system

Abstract

An object of the system according to the embodiment is to analyze inactive information of a user and collect detailed data.SOLUTION: A system includes an information interpretation part, a clone generation part, a simulation part, and a data collection part. The information interpretation unit analyzes the user's inactive information collected from the SNS. The alter ego generation section generates an alter ego of the user based on the inactive information analyzed by the information interpretation section. The simulation part makes the alter ego generated by the alter ego generation part live in a virtual environment. The data collection unit collects detailed data from the life of the avatar simulated by the simulation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies make it difficult to collect large amounts of high-quality data from users, leaving room for improvement in corporate value creation.

[0005] The system according to the embodiment aims to analyze passive information of users and collect detailed data. [Means for solving the problem]

[0006] The system according to the embodiment includes an information interpretation unit, an avatar generation unit, a simulation unit, and a data collection unit. The information interpretation unit analyzes inactive information about a user collected from SNS. The avatar generation unit generates an avatar of the user based on the inactive information analyzed by the information interpretation unit. The simulation unit makes the avatar generated by the avatar generation unit live in a virtual environment. The data collection unit collects detailed data from the life of the avatar simulated by the simulation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the user's passive information and collect detailed data. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The data collection platform according to an embodiment of the present invention is a system in which a generation AI interprets passive information of users collected from social media and other sources, creates an avatar of the user, and makes that avatar live in a virtual environment. This allows companies to efficiently collect detailed data and create unique value.

[0029] A data collection platform according to an embodiment includes an information interpretation unit, an avatar generation unit, a simulation unit, and a data collection unit. The information interpretation unit analyzes inactive information about a user collected from social networking sites and the like. For example, the information interpretation unit analyzes the user's posted content, profile information, behavioral history, and the like to model the user's personality, preferences, and behavioral patterns. The information interpretation unit can also analyze the user's inactive information using a generation AI. The avatar generation unit generates an avatar of the user based on the inactive information analyzed by the information interpretation unit. For example, the avatar generation unit uses a generation AI to generate an avatar that reflects the user's personality, preferences, and behavioral patterns. The avatar generation unit can also generate a virtual entity that mimics the user's characteristics. The simulation unit makes the avatar generated by the avatar generation unit live in a virtual environment. For example, the simulation unit sets up a scenario in which the avatar shops in a virtual city and interacts with friends. The simulation unit can also collect detailed data by simulating the avatar's life in the virtual environment. The data collection unit collects detailed data from the life of the avatar simulated by the simulation unit. For example, the data collection unit collects detailed information such as what products the avatar prefers, what behavioral patterns the avatar has, and what communication style the avatar has. The data collection unit can also analyze the collected data and provide valuable information that companies can use in their marketing strategies and product development. As a result, the data collection platform according to the embodiment can collect detailed data by analyzing inactive information about the user collected from social media and the like, and generating an avatar of the user to live in a virtual environment.

[0030] The information interpretation unit analyzes the user's biometric data to generate a more sophisticated avatar. For example, the information interpretation unit collects the user's heart rate and electrodermal activity in real time, and the generation AI analyzes that data to adjust the avatar's behavior. For example, if the user is tense, the avatar will take actions to relax. The information interpretation unit also analyzes the user's stress level and relaxation level based on the biometric data, and the generation AI sets the avatar's behavior based on that information. For example, if the user is relaxed, the avatar will take relaxed actions. The information interpretation unit also collects the user's biometric data over a long period of time, and the generation AI learns from that data to optimize the avatar's behavioral patterns. For example, if the user is prone to stress during certain times of the day, the avatar will be set to take relaxing actions during those times. In this way, a more sophisticated avatar can be generated by analyzing the user's biometric data.

[0031] The information interpretation unit can incorporate an algorithm that learns the user's past behavioral history and predicts future behavior. For example, the information interpretation unit analyzes the user's past social media posts and behavioral history, and the generation AI incorporates an algorithm that predicts future behavior based on that data. For example, if a user has participated in a specific event in the past, it predicts that the user is likely to participate in a similar event in the future. The information interpretation unit also allows the generation AI to learn the user's behavioral history, extract specific patterns, and predict future behavior. For example, if a user has a habit of going to a specific place every weekend, it predicts that behavior and reflects it in the avatar's behavior. The information interpretation unit also allows the generation AI to simulate future behavior based on the user's behavioral history and reflect the results in the avatar's behavior. For example, it predicts future behavior based on how the user has behaved in specific situations in the past. This allows for more accurate simulations by learning the user's past behavioral history and predicting future behavior.

[0032] The avatar generation unit can generate an avatar from a global perspective, taking into account the characteristics of different cultural spheres or regions. For example, the generation AI of the avatar generation unit analyzes the characteristics of the user's cultural sphere or region and generates an avatar based on that information. For example, if the user lives in Asia, the avatar generation unit generates an avatar that reflects the culture and customs of that region. The avatar generation unit also collects data from different cultural spheres, and the generation AI generates an avatar from a global perspective based on that data. For example, if the user has a multicultural background, the avatar generation unit generates an avatar that reflects those characteristics. The avatar generation unit also learns the user's regional characteristics and sets the avatar's behavior pattern based on that information. For example, if the user has a habit of participating in festivals or events in a particular region, the avatar will reflect that behavior. This allows the avatar generation unit to generate an avatar from a global perspective by taking into account the characteristics of different cultural spheres and regions.

[0033] The avatar generation unit can generate a variety of avatars that reflect the characteristics of different age groups and genders. In the avatar generation unit, for example, the generation AI analyzes the user's age group and gender and generates an avatar based on that information. For example, if the user is young, an avatar that reflects those characteristics is generated. The avatar generation unit also collects data on different age groups and genders, and the generation AI generates a variety of avatars based on that data. For example, if the user is middle-aged or older, an avatar that reflects those characteristics is generated. In addition, the avatar generation unit sets the behavior pattern of the avatar based on the user's age group and gender. For example, if the user is female, a behavior pattern that reflects those characteristics is set for the avatar. In this way, a variety of avatars can be generated by reflecting the characteristics of different age groups and genders.

[0034] The simulation unit can add a scenario in which an avatar cooperates with other avatars to accomplish a task while living in a virtual environment. The simulation unit adds, for example, a scenario in which an avatar cooperates with other avatars to accomplish a task while living in a virtual environment. For example, a scenario is set in which the avatars work together on a project. The simulation unit also introduces an algorithm for accomplishing a task in cooperation with other avatars and adjusts the avatars' actions. For example, the avatars divide up roles and efficiently progress through the task. The simulation unit also sets a scenario in which the avatars accomplish a task while communicating with other avatars, and analyzes data collected in the process. For example, a scenario is set in which the avatars demonstrate teamwork. This makes it possible to simulate cooperative behavior by accomplishing a task while living in a virtual environment in cooperation with other avatars.

[0035] The simulation unit can reflect different seasons and weather conditions in the simulation of the avatar's life in the virtual environment. The simulation unit, for example, reflects different seasons and weather conditions in the simulation of the avatar's life in the virtual environment. For example, a scenario is set in which the avatar wears warm clothes on a cold winter day. The simulation unit also changes the seasons and weather conditions in real time and adjusts the avatar's behavior in response to the changes. For example, a scenario is set in which the avatar goes out with an umbrella on a rainy day. The simulation unit also performs simulations that reflect different seasons and weather conditions and analyzes data collected during the simulation. For example, the simulation unit analyzes the avatar's behavior of choosing a cold drink on a hot summer day. In this way, by reflecting different seasons and weather conditions in the simulation of the avatar's life in the virtual environment, a more realistic simulation is possible.

[0036] The simulation unit can add a scenario in which the avatar experiences different occupations and roles within the virtual environment. For example, the simulation unit adds a scenario in which the avatar experiences different occupations and roles within the virtual environment. For example, a scenario in which the avatar works as a doctor or a teacher is set up. The simulation unit also introduces an algorithm for experiencing different occupations and roles and adjusts the avatar's behavior. For example, a scenario in which the avatar learns skills necessary for a specific occupation is set up. The simulation unit also sets up a scenario in which the avatar experiences different occupations and roles and analyzes data collected in the process. For example, the simulation unit analyzes the stress level of the avatar in different occupations. This enables a variety of simulations to be performed by experiencing different occupations and roles within the virtual environment.

[0037] The data collection unit can analyze the collected data in time series and track changes in the user's behavioral patterns. The data collection unit, for example, analyzes the collected data in time series and tracks changes in the user's behavioral patterns. For example, it analyzes what actions the user took over a specific period of time. The data collection unit also visualizes changes in the user's behavioral patterns based on the time series data and develops a marketing strategy based on that information. For example, it analyzes what products the user will purchase in a specific season. The data collection unit also analyzes the collected data in time series and introduces an algorithm that tracks changes in the user's behavioral patterns. For example, it analyzes how often the user participates in a specific event. In this way, it is possible to track changes in the user's behavioral patterns by analyzing the collected data in time series.

[0038] The data collection unit can build a model that predicts the user's future behavior based on the collected data. The data collection unit, for example, builds a model that predicts the user's future behavior based on the collected data. For example, it predicts what product the user will purchase next. The data collection unit also analyzes the collected data, extracts the user's behavioral patterns, and introduces an algorithm that predicts future behavior. For example, it predicts the possibility that the user will participate in a particular event. The data collection unit also simulates the user's future behavior based on the collected data and reflects the results in a marketing strategy. For example, it predicts what service the user will use next. In this way, future behavior can be predicted by building a model that predicts the user's future behavior based on the collected data.

[0039] The data collection unit can classify the collected data by different industries or uses and perform analysis specialized for each industry. For example, the data collection unit classifies the collected data by different industries or uses and performs analysis specialized for each industry. For example, data from the medical industry is analyzed to extract information about specific diseases. The data collection unit also collects data from different industries and introduces an algorithm that performs specialized analysis based on that data. For example, data from the financial industry is analyzed to develop an investment strategy. The data collection unit also classifies the collected data by use and performs analysis specialized for each use. For example, consumer behavior data is analyzed to develop a marketing strategy. In this way, by classifying the collected data by different industries or uses and performing analysis specialized for each industry, data analysis tailored to the characteristics of each industry becomes possible.

[0040] The data collection unit can integrate the collected data with other datasets to perform more comprehensive analysis. For example, the data collection unit integrates the collected data with other datasets to perform more comprehensive analysis. For example, health data and purchase history are integrated to analyze the relationship between a user's health condition and consumption behavior. The data collection unit also integrates different datasets and introduces an algorithm to perform comprehensive analysis based on the data. For example, SNS data and location information data are integrated to analyze user behavior patterns. The data collection unit also integrates the collected data with other datasets and uses the information to develop marketing strategies. For example, purchase history and SNS data are integrated to analyze a user's purchasing intentions. In this way, more comprehensive analysis is possible by integrating the collected data with other datasets.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The data collection platform comprises an information interpretation unit that analyzes the user's passive information, an avatar generation unit that generates the user's avatar, a simulation unit that makes the avatar live in a virtual environment, and a data collection unit that collects detailed data. The information interpretation unit analyzes the user's passive information collected from social media and other sources, and models the user's personality, preferences, and behavioral patterns. The avatar generation unit generates the user's avatar based on the information analyzed by the information interpretation unit, and the simulation unit makes the avatar live in a virtual environment. The data collection unit collects detailed data from the avatar's life simulated by the simulation unit. This allows companies to efficiently collect detailed data and create unique value.

[0043] The information interpretation unit can analyze the user's biometric data to generate a more sophisticated avatar. For example, the user's heart rate and electrodermal activity can be collected in real time, and the generation AI can analyze that data to adjust the avatar's behavior. If the user is tense, the avatar will take actions to relax. The biometric data can also be used to analyze the user's stress level and relaxation level, and the generation AI can use that information to set the avatar's behavior. If the user is relaxed, the avatar will take relaxed actions. Furthermore, the user's biometric data can be collected over a long period of time, and the generation AI can learn from that data to optimize the avatar's behavior patterns. This makes it possible to generate a more sophisticated avatar by analyzing the user's biometric data.

[0044] The information interpretation unit can incorporate an algorithm that learns a user's past behavioral history and predicts future behavior. For example, the generation AI can analyze a user's past social media posts and behavioral history, and then use that data to implement an algorithm that predicts future behavior. If a user has participated in a specific event in the past, it can predict that they are likely to participate in a similar event in the future. The generation AI can also learn the user's behavioral history, extract specific patterns, and predict future behavior. If a user has a habit of going to a specific place every weekend, it can predict that behavior and reflect it in the avatar's behavior. Furthermore, the generation AI can simulate future behavior based on the user's behavioral history and reflect the results in the avatar's behavior. This allows for more accurate simulations by learning the user's past behavioral history and predicting future behavior.

[0045] The avatar generation unit can generate avatars from a global perspective, taking into account the characteristics of different cultural spheres or regions. For example, the generation AI analyzes the characteristics of the user's cultural sphere or region and generates an avatar based on that information. If the user lives in Asia, an avatar that reflects the culture and customs of that region is generated. Data from different cultural spheres is also collected, and the generation AI generates avatars from a global perspective based on that data. If the user has a multicultural background, an avatar that reflects those characteristics is generated. Furthermore, the generation AI can learn the user's regional characteristics and set the avatar's behavioral patterns based on that information. If the user has a habit of participating in festivals or events in a particular region, that behavior can be reflected in the avatar. This allows avatars to be generated from a global perspective by taking into account the characteristics of different cultural spheres and regions.

[0046] The avatar generation unit can generate a variety of avatars that reflect the characteristics of different age groups and genders. For example, the generation AI analyzes the user's age group and gender and generates an avatar based on that information. If the user is young, an avatar that reflects those characteristics is generated. The generation AI also collects data on different age groups and genders, and generates a variety of avatars based on that data. If the user is middle-aged or older, an avatar that reflects those characteristics is generated. Furthermore, the generation AI can set the avatar's behavioral patterns based on the user's age group and gender. If the user is female, a behavioral pattern that reflects her characteristics is set for the avatar. This makes it possible to generate a variety of avatars by reflecting the characteristics of different age groups and genders.

[0047] The simulation unit can add scenarios in which avatars cooperate with other avatars to accomplish tasks while living in a virtual environment. For example, a scenario can be added in which an avatar cooperates with other avatars to accomplish tasks while living in a virtual environment. A scenario can be set in which avatars work together on a project. An algorithm can also be introduced to help avatars cooperate with other avatars to accomplish tasks, and the avatars' actions can be adjusted. The avatars can assign roles and efficiently complete tasks. Furthermore, a scenario can be set in which avatars communicate with other avatars to accomplish tasks, and data collected in the process can be analyzed. A scenario can be set in which avatars demonstrate teamwork. This makes it possible to simulate cooperative behavior by avatars cooperating with other avatars to accomplish tasks while living in a virtual environment.

[0048] The simulation unit can reflect different seasons and weather conditions in the simulation of the avatar's life in the virtual environment. For example, different seasons and weather conditions can be reflected in the simulation of the avatar's life in the virtual environment. A scenario can be set in which the avatar wears warm clothes on a cold winter day. The seasons and weather conditions can also be changed in real time, and the avatar's behavior can be adjusted in response to these changes. A scenario can be set in which the avatar goes out with an umbrella on a rainy day. Furthermore, simulations that reflect different seasons and weather conditions can be performed, and data collected during the process can be analyzed. The behavior of the avatar choosing a cold drink on a hot summer day can be analyzed. In this way, by reflecting different seasons and weather conditions in the simulation of the avatar's life in the virtual environment, more realistic simulations can be achieved.

[0049] The simulation unit can add scenarios in which the avatar experiences different occupations and roles within the virtual environment. For example, a scenario can be added in which the avatar experiences different occupations and roles within the virtual environment. A scenario can be set in which the avatar works as a doctor or a teacher. An algorithm can also be introduced to experience different occupations and roles and adjust the avatar's behavior. A scenario can be set in which the avatar learns the skills required for a specific occupation. Furthermore, a scenario can be set in which the avatar experiences different occupations and roles, and data collected during the process can be analyzed. The stress level of the avatar in different occupations can be analyzed. This allows a variety of simulations to be performed by experiencing different occupations and roles within the virtual environment.

[0050] The data collection unit can analyze the collected data in chronological order and track changes in user behavior patterns. For example, the collected data can be analyzed in chronological order to track changes in user behavior patterns. The actions taken by the user over a specific period can be analyzed. The changes in user behavior patterns can be visualized based on the time-series data, and a marketing strategy can be developed based on this information. The types of products a user will purchase in a specific season can be analyzed. Furthermore, an algorithm can be introduced to analyze the collected data in chronological order and track changes in user behavior patterns. The frequency with which a user participates in a specific event can be analyzed. In this way, by analyzing the collected data in chronological order, changes in user behavior patterns can be tracked.

[0051] The data collection unit can build a model that predicts a user's future behavior based on the collected data. For example, a model that predicts a user's future behavior is built based on the collected data. It predicts what product the user will purchase next. It also analyzes the collected data, extracts the user's behavior patterns, and introduces an algorithm that predicts future behavior. It predicts the likelihood that the user will participate in a particular event. Furthermore, it can simulate the user's future behavior based on the collected data and reflect the results in marketing strategies. It predicts what service the user will use next. In this way, future behavior can be predicted by building a model that predicts a user's future behavior based on the collected data.

[0052] The data collection unit can classify the collected data by different industries or uses and perform analysis specialized for each industry. For example, the collected data can be classified by different industries or uses and analysis specialized for each industry can be performed. Data from the medical industry can be analyzed to extract information about specific diseases. Data from different industries can also be collected and an algorithm can be introduced to perform specialized analysis based on that data. Data from the financial industry can be analyzed to develop investment strategies. Furthermore, the collected data can be classified by use and analysis specialized for each use can be performed. Consumer behavior data can be analyzed to develop marketing strategies. This makes it possible to classify the collected data by different industries or uses and perform analysis specialized for each industry, thereby enabling data analysis tailored to the characteristics of each industry.

[0053] The data collection unit can integrate the collected data with other datasets to perform more comprehensive analysis. For example, the collected data can be integrated with other datasets to perform more comprehensive analysis. Health data and purchase history can be integrated to analyze the relationship between a user's health condition and consumption behavior. It is also possible to integrate different datasets and introduce algorithms that perform comprehensive analysis based on that data. Social media data and location data can be integrated to analyze user behavior patterns. Furthermore, the collected data can be integrated with other datasets to develop marketing strategies based on that information. Purchase history and social media data can be integrated to analyze a user's purchasing intentions. In this way, more comprehensive analysis can be performed by integrating the collected data with other datasets.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The information interpretation unit analyzes the user's passive information collected from social media and other sources. For example, the information interpretation unit analyzes the user's posted content, profile information, and behavioral history to model the user's personality, preferences, and behavioral patterns. The information interpretation unit can also use generative AI to analyze the user's passive information. Step 2: The avatar generation unit generates an avatar for the user based on the passive information analyzed by the information interpretation unit. For example, the avatar generation unit uses a generation AI to generate an avatar that reflects the user's personality, preferences, and behavioral patterns. The avatar generation unit can also generate a virtual being that mimics the user's characteristics. Step 3: The simulation unit makes the avatar generated by the avatar generation unit live in a virtual environment. For example, the simulation unit sets up a scenario in which the avatar goes shopping in a virtual city and interacts with friends. The simulation unit can also collect detailed data by simulating the avatar's life in the virtual environment. Step 4: The data collection unit collects detailed data from the life of the avatar simulated by the simulation unit. For example, the data collection unit collects detailed information such as what products the avatar prefers, what behavioral patterns it has, and what communication style it has. The data collection unit can also analyze the collected data and provide valuable information that companies can use in their marketing strategies and product development.

[0056] (Example 2) The data collection platform according to an embodiment of the present invention is a system in which a generation AI interprets passive information of users collected from social media and other sources, creates an avatar of the user, and makes that avatar live in a virtual environment. This allows companies to efficiently collect detailed data and create unique value.

[0057] A data collection platform according to an embodiment includes an information interpretation unit, an avatar generation unit, a simulation unit, and a data collection unit. The information interpretation unit analyzes inactive information about a user collected from social networking sites and the like. For example, the information interpretation unit analyzes the user's posted content, profile information, behavioral history, and the like to model the user's personality, preferences, and behavioral patterns. The information interpretation unit can also analyze the user's inactive information using a generation AI. The avatar generation unit generates an avatar of the user based on the inactive information analyzed by the information interpretation unit. For example, the avatar generation unit uses a generation AI to generate an avatar that reflects the user's personality, preferences, and behavioral patterns. The avatar generation unit can also generate a virtual entity that mimics the user's characteristics. The simulation unit makes the avatar generated by the avatar generation unit live in a virtual environment. For example, the simulation unit sets up a scenario in which the avatar shops in a virtual city and interacts with friends. The simulation unit can also collect detailed data by simulating the avatar's life in the virtual environment. The data collection unit collects detailed data from the life of the avatar simulated by the simulation unit. For example, the data collection unit collects detailed information such as what products the avatar prefers, what behavioral patterns the avatar has, and what communication style the avatar has. The data collection unit can also analyze the collected data and provide valuable information that companies can use in their marketing strategies and product development. As a result, the data collection platform according to the embodiment can collect detailed data by analyzing inactive information about the user collected from social media and the like, and generating an avatar of the user to live in a virtual environment.

[0058] The information interpretation unit can estimate the user's emotions and adjust the avatar's behavioral patterns based on those emotions. For example, the generation AI analyzes emotions from the user's social media posts and comments and adjusts the avatar's behavioral patterns based on those emotions. For example, if the user has positive emotions, the avatar is set to behave proactively. The information interpretation unit also collects the user's emotional data in real time, and the generation AI dynamically adjusts the avatar's behavior based on that data. For example, if the user is feeling stressed, the avatar will take actions to relax. The information interpretation unit also allows the generation AI to learn the user's emotional history, predict future behavior based on past emotional patterns, and adjust the avatar's behavior. For example, the avatar's behavior is set based on how the user felt in specific situations in the past. This allows the avatar's behavioral patterns to be adjusted based on the user's emotions, enabling a more realistic simulation.

[0059] The information interpretation unit analyzes the user's biometric data to generate a more sophisticated avatar. For example, the information interpretation unit collects the user's heart rate and electrodermal activity in real time, and the generation AI analyzes that data to adjust the avatar's behavior. For example, if the user is tense, the avatar will take actions to relax. The information interpretation unit also analyzes the user's stress level and relaxation level based on the biometric data, and the generation AI sets the avatar's behavior based on that information. For example, if the user is relaxed, the avatar will take relaxed actions. The information interpretation unit also collects the user's biometric data over a long period of time, and the generation AI learns from that data to optimize the avatar's behavioral patterns. For example, if the user is prone to stress during certain times of the day, the avatar will be set to take relaxing actions during those times. In this way, a more sophisticated avatar can be generated by analyzing the user's biometric data.

[0060] The information interpretation unit can incorporate an algorithm that learns the user's past behavioral history and predicts future behavior. For example, the information interpretation unit analyzes the user's past social media posts and behavioral history, and the generation AI incorporates an algorithm that predicts future behavior based on that data. For example, if a user has participated in a specific event in the past, it predicts that the user is likely to participate in a similar event in the future. The information interpretation unit also allows the generation AI to learn the user's behavioral history, extract specific patterns, and predict future behavior. For example, if a user has a habit of going to a specific place every weekend, it predicts that behavior and reflects it in the avatar's behavior. The information interpretation unit also allows the generation AI to simulate future behavior based on the user's behavioral history and reflect the results in the avatar's behavior. For example, it predicts future behavior based on how the user has behaved in specific situations in the past. This allows for more accurate simulations by learning the user's past behavioral history and predicting future behavior.

[0061] The avatar generation unit can generate an avatar from a global perspective, taking into account the characteristics of different cultural spheres or regions. For example, the generation AI of the avatar generation unit analyzes the characteristics of the user's cultural sphere or region and generates an avatar based on that information. For example, if the user lives in Asia, the avatar generation unit generates an avatar that reflects the culture and customs of that region. The avatar generation unit also collects data from different cultural spheres, and the generation AI generates an avatar from a global perspective based on that data. For example, if the user has a multicultural background, the avatar generation unit generates an avatar that reflects those characteristics. The avatar generation unit also learns the user's regional characteristics and sets the avatar's behavior pattern based on that information. For example, if the user has a habit of participating in festivals or events in a particular region, the avatar will reflect that behavior. This allows the avatar generation unit to generate an avatar from a global perspective by taking into account the characteristics of different cultural spheres and regions.

[0062] The avatar generation unit can generate a variety of avatars that reflect the characteristics of different age groups and genders. In the avatar generation unit, for example, the generation AI analyzes the user's age group and gender and generates an avatar based on that information. For example, if the user is young, an avatar that reflects those characteristics is generated. The avatar generation unit also collects data on different age groups and genders, and the generation AI generates a variety of avatars based on that data. For example, if the user is middle-aged or older, an avatar that reflects those characteristics is generated. In addition, the avatar generation unit sets the behavior pattern of the avatar based on the user's age group and gender. For example, if the user is female, a behavior pattern that reflects those characteristics is set for the avatar. In this way, a variety of avatars can be generated by reflecting the characteristics of different age groups and genders.

[0063] The avatar generation unit uses the emotion estimation function to generate an avatar based on the user's emotions, making it possible to create an avatar that is easy to empathize with emotionally. The avatar generation unit, for example, uses the emotion estimation function to analyze the user's emotion data and generate an avatar based on that information. For example, if the user has positive emotions, it generates an avatar that reflects those emotions. The avatar generation unit also uses a generation AI to learn the user's emotion history and generate an avatar that is easy to empathize with emotionally based on that information. For example, it sets the avatar based on how the user felt in specific situations in the past. The avatar generation unit also uses the emotion estimation function to set a behavior pattern for the avatar based on the user's emotions. For example, if the user is feeling stressed, it sets the avatar to behave in a way that reflects those emotions. In this way, by using the emotion estimation function, it is possible to create an avatar that is easy to empathize with emotionally.

[0064] The simulation unit can estimate emotions of the alter-ego while it is living in the virtual environment and change its behavior based on those emotions. For example, the simulation unit estimates emotions of the alter-ego while it is living in the virtual environment and changes its behavior based on those emotions. For example, if the alter-ego is feeling stressed, it takes action to relax. The simulation unit also collects emotional data of the alter-ego in the virtual environment in real time and dynamically adjusts the alter-ego's behavior based on that data. For example, if the alter-ego is feeling positive, it takes proactive action. The simulation unit also learns the alter-ego's emotional history, predicts future behavior based on past emotional patterns, and adjusts the alter-ego's behavior. For example, it sets future behavior based on how the alter-ego felt in specific situations in the past. This enables a more realistic simulation by estimating emotions of the alter-ego while it is living in the virtual environment and changing its behavior based on those emotions.

[0065] The simulation unit can add a scenario in which an avatar cooperates with other avatars to accomplish a task while living in a virtual environment. The simulation unit adds, for example, a scenario in which an avatar cooperates with other avatars to accomplish a task while living in a virtual environment. For example, a scenario is set in which the avatars work together on a project. The simulation unit also introduces an algorithm for accomplishing a task in cooperation with other avatars and adjusts the avatars' actions. For example, the avatars divide up roles and efficiently progress through the task. The simulation unit also sets a scenario in which the avatars accomplish a task while communicating with other avatars, and analyzes data collected in the process. For example, a scenario is set in which the avatars demonstrate teamwork. This makes it possible to simulate cooperative behavior by accomplishing a task while living in a virtual environment in cooperation with other avatars.

[0066] The simulation unit can reflect different seasons and weather conditions in the simulation of the avatar's life in the virtual environment. The simulation unit, for example, reflects different seasons and weather conditions in the simulation of the avatar's life in the virtual environment. For example, a scenario is set in which the avatar wears warm clothes on a cold winter day. The simulation unit also changes the seasons and weather conditions in real time and adjusts the avatar's behavior in response to the changes. For example, a scenario is set in which the avatar goes out with an umbrella on a rainy day. The simulation unit also performs simulations that reflect different seasons and weather conditions and analyzes data collected during the simulation. For example, the simulation unit analyzes the avatar's behavior of choosing a cold drink on a hot summer day. In this way, by reflecting different seasons and weather conditions in the simulation of the avatar's life in the virtual environment, a more realistic simulation is possible.

[0067] The simulation unit can add a scenario in which the avatar experiences different occupations and roles within the virtual environment. For example, the simulation unit adds a scenario in which the avatar experiences different occupations and roles within the virtual environment. For example, a scenario in which the avatar works as a doctor or a teacher is set up. The simulation unit also introduces an algorithm for experiencing different occupations and roles and adjusts the avatar's behavior. For example, a scenario in which the avatar learns skills necessary for a specific occupation is set up. The simulation unit also sets up a scenario in which the avatar experiences different occupations and roles and analyzes data collected in the process. For example, the simulation unit analyzes the stress level of the avatar in different occupations. This enables a variety of simulations to be performed by experiencing different occupations and roles within the virtual environment.

[0068] The simulation unit uses the emotion estimation function to analyze in real time the emotions felt by the alter-ego while living in the virtual environment and promote behavior based on those emotions. The simulation unit, for example, uses the emotion estimation function to analyze in real time the emotions felt by the alter-ego while living in the virtual environment. For example, if the alter-ego is feeling stressed, the simulation unit promotes behavior to relax based on that emotion. The simulation unit also collects emotion data of the alter-ego in real time and sets emotion-based behavior based on that data. For example, if the alter-ego has positive emotions, the simulation unit sets the alter-ego to take proactive behavior. The simulation unit also uses the emotion estimation function to learn the alter-ego's emotion history, predict future behavior based on past emotion patterns, and promote emotion-based behavior. For example, future behavior is set based on how the alter-ego felt in specific situations in the past. In this way, by using the emotion estimation function, the simulation unit can analyze in real time the emotions felt by the alter-ego while living in the virtual environment and promote emotion-based behavior.

[0069] The data collection unit can use an emotion estimation function on the collected data to analyze the user's emotional response. The data collection unit, for example, uses the emotion estimation function on the collected data to analyze the user's emotional response. For example, it analyzes whether the user has positive emotions toward a particular product. The data collection unit also uses the emotion estimation function to extract the user's emotional pattern from the collected data and develops a marketing strategy based on that information. For example, it analyzes how the user feels about a particular brand. The data collection unit also analyzes the user's emotional response based on the collected data and reflects the results in product development. For example, if the user has positive emotions toward a particular function, it strengthens that function. In this way, the emotion estimation function can be used on the collected data to analyze the user's emotional response.

[0070] The data collection unit can analyze the collected data in time series and track changes in the user's behavioral patterns. The data collection unit, for example, analyzes the collected data in time series and tracks changes in the user's behavioral patterns. For example, it analyzes what actions the user took over a specific period of time. The data collection unit also visualizes changes in the user's behavioral patterns based on the time series data and develops a marketing strategy based on that information. For example, it analyzes what products the user will purchase in a specific season. The data collection unit also analyzes the collected data in time series and introduces an algorithm that tracks changes in the user's behavioral patterns. For example, it analyzes how often the user participates in a specific event. In this way, it is possible to track changes in the user's behavioral patterns by analyzing the collected data in time series.

[0071] The data collection unit can build a model that predicts the user's future behavior based on the collected data. The data collection unit, for example, builds a model that predicts the user's future behavior based on the collected data. For example, it predicts what product the user will purchase next. The data collection unit also analyzes the collected data, extracts the user's behavioral patterns, and introduces an algorithm that predicts future behavior. For example, it predicts the possibility that the user will participate in a particular event. The data collection unit also simulates the user's future behavior based on the collected data and reflects the results in a marketing strategy. For example, it predicts what service the user will use next. In this way, future behavior can be predicted by building a model that predicts the user's future behavior based on the collected data.

[0072] The data collection unit can classify the collected data by different industries or uses and perform analysis specialized for each industry. For example, the data collection unit classifies the collected data by different industries or uses and performs analysis specialized for each industry. For example, data from the medical industry is analyzed to extract information about specific diseases. The data collection unit also collects data from different industries and introduces an algorithm that performs specialized analysis based on that data. For example, data from the financial industry is analyzed to develop an investment strategy. The data collection unit also classifies the collected data by use and performs analysis specialized for each use. For example, consumer behavior data is analyzed to develop a marketing strategy. In this way, by classifying the collected data by different industries or uses and performing analysis specialized for each industry, data analysis tailored to the characteristics of each industry becomes possible.

[0073] The data collection unit can integrate the collected data with other datasets to perform more comprehensive analysis. For example, the data collection unit integrates the collected data with other datasets to perform more comprehensive analysis. For example, health data and purchase history are integrated to analyze the relationship between a user's health condition and consumption behavior. The data collection unit also integrates different datasets and introduces an algorithm to perform comprehensive analysis based on the data. For example, SNS data and location information data are integrated to analyze user behavior patterns. The data collection unit also integrates the collected data with other datasets and uses the information to develop marketing strategies. For example, purchase history and SNS data are integrated to analyze a user's purchasing intentions. In this way, more comprehensive analysis is possible by integrating the collected data with other datasets.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] The data collection platform comprises an information interpretation unit that analyzes the user's passive information, an avatar generation unit that generates the user's avatar, a simulation unit that makes the avatar live in a virtual environment, and a data collection unit that collects detailed data. The information interpretation unit analyzes the user's passive information collected from social media and other sources, and models the user's personality, preferences, and behavioral patterns. The avatar generation unit generates the user's avatar based on the information analyzed by the information interpretation unit, and the simulation unit makes the avatar live in a virtual environment. The data collection unit collects detailed data from the avatar's life simulated by the simulation unit. This allows companies to efficiently collect detailed data and create unique value.

[0076] The information interpretation unit can estimate the user's emotions and adjust the avatar's behavioral patterns based on those emotions. For example, the generation AI analyzes emotions from the user's social media posts and comments and adjusts the avatar's behavioral patterns based on those emotions. If the user has positive emotions, the avatar is set to take proactive actions. In addition, the generation AI collects the user's emotional data in real time and dynamically adjusts the avatar's behavior based on that data. If the user is feeling stressed, the avatar will take actions to relax. Furthermore, the generation AI can learn the user's emotional history, predict future actions based on past emotional patterns, and adjust the avatar's behavior. This allows for more realistic simulations by adjusting the avatar's behavioral patterns based on the user's emotions.

[0077] The information interpretation unit can analyze the user's biometric data to generate a more sophisticated avatar. For example, the user's heart rate and electrodermal activity can be collected in real time, and the generation AI can analyze that data to adjust the avatar's behavior. If the user is tense, the avatar will take actions to relax. The biometric data can also be used to analyze the user's stress level and relaxation level, and the generation AI can use that information to set the avatar's behavior. If the user is relaxed, the avatar will take relaxed actions. Furthermore, the user's biometric data can be collected over a long period of time, and the generation AI can learn from that data to optimize the avatar's behavior patterns. This makes it possible to generate a more sophisticated avatar by analyzing the user's biometric data.

[0078] The information interpretation unit can incorporate an algorithm that learns a user's past behavioral history and predicts future behavior. For example, the generation AI can analyze a user's past social media posts and behavioral history, and then use that data to implement an algorithm that predicts future behavior. If a user has participated in a specific event in the past, it can predict that they are likely to participate in a similar event in the future. The generation AI can also learn the user's behavioral history, extract specific patterns, and predict future behavior. If a user has a habit of going to a specific place every weekend, it can predict that behavior and reflect it in the avatar's behavior. Furthermore, the generation AI can simulate future behavior based on the user's behavioral history and reflect the results in the avatar's behavior. This allows for more accurate simulations by learning the user's past behavioral history and predicting future behavior.

[0079] The avatar generation unit can generate avatars from a global perspective, taking into account the characteristics of different cultural spheres or regions. For example, the generation AI analyzes the characteristics of the user's cultural sphere or region and generates an avatar based on that information. If the user lives in Asia, an avatar that reflects the culture and customs of that region is generated. Data from different cultural spheres is also collected, and the generation AI generates avatars from a global perspective based on that data. If the user has a multicultural background, an avatar that reflects those characteristics is generated. Furthermore, the generation AI can learn the user's regional characteristics and set the avatar's behavioral patterns based on that information. If the user has a habit of participating in festivals or events in a particular region, that behavior can be reflected in the avatar. This allows avatars to be generated from a global perspective by taking into account the characteristics of different cultural spheres and regions.

[0080] The avatar generation unit can generate a variety of avatars that reflect the characteristics of different age groups and genders. For example, the generation AI analyzes the user's age group and gender and generates an avatar based on that information. If the user is young, an avatar that reflects those characteristics is generated. The generation AI also collects data on different age groups and genders, and generates a variety of avatars based on that data. If the user is middle-aged or older, an avatar that reflects those characteristics is generated. Furthermore, the generation AI can set the avatar's behavioral patterns based on the user's age group and gender. If the user is female, a behavioral pattern that reflects her characteristics is set for the avatar. This makes it possible to generate a variety of avatars by reflecting the characteristics of different age groups and genders.

[0081] The avatar generation unit uses the emotion estimation function to generate an avatar based on the user's emotions, making it possible to create an avatar that is easy to empathize with emotionally. For example, the emotion estimation function is used to analyze the user's emotional data and generate an avatar based on that information. If the user has positive emotions, an avatar that reflects those emotions is generated. The generation AI also learns the user's emotional history and generates an avatar that is easy to empathize with emotionally based on that information. The avatar is set based on how the user felt in specific situations in the past. Furthermore, the emotion estimation function can also be used to set the avatar's behavior pattern based on the user's emotions. If the user is feeling stressed, the avatar is set to behave in a way that reflects those emotions. In this way, the emotion estimation function makes it possible to create an avatar that is easy to empathize with emotionally.

[0082] The simulation unit can estimate emotions while the avatar is living in the virtual environment and change its behavior based on those emotions. For example, the simulation unit estimates emotions while the avatar is living in the virtual environment and changes its behavior based on those emotions. If the avatar is feeling stressed, it takes action to relax. It also collects emotional data of the avatar in the virtual environment in real time and dynamically adjusts the avatar's behavior based on that data. If the avatar is feeling positive emotions, it takes proactive action. It can also learn the avatar's emotional history, predict future behavior based on past emotional patterns, and adjust the avatar's behavior. This makes it possible to estimate emotions while the avatar is living in the virtual environment and change its behavior based on those emotions, enabling a more realistic simulation.

[0083] The simulation unit can add scenarios in which avatars cooperate with other avatars to accomplish tasks while living in a virtual environment. For example, a scenario can be added in which an avatar cooperates with other avatars to accomplish tasks while living in a virtual environment. A scenario can be set in which avatars work together on a project. An algorithm can also be introduced to help avatars cooperate with other avatars to accomplish tasks, and the avatars' actions can be adjusted. The avatars can assign roles and efficiently complete tasks. Furthermore, a scenario can be set in which avatars communicate with other avatars to accomplish tasks, and data collected in the process can be analyzed. A scenario can be set in which avatars demonstrate teamwork. This makes it possible to simulate cooperative behavior by avatars cooperating with other avatars to accomplish tasks while living in a virtual environment.

[0084] The simulation unit can reflect different seasons and weather conditions in the simulation of the avatar's life in the virtual environment. For example, different seasons and weather conditions can be reflected in the simulation of the avatar's life in the virtual environment. A scenario can be set in which the avatar wears warm clothes on a cold winter day. The seasons and weather conditions can also be changed in real time, and the avatar's behavior can be adjusted in response to these changes. A scenario can be set in which the avatar goes out with an umbrella on a rainy day. Furthermore, simulations that reflect different seasons and weather conditions can be performed, and data collected during the process can be analyzed. The behavior of the avatar choosing a cold drink on a hot summer day can be analyzed. In this way, by reflecting different seasons and weather conditions in the simulation of the avatar's life in the virtual environment, more realistic simulations can be achieved.

[0085] The simulation unit can add scenarios in which the avatar experiences different occupations and roles within the virtual environment. For example, a scenario can be added in which the avatar experiences different occupations and roles within the virtual environment. A scenario can be set in which the avatar works as a doctor or a teacher. An algorithm can also be introduced to experience different occupations and roles and adjust the avatar's behavior. A scenario can be set in which the avatar learns the skills required for a specific occupation. Furthermore, a scenario can be set in which the avatar experiences different occupations and roles, and data collected during the process can be analyzed. The stress level of the avatar in different occupations can be analyzed. This allows a variety of simulations to be performed by experiencing different occupations and roles within the virtual environment.

[0086] The simulation unit can use the emotion estimation function to analyze in real time the emotions felt by the avatar while living in the virtual environment and encourage behavior based on those emotions. For example, the emotion estimation function can be used to analyze in real time the emotions felt by the avatar while living in the virtual environment. If the avatar is feeling stressed, the simulation unit can encourage behavior to relax based on those emotions. The simulation unit can also collect emotion data of the avatar in real time and set behavior based on the emotions based on that data. If the avatar has positive emotions, the simulation unit can set behavior to be proactive. Furthermore, the emotion estimation function can be used to learn the emotion history of the avatar, predict future behavior based on past emotion patterns, and encourage behavior based on those emotions. As a result, the emotion estimation function can be used to analyze in real time the emotions felt by the avatar while living in the virtual environment and encourage behavior based on those emotions.

[0087] The data collection unit can use an emotion estimation function on the collected data to analyze the user's emotional response. For example, the emotion estimation function is used on the collected data to analyze the user's emotional response. It analyzes whether the user has positive emotions toward a particular product. The emotion estimation function can also be used to extract the user's emotional patterns from the collected data and develop a marketing strategy based on that information. It can also analyze the user's emotions toward a particular brand. Furthermore, the user's emotional response can be analyzed based on the collected data, and the results can be reflected in product development. If the user has positive emotions toward a particular function, that function can be enhanced. In this way, the emotion estimation function can be used on the collected data to analyze the user's emotional response.

[0088] The data collection unit can analyze the collected data in chronological order and track changes in user behavior patterns. For example, the collected data can be analyzed in chronological order to track changes in user behavior patterns. The actions taken by the user over a specific period can be analyzed. The changes in user behavior patterns can be visualized based on the time-series data, and a marketing strategy can be developed based on this information. The types of products a user will purchase in a specific season can be analyzed. Furthermore, an algorithm can be introduced to analyze the collected data in chronological order and track changes in user behavior patterns. The frequency with which a user participates in a specific event can be analyzed. In this way, by analyzing the collected data in chronological order, changes in user behavior patterns can be tracked.

[0089] The data collection unit can build a model that predicts a user's future behavior based on the collected data. For example, a model that predicts a user's future behavior is built based on the collected data. It predicts what product the user will purchase next. It also analyzes the collected data, extracts the user's behavior patterns, and introduces an algorithm that predicts future behavior. It predicts the likelihood that the user will participate in a particular event. Furthermore, it can simulate the user's future behavior based on the collected data and reflect the results in marketing strategies. It predicts what service the user will use next. In this way, future behavior can be predicted by building a model that predicts a user's future behavior based on the collected data.

[0090] The data collection unit can classify the collected data by different industries or uses and perform analysis specialized for each industry. For example, the collected data can be classified by different industries or uses and analysis specialized for each industry can be performed. Data from the medical industry can be analyzed to extract information about specific diseases. Data from different industries can also be collected and an algorithm can be introduced to perform specialized analysis based on that data. Data from the financial industry can be analyzed to develop investment strategies. Furthermore, the collected data can be classified by use and analysis specialized for each use can be performed. Consumer behavior data can be analyzed to develop marketing strategies. This makes it possible to classify the collected data by different industries or uses and perform analysis specialized for each industry, thereby enabling data analysis tailored to the characteristics of each industry.

[0091] The data collection unit can integrate the collected data with other datasets to perform more comprehensive analysis. For example, the collected data can be integrated with other datasets to perform more comprehensive analysis. Health data and purchase history can be integrated to analyze the relationship between a user's health condition and consumption behavior. It is also possible to integrate different datasets and introduce algorithms that perform comprehensive analysis based on that data. Social media data and location data can be integrated to analyze user behavior patterns. Furthermore, the collected data can be integrated with other datasets to develop marketing strategies based on that information. Purchase history and social media data can be integrated to analyze a user's purchasing intentions. In this way, more comprehensive analysis can be performed by integrating the collected data with other datasets.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The information interpretation unit analyzes the user's passive information collected from social media and other sources. For example, the information interpretation unit analyzes the user's posted content, profile information, and behavioral history to model the user's personality, preferences, and behavioral patterns. The information interpretation unit can also use generative AI to analyze the user's passive information. Step 2: The avatar generation unit generates an avatar for the user based on the passive information analyzed by the information interpretation unit. For example, the avatar generation unit uses a generation AI to generate an avatar that reflects the user's personality, preferences, and behavioral patterns. The avatar generation unit can also generate a virtual being that mimics the user's characteristics. Step 3: The simulation unit makes the avatar generated by the avatar generation unit live in a virtual environment. For example, the simulation unit sets up a scenario in which the avatar goes shopping in a virtual city and interacts with friends. The simulation unit can also collect detailed data by simulating the avatar's life in the virtual environment. Step 4: The data collection unit collects detailed data from the life of the avatar simulated by the simulation unit. For example, the data collection unit collects detailed information such as what products the avatar prefers, what behavioral patterns it has, and what communication style it has. The data collection unit can also analyze the collected data and provide valuable information that companies can use in their marketing strategies and product development.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] 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.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] 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.

[0153] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information interpretation unit that analyzes passive information of users collected from SNS; an avatar generation unit that generates an avatar of the user based on the non-active information analyzed by the information interpretation unit; a simulation unit that makes the alter-ego generated by the alter-ego generation unit live in a virtual environment; a data collection unit that collects detailed data from the life of the alter ego simulated by the simulation unit; A system characterized by:

2. The information interpretation unit Estimating the user's emotions and adjusting the behavioral patterns of the avatar based on the emotions 2. The system of claim 1.

3. The information interpretation unit Analyzing the user's biometric data and generating a more sophisticated avatar 2. The system of claim 1.

4. The information interpretation unit Introducing an algorithm that learns the user's past behavior history and predicts future behavior.

2. The system of claim 1.

5. The avatar generation unit Generate the avatar from a global perspective, taking into account the characteristics of different cultural spheres or regions.

2. The system of claim 1.

6. The avatar generation unit Generate a variety of avatars that reflect the characteristics of different age groups and genders 2. The system of claim 1.

7. The avatar generation unit The alter-ego is generated based on the emotions of the user, and the alter-ego is created so that the user can easily empathize with the alter-ego emotionally.

2. The system of claim 1.

8. The simulation unit Inferring emotions while the avatar lives in the virtual environment and changing behavior based on the emotions.

2. The system of claim 1.

Images