system

Abstract

The system according to the embodiment aims to generate an avatar of the user based on information extracted from an SNS and to collect detailed data through the avatar. [Solution] A system according to an embodiment includes an information extraction unit, an avatar generation unit, and a behavior collection unit. The information extraction unit extracts user information from a social networking service. The avatar generation unit generates an avatar of the user based on the information extracted by the information extraction unit. The behavior collection unit collects behavioral data of the avatar generated by the avatar generation unit as it acts in a virtual environment.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have made it difficult to collect large amounts of high-quality data from users, which has limited the value that companies can create.

[0005] The system according to the embodiment aims to generate an avatar of the user based on information extracted from an SNS and to collect detailed data through the avatar. [Means for solving the problem]

[0006] The system according to the embodiment includes an information extraction unit, an avatar generation unit, and a behavior collection unit. The information extraction unit extracts user information from SNS. The avatar generation unit generates an avatar of the user based on the information extracted by the information extraction unit. The behavior collection unit collects behavioral data of the avatar generated by the avatar generation unit as it acts in a virtual environment. [Effects of the Invention]

[0007] The system according to the embodiment generates an avatar of the user based on information extracted from the SNS, and can collect detailed data through the avatar. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A data collection platform according to an embodiment of the present invention uses a generation AI to extract passive information about a user from social media platforms and generate an avatar for the user based on that information. This platform extracts information about the user's behavior and preferences from social media platforms and generates an avatar for the user based on the extracted information. The generated avatar lives with other agents in a virtual environment and collects their behavioral data. This platform enables companies to collect large amounts of high-quality data and pioneers next-generation data collection. For example, the generation AI extracts information about the user's behavior and preferences from social media platforms and collects behavioral data such as content posted by the user on the social media platform, as well as likes and shares. For example, if a user posts "I want to travel" on the social media platform, this information is extracted. Next, the generation AI generates an avatar for the user based on the extracted information. The generation AI analyzes the extracted information to understand the user's behavioral patterns and preferences. For example, if a user is interested in traveling, an avatar reflecting that preference is generated. The generated avatar lives with other agents in a virtual environment. For example, the avatar may plan a trip or interact with other agents within the virtual environment. In this way, data on the avatar's behavior is collected. This platform enables companies to collect large amounts of high-quality data. For example, it can create marketing strategies based on user preferences and behavioral patterns. Furthermore, analyzing how the user's avatar behaves within the virtual environment can provide a more detailed understanding of the user's needs. This allows the data collection platform to collect large amounts of high-quality data for companies, leading the way in next-generation data collection.

[0029] A data collection platform according to an embodiment includes an information extraction unit, an avatar generation unit, and a behavior collection unit. The information extraction unit extracts user information from a social networking site (SNS). The information extraction unit collects behavioral data, such as the content of posts, likes, and shares on the SNS. For example, if a user posts "I want to go traveling" on the SNS, the information extraction unit extracts the information. The information extraction unit also analyzes the user's behavioral data to understand the user's preferences and behavioral patterns. For example, if a user frequently posts about "travel," the information extraction unit extracts the user's preferences. The avatar generation unit generates an avatar for the user based on the information extracted by the information extraction unit. For example, the avatar generation unit analyzes the extracted information to understand the user's behavioral patterns and preferences. For example, if a user is interested in traveling, the avatar generation unit generates an avatar that reflects the user's preferences. The avatar generation unit also configures the generated avatar to live together with other agents in a virtual environment. For example, the avatar generation unit sets the avatar to make travel plans and interact with other agents within the virtual environment. The behavior collection unit causes the generated avatar to act within the virtual environment and collects behavioral data. The behavior collection unit, for example, records how the avatar acts within the virtual environment and collects that data. For example, the behavior collection unit collects behavioral data on the avatar when making travel plans and when interacting with other agents. This enables the data collection platform according to the embodiment to efficiently extract user information, generate avatars, and collect behavioral data.

[0030] The information extraction unit can extract user behavioral data from SNS. The information extraction unit collects behavioral data such as the content of posts on SNS, likes, and shares. For example, if a user posts "I want to go on a trip" on SNS, the information extraction unit extracts that information. The information extraction unit also analyzes the user behavioral data to understand the user's preferences and behavioral patterns. For example, if a user frequently posts about "travel," the information extraction unit extracts that preference. This makes it possible to efficiently extract user behavioral data from SNS.

[0031] The avatar generation unit can analyze the user's behavioral patterns or preferences based on the extracted information and generate an avatar for the user. The avatar generation unit, for example, analyzes the extracted information to understand the user's behavioral patterns and preferences. For example, if the user is interested in traveling, the avatar generation unit generates an avatar that reflects the user's preferences. The avatar generation unit also sets the generated avatar to live together with other agents in the virtual environment. For example, the avatar generation unit sets the avatar to plan travel and interact with other agents in the virtual environment. This makes it possible to generate an avatar that reflects the user's behavioral patterns and preferences.

[0032] The behavior collection unit can collect behavioral data of the generated avatar as it lives together with other virtual agents in the virtual environment. The behavior collection unit, for example, records how the avatar behaves in the virtual environment and collects that data. For example, the behavior collection unit collects behavioral data of the avatar when planning a trip or when interacting with other agents. This makes it possible to efficiently collect behavioral data of the avatar in the virtual environment.

[0033] The behavior collection unit can analyze the behavioral data of the avatar to understand the needs of the user. For example, the behavior collection unit analyzes the behavioral data of the avatar to understand the needs of the user. For example, the behavior collection unit analyzes the behavioral data of the avatar when planning a trip or when interacting with other agents to understand the preferences and behavioral patterns of the user. In this way, by analyzing the behavioral data of the avatar, it is possible to understand the needs of the user in detail.

[0034] When extracting information from SNS, the information extraction unit can analyze the user's past posting history and select an appropriate extraction method. For example, the information extraction unit uses a generation AI to extract related information based on keywords that the user has frequently posted in the past. The information extraction unit can also prioritize the extraction of information on specific themes from the user's past posting history. Furthermore, the information extraction unit can analyze the user's posting frequency and time period to extract information at the optimal timing. This makes it possible to select the optimal information extraction method by analyzing the user's past posting history.

[0035] When extracting information, the information extraction unit can filter based on the user's current interests and trends. For example, if the user has recently shown an interest in "travel," the generation AI can prioritize extracting information related to travel. Also, if the user has shown an interest in "health," which is a current trend, the information extraction unit can also prioritize extracting information related to health. Furthermore, if the user has shown an interest in a specific event, the information extraction unit can also extract information related to that event. This makes it possible to extract more relevant information by filtering information based on the user's current interests and trends.

[0036] When extracting information, the information extraction unit can prioritize extracting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific city, the generation AI can extract event and tourist information related to that city. In addition, if the user is in a specific region, the generation AI can extract weather and traffic information for that region. Furthermore, if the user is traveling, the information extraction unit can extract tourist spot and restaurant information for the travel destination. This makes it possible to extract more relevant information by taking into account the user's geographical location information.

[0037] When extracting information, the information extraction unit can analyze the user's social media activity and extract related information. For example, if the user frequently uses a specific hashtag, the information extraction unit causes the generation AI to extract information related to that hashtag. In addition, if the user is a member of a specific group or community, the information extraction unit can also cause the generation AI to extract information related to that group. Furthermore, if the user follows a specific account, the information extraction unit can also cause the generation AI to extract information related to that account. This makes it possible to efficiently extract related information by analyzing the user's social media activity.

[0038] When generating an avatar, the avatar generation unit can generate an optimal avatar by referencing the user's past behavioral data. In the avatar generation unit, for example, the generation AI sets the avatar's behavior pattern based on the user's frequent past behavior. The avatar generation unit can also generate an avatar that reflects specific preferences from the user's past behavioral data. Furthermore, the avatar generation unit can analyze the user's past behavioral data and set the most suitable avatar characteristics. This makes it possible to generate an optimal avatar by referencing the user's past behavioral data.

[0039] When generating an avatar, the avatar generation unit can customize the avatar's characteristics based on the user's current living situation or area of ​​interest. For example, if the user is busy in their current living situation, the generation AI sets an efficient behavior pattern for the avatar. Also, if the user is interested in a particular area of ​​interest, the avatar generation unit can reflect characteristics related to that area in the avatar. Furthermore, the avatar generation unit can set characteristics suitable for the avatar, taking into account the user's current living situation. This makes it possible to generate a more appropriate avatar by customizing the avatar's characteristics based on the user's current living situation and area of ​​interest.

[0040] When generating an avatar, the avatar generation unit can generate an optimal avatar by taking into account the user's geographical location information. For example, if the user lives in a specific city, the avatar generation unit generates an avatar that reflects the culture and customs of that city. Furthermore, if the user lives in a specific region, the avatar generation unit can also generate an avatar that takes into account the climate and environment of that region. Furthermore, if the user is traveling, the avatar generation unit can also generate an avatar that reflects information about the user's travel destination. This makes it possible to generate a more appropriate avatar by taking into account the user's geographical location information.

[0041] The avatar generation unit can analyze the user's social media activities and reflect related behavioral patterns when generating the avatar. For example, if the user frequently uses a specific hashtag, the avatar generation unit can reflect behavioral patterns related to the hashtag in the avatar. In addition, if the user participates in a specific group or community, the avatar generation unit can also reflect behavioral patterns related to the group in the avatar. Furthermore, if the user follows a specific account, the avatar generation unit can also reflect behavioral patterns related to the account in the avatar. This makes it possible to generate an avatar that reflects related behavioral patterns by analyzing the user's social media activities.

[0042] When collecting behavior, the behavior collection unit can select the optimal collection method by referring to the alter's past behavior data. For example, the behavior collection unit uses the generation AI to select the optimal collection method based on the alter's frequent behavior in the past. The behavior collection unit can also prioritize the collection of behavior data that reflects specific preferences from the alter's past behavior data. Furthermore, the behavior collection unit can analyze the alter's past behavior data and select the most efficient collection method. This makes it possible to select the optimal collection method by referring to the alter's past behavior data.

[0043] When collecting behavior, the behavior collection unit can filter the behavior based on the avatar's current activity status and areas of interest. For example, if the avatar is busy in its current activity status, the behavior collection unit will prioritize collecting behavior data that the generation AI is efficient at. Also, if the avatar is interested in a particular area of ​​interest, the behavior collection unit can prioritize collecting behavior data related to that area. Furthermore, the behavior collection unit can also collect optimal behavior data by taking into account the avatar's current activity status. This makes it possible to collect more relevant data by filtering behavior data based on the avatar's current activity status and areas of interest.

[0044] When collecting behavior, the behavior collection unit can prioritize collecting highly relevant behavioral data by taking into account the avatar's geographical location information. For example, if the avatar is in a specific city, the behavior collection unit allows the generation AI to prioritize collecting behavioral data related to that city. Also, if the avatar is in a specific region, the behavior collection unit can also allow the generation AI to prioritize collecting behavioral data related to the climate and environment of that region. Furthermore, if the avatar is traveling, the behavior collection unit can also allow the generation AI to prioritize collecting behavioral data from travel destinations. This makes it possible to collect more relevant behavioral data by taking into account the avatar's geographical location information.

[0045] The behavior collection unit can analyze the avatar's social media activities and collect related behavioral data when collecting behavior. For example, if the avatar frequently uses a specific hashtag, the behavior collection unit can cause the generation AI to prioritize collection of behavioral data related to that hashtag. Also, if the avatar participates in a specific group or community, the behavior collection unit can cause the generation AI to prioritize collection of behavioral data related to that group. Furthermore, if the avatar follows a specific account, the behavior collection unit can cause the generation AI to prioritize collection of behavioral data related to that account. This makes it possible to efficiently collect related behavioral data by analyzing the avatar's social media activities.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The data collection platform may further include a health data collection unit that collects the user's health data. The health data collection unit may collect, for example, data such as heart rate, step count, and sleep patterns from the user's smartwatch or fitness tracker. The health data collection unit may also collect the user's food and exercise records to comprehensively understand the user's health status. Furthermore, the health data collection unit may analyze the user's health data and adjust the avatar's behavioral patterns based on the user's health status. This allows the user to support a healthier lifestyle by collecting the user's health data and reflecting it in the avatar's behavior.

[0048] The information extraction unit can analyze a user's purchasing history and extract related information. For example, the generation AI extracts information about related products based on the user's purchase history on an online shopping site. The information extraction unit can also analyze reviews and ratings of products the user has previously purchased to understand the user's preferences. Furthermore, the information extraction unit can preferentially extract information about specific brands or categories from the user's purchasing history. This makes it possible to extract more relevant information by analyzing the user's purchasing history.

[0049] The behavior collection unit can analyze the avatar's behavioral data in real time and provide feedback to the user. For example, the behavior collection unit can suggest healthy lifestyle habits to the user based on the avatar's behavior in the virtual environment. The behavior collection unit can also suggest stress management and relaxation methods to the user based on the avatar's behavioral data. Furthermore, the behavior collection unit can analyze the avatar's behavioral data and suggest efficient time management methods to the user. This makes it possible to support the user's lifestyle by analyzing the avatar's behavioral data in real time and providing feedback to the user.

[0050] When extracting information from social media, the information extraction unit can analyze a user's past posting history and select an appropriate extraction method. For example, the generation AI extracts related information based on keywords that the user has frequently posted in the past. The information extraction unit can also prioritize the extraction of information on specific themes from the user's past posting history. Furthermore, the information extraction unit can analyze the user's posting frequency and time period to extract information at the optimal timing. This makes it possible to select the optimal information extraction method by analyzing the user's past posting history.

[0051] When extracting information, the information extraction unit can filter based on the user's current interests and trends. For example, if the user has recently shown an interest in "travel," the generation AI can prioritize extracting information related to travel. Furthermore, if the user has shown an interest in "health," a current trend, the information extraction unit can also prioritize extracting information related to health. Furthermore, if the user has shown an interest in a specific event, the information extraction unit can also extract information related to that event. This makes it possible to extract more relevant information by filtering information based on the user's current interests and trends.

[0052] When extracting information, the information extraction unit can prioritize extracting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific city, the generation AI can extract event and tourist information related to that city. In addition, if the user is in a specific region, the information extraction unit can also extract weather and traffic information for that region. Furthermore, if the user is traveling, the information extraction unit can also extract tourist spot and restaurant information for the travel destination. This makes it possible to extract more relevant information by taking into account the user's geographical location information.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The information extraction unit extracts user information from social media. Specifically, it collects behavioral data such as social media posts, likes, and shares, and analyzes it to understand the user's preferences and behavioral patterns. For example, if a user posts that they "want to go on a trip," that information is extracted. If the user frequently posts about "travel," their preferences are extracted. Step 2: The avatar generation unit generates an avatar for the user based on the information extracted by the information extraction unit. Specifically, it analyzes the extracted information to understand the user's behavioral patterns and preferences, and generates an avatar that reflects them. For example, if the user is interested in traveling, an avatar that reflects those preferences is generated and set up to live together with other agents in the virtual environment. Step 3: The behavior collection unit collects behavioral data of the created avatar as it acts within the virtual environment. Specifically, it records how the avatar behaves within the virtual environment and collects that data. For example, it collects behavioral data when the avatar plans a trip or when interacting with other agents.

[0055] (Example 2) A data collection platform according to an embodiment of the present invention uses a generation AI to extract passive information about a user from social media platforms and generate an avatar for the user based on that information. This platform extracts information about the user's behavior and preferences from social media platforms and generates an avatar for the user based on the extracted information. The generated avatar lives with other agents in a virtual environment and collects their behavioral data. This platform enables companies to collect large amounts of high-quality data and pioneers next-generation data collection. For example, the generation AI extracts information about the user's behavior and preferences from social media platforms and collects behavioral data such as content posted by the user on the social media platform, as well as likes and shares. For example, if a user posts "I want to travel" on the social media platform, this information is extracted. Next, the generation AI generates an avatar for the user based on the extracted information. The generation AI analyzes the extracted information to understand the user's behavioral patterns and preferences. For example, if a user is interested in traveling, an avatar reflecting that preference is generated. The generated avatar lives with other agents in a virtual environment. For example, the avatar may plan a trip or interact with other agents within the virtual environment. In this way, data on the avatar's behavior is collected. This platform enables companies to collect large amounts of high-quality data. For example, it can create marketing strategies based on user preferences and behavioral patterns. Furthermore, analyzing how the user's avatar behaves within the virtual environment can provide a more detailed understanding of the user's needs. This allows the data collection platform to collect large amounts of high-quality data for companies, leading the way in next-generation data collection.

[0056] A data collection platform according to an embodiment includes an information extraction unit, an avatar generation unit, and a behavior collection unit. The information extraction unit extracts user information from a social networking site (SNS). The information extraction unit collects behavioral data, such as the content of posts, likes, and shares on the SNS. For example, if a user posts "I want to go traveling" on the SNS, the information extraction unit extracts the information. The information extraction unit also analyzes the user's behavioral data to understand the user's preferences and behavioral patterns. For example, if a user frequently posts about "travel," the information extraction unit extracts the user's preferences. The avatar generation unit generates an avatar for the user based on the information extracted by the information extraction unit. For example, the avatar generation unit analyzes the extracted information to understand the user's behavioral patterns and preferences. For example, if a user is interested in traveling, the avatar generation unit generates an avatar that reflects the user's preferences. The avatar generation unit also configures the generated avatar to live together with other agents in a virtual environment. For example, the avatar generation unit sets the avatar to make travel plans and interact with other agents within the virtual environment. The behavior collection unit causes the generated avatar to act within the virtual environment and collects behavioral data. The behavior collection unit, for example, records how the avatar acts within the virtual environment and collects that data. For example, the behavior collection unit collects behavioral data on the avatar when making travel plans and when interacting with other agents. This enables the data collection platform according to the embodiment to efficiently extract user information, generate avatars, and collect behavioral data.

[0057] The information extraction unit can extract user behavioral data from SNS. The information extraction unit collects behavioral data such as the content of posts on SNS, likes, and shares. For example, if a user posts "I want to go on a trip" on SNS, the information extraction unit extracts that information. The information extraction unit also analyzes the user behavioral data to understand the user's preferences and behavioral patterns. For example, if a user frequently posts about "travel," the information extraction unit extracts that preference. This makes it possible to efficiently extract user behavioral data from SNS.

[0058] The avatar generation unit can analyze the user's behavioral patterns or preferences based on the extracted information and generate an avatar for the user. The avatar generation unit, for example, analyzes the extracted information to understand the user's behavioral patterns and preferences. For example, if the user is interested in traveling, the avatar generation unit generates an avatar that reflects the user's preferences. The avatar generation unit also sets the generated avatar to live together with other agents in the virtual environment. For example, the avatar generation unit sets the avatar to plan travel and interact with other agents in the virtual environment. This makes it possible to generate an avatar that reflects the user's behavioral patterns and preferences.

[0059] The behavior collection unit can collect behavioral data of the generated avatar as it lives together with other virtual agents in the virtual environment. The behavior collection unit, for example, records how the avatar behaves in the virtual environment and collects that data. For example, the behavior collection unit collects behavioral data of the avatar when planning a trip or when interacting with other agents. This makes it possible to efficiently collect behavioral data of the avatar in the virtual environment.

[0060] The behavior collection unit can analyze the behavioral data of the avatar to understand the needs of the user. For example, the behavior collection unit analyzes the behavioral data of the avatar to understand the needs of the user. For example, the behavior collection unit analyzes the behavioral data of the avatar when planning a trip or when interacting with other agents to understand the preferences and behavioral patterns of the user. In this way, by analyzing the behavioral data of the avatar, it is possible to understand the needs of the user in detail.

[0061] The information extraction unit can estimate the user's emotions and adjust the type of information to be extracted based on the estimated user emotions. For example, if the user is expressing positive emotions, the information extraction unit can cause the generation AI to preferentially extract information about travel and hobbies. In addition, if the user is expressing negative emotions, the information extraction unit can cause the generation AI to preferentially extract information about stress relief and relaxation. Furthermore, if the user is expressing neutral emotions, the information extraction unit can cause the generation AI to extract information about daily life in a balanced manner. This makes it possible to extract more appropriate information by adjusting the type of information to be extracted according to the user's emotions.

[0062] When extracting information from SNS, the information extraction unit can analyze the user's past posting history and select an appropriate extraction method. For example, the information extraction unit uses a generation AI to extract related information based on keywords that the user has frequently posted in the past. The information extraction unit can also prioritize the extraction of information on specific themes from the user's past posting history. Furthermore, the information extraction unit can analyze the user's posting frequency and time period to extract information at the optimal timing. This makes it possible to select the optimal information extraction method by analyzing the user's past posting history.

[0063] When extracting information, the information extraction unit can filter based on the user's current interests and trends. For example, if the user has recently shown an interest in "travel," the generation AI can prioritize extracting information related to travel. Also, if the user has shown an interest in "health," which is a current trend, the information extraction unit can also prioritize extracting information related to health. Furthermore, if the user has shown an interest in a specific event, the information extraction unit can also extract information related to that event. This makes it possible to extract more relevant information by filtering information based on the user's current interests and trends.

[0064] The information extraction unit can estimate the user's emotions and determine the priority of information to be extracted based on the estimated user emotions. For example, if the user is excited, the information extraction unit can cause the generation AI to preferentially extract information related to entertainment. Furthermore, if the user is calm, the information extraction unit can cause the generation AI to preferentially extract information related to education and learning. Furthermore, if the user is tired, the information extraction unit can cause the generation AI to preferentially extract information related to relaxation and rest. This makes it possible to extract more appropriate information by determining the priority of information according to the user's emotions.

[0065] When extracting information, the information extraction unit can prioritize extracting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific city, the generation AI can extract event and tourist information related to that city. In addition, if the user is in a specific region, the generation AI can extract weather and traffic information for that region. Furthermore, if the user is traveling, the information extraction unit can extract tourist spot and restaurant information for the travel destination. This makes it possible to extract more relevant information by taking into account the user's geographical location information.

[0066] When extracting information, the information extraction unit can analyze the user's social media activity and extract related information. For example, if the user frequently uses a specific hashtag, the information extraction unit causes the generation AI to extract information related to that hashtag. In addition, if the user is a member of a specific group or community, the information extraction unit can also cause the generation AI to extract information related to that group. Furthermore, if the user follows a specific account, the information extraction unit can also cause the generation AI to extract information related to that account. This makes it possible to efficiently extract related information by analyzing the user's social media activity.

[0067] The avatar generation unit can estimate the user's emotions and adjust the behavior pattern of the avatar based on the estimated user's emotions. For example, when the user is expressing positive emotions, the generation AI of the avatar generation unit sets an active behavior pattern for the avatar. In addition, when the user is expressing negative emotions, the generation AI of the avatar generation unit can set an behavior pattern that emphasizes relaxation for the avatar. Furthermore, when the user is expressing neutral emotions, the generation AI of the avatar generation unit can set a balanced behavior pattern for the avatar. This makes it possible to generate a more appropriate avatar by adjusting the behavior pattern of the avatar according to the user's emotions.

[0068] When generating an avatar, the avatar generation unit can generate an optimal avatar by referencing the user's past behavioral data. In the avatar generation unit, for example, the generation AI sets the avatar's behavior pattern based on the user's frequent past behavior. The avatar generation unit can also generate an avatar that reflects specific preferences from the user's past behavioral data. Furthermore, the avatar generation unit can analyze the user's past behavioral data and set the most suitable avatar characteristics. This makes it possible to generate an optimal avatar by referencing the user's past behavioral data.

[0069] When generating an avatar, the avatar generation unit can customize the avatar's characteristics based on the user's current living situation or area of ​​interest. For example, if the user is busy in their current living situation, the generation AI sets an efficient behavior pattern for the avatar. Also, if the user is interested in a particular area of ​​interest, the avatar generation unit can reflect characteristics related to that area in the avatar. Furthermore, the avatar generation unit can set characteristics suitable for the avatar, taking into account the user's current living situation. This makes it possible to generate a more appropriate avatar by customizing the avatar's characteristics based on the user's current living situation and area of ​​interest.

[0070] The avatar generation unit can estimate the user's emotions and determine the avatar's action priorities based on the estimated user's emotions. For example, if the user is excited, the avatar generation unit sets a behavior pattern in which the generation AI prioritizes entertainment for the avatar. In addition, if the user is calm, the avatar generation unit can also set a behavior pattern in which the generation AI prioritizes study or work for the avatar. Furthermore, if the user is tired, the avatar generation unit can also set a behavior pattern in which the generation AI prioritizes rest for the avatar. This makes it possible to generate more appropriate avatars by determining the avatar's action priorities according to the user's emotions.

[0071] When generating an avatar, the avatar generation unit can generate an optimal avatar by taking into account the user's geographical location information. For example, if the user lives in a specific city, the avatar generation unit generates an avatar that reflects the culture and customs of that city. Furthermore, if the user lives in a specific region, the avatar generation unit can also generate an avatar that takes into account the climate and environment of that region. Furthermore, if the user is traveling, the avatar generation unit can also generate an avatar that reflects information about the user's travel destination. This makes it possible to generate a more appropriate avatar by taking into account the user's geographical location information.

[0072] The avatar generation unit can analyze the user's social media activities and reflect related behavioral patterns when generating the avatar. For example, if the user frequently uses a specific hashtag, the avatar generation unit can reflect behavioral patterns related to the hashtag in the avatar. In addition, if the user participates in a specific group or community, the avatar generation unit can also reflect behavioral patterns related to the group in the avatar. Furthermore, if the user follows a specific account, the avatar generation unit can also reflect behavioral patterns related to the account in the avatar. This makes it possible to generate an avatar that reflects related behavioral patterns by analyzing the user's social media activities.

[0073] The behavior collection unit can estimate the user's emotions and adjust the type of behavioral data to be collected based on the estimated user emotions. For example, when the user is expressing positive emotions, the behavior collection unit causes the generation AI to prioritize collecting sociable behavioral data of the avatar. In addition, when the user is expressing negative emotions, the behavior collection unit can also cause the generation AI to prioritize collecting behavioral data related to the avatar's relaxation. Furthermore, when the user is expressing neutral emotions, the behavior collection unit can also cause the generation AI to collect balanced behavioral data related to the avatar's daily life. This makes it possible to collect more appropriate data by adjusting the type of behavioral data to be collected according to the user's emotions.

[0074] When collecting behavior, the behavior collection unit can select the optimal collection method by referring to the alter's past behavior data. For example, the behavior collection unit uses the generation AI to select the optimal collection method based on the alter's frequent behavior in the past. The behavior collection unit can also prioritize the collection of behavior data that reflects specific preferences from the alter's past behavior data. Furthermore, the behavior collection unit can analyze the alter's past behavior data and select the most efficient collection method. This makes it possible to select the optimal collection method by referring to the alter's past behavior data.

[0075] When collecting behavior, the behavior collection unit can filter the behavior based on the avatar's current activity status and areas of interest. For example, if the avatar is busy in its current activity status, the behavior collection unit will prioritize collecting behavior data that the generation AI is efficient at. Also, if the avatar is interested in a particular area of ​​interest, the behavior collection unit can prioritize collecting behavior data related to that area. Furthermore, the behavior collection unit can also collect optimal behavior data by taking into account the avatar's current activity status. This makes it possible to collect more relevant data by filtering behavior data based on the avatar's current activity status and areas of interest.

[0076] The behavior collection unit can estimate the user's emotions and determine the priority of behavioral data to be collected based on the estimated user's emotions. For example, if the user is excited, the behavior collection unit can cause the generation AI to prioritize collecting behavioral data related to the avatar's entertainment. Also, if the user is calm, the behavior collection unit can cause the generation AI to prioritize collecting behavioral data related to the avatar's learning or work. Furthermore, if the user is tired, the behavior collection unit can cause the generation AI to prioritize collecting behavioral data related to the avatar's rest. This makes it possible to collect more appropriate data by determining the priority of behavioral data according to the user's emotions.

[0077] When collecting behavior, the behavior collection unit can prioritize collecting highly relevant behavioral data by taking into account the avatar's geographical location information. For example, if the avatar is in a specific city, the behavior collection unit allows the generation AI to prioritize collecting behavioral data related to that city. Also, if the avatar is in a specific region, the behavior collection unit can also allow the generation AI to prioritize collecting behavioral data related to the climate and environment of that region. Furthermore, if the avatar is traveling, the behavior collection unit can also allow the generation AI to prioritize collecting behavioral data from travel destinations. This makes it possible to collect more relevant behavioral data by taking into account the avatar's geographical location information.

[0078] The behavior collection unit can analyze the avatar's social media activities and collect related behavioral data when collecting behavior. For example, if the avatar frequently uses a specific hashtag, the behavior collection unit can cause the generation AI to prioritize collection of behavioral data related to that hashtag. Also, if the avatar participates in a specific group or community, the behavior collection unit can cause the generation AI to prioritize collection of behavioral data related to that group. Furthermore, if the avatar follows a specific account, the behavior collection unit can cause the generation AI to prioritize collection of behavioral data related to that account. This makes it possible to efficiently collect related behavioral data by analyzing the avatar's social media activities. === Hard Collateral 1-1 === Each of the multiple elements including the information extraction unit, the avatar generation unit, and the behavior collection unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the information extraction unit extracts user information from an SNS by the control unit 46A of the smart device 14. The avatar generation unit generates an avatar of the user based on the information extracted by the specific processing unit 290 of the data processing device 12. The behavior collection unit collects behavior data of the avatar in the virtual environment by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the information extraction unit, the avatar generation unit, and the behavior collection unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the information extraction unit extracts user information from an SNS by the control unit 46A of the smart glasses 214. Furthermore, the avatar generation unit generates an avatar of the user based on the information extracted by the specific processing unit 290 of the data processing device 12. The behavior collection unit collects behavior data of the avatar in the virtual environment by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the information extraction unit, avatar generation unit, and behavior collection unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the information extraction unit extracts user information from an SNS by the control unit 46A of the headset type terminal 314. Furthermore, the avatar generation unit generates an avatar of the user based on the information extracted by the specific processing unit 290 of the data processing device 12. The behavior collection unit collects behavior data of the avatar in the virtual environment by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the information extraction unit, avatar generation unit, and behavior collection unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the information extraction unit extracts user information from an SNS by the control unit 46A of the robot 414. Furthermore, the avatar generation unit generates an avatar of the user based on the information extracted by the specific processing unit 290 of the data processing device 12. The behavior collection unit collects behavior data of the avatar in the virtual environment by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The data collection platform may further include a health data collection unit that collects the user's health data. The health data collection unit may collect, for example, data such as heart rate, step count, and sleep patterns from the user's smartwatch or fitness tracker. The health data collection unit may also collect the user's food and exercise records to comprehensively understand the user's health status. Furthermore, the health data collection unit may analyze the user's health data and adjust the avatar's behavioral patterns based on the user's health status. This allows the user to support a healthier lifestyle by collecting the user's health data and reflecting it in the avatar's behavior.

[0081] The information extraction unit can analyze a user's purchasing history and extract related information. For example, the generation AI extracts information about related products based on the user's purchase history on an online shopping site. The information extraction unit can also analyze reviews and ratings of products the user has previously purchased to understand the user's preferences. Furthermore, the information extraction unit can preferentially extract information about specific brands or categories from the user's purchasing history. This makes it possible to extract more relevant information by analyzing the user's purchasing history.

[0082] The avatar generation unit can estimate the user's emotions and customize the appearance of the avatar based on the estimated user emotions. For example, if the user is expressing positive emotions, the generation AI can assign bright-colored clothing and facial expressions to the avatar. Alternatively, if the user is expressing negative emotions, the generation AI can assign subdued-colored clothing and facial expressions to the avatar. Furthermore, if the user is expressing neutral emotions, the generation AI can assign a balanced appearance to the avatar. This makes it possible to generate more realistic avatars by customizing the appearance of the avatar according to the user's emotions.

[0083] The behavior collection unit can analyze the avatar's behavioral data in real time and provide feedback to the user. For example, the behavior collection unit can suggest healthy lifestyle habits to the user based on the avatar's behavior in the virtual environment. The behavior collection unit can also suggest stress management and relaxation methods to the user based on the avatar's behavioral data. Furthermore, the behavior collection unit can analyze the avatar's behavioral data and suggest efficient time management methods to the user. This makes it possible to support the user's lifestyle by analyzing the avatar's behavioral data in real time and providing feedback to the user.

[0084] The behavior collection unit can estimate the user's emotions and adjust the type of behavioral data to be collected based on the estimated user emotions. For example, if the user is expressing positive emotions, the generation AI can prioritize collecting sociable behavioral data of the avatar. In addition, if the user is expressing negative emotions, the behavior collection unit can also prioritize collecting behavioral data related to the avatar's relaxation. Furthermore, if the user is expressing neutral emotions, the behavior collection unit can also collect balanced behavioral data related to the avatar's daily life. This makes it possible to collect more appropriate data by adjusting the type of behavioral data to be collected according to the user's emotions.

[0085] The information extraction unit can estimate the user's emotions and adjust the type of information to be extracted based on the estimated user emotions. For example, if the user is expressing positive emotions, the generation AI can prioritize extracting information related to travel and hobbies. In addition, if the user is expressing negative emotions, the information extraction unit can also prioritize extracting information related to stress relief and relaxation. Furthermore, if the user is expressing neutral emotions, the information extraction unit can also extract information related to daily life in a balanced manner. This makes it possible to extract more appropriate information by adjusting the type of information to be extracted according to the user's emotions.

[0086] When extracting information from social media, the information extraction unit can analyze a user's past posting history and select an appropriate extraction method. For example, the generation AI extracts related information based on keywords that the user has frequently posted in the past. The information extraction unit can also prioritize the extraction of information on specific themes from the user's past posting history. Furthermore, the information extraction unit can analyze the user's posting frequency and time period to extract information at the optimal timing. This makes it possible to select the optimal information extraction method by analyzing the user's past posting history.

[0087] When extracting information, the information extraction unit can filter based on the user's current interests and trends. For example, if the user has recently shown an interest in "travel," the generation AI can prioritize extracting information related to travel. Furthermore, if the user has shown an interest in "health," a current trend, the information extraction unit can also prioritize extracting information related to health. Furthermore, if the user has shown an interest in a specific event, the information extraction unit can also extract information related to that event. This makes it possible to extract more relevant information by filtering information based on the user's current interests and trends.

[0088] The information extraction unit can estimate the user's emotions and determine the priority of information to be extracted based on the estimated user emotions. For example, if the user is excited, the generation AI can prioritize extracting information related to entertainment. In addition, if the user is calm, the information extraction unit can also prioritize extracting information related to education and learning. Furthermore, if the user is tired, the information extraction unit can also prioritize extracting information related to relaxation and rest. This makes it possible to extract more appropriate information by determining the priority of information according to the user's emotions.

[0089] When extracting information, the information extraction unit can prioritize extracting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific city, the generation AI can extract event and tourist information related to that city. In addition, if the user is in a specific region, the information extraction unit can also extract weather and traffic information for that region. Furthermore, if the user is traveling, the information extraction unit can also extract tourist spot and restaurant information for the travel destination. This makes it possible to extract more relevant information by taking into account the user's geographical location information.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The information extraction unit extracts user information from social media. Specifically, it collects behavioral data such as social media posts, likes, and shares, and analyzes it to understand the user's preferences and behavioral patterns. For example, if a user posts that they "want to go on a trip," that information is extracted. If the user frequently posts about "travel," their preferences are extracted. Step 2: The avatar generation unit generates an avatar for the user based on the information extracted by the information extraction unit. Specifically, it analyzes the extracted information to understand the user's behavioral patterns and preferences, and generates an avatar that reflects them. For example, if the user is interested in traveling, an avatar that reflects those preferences is generated and set up to live together with other agents in the virtual environment. Step 3: The behavior collection unit collects behavioral data of the created avatar as it acts within the virtual environment. Specifically, it records how the avatar behaves within the virtual environment and collects that data. For example, it collects behavioral data when the avatar plans a trip or when interacting with other agents.

[0092] 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0094] 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.

[0095] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] [Explanation of symbols]

[0164] 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 extraction unit that extracts user information from the SNS; an avatar generation unit that generates an avatar of the user based on the information extracted by the information extraction unit; a behavior collection unit that collects behavior data of the avatar generated by the avatar generation unit acting in a virtual environment. A system characterized by:

2. The information extraction unit Extract user behavior data from social media 2. The system of claim 1.

3. The avatar generation unit Based on the extracted information, the user's behavioral patterns or preferences are analyzed and an avatar of the user is generated.

2. The system of claim 1.

4. The behavior collection unit The generated avatar lives together with other virtual agents in a virtual environment, and its behavioral data is collected.

2. The system of claim 1.

5. The behavior collection unit Analyzing the behavioral data of avatars to understand user needs 2. The system of claim 1.

6. The information extraction unit Estimate the user's emotions and adjust the type of information to be extracted based on the estimated user emotions.

2. The system of claim 1.

7. The information extraction unit When extracting information from SNS, analyze the user's past posting history and select the appropriate extraction method.

2. The system of claim 1.

8. The information extraction unit When extracting information, filtering is performed based on the user's current interests and trends.

2. The system of claim 1.

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