessing" refers to the procedure of preparing collected raw data by filling in missing information and removing outliers, thereby creating a format suitable for analysis.

[0314] "Consumer behavior trends" refer to a set of statistical and behavioral characteristics that indicate what kind of consumption patterns an individual typically has.

[0315] A "virtual model" is a digital model created on a computer for simulation purposes, with the aim of mimicking an individual's consumption activities.

[0316] "Simulation of future consumer behavior" is an analytical process that uses virtual models to predict an individual's future consumption patterns and financial situation.

[0317] "Visualization" refers to the visual representation of analysis results or simulation results using graphs and charts.

[0318] "Budget management" is the process of planning and adjusting spending based on financial goals set by an individual.

[0319] To realize this invention, it is necessary to configure a system in cooperation with a server, terminal, and user. The server first collects individual behavioral history information and stores it in an information storage device. Behavioral history information includes shopping history, travel routes, and online activities. This uses data acquired via devices such as smartphones and smart glasses.

[0320] Next, the server preprocesses the collected behavioral history information. Specifically, it fills in missing information using statistical inference and external databases, and removes outliers to form a dataset suitable for analysis. Data cleaning software and data analysis platforms are used for this process.

[0321] Based on the pre-processed data, the server extracts individual consumer behavior trends. Here, machine learning algorithms (e.g., clustering techniques) are applied to identify individual purchasing patterns. Machine learning libraries in Python or R are used for this process.

[0322] Using the extracted features, the server generates a virtual model and simulates future consumer behavior in a virtual space. This allows for the evaluation of the impact of specific purchasing behaviors on individual budgets and financial goals. Virtualization technology and simulation software are used in this simulation.

[0323] The user enters the items they are considering purchasing through their device. As the smart glasses scan the items, the server visualizes the impact of purchasing those items on the user's budget in real time and displays it on the device. This visualization utilizes augmented reality technology and data visualization tools.

[0324] For example, if a user is considering purchasing high-end coffee equipment, the server predicts the financial impact of that purchase based on the information scanned by the terminal. The system then uses the simulation results to help the user decide whether or not to make the purchase.

[0325] An example of a prompt is: "Please provide the steps to develop an application that analyzes in real time how a user's purchase of an expensive product impacts their monthly budget and displays the results visually."

[0326] This system enables users to consistently make appropriate consumption choices, helping them maintain long-term financial health.

[0327] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0328] Step 1:

[0329] Users generate their own behavioral history information. This is done by collecting daily consumption activities and travel history using smartphones or smart glasses. This information is recorded as time-series data and transmitted to a server.

[0330] Step 2:

[0331] The server stores the received behavioral history information in its data storage device. Since the stored data is unsuitable for analysis in its raw state, preprocessing is required.

[0332] Step 3:

[0333] The server preprocesses the behavioral history data. This process involves estimating and imputing missing information using data cleaning techniques, and detecting and removing outliers using statistical methods. As a result, a formatted dataset is obtained.

[0334] Step 4:

[0335] The server uses pre-processed data to extract individual consumer behavior trends. Here, machine learning algorithms are applied, and consumption patterns are identified through clustering. The input is formatted data, and the output is the consumption trends of each user.

[0336] Step 5:

[0337] The server generates a virtual model from extracted consumer behavior trends and runs a digital simulation. This simulation recreates future consumer behavior in a virtual space and analyzes its impact on the user's budget. The output is the simulation result.

[0338] Step 6:

[0339] The user scans the product they are considering purchasing using their device. Based on this, the server calculates the budget impact of purchasing that product in real time and displays it visually on the device. Augmented reality technology and data visualization tools are used for this.

[0340] Step 7:

[0341] Users review the simulation results visualized on their devices and make purchasing decisions. The results serve as information to help them make the best choices for achieving long-term financial goals.

[0342] In each step, a generative AI model is used to generate prompt messages, supporting necessary analysis and decision-making. This process enables users to make informed purchasing decisions.

[0343] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0344] Embodiments of the present invention include a life simulation system combined with an emotion engine. The server automatically collects life log data from the user's terminal, wearable device, online activity, etc. Next, the emotion engine is used to analyze the user's emotional state from the collected data and record the emotional state in a database. The emotion engine can evaluate emotions from voice tone, message content, behavioral history, etc.

[0345] The server integrates and preprocesses the aforementioned lifelog data and emotional data, filling in missing data and removing outliers. Next, the server extracts characteristics of the user's behavioral patterns, hobbies, and emotional states from the preprocessed data. Based on this characteristic information, the server generates a digital twin and simulates future choices and actions in the virtual space.

[0346] The user uses a terminal to input specific choices or life events they want to simulate into the server. The server sets the simulation parameters based on this information and extracted emotional characteristics. During the simulation, the emotional engine's emotional influence is reflected in the digital twin's behavior, generating multiple scenarios that take the user's emotional state into account.

[0347] The server analyzes the generated scenarios in detail and evaluates the impact each option has on the user's emotions and life. The results are then sent to the terminal in a visual format for the user to see. The user can then use the presented results to make decisions that take emotional aspects into consideration. In this form, the present invention provides support for individuals to make choices that are more emotionally satisfying.

[0348] The following describes the processing flow.

[0349] Step 1:

[0350] The server collects lifelog data from the user's terminal or wearable device. This data includes movement history, heart rate, and social media message history. This allows the server to understand the user's daily activities and psychological changes.

[0351] Step 2:

[0352] The server uses an emotion engine to analyze the user's emotional state from the collected data. The emotion engine analyzes message content, voice tone, and facial recognition data to evaluate the user's emotional state.

[0353] Step 3:

[0354] The server integrates life log data and emotional data and preprocesses this data. This includes imputing missing data and removing outliers, with the goal of preparing the data in a format that is easy to analyze.

[0355] Step 4:

[0356] The server extracts user behavior patterns, hobbies, and emotional characteristics from pre-processed data. This allows for a numerical representation of individual-specific behaviors and emotions, forming the basis for digital twin generation.

[0357] Step 5:

[0358] The user sends requests to the server via their device regarding specific choices or life events they want to simulate. For example, they might input their intention to change jobs or start a new hobby.

[0359] Step 6:

[0360] The server sets the simulation parameters for the digital twin based on the user's input information and emotional characteristics. These settings reflect the user's emotional tendencies and past behavioral history.

[0361] Step 7:

[0362] The server executes multiple simulation scenarios using a digital twin, according to the configured parameters. Because an emotion engine is integrated into the simulation, each scenario progresses while considering the user's emotional responses.

[0363] Step 8:

[0364] The server analyzes the simulation results and evaluates the emotional impact each option has on the user and its effect on their life. The analysis results are sent to the terminal as visual information for the user to review.

[0365] Step 9:

[0366] Users review the simulation results presented through their devices and make decisions after considering their emotional impact. This allows users to make choices that lead to desirable outcomes.

[0367] (Example 2)

[0368] Next, we will describe Example 2. 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".

[0369] In modern society, there is a need to accurately understand the emotional impact individuals experience in their daily lives and to make more satisfying decisions. However, conventional technologies have struggled to simulate situations that take an individual's emotional state into account, resulting in insufficient evaluation of future choices that reflect emotional factors. Therefore, there is a need for decision-making support that takes emotions into account.

[0370] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0371] In this invention, the server includes means for collecting personal life log information and storing it in an information storage device, means for preprocessing the collected life log information to fill in missing information and remove outliers, and means for integrating the collected life log and emotional information using an emotion analysis function and reflecting the emotional state in the simulation. This makes it possible to support decision-making to enhance an individual's emotional satisfaction through a future life simulation that takes emotional state into account.

[0372] "Life log information" refers to data about an individual's activities and behaviors in their daily life.

[0373] "Information storage device" refers to a system or medium for storing collected data.

[0374] "Preprocessing" refers to the techniques of processing and cleaning applied to raw data, and is a process that improves the quality of the data.

[0375] "Imputing missing information" refers to filling in incomplete information in a dataset using existing data or estimates.

[0376] "Outlier removal" is the process of detecting extreme data points in a dataset based on statistical methods and removing those that may negatively impact the analysis.

[0377] "Emotional analysis function" refers to a technology or algorithm for evaluating an individual's emotional state and integrating related data.

[0378] A "virtual model" refers to a digital representation created to reproduce an individual's characteristics and perform simulations in a virtual environment.

[0379] A "virtual environment" refers to a computer-generated simulation space through which users can experience various scenarios.

[0380] "Future life simulation" refers to an attempt to predict future choices and actions within a virtual environment and analyze the results under specific conditions.

[0381] "Emotional state" is a term that describes an individual's psychological or emotional state and is a factor that influences their individual behavior and choices.

[0382] "Decision support" refers to the process of providing information and insights to help a system improve an individual's choices and make more satisfying decisions.

[0383] In implementing this invention, the system consists of a server, a terminal, and a user. The server automatically collects personal life log information from various devices and stores it in an information storage device. The devices from which data is collected include smartphones, wearable devices, and internet platforms. This data includes user behavior, message content, and voice tone.

[0384] The server preprocesses the collected data. It improves data quality by using statistical software and data analysis tools to impart missing information and remove outliers. Based on this preprocessed data, the server uses machine learning algorithms to extract features related to individual behavior patterns and preferences.

[0385] This feature information is used in combination with emotion analysis functions to evaluate the user's emotional state. The evaluated emotion data forms the basis for building a virtual model of the user. The generation of the virtual model takes place on a digital simulation platform, enabling simulations of future life.

[0386] The user uses a terminal to input the choices or events they want to simulate into the server. This input uses prompts for the generated AI model. For example, a user might send the prompt "I want to know how relationships in a new workplace affect emotions" to the server. Based on this information, the server sets the control variables for the simulation and simulates multiple situations.

[0387] The server analyzes the generated simulation results and provides visually clear results to the terminal to support the user's decision-making. This allows the user to make decisions based on their emotional state, supporting choices that enhance their emotional satisfaction.

[0388] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0389] Step 1:

[0390] The server collects user life log information. Inputs include data from smartphones and wearable devices, as well as activity history from online platforms. The server aggregates this information in an information storage device and stores it in a database. Specifically, it retrieves data from devices via APIs and updates the database periodically.

[0391] Step 2:

[0392] The server preprocesses the collected lifelog information. The input is the stored raw data, and the output is a clean, integrated dataset. The server uses statistical analysis software to fill in missing information with historical data and estimates, and to detect and remove outliers. Specifically, it makes full use of filtering algorithms and interpolation techniques.

[0393] Step 3:

[0394] The server extracts feature information from pre-processed data. The input is a clean dataset, and the output is a feature vector relating to user behavior patterns and preferences. Machine learning algorithms are used to analyze the data using clustering and classifiers. A specific example is pattern recognition based on user activity frequency and preferences.

[0395] Step 4:

[0396] The server integrates life logs and feature information collected using sentiment analysis capabilities. The input is a feature vector, and the output is a dataset containing the user's emotional state. Natural language processing techniques and speech tone analysis tools are used to estimate the user's emotions. Specifically, emotions are classified from text messages.

[0397] Step 5:

[0398] The server generates a virtual model based on extracted feature information and emotional states. The input is an integrated dataset, and the output is a user-specific digital twin. Simulations are performed within the virtual environment to model the user's future life. Specifically, 3D model generation tools and simulation engines are used.

[0399] Step 6:

[0400] The user uses a terminal to input specific choices they want to simulate into the server. The input is a prompt message for the generated AI model, and the output is the simulation control variables set by the server. Based on the prompt message, the server reflects various conditions. A concrete example of a prompt might be, "I want to analyze the impact of a new work environment on emotions."

[0401] Step 7:

[0402] The server performs simulations based on configured control variables and generates multiple scenarios. The inputs are the control variables and a digital twin, while the output is the simulation results reflecting various situations. The server utilizes emotion analysis capabilities to reflect the user's emotional state in the scenarios. Specifically, dynamic simulation software is used.

[0403] Step 8:

[0404] The server analyzes the generated simulation results and provides the user with information to support their decision-making. The input is the simulation output, and the output is a visually easy-to-understand insight. The server sends the results to the terminal to help the user make choices that enhance their emotional satisfaction. For example, the results can be displayed as graphs or charts.

[0405] (Application Example 2)

[0406] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0407] The system did not adequately present options that took into account individual emotional states, resulting in users having difficulty making emotionally satisfying decisions. In particular, insights that reflected emotions were not provided in real-world behaviors such as shopping and service selection.

[0408] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0409] In this invention, the server includes means for collecting personal life log data and storing it in a data storage device, means for preprocessing the collected life log data to complete missing data and remove outliers, and means for analyzing emotional data and presenting optimal choices based on the user's emotional state. As a result, the user receives choices that match their emotional state and can make emotionally satisfying decisions.

[0410] "Life log data" refers to digital information that shows an individual's actions and state in their daily life, recording their daily activities and changes.

[0411] A "data storage device" is a storage device for securely storing digital data, and is a hardware or software system that enables data management and access.

[0412] "Data imputation" is the process of inferring and filling in missing information to make a dataset complete.

[0413] "Outlier removal" is a technique that improves data accuracy by identifying and removing or adjusting statistically abnormal values ​​present in a dataset.

[0414] "Behavioral patterns" refer to tendencies in actions and activities that an individual repeatedly performs.

[0415] "Preferences" refer to an individual's likes and tendencies, and can mean a strong attachment to a particular activity or thing.

[0416] A "digital twin" is an accurate digital representation of an object or process in the real world, enabling simulations between reality and the virtual world.

[0417] A "virtual realm" is a digital space that mimics the real world, created using computer technology.

[0418] "Insight" refers to a deep understanding or knowledge gained through analysis and observation, providing crucial information to guide actions and decisions in specific situations.

[0419] "Emotional data" refers to information that quantitatively or qualitatively indicates an individual's emotional state, and is obtained from data such as voice tone, facial expressions, and behavioral history.

[0420] In this invention, the server collects personal lifelog data and processes it for storage in a data storage device. This includes a communication module for acquiring data from smart devices and wearable devices. The server preprocesses the collected lifelog data, fills in missing data, and removes outliers. Data processing software such as Python or TensorFlow is used in this process.

[0421] The server also extracts individual behavioral patterns and preferences based on pre-processed data. This allows for the creation of a digital twin and the simulation of future life in a virtual realm. Simultaneously analyzing the simulation results and user emotional data, the server presents optimal choices. Analysis tools such as Amazon SageMaker are used in this process.

[0422] The device receives insights from the server and functions as an interface that presents the user with options tailored to their emotional state. For example, if a user is determined to be stressed, the system can recommend purchasing products that help with relaxation.

[0423] An example of a prompt using a generative AI model is, "If the customer's emotional state is 'stressed', generate a list of recommended products in the shopping guide." In this way, it is possible to help users make emotionally satisfying decisions.

[0424] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0425] Step 1:

[0426] The server acquires lifelog data from smart devices and wearable devices and stores it in a data storage device. The input here is digital information about daily activities transmitted from each device, and the output is a structured dataset. This process involves receiving and formatting data from devices and appropriately storing it in a database.

[0427] Step 2:

[0428] The server preprocesses the collected lifelog data, filling in missing data and removing outliers. The input is the stored raw data, and the output is a clean dataset with outliers removed. Data processing scripts using Python or Pandas estimate and fill in missing parts, and detect and remove outliers.

[0429] Step 3:

[0430] The server extracts individual behavioral patterns and preferences based on pre-processed data. The input is a clean dataset, and the output is feature vectors related to behavior and preferences. It uses a machine learning model to analyze the data and extract those features.

[0431] Step 4:

[0432] The server generates a digital twin using extracted features and simulates future life in a virtual realm. The input is a feature vector, and the output is the result of the digital twin simulation. It executes a simulation algorithm to generate diverse future scenarios.

[0433] Step 5:

[0434] The server analyzes the simulation results and uses sentiment data to present the optimal choices based on the user's current emotional state. The input is the simulation results and sentiment data, and the output is a list of choices to aid in the user's decision-making. This includes the operation of the sentiment engine, which evaluates the emotional state and optimizes the choices.

[0435] Step 6:

[0436] The terminal presents the user with choices and insights sent from the server. The input is the choices and insights received from the server, and the output is visually formatted information. The terminal displays the information through a user interface, making it easy for the user to understand.

[0437] Step 7:

[0438] The user makes a decision based on the information presented, taking emotional aspects into consideration. The input is the information presented from the device, and the output is the user's chosen actions and decisions. This includes making judgments based on the given information and deciding on the next course of action.

[0439] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0440] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0441] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0442] [Third Embodiment]

[0443] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0444] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0445] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0446] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0447] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0448] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0449] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0450] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0451] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0452] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0453] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0454] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0455] An embodiment for carrying out the present invention consists of the following steps.

[0456] First, after obtaining permission from the user, the server automatically collects lifelog data from the user's terminal, wearable devices, and social media. This data includes the user's movement history, fitness activities, and online activities. Next, the server preprocesses the collected data by organizing it, imputing missing data, and removing outliers. This creates a well-structured dataset suitable for analysis.

[0457] Subsequently, the server uses the pre-processed data to clarify the user's behavioral patterns and hobbies / preferences using a feature extraction algorithm. Based on the feature information obtained in this process, the server generates a digital twin unique to the user. The digital twin operates in a virtual environment that mimics the real world, recreating situations where the user needs to consider specific choices.

[0458] Users input choices related to life events such as changing jobs, moving, or relationships into the server via a terminal. Based on this input, the server sets the parameters for the simulation. According to the set parameters, the digital twin considers multiple choices and simulates actions in a virtual space. Each simulation result is recorded in detail, and the server analyzes them to predict the possible consequences of a particular choice.

[0459] Ultimately, the server visualizes and provides the results to the user's terminal to support their decision-making. The user can review the presented data and make the decision best suited to their situation. Through this mechanism, the present invention aims to strongly support individual decision-making in today's complex society.

[0460] The following describes the processing flow.

[0461] Step 1:

[0462] The server automatically collects life log data from the user's terminal or wearable device. This data includes location information, fitness information, and online activity, and is obtained in a secure manner.

[0463] Step 2:

[0464] The server performs preprocessing to format the collected lifelog data. This process involves imputing missing data, removing outliers, and converting the data into a format suitable for analysis.

[0465] Step 3:

[0466] The server uses pre-processed data to extract user behavior patterns, hobbies, and preferences. This process uses data mining techniques to clarify the characteristics of individual users.

[0467] Step 4:

[0468] The server generates a digital twin of the user based on the extracted feature information. The digital twin is a model that reproduces the user's real-world behavior in a virtual space.

[0469] Step 5:

[0470] Users use a terminal to input information into the server about choices and life events they want to simulate. This includes things like changing jobs or moving.

[0471] Step 6:

[0472] The server sets the simulation parameters based on the input information. This setting provides the foundation for the digital twin to perform simulations in a virtual space.

[0473] Step 7:

[0474] The server runs simulations under multiple scenarios and predicts the outcome based on each choice. It meticulously records the obtained data and analyzes the results for each scenario.

[0475] Step 8:

[0476] Based on the analyzed simulation results, the server provides users with insights via their terminals to help them make optimal choices. Users can then use this information to make their actual decisions.

[0477] (Example 1)

[0478] Next, we will describe Example 1. 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."

[0479] In modern society, the complexity of choices and decision-making that individuals face is increasing. Appropriate judgments are required for various life events, such as changing jobs, moving, or starting a new hobby, but it is difficult to assess the impact of each choice in advance. Therefore, predicting the consequences of future choices based on an individual's behavior and preferences, and supporting their decision-making, is a crucial challenge.

[0480] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0481] In this invention, the server includes means for collecting and storing information related to an individual's behavior in an information storage medium, means for preprocessing the collected behavior-related information to fill in missing information and remove abnormal values, and means for extracting characteristics related to the individual's behavioral traits and preferences based on the preprocessed information. This makes it possible to simulate future outcomes and impacts based on various choices that an individual should consider, and to support decision-making.

[0482] "Information related to individual behavior" refers to data about a user's daily life and online activities, such as their travel history, fitness activities, and online activities.

[0483] "Information storage media" refers to devices and systems for storing data, and includes databases and cloud storage.

[0484] "Preprocessing" refers to processes performed to prepare collected data into an analyzable format, such as organizing the data, imputing missing values, and removing outliers.

[0485] "Behavioral characteristics and preferences" refers to information that quantifies or categorizes an individual's behavioral patterns, hobbies, and interests.

[0486] A "virtual model" refers to a simulated data model that is recreated in a digital space based on an individual's characteristics.

[0487] A "virtual domain" refers to a simulated environment that resembles the real world, reproduced within a computer.

[0488] "Future behavior simulation" refers to a process that uses a user-specific digital data model to virtually recreate future scenarios based on various choices and evaluate their impact in advance.

[0489] "Insight" refers to useful knowledge and suggestions derived from analyzing the results of a simulation.

[0490] "Communication equipment" refers to devices used by users to input information and send and receive data to and from a server.

[0491] A "choice pattern" refers to a set of various options or scenarios that a user considers.

[0492] A "situation" refers to a hypothetical scenario generated based on a specific selection pattern, describing how the digital model behaves within that scenario.

[0493] A description of embodiments for carrying out the present invention will be provided.

[0494] In this system, the server plays a central role. First, after obtaining user permission, the server collects "personal behavior-related information" from the user's terminal, wearable devices, and social media. This information includes travel history, fitness activities, and online activity. The collected data is stored in "information storage media" such as cloud storage. For data collection, the server uses dedicated APIs and database management systems.

[0495] Next, the server "preprocesses" the collected information. In this step, libraries such as Python's Pandas are used to impute missing information and remove abnormal values. During this process, the data is prepared into a user-friendly format using statistical analysis and machine learning algorithms.

[0496] Subsequently, the server uses extraction algorithms to extract "behavioral characteristics and preferences." As part of this process, machine learning libraries such as Scikit-learn are used. This clarifies an individual's behavioral patterns as numerical values ​​and categories.

[0497] Next, based on the extracted features, the server generates a "virtual model," or digital twin. This model operates in a "virtual domain" using Unity or other 3D simulation tools, enabling the simulation of the user's future actions and choices.

[0498] The user sends the "selection patterns" they want to simulate as prompt messages to the server via their terminal. For example, they might enter a prompt such as, "Please simulate the choice of taking up yoga as a hobby." This sets the conditions for the simulation, and the server virtually reproduces various scenarios.

[0499] Ultimately, the server analyzes the simulation results and provides "insights" to support the user's decision-making. These results are visualized on the terminal as graphs and charts, allowing the user to make informed decisions based on them.

[0500] This system aims to support users in making appropriate decisions by utilizing generative AI models to proactively evaluate the future impact of various choices they face.

[0501] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0502] Step 1:

[0503] After obtaining user permission, the server collects personal behavior-related information from the user's device, wearable devices, and social media. Inputs include data on movement history, fitness activities, and online activities. This data is retrieved using APIs and data collection programs and stored in cloud storage. Outputs are collected information in a structured data format.

[0504] Step 2:

[0505] The server preprocesses the collected information. The input is the raw data obtained in step 1. The data is organized using the Pandas library, and the mean or linear interpolation is used to impute missing values. Outliers are identified and removed based on the standard deviation. The output is a clean dataset prepared for analysis.

[0506] Step 3:

[0507] The server extracts features related to behavioral characteristics and preferences based on preprocessed data. The input is the clean data obtained in step 2. The server uses the Scikit-learn library to perform feature extraction based on clustering and principal component analysis. The output is numerical or categorical features related to user behavior patterns and preferences.

[0508] Step 4:

[0509] The server generates a virtual model based on the extracted features. The input is the feature data obtained in step 3. Using a 3D simulation tool such as Unity, a digital twin is created in a virtual space, constructing an environment that mimics the real world. The output is a digital twin that can be operated in the virtual space.

[0510] Step 5:

[0511] The user sends the selection patterns they want to simulate as prompts to the server via their terminal. The input is the prompt entered by the user from their terminal. An example would be a specific request such as, "Please simulate the selection of taking up yoga as a hobby." The output is the parameters necessary for the simulation, which the server receives.

[0512] Step 6:

[0513] The server performs a digital twin simulation according to the configured parameters. The input is the simulation parameters obtained in step 5. The server runs multiple scenarios in the virtual environment and records the results of each. The output is the detailed simulation results, which are data on the impact of each choice.

[0514] Step 7:

[0515] The server analyzes the simulation results and provides insights to support user decision-making. The input is the simulation results obtained in step 6. Machine learning algorithms are used to analyze the results and extract suggestions and trends for the user. The output is visualized insights on the terminal and presented to the user as graphs and charts.

[0516] (Application Example 1)

[0517] Next, we will explain Application Example 1. In the following explanation, 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."

[0518] Individual consumer behavior often presents challenges in budget management and rational decision-making. This is because it's difficult to understand in real time how the goods and services being considered for purchase will impact one's individual financial situation. This problem frequently leads to overconsumption and unplanned spending, hindering the achievement of long-term financial goals. Therefore, there is a need for purchasing support tools based on individual consumption trends.

[0519] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0520] In this invention, the server includes means for collecting and storing an individual's behavioral history information in an information storage device, means for preprocessing the collected behavioral history information to fill in missing information and remove outliers, and means for generating a virtual model using the extracted characteristics and simulating future consumption behavior in a virtual space. This makes it possible to visualize the impact of an individual's consumption behavior in real time and support budget management.

[0521] "Behavioral history information" refers to data that shows a record of an individual's daily consumption-related behaviors and movements.

[0522] An "information storage device" is a digital storage medium or system used to store collected data.

[0523] "Preprocessing" refers to the procedure of preparing collected raw data by filling in missing information and removing outliers, thereby creating a format suitable for analysis.

[0524] "Consumer behavior trends" refer to a set of statistical and behavioral characteristics that indicate what kind of consumption patterns an individual typically has.

[0525] A "virtual model" is a digital model created on a computer for simulation purposes, with the aim of mimicking an individual's consumption activities.

[0526] "Simulation of future consumer behavior" is an analytical process that uses virtual models to predict an individual's future consumption patterns and financial situation.

[0527] "Visualization" refers to the visual representation of analysis results or simulation results using graphs and charts.

[0528] "Budget management" is the process of planning and adjusting spending based on financial goals set by an individual.

[0529] To realize this invention, it is necessary to configure a system in cooperation with a server, terminal, and user. The server first collects individual behavioral history information and stores it in an information storage device. Behavioral history information includes shopping history, travel routes, and online activities. This uses data acquired via devices such as smartphones and smart glasses.

[0530] Next, the server preprocesses the collected behavioral history information. Specifically, it fills in missing information using statistical inference and external databases, and removes outliers to form a dataset suitable for analysis. Data cleaning software and data analysis platforms are used for this process.

[0531] Based on the pre-processed data, the server extracts individual consumer behavior trends. Here, machine learning algorithms (e.g., clustering techniques) are applied to identify individual purchasing patterns. Machine learning libraries in Python or R are used for this process.

[0532] Using the extracted features, the server generates a virtual model and simulates future consumer behavior in a virtual space. This allows for the evaluation of the impact of specific purchasing behaviors on individual budgets and financial goals. Virtualization technology and simulation software are used in this simulation.

[0533] The user enters the items they are considering purchasing through their device. As the smart glasses scan the items, the server visualizes the impact of purchasing those items on the user's budget in real time and displays it on the device. This visualization utilizes augmented reality technology and data visualization tools.

[0534] For example, if a user is considering purchasing high-end coffee equipment, the server predicts the financial impact of that purchase based on the information scanned by the terminal. The system then uses the simulation results to help the user decide whether or not to make the purchase.

[0535] An example of a prompt is: "Please provide the steps to develop an application that analyzes in real time how a user's purchase of an expensive product impacts their monthly budget and displays the results visually."

[0536] This system enables users to consistently make appropriate consumption choices, helping them maintain long-term financial health.

[0537] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0538] Step 1:

[0539] Users generate their own behavioral history information. This is done by collecting daily consumption activities and travel history using smartphones or smart glasses. This information is recorded as time-series data and transmitted to a server.

[0540] Step 2:

[0541] The server stores the received behavioral history information in its data storage device. Since the stored data is unsuitable for analysis in its raw state, preprocessing is required.

[0542] Step 3:

[0543] The server preprocesses the behavioral history data. This process involves estimating and imputing missing information using data cleaning techniques, and detecting and removing outliers using statistical methods. As a result, a formatted dataset is obtained.

[0544] Step 4:

[0545] The server uses pre-processed data to extract individual consumer behavior trends. Here, machine learning algorithms are applied, and consumption patterns are identified through clustering. The input is formatted data, and the output is the consumption trends of each user.

[0546] Step 5:

[0547] The server generates a virtual model from extracted consumer behavior trends and runs a digital simulation. This simulation recreates future consumer behavior in a virtual space and analyzes its impact on the user's budget. The output is the simulation result.

[0548] Step 6:

[0549] The user scans the product they are considering purchasing using their device. Based on this, the server calculates the budget impact of purchasing that product in real time and displays it visually on the device. Augmented reality technology and data visualization tools are used for this.

[0550] Step 7:

[0551] Users review the simulation results visualized on their devices and make purchasing decisions. The results serve as information to help them make the best choices for achieving long-term financial goals.

[0552] In each step, a generative AI model is used to generate prompt messages, supporting necessary analysis and decision-making. This process enables users to make informed purchasing decisions.

[0553] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0554] Embodiments of the present invention include a life simulation system combined with an emotion engine. The server automatically collects life log data from the user's terminal, wearable device, online activity, etc. Next, the emotion engine is used to analyze the user's emotional state from the collected data and record the emotional state in a database. The emotion engine can evaluate emotions from voice tone, message content, behavioral history, etc.

[0555] The server integrates and preprocesses the aforementioned lifelog data and emotional data, filling in missing data and removing outliers. Next, the server extracts characteristics of the user's behavioral patterns, hobbies, and emotional states from the preprocessed data. Based on this characteristic information, the server generates a digital twin and simulates future choices and actions in the virtual space.

[0556] The user uses a terminal to input specific choices or life events they want to simulate into the server. The server sets the simulation parameters based on this information and extracted emotional characteristics. During the simulation, the emotional engine's emotional influence is reflected in the digital twin's behavior, generating multiple scenarios that take the user's emotional state into account.

[0557] The server analyzes the generated scenarios in detail and evaluates the impact each option has on the user's emotions and life. The results are then sent to the terminal in a visual format for the user to see. The user can then use the presented results to make decisions that take emotional aspects into consideration. In this form, the present invention provides support for individuals to make choices that are more emotionally satisfying.

[0558] The following describes the processing flow.

[0559] Step 1:

[0560] The server collects lifelog data from the user's terminal or wearable device. This data includes movement history, heart rate, and social media message history. This allows the server to understand the user's daily activities and psychological changes.

[0561] Step 2:

[0562] The server uses an emotion engine to analyze the user's emotional state from the collected data. The emotion engine analyzes message content, voice tone, and facial recognition data to evaluate the user's emotional state.

[0563] Step 3:

[0564] The server integrates life log data and emotional data and preprocesses this data. This includes imputing missing data and removing outliers, with the goal of preparing the data in a format that is easy to analyze.

[0565] Step 4:

[0566] The server extracts user behavior patterns, hobbies, and emotional characteristics from pre-processed data. This allows for a numerical representation of individual-specific behaviors and emotions, forming the basis for digital twin generation.

[0567] Step 5:

[0568] The user sends requests to the server via their device regarding specific choices or life events they want to simulate. For example, they might input their intention to change jobs or start a new hobby.

[0569] Step 6:

[0570] The server sets the simulation parameters for the digital twin based on the user's input information and emotional characteristics. These settings reflect the user's emotional tendencies and past behavioral history.

[0571] Step 7:

[0572] The server executes multiple simulation scenarios using a digital twin, according to the configured parameters. Because an emotion engine is integrated into the simulation, each scenario progresses while considering the user's emotional responses.

[0573] Step 8:

[0574] The server analyzes the simulation results and evaluates the emotional impact each option has on the user and its effect on their life. The analysis results are sent to the terminal as visual information for the user to review.

[0575] Step 9:

[0576] Users review the simulation results presented through their devices and make decisions after considering their emotional impact. This allows users to make choices that lead to desirable outcomes.

[0577] (Example 2)

[0578] Next, we will describe Example 2. 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."

[0579] In modern society, there is a need to accurately understand the emotional impact individuals experience in their daily lives and to make more satisfying decisions. However, conventional technologies have struggled to simulate situations that take an individual's emotional state into account, resulting in insufficient evaluation of future choices that reflect emotional factors. Therefore, there is a need for decision-making support that takes emotions into account.

[0580] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0581] In this invention, the server includes means for collecting personal life log information and storing it in an information storage device, means for preprocessing the collected life log information to fill in missing information and remove outliers, and means for integrating the collected life log and emotional information using an emotion analysis function and reflecting the emotional state in the simulation. This makes it possible to support decision-making to enhance an individual's emotional satisfaction through a future life simulation that takes emotional state into account.

[0582] "Life log information" refers to data about an individual's activities and behaviors in their daily life.

[0583] "Information storage device" refers to a system or medium for storing collected data.

[0584] "Preprocessing" refers to the techniques of processing and cleaning applied to raw data, and is a process that improves the quality of the data.

[0585] "Imputing missing information" refers to filling in incomplete information in a dataset using existing data or estimates.

[0586] "Outlier removal" is the process of detecting extreme data points in a dataset based on statistical methods and removing those that may negatively impact the analysis.

[0587] "Emotional analysis function" refers to a technology or algorithm for evaluating an individual's emotional state and integrating related data.

[0588] A "virtual model" refers to a digital representation created to reproduce an individual's characteristics and perform simulations in a virtual environment.

[0589] A "virtual environment" refers to a computer-generated simulation space through which users can experience various scenarios.

[0590] "Future life simulation" refers to an attempt to predict future choices and actions within a virtual environment and analyze the results under specific conditions.

[0591] "Emotional state" is a term that describes an individual's psychological or emotional state and is a factor that influences their individual behavior and choices.

[0592] "Decision support" refers to the process of providing information and insights to help a system improve an individual's choices and make more satisfying decisions.

[0593] In implementing this invention, the system consists of a server, a terminal, and a user. The server automatically collects personal life log information from various devices and stores it in an information storage device. The devices from which data is collected include smartphones, wearable devices, and internet platforms. This data includes user behavior, message content, and voice tone.

[0594] The server preprocesses the collected data. It improves data quality by using statistical software and data analysis tools to impart missing information and remove outliers. Based on this preprocessed data, the server uses machine learning algorithms to extract features related to individual behavior patterns and preferences.

[0595] This feature information is used in combination with emotion analysis functions to evaluate the user's emotional state. The evaluated emotion data forms the basis for building a virtual model of the user. The generation of the virtual model takes place on a digital simulation platform, enabling simulations of future life.

[0596] The user uses a terminal to input the choices or events they want to simulate into the server. This input uses prompts for the generated AI model. For example, a user might send the prompt "I want to know how relationships in a new workplace affect emotions" to the server. Based on this information, the server sets the control variables for the simulation and simulates multiple situations.

[0597] The server analyzes the generated simulation results and provides visually clear results to the terminal to support the user's decision-making. This allows the user to make decisions based on their emotional state, supporting choices that enhance their emotional satisfaction.

[0598] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0599] Step 1:

[0600] The server collects user life log information. Inputs include data from smartphones and wearable devices, as well as activity history from online platforms. The server aggregates this information in an information storage device and stores it in a database. Specifically, it retrieves data from devices via APIs and updates the database periodically.

[0601] Step 2:

[0602] The server preprocesses the collected lifelog information. The input is the stored raw data, and the output is a clean, integrated dataset. The server uses statistical analysis software to fill in missing information with historical data and estimates, and to detect and remove outliers. Specifically, it makes full use of filtering algorithms and interpolation techniques.

[0603] Step 3:

[0604] The server extracts feature information from pre-processed data. The input is a clean dataset, and the output is a feature vector relating to user behavior patterns and preferences. Machine learning algorithms are used to analyze the data using clustering and classifiers. A specific example is pattern recognition based on user activity frequency and preferences.

[0605] Step 4:

[0606] The server integrates life logs and feature information collected using sentiment analysis capabilities. The input is a feature vector, and the output is a dataset containing the user's emotional state. Natural language processing techniques and speech tone analysis tools are used to estimate the user's emotions. Specifically, emotions are classified from text messages.

[0607] Step 5:

[0608] The server generates a virtual model based on extracted feature information and emotional states. The input is an integrated dataset, and the output is a user-specific digital twin. Simulations are performed within the virtual environment to model the user's future life. Specifically, 3D model generation tools and simulation engines are used.

[0609] Step 6:

[0610] The user uses a terminal to input specific choices they want to simulate into the server. The input is a prompt message for the generated AI model, and the output is the simulation control variables set by the server. Based on the prompt message, the server reflects various conditions. A concrete example of a prompt might be, "I want to analyze the impact of a new work environment on emotions."

[0611] Step 7:

[0612] The server performs simulations based on configured control variables and generates multiple scenarios. The inputs are the control variables and a digital twin, while the output is the simulation results reflecting various situations. The server utilizes emotion analysis capabilities to reflect the user's emotional state in the scenarios. Specifically, dynamic simulation software is used.

[0613] Step 8:

[0614] The server analyzes the generated simulation results and provides the user with information to support their decision-making. The input is the simulation output, and the output is a visually easy-to-understand insight. The server sends the results to the terminal to help the user make choices that enhance their emotional satisfaction. For example, the results can be displayed as graphs or charts.

[0615] (Application Example 2)

[0616] Next, we will explain application example 2. In the following explanation, 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."

[0617] The system did not adequately present options that took into account individual emotional states, resulting in users having difficulty making emotionally satisfying decisions. In particular, insights that reflected emotions were not provided in real-world behaviors such as shopping and service selection.

[0618] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0619] In this invention, the server includes means for collecting personal life log data and storing it in a data storage device, means for preprocessing the collected life log data to complete missing data and remove outliers, and means for analyzing emotional data and presenting optimal choices based on the user's emotional state. As a result, the user receives choices that match their emotional state and can make emotionally satisfying decisions.

[0620] "Life log data" refers to digital information that shows an individual's actions and state in their daily life, recording their daily activities and changes.

[0621] A "data storage device" is a storage device for securely storing digital data, and is a hardware or software system that enables data management and access.

[0622] "Data imputation" is the process of inferring and filling in missing information to make a dataset complete.

[0623] "Outlier removal" is a technique that improves data accuracy by identifying and removing or adjusting statistically abnormal values ​​present in a dataset.

[0624] "Behavioral patterns" refer to tendencies in actions and activities that an individual repeatedly performs.

[0625] "Preferences" refer to an individual's likes and tendencies, and can mean a strong attachment to a particular activity or thing.

[0626] A "digital twin" is an accurate digital representation of an object or process in the real world, enabling simulations between reality and the virtual world.

[0627] A "virtual realm" is a digital space that mimics the real world, created using computer technology.

[0628] "Insight" refers to a deep understanding or knowledge gained through analysis and observation, providing crucial information to guide actions and decisions in specific situations.

[0629] "Emotional data" refers to information that quantitatively or qualitatively indicates an individual's emotional state, and is obtained from data such as voice tone, facial expressions, and behavioral history.

[0630] In this invention, the server collects personal lifelog data and processes it for storage in a data storage device. This includes a communication module for acquiring data from smart devices and wearable devices. The server preprocesses the collected lifelog data, fills in missing data, and removes outliers. Data processing software such as Python or TensorFlow is used in this process.

[0631] The server also extracts individual behavioral patterns and preferences based on pre-processed data. This allows for the creation of a digital twin and the simulation of future life in a virtual realm. Simultaneously analyzing the simulation results and user emotional data, the server presents optimal choices. Analysis tools such as Amazon SageMaker are used in this process.

[0632] The device receives insights from the server and functions as an interface that presents the user with options tailored to their emotional state. For example, if a user is determined to be stressed, the system can recommend purchasing products that help with relaxation.

[0633] An example of a prompt using a generative AI model is, "If the customer's emotional state is 'stressed', generate a list of recommended products in the shopping guide." In this way, it is possible to help users make emotionally satisfying decisions.

[0634] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0635] Step 1:

[0636] The server acquires lifelog data from smart devices and wearable devices and stores it in a data storage device. The input here is digital information about daily activities transmitted from each device, and the output is a structured dataset. This process involves receiving and formatting data from devices and appropriately storing it in a database.

[0637] Step 2:

[0638] The server preprocesses the collected lifelog data, filling in missing data and removing outliers. The input is the stored raw data, and the output is a clean dataset with outliers removed. Data processing scripts using Python or Pandas estimate and fill in missing parts, and detect and remove outliers.

[0639] Step 3:

[0640] The server extracts individual behavioral patterns and preferences based on pre-processed data. The input is a clean dataset, and the output is feature vectors related to behavior and preferences. It uses a machine learning model to analyze the data and extract those features.

[0641] Step 4:

[0642] The server generates a digital twin using extracted features and simulates future life in a virtual realm. The input is a feature vector, and the output is the result of the digital twin simulation. It executes a simulation algorithm to generate diverse future scenarios.

[0643] Step 5:

[0644] The server analyzes the simulation results and uses sentiment data to present the optimal choices based on the user's current emotional state. The input is the simulation results and sentiment data, and the output is a list of choices to aid in the user's decision-making. This includes the operation of the sentiment engine, which evaluates the emotional state and optimizes the choices.

[0645] Step 6:

[0646] The terminal presents the user with choices and insights sent from the server. The input is the choices and insights received from the server, and the output is visually formatted information. The terminal displays the information through a user interface, making it easy for the user to understand.

[0647] Step 7:

[0648] The user makes a decision based on the information presented, taking emotional aspects into consideration. The input is the information presented from the device, and the output is the user's chosen actions and decisions. This includes making judgments based on the given information and deciding on the next course of action.

[0649] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0650] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0651] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0652] [Fourth Embodiment]

[0653] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0654] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0655] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0656] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0657] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0658] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0659] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0660] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0661] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0662] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0663] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0664] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0665] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0666] An embodiment for carrying out the present invention consists of the following steps.

[0667] First, after obtaining permission from the user, the server automatically collects lifelog data from the user's terminal, wearable devices, and social media. This data includes the user's movement history, fitness activities, and online activities. Next, the server preprocesses the collected data by organizing it, imputing missing data, and removing outliers. This creates a well-structured dataset suitable for analysis.

[0668] Subsequently, the server uses the pre-processed data to clarify the user's behavioral patterns and hobbies / preferences using a feature extraction algorithm. Based on the feature information obtained in this process, the server generates a digital twin unique to the user. The digital twin operates in a virtual environment that mimics the real world, recreating situations where the user needs to consider specific choices.

[0669] Users input choices related to life events such as changing jobs, moving, or relationships into the server via a terminal. Based on this input, the server sets the parameters for the simulation. According to the set parameters, the digital twin considers multiple choices and simulates actions in a virtual space. Each simulation result is recorded in detail, and the server analyzes them to predict the possible consequences of a particular choice.

[0670] Ultimately, the server visualizes and provides the results to the user's terminal to support their decision-making. The user can review the presented data and make the decision best suited to their situation. Through this mechanism, the present invention aims to strongly support individual decision-making in today's complex society.

[0671] The following describes the processing flow.

[0672] Step 1:

[0673] The server automatically collects life log data from the user's terminal or wearable device. This data includes location information, fitness information, and online activity, and is obtained in a secure manner.

[0674] Step 2:

[0675] The server performs preprocessing to format the collected lifelog data. This process involves imputing missing data, removing outliers, and converting the data into a format suitable for analysis.

[0676] Step 3:

[0677] The server uses pre-processed data to extract user behavior patterns, hobbies, and preferences. This process uses data mining techniques to clarify the characteristics of individual users.

[0678] Step 4:

[0679] The server generates a digital twin of the user based on the extracted feature information. The digital twin is a model that reproduces the user's real-world behavior in a virtual space.

[0680] Step 5:

[0681] Users use a terminal to input information into the server about choices and life events they want to simulate. This includes things like changing jobs or moving.

[0682] Step 6:

[0683] The server sets the simulation parameters based on the input information. This setting provides the foundation for the digital twin to perform simulations in a virtual space.

[0684] Step 7:

[0685] The server runs simulations under multiple scenarios and predicts the outcome based on each choice. It meticulously records the obtained data and analyzes the results for each scenario.

[0686] Step 8:

[0687] Based on the analyzed simulation results, the server provides users with insights via their terminals to help them make optimal choices. Users can then use this information to make their actual decisions.

[0688] (Example 1)

[0689] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0690] In modern society, the complexity of choices and decision-making that individuals face is increasing. Appropriate judgments are required for various life events, such as changing jobs, moving, or starting a new hobby, but it is difficult to assess the impact of each choice in advance. Therefore, predicting the consequences of future choices based on an individual's behavior and preferences, and supporting their decision-making, is a crucial challenge.

[0691] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0692] In this invention, the server includes means for collecting and storing information related to an individual's behavior in an information storage medium, means for preprocessing the collected behavior-related information to fill in missing information and remove abnormal values, and means for extracting characteristics related to the individual's behavioral traits and preferences based on the preprocessed information. This makes it possible to simulate future outcomes and impacts based on various choices that an individual should consider, and to support decision-making.

[0693] "Information related to individual behavior" refers to data about a user's daily life and online activities, such as their travel history, fitness activities, and online activities.

[0694] "Information storage media" refers to devices and systems for storing data, and includes databases and cloud storage.

[0695] "Preprocessing" refers to processes performed to prepare collected data into an analyzable format, such as organizing the data, imputing missing values, and removing outliers.

[0696] "Behavioral characteristics and preferences" refers to information that quantifies or categorizes an individual's behavioral patterns, hobbies, and interests.

[0697] A "virtual model" refers to a simulated data model that is recreated in a digital space based on an individual's characteristics.

[0698] A "virtual domain" refers to a simulated environment that resembles the real world, reproduced within a computer.

[0699] "Future behavior simulation" refers to a process that uses a user-specific digital data model to virtually recreate future scenarios based on various choices and evaluate their impact in advance.

[0700] "Insight" refers to useful knowledge and suggestions derived from analyzing the results of a simulation.

[0701] "Communication equipment" refers to devices used by users to input information and send and receive data to and from a server.

[0702] A "choice pattern" refers to a set of various options or scenarios that a user considers.

[0703] A "situation" refers to a hypothetical scenario generated based on a specific selection pattern, describing how the digital model behaves within that scenario.

[0704] A description of embodiments for carrying out the present invention will be provided.

[0705] In this system, the server plays a central role. First, after obtaining user permission, the server collects "personal behavior-related information" from the user's terminal, wearable devices, and social media. This information includes travel history, fitness activities, and online activity. The collected data is stored in "information storage media" such as cloud storage. For data collection, the server uses dedicated APIs and database management systems.

[0706] Next, the server "preprocesses" the collected information. In this step, libraries such as Python's Pandas are used to impute missing information and remove abnormal values. During this process, the data is prepared into a user-friendly format using statistical analysis and machine learning algorithms.

[0707] Subsequently, the server uses extraction algorithms to extract "behavioral characteristics and preferences." As part of this process, machine learning libraries such as Scikit-learn are used. This clarifies an individual's behavioral patterns as numerical values ​​and categories.

[0708] Next, based on the extracted features, the server generates a "virtual model," or digital twin. This model operates in a "virtual domain" using Unity or other 3D simulation tools, enabling the simulation of the user's future actions and choices.

[0709] The user sends the "selection patterns" they want to simulate as prompt messages to the server via their terminal. For example, they might enter a prompt such as, "Please simulate the choice of taking up yoga as a hobby." This sets the conditions for the simulation, and the server virtually reproduces various scenarios.

[0710] Ultimately, the server analyzes the simulation results and provides "insights" to support the user's decision-making. These results are visualized on the terminal as graphs and charts, allowing the user to make informed decisions based on them.

[0711] This system aims to support users in making appropriate decisions by utilizing generative AI models to proactively evaluate the future impact of various choices they face.

[0712] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0713] Step 1:

[0714] After obtaining user permission, the server collects personal behavior-related information from the user's device, wearable devices, and social media. Inputs include data on movement history, fitness activities, and online activities. This data is retrieved using APIs and data collection programs and stored in cloud storage. Outputs are collected information in a structured data format.

[0715] Step 2:

[0716] The server preprocesses the collected information. The input is the raw data obtained in step 1. The data is organized using the Pandas library, and the mean or linear interpolation is used to impute missing values. Outliers are identified and removed based on the standard deviation. The output is a clean dataset prepared for analysis.

[0717] Step 3:

[0718] The server extracts features related to behavioral characteristics and preferences based on preprocessed data. The input is the clean data obtained in step 2. The server uses the Scikit-learn library to perform feature extraction based on clustering and principal component analysis. The output is numerical or categorical features related to user behavior patterns and preferences.

[0719] Step 4:

[0720] The server generates a virtual model based on the extracted features. The input is the feature data obtained in step 3. Using a 3D simulation tool such as Unity, a digital twin is created in a virtual space, constructing an environment that mimics the real world. The output is a digital twin that can be operated in the virtual space.

[0721] Step 5:

[0722] The user sends the selection patterns they want to simulate as prompts to the server via their terminal. The input is the prompt entered by the user from their terminal. An example would be a specific request such as, "Please simulate the selection of taking up yoga as a hobby." The output is the parameters necessary for the simulation, which the server receives.

[0723] Step 6:

[0724] The server performs a digital twin simulation according to the configured parameters. The input is the simulation parameters obtained in step 5. The server runs multiple scenarios in the virtual environment and records the results of each. The output is the detailed simulation results, which are data on the impact of each choice.

[0725] Step 7:

[0726] The server analyzes the simulation results and provides insights to support user decision-making. The input is the simulation results obtained in step 6. Machine learning algorithms are used to analyze the results and extract suggestions and trends for the user. The output is visualized insights on the terminal and presented to the user as graphs and charts.

[0727] (Application Example 1)

[0728] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0729] Individual consumer behavior often presents challenges in budget management and rational decision-making. This is because it's difficult to understand in real time how the goods and services being considered for purchase will impact one's individual financial situation. This problem frequently leads to overconsumption and unplanned spending, hindering the achievement of long-term financial goals. Therefore, there is a need for purchasing support tools based on individual consumption trends.

[0730] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0731] In this invention, the server includes means for collecting and storing an individual's behavioral history information in an information storage device, means for preprocessing the collected behavioral history information to fill in missing information and remove outliers, and means for generating a virtual model using the extracted characteristics and simulating future consumption behavior in a virtual space. This makes it possible to visualize the impact of an individual's consumption behavior in real time and support budget management.

[0732] "Behavioral history information" refers to data that shows a record of an individual's daily consumption-related behaviors and movements.

[0733] An "information storage device" is a digital storage medium or system used to store collected data.

[0734] "Preprocessing" refers to the procedure of preparing collected raw data by filling in missing information and removing outliers, thereby creating a format suitable for analysis.

[0735] "Consumer behavior trends" refer to a set of statistical and behavioral characteristics that indicate what kind of consumption patterns an individual typically has.

[0736] A "virtual model" is a digital model created on a computer for simulation purposes, with the aim of mimicking an individual's consumption activities.

[0737] "Simulation of future consumer behavior" is an analytical process that uses virtual models to predict an individual's future consumption patterns and financial situation.

[0738] "Visualization" refers to the visual representation of analysis results or simulation results using graphs and charts.

[0739] "Budget management" is the process of planning and adjusting spending based on financial goals set by an individual.

[0740] To realize this invention, it is necessary to configure a system in cooperation with a server, terminal, and user. The server first collects individual behavioral history information and stores it in an information storage device. Behavioral history information includes shopping history, travel routes, and online activities. This uses data acquired via devices such as smartphones and smart glasses.

[0741] Next, the server preprocesses the collected behavioral history information. Specifically, it fills in missing information using statistical inference and external databases, and removes outliers to form a dataset suitable for analysis. Data cleaning software and data analysis platforms are used for this process.

[0742] Based on the pre-processed data, the server extracts individual consumer behavior trends. Here, machine learning algorithms (e.g., clustering techniques) are applied to identify individual purchasing patterns. Machine learning libraries in Python or R are used for this process.

[0743] Using the extracted features, the server generates a virtual model and simulates future consumer behavior in a virtual space. This allows for the evaluation of the impact of specific purchasing behaviors on individual budgets and financial goals. Virtualization technology and simulation software are used in this simulation.

[0744] The user enters the items they are considering purchasing through their device. As the smart glasses scan the items, the server visualizes the impact of purchasing those items on the user's budget in real time and displays it on the device. This visualization utilizes augmented reality technology and data visualization tools.

[0745] For example, if a user is considering purchasing high-end coffee equipment, the server predicts the financial impact of that purchase based on the information scanned by the terminal. The system then uses the simulation results to help the user decide whether or not to make the purchase.

[0746] An example of a prompt is: "Please provide the steps to develop an application that analyzes in real time how a user's purchase of an expensive product impacts their monthly budget and displays the results visually."

[0747] This system enables users to consistently make appropriate consumption choices, helping them maintain long-term financial health.

[0748] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0749] Step 1:

[0750] Users generate their own behavioral history information. This is done by collecting daily consumption activities and travel history using smartphones or smart glasses. This information is recorded as time-series data and transmitted to a server.

[0751] Step 2:

[0752] The server stores the received behavioral history information in its data storage device. Since the stored data is unsuitable for analysis in its raw state, preprocessing is required.

[0753] Step 3:

[0754] The server preprocesses the behavioral history data. This process involves estimating and imputing missing information using data cleaning techniques, and detecting and removing outliers using statistical methods. As a result, a formatted dataset is obtained.

[0755] Step 4:

[0756] The server uses pre-processed data to extract individual consumer behavior trends. Here, machine learning algorithms are applied, and consumption patterns are identified through clustering. The input is formatted data, and the output is the consumption trends of each user.

[0757] Step 5:

[0758] The server generates a virtual model from extracted consumer behavior trends and runs a digital simulation. This simulation recreates future consumer behavior in a virtual space and analyzes its impact on the user's budget. The output is the simulation result.

[0759] Step 6:

[0760] The user scans the product they are considering purchasing using their device. Based on this, the server calculates the budget impact of purchasing that product in real time and displays it visually on the device. Augmented reality technology and data visualization tools are used for this.

[0761] Step 7:

[0762] Users review the simulation results visualized on their devices and make purchasing decisions. The results serve as information to help them make the best choices for achieving long-term financial goals.

[0763] In each step, a generative AI model is used to generate prompt messages, supporting necessary analysis and decision-making. This process enables users to make informed purchasing decisions.

[0764] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0765] Embodiments of the present invention include a life simulation system combined with an emotion engine. The server automatically collects life log data from the user's terminal, wearable device, online activity, etc. Next, the emotion engine is used to analyze the user's emotional state from the collected data and record the emotional state in a database. The emotion engine can evaluate emotions from voice tone, message content, behavioral history, etc.

[0766] The server integrates and preprocesses the aforementioned lifelog data and emotional data, filling in missing data and removing outliers. Next, the server extracts characteristics of the user's behavioral patterns, hobbies, and emotional states from the preprocessed data. Based on this characteristic information, the server generates a digital twin and simulates future choices and actions in the virtual space.

[0767] The user uses a terminal to input specific choices or life events they want to simulate into the server. The server sets the simulation parameters based on this information and extracted emotional characteristics. During the simulation, the emotional engine's emotional influence is reflected in the digital twin's behavior, generating multiple scenarios that take the user's emotional state into account.

[0768] The server analyzes the generated scenarios in detail and evaluates the impact each option has on the user's emotions and life. The results are then sent to the terminal in a visual format for the user to see. The user can then use the presented results to make decisions that take emotional aspects into consideration. In this form, the present invention provides support for individuals to make choices that are more emotionally satisfying.

[0769] The following describes the processing flow.

[0770] Step 1:

[0771] The server collects lifelog data from the user's terminal or wearable device. This data includes movement history, heart rate, and social media message history. This allows the server to understand the user's daily activities and psychological changes.

[0772] Step 2:

[0773] The server uses an emotion engine to analyze the user's emotional state from the collected data. The emotion engine analyzes message content, voice tone, and facial recognition data to evaluate the user's emotional state.

[0774] Step 3:

[0775] The server integrates life log data and emotional data and preprocesses this data. This includes imputing missing data and removing outliers, with the goal of preparing the data in a format that is easy to analyze.

[0776] Step 4:

[0777] The server extracts user behavior patterns, hobbies, and emotional characteristics from pre-processed data. This allows for a numerical representation of individual-specific behaviors and emotions, forming the basis for digital twin generation.

[0778] Step 5:

[0779] The user sends requests to the server via their device regarding specific choices or life events they want to simulate. For example, they might input their intention to change jobs or start a new hobby.

[0780] Step 6:

[0781] The server sets the simulation parameters for the digital twin based on the user's input information and emotional characteristics. These settings reflect the user's emotional tendencies and past behavioral history.

[0782] Step 7:

[0783] The server executes multiple simulation scenarios using a digital twin, according to the configured parameters. Because an emotion engine is integrated into the simulation, each scenario progresses while considering the user's emotional responses.

[0784] Step 8:

[0785] The server analyzes the simulation results and evaluates the emotional impact each option has on the user and its effect on their life. The analysis results are sent to the terminal as visual information for the user to review.

[0786] Step 9:

[0787] Users review the simulation results presented through their devices and make decisions after considering their emotional impact. This allows users to make choices that lead to desirable outcomes.

[0788] (Example 2)

[0789] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0790] In modern society, there is a need to accurately understand the emotional impact individuals experience in their daily lives and to make more satisfying decisions. However, conventional technologies have struggled to simulate situations that take an individual's emotional state into account, resulting in insufficient evaluation of future choices that reflect emotional factors. Therefore, there is a need for decision-making support that takes emotions into account.

[0791] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0792] In this invention, the server includes means for collecting personal life log information and storing it in an information storage device, means for preprocessing the collected life log information to fill in missing information and remove outliers, and means for integrating the collected life log and emotional information using an emotion analysis function and reflecting the emotional state in the simulation. This makes it possible to support decision-making to enhance an individual's emotional satisfaction through a future life simulation that takes emotional state into account.

[0793] "Life log information" refers to data about an individual's activities and behaviors in their daily life.

[0794] "Information storage device" refers to a system or medium for storing collected data.

[0795] "Preprocessing" refers to the techniques of processing and cleaning applied to raw data, and is a process that improves the quality of the data.

[0796] "Imputing missing information" refers to filling in incomplete information in a dataset using existing data or estimates.

[0797] "Outlier removal" is the process of detecting extreme data points in a dataset based on statistical methods and removing those that may negatively impact the analysis.

[0798] "Emotional analysis function" refers to a technology or algorithm for evaluating an individual's emotional state and integrating related data.

[0799] A "virtual model" refers to a digital representation created to reproduce an individual's characteristics and perform simulations in a virtual environment.

[0800] A "virtual environment" refers to a computer-generated simulation space through which users can experience various scenarios.

[0801] "Future life simulation" refers to an attempt to predict future choices and actions within a virtual environment and analyze the results under specific conditions.

[0802] "Emotional state" is a term that describes an individual's psychological or emotional state and is a factor that influences their individual behavior and choices.

[0803] "Decision support" refers to the process of providing information and insights to help a system improve an individual's choices and make more satisfying decisions.

[0804] In implementing this invention, the system consists of a server, a terminal, and a user. The server automatically collects personal life log information from various devices and stores it in an information storage device. The devices from which data is collected include smartphones, wearable devices, and internet platforms. This data includes user behavior, message content, and voice tone.

[0805] The server preprocesses the collected data. It improves data quality by using statistical software and data analysis tools to impart missing information and remove outliers. Based on this preprocessed data, the server uses machine learning algorithms to extract features related to individual behavior patterns and preferences.

[0806] This feature information is used in combination with emotion analysis functions to evaluate the user's emotional state. The evaluated emotion data forms the basis for building a virtual model of the user. The generation of the virtual model takes place on a digital simulation platform, enabling simulations of future life.

[0807] The user uses a terminal to input the choices or events they want to simulate into the server. This input uses prompts for the generated AI model. For example, a user might send the prompt "I want to know how relationships in a new workplace affect emotions" to the server. Based on this information, the server sets the control variables for the simulation and simulates multiple situations.

[0808] The server analyzes the generated simulation results and provides visually clear results to the terminal to support the user's decision-making. This allows the user to make decisions based on their emotional state, supporting choices that enhance their emotional satisfaction.

[0809] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0810] Step 1:

[0811] The server collects user life log information. Inputs include data from smartphones and wearable devices, as well as activity history from online platforms. The server aggregates this information in an information storage device and stores it in a database. Specifically, it retrieves data from devices via APIs and updates the database periodically.

[0812] Step 2:

[0813] The server preprocesses the collected lifelog information. The input is the stored raw data, and the output is a clean, integrated dataset. The server uses statistical analysis software to fill in missing information with historical data and estimates, and to detect and remove outliers. Specifically, it makes full use of filtering algorithms and interpolation techniques.

[0814] Step 3:

[0815] The server extracts feature information from pre-processed data. The input is a clean dataset, and the output is a feature vector relating to user behavior patterns and preferences. Machine learning algorithms are used to analyze the data using clustering and classifiers. A specific example is pattern recognition based on user activity frequency and preferences.

[0816] Step 4:

[0817] The server integrates life logs and feature information collected using sentiment analysis capabilities. The input is a feature vector, and the output is a dataset containing the user's emotional state. Natural language processing techniques and speech tone analysis tools are used to estimate the user's emotions. Specifically, emotions are classified from text messages.

[0818] Step 5:

[0819] The server generates a virtual model based on extracted feature information and emotional states. The input is an integrated dataset, and the output is a user-specific digital twin. Simulations are performed within the virtual environment to model the user's future life. Specifically, 3D model generation tools and simulation engines are used.

[0820] Step 6:

[0821] The user uses a terminal to input specific choices they want to simulate into the server. The input is a prompt message for the generated AI model, and the output is the simulation control variables set by the server. Based on the prompt message, the server reflects various conditions. A concrete example of a prompt might be, "I want to analyze the impact of a new work environment on emotions."

[0822] Step 7:

[0823] The server performs simulations based on configured control variables and generates multiple scenarios. The inputs are the control variables and a digital twin, while the output is the simulation results reflecting various situations. The server utilizes emotion analysis capabilities to reflect the user's emotional state in the scenarios. Specifically, dynamic simulation software is used.

[0824] Step 8:

[0825] The server analyzes the generated simulation results and provides the user with information to support their decision-making. The input is the simulation output, and the output is a visually easy-to-understand insight. The server sends the results to the terminal to help the user make choices that enhance their emotional satisfaction. For example, the results can be displayed as graphs or charts.

[0826] (Application Example 2)

[0827] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0828] The system did not adequately present options that took into account individual emotional states, resulting in users having difficulty making emotionally satisfying decisions. In particular, insights that reflected emotions were not provided in real-world behaviors such as shopping and service selection.

[0829] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0830] In this invention, the server includes means for collecting personal life log data and storing it in a data storage device, means for preprocessing the collected life log data to complete missing data and remove outliers, and means for analyzing emotional data and presenting optimal choices based on the user's emotional state. As a result, the user receives choices that match their emotional state and can make emotionally satisfying decisions.

[0831] "Life log data" refers to digital information that shows an individual's actions and state in their daily life, recording their daily activities and changes.

[0832] A "data storage device" is a storage device for securely storing digital data, and is a hardware or software system that enables data management and access.

[0833] "Data imputation" is the process of inferring and filling in missing information to make a dataset complete.

[0834] "Outlier removal" is a technique that improves data accuracy by identifying and removing or adjusting statistically abnormal values ​​present in a dataset.

[0835] "Behavioral patterns" refer to tendencies in actions and activities that an individual repeatedly performs.

[0836] "Preferences" refer to an individual's likes and tendencies, and can mean a strong attachment to a particular activity or thing.

[0837] A "digital twin" is an accurate digital representation of an object or process in the real world, enabling simulations between reality and the virtual world.

[0838] A "virtual realm" is a digital space that mimics the real world, created using computer technology.

[0839] "Insight" refers to a deep understanding or knowledge gained through analysis and observation, providing crucial information to guide actions and decisions in specific situations.

[0840] "Emotional data" refers to information that quantitatively or qualitatively indicates an individual's emotional state, and is obtained from data such as voice tone, facial expressions, and behavioral history.

[0841] In this invention, the server collects personal lifelog data and processes it for storage in a data storage device. This includes a communication module for acquiring data from smart devices and wearable devices. The server preprocesses the collected lifelog data, fills in missing data, and removes outliers. Data processing software such as Python or TensorFlow is used in this process.

[0842] The server also extracts individual behavioral patterns and preferences based on pre-processed data. This allows for the creation of a digital twin and the simulation of future life in a virtual realm. Simultaneously analyzing the simulation results and user emotional data, the server presents optimal choices. Analysis tools such as Amazon SageMaker are used in this process.

[0843] The device receives insights from the server and functions as an interface that presents the user with options tailored to their emotional state. For example, if a user is determined to be stressed, the system can recommend purchasing products that help with relaxation.

[0844] An example of a prompt using a generative AI model is, "If the customer's emotional state is 'stressed', generate a list of recommended products in the shopping guide." In this way, it is possible to help users make emotionally satisfying decisions.

[0845] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0846] Step 1:

[0847] The server acquires lifelog data from smart devices and wearable devices and stores it in a data storage device. The input here is digital information about daily activities transmitted from each device, and the output is a structured dataset. This process involves receiving and formatting data from devices and appropriately storing it in a database.

[0848] Step 2:

[0849] The server preprocesses the collected lifelog data, filling in missing data and removing outliers. The input is the stored raw data, and the output is a clean dataset with outliers removed. Data processing scripts using Python or Pandas estimate and fill in missing parts, and detect and remove outliers.

[0850] Step 3:

[0851] The server extracts individual behavioral patterns and preferences based on pre-processed data. The input is a clean dataset, and the output is feature vectors related to behavior and preferences. It uses a machine learning model to analyze the data and extract those features.

[0852] Step 4:

[0853] The server generates a digital twin using extracted features and simulates future life in a virtual realm. The input is a feature vector, and the output is the result of the digital twin simulation. It executes a simulation algorithm to generate diverse future scenarios.

[0854] Step 5:

[0855] The server analyzes the simulation results and uses sentiment data to present the optimal choices based on the user's current emotional state. The input is the simulation results and sentiment data, and the output is a list of choices to aid in the user's decision-making. This includes the operation of the sentiment engine, which evaluates the emotional state and optimizes the choices.

[0856] Step 6:

[0857] The terminal presents the user with choices and insights sent from the server. The input is the choices and insights received from the server, and the output is visually formatted information. The terminal displays the information through a user interface, making it easy for the user to understand.

[0858] Step 7:

[0859] The user makes a decision based on the information presented, taking emotional aspects into consideration. The input is the information presented from the device, and the output is the user's chosen actions and decisions. This includes making judgments based on the given information and deciding on the next course of action.

[0860] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0861] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0862] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0863] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0864] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0865] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0866] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0867] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0868] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0869] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0870] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0871] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0872] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0873] 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.

[0874] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0875] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0876] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0877] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0878] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0879] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0880] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0881] The following is further disclosed regarding the embodiments described above.

[0882] (Claim 1)

[0883] A means of collecting personal life log data and storing it in a database,

[0884] A means for preprocessing collected lifelog data, including imputing missing data and removing outliers,

[0885] A means for extracting characteristics about an individual's behavioral patterns and hobbies based on preprocessed data,

[0886] A means of generating a digital twin using extracted features and performing a simulation of future life in a virtual space,

[0887] A means of analyzing simulation results and providing insights to support individual decision-making,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, in which an individual inputs options they wish to simulate via a terminal, and the simulation parameters are set based on that information.

[0891] (Claim 3)

[0892] The system according to claim 1, which generates multiple scenarios based on different choices in a simulation and compares and evaluates the results.

[0893] "Example 1"

[0894] (Claim 1)

[0895] A means of collecting information related to an individual's behavior and storing it in an information storage medium,

[0896] A means for preprocessing collected behavior-related information, including imputing missing information and removing abnormal values,

[0897] A means for extracting characteristics related to an individual's behavioral traits and preferences based on preprocessed information,

[0898] A means for generating a virtual model using extracted features and performing future behavior simulations in a virtual domain,

[0899] A means of analyzing the results of simulations and providing insights to support individual decision-making,

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, wherein an individual inputs a selection pattern they wish to simulate via a communication device, and the simulation conditions are set based on that information.

[0903] (Claim 3)

[0904] The system according to claim 1, which generates multiple situations based on different selection patterns in a simulation and compares and evaluates the results.

[0905] "Application Example 1"

[0906] (Claim 1)

[0907] A means for collecting an individual's behavioral history information and storing it in an information storage device,

[0908] A means for preprocessing collected behavioral history information, including imputing missing information and removing outliers,

[0909] A means for extracting individual consumer behavior trends based on pre-processed information,

[0910] A means for generating a virtual model using extracted characteristics and simulating future consumer behavior in a virtual space,

[0911] A means of analyzing simulation results and providing insights to support individual decision-making,

[0912] A means of using a display device to visualize the impact of consumer behavior in real time and support consumers in managing their budgets,

[0913] A system that includes this.

[0914] (Claim 2)

[0915] The system according to claim 1, wherein an individual inputs purchasing behavior they wish to simulate via an information terminal, and the conditions for the simulation are set based on that information.

[0916] (Claim 3)

[0917] The system according to claim 1, which generates multiple scenarios based on different purchasing options in a simulation and compares and evaluates the results.

[0918] "Example 2 of combining an emotion engine"

[0919] (Claim 1)

[0920] A means for collecting personal life log information and storing it in an information storage device,

[0921] A means for preprocessing collected lifelog information, including imputing missing information and removing outliers,

[0922] A means for extracting characteristics of an individual's behavioral patterns and preferences based on pre-processed information,

[0923] A means of generating a virtual model using extracted features and performing a simulation of future life in a virtual environment,

[0924] A means of integrating life logs and emotional information collected using emotion analysis functions and reflecting emotional states in simulations,

[0925] A means of analyzing simulation results and providing insights to support individual decision-making,

[0926] A system that includes this.

[0927] (Claim 2)

[0928] The system according to claim 1, wherein an individual inputs a selection to be simulated through an output device, and the control variables of the simulation are set based on that information.

[0929] (Claim 3)

[0930] The system according to claim 1, which generates multiple situations based on different choices in a simulation and compares and evaluates the results using an emotion analysis function.

[0931] "Application example 2 when combining with an emotional engine"

[0932] (Claim 1)

[0933] A means for collecting personal life log data and storing it in a data storage device,

[0934] A means for preprocessing collected lifelog data, including filling in missing data and removing outliers,

[0935] A means for extracting characteristics of an individual's behavioral patterns and preferences based on preprocessed data,

[0936] A means of generating a digital twin using extracted features and performing a simulation of future life in a virtual domain,

[0937] A means of analyzing simulation results and providing insights to support individual decision-making,

[0938] A means of analyzing emotional data and presenting the optimal choice based on the user's emotional state,

[0939] A system that includes this.

[0940] (Claim 2)

[0941] The system according to claim 1, wherein an individual inputs a selection of options they wish to simulate through an information processing device, and the system sets the simulation variables based on that information.

[0942] (Claim 3)

[0943] The system according to claim 1, which generates multiple scenarios based on different choices in a simulation, compares and evaluates the results, and presents choices that match the emotional state. [Explanation of Symbols]

[0944] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting personal life log data and storing it in a database, A means for preprocessing collected lifelog data, including imputing missing data and removing outliers, A means for extracting characteristics about an individual's behavioral patterns and hobbies based on preprocessed data, A means of generating a digital twin using extracted features and performing a simulation of future life in a virtual space, A means of analyzing simulation results and providing insights to support individual decision-making, A system that includes this.

2. The system according to claim 1, wherein an individual inputs the options they wish to simulate via a terminal, and the simulation parameters are set based on that information.

3. The system according to claim 1, which generates multiple scenarios based on different choices in a simulation and compares and evaluates the results.

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