system

Abstract

The system according to the embodiment aims to generate a virtual twin based on the user's information and provide personalized advice and options. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a provision unit, an encryption unit, and a reliability evaluation unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a virtual twin based on the information analyzed by the analysis unit. The provision unit allows the virtual twin generated by the generation unit to present options or advice to the user. The encryption unit encrypts the information collected by the collection unit. The reliability evaluation unit evaluates the reliability of the options or advice provided by the provision unit.

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 do not adequately provide personalized advice and options based on user information, and there is room for improvement.

[0005] The system according to the embodiment aims to generate a virtual twin based on the user's information and provide personalized advice and options. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, a provision unit, an encryption unit, and a reliability evaluation unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a virtual twin based on the information analyzed by the analysis unit. The provision unit allows the virtual twin generated by the generation unit to present options or advice to the user. The encryption unit encrypts the information collected by the collection unit. The reliability evaluation unit evaluates the reliability of the options or advice provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate a virtual twin based on the user's information and provide personalized advice and options. [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 virtual twin generation system according to an embodiment of the present invention generates virtual twins using all personal information as training data, and provides services such as suggesting options, providing advice, and predicting the future to users. This virtual twin generation system begins when a user enters all information related to themselves, including their parents, relatives, and ancestors (summary, photos, behavioral history, and purchase history) as training data on their personal page. Next, a generation AI analyzes this information and generates the user's virtual twin. This virtual twin provides services such as suggesting options, providing advice (recommending options, presenting risk levels), and predicting the future when the user engages in an activity. For example, if a user is considering investing, the virtual twin can recommend optimal investments and present risk levels based on past data. Similarly, if a user is planning a trip, the virtual twin can suggest optimal travel plans and provide advice on avoiding risks. In this way, users can utilize their own virtual twins to make better decisions and avoid risks. Furthermore, the system includes a collection unit, analysis unit, generation unit, provision unit, encryption unit, and reliability evaluation unit, and these units work together to safely manage user information and provide highly reliable services. As a result, the virtual twin generation system can provide highly reliable services to users by collecting, analyzing, generating, providing, encrypting, and evaluating the reliability of user information.

[0029] A virtual twin generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a provision unit, an encryption unit, and a reliability evaluation unit. The collection unit collects user information. The user information includes, but is not limited to, personal information, behavioral history, and emotional data. The collection unit collects user behavioral data using, for example, a sensor. The collection unit can also collect user opinions and emotions through questionnaires. The collection unit can also analyze log data to identify user behavioral patterns. For example, the collection unit collects GPS data from the user's smartphone to understand the user's movement history. The collection unit can also collect the user's web browsing history to identify the user's interests. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the user's behavioral patterns using data mining technology. The analysis unit can also estimate the user's emotions using a machine learning algorithm. The analysis unit can also analyze the user's text data using natural language processing technology. For example, the analysis unit analyzes a user's social media posts to estimate the user's emotions. The analysis unit can also analyze the user's purchasing history to identify the user's purchasing trends. The generation unit generates a virtual twin based on the information analyzed by the analysis unit. The generation unit, for example, generates a digital avatar of the user using a generation AI. The generation unit can also predict the user's future behavior using a simulation model. The generation unit can also generate a virtual twin based on the user's behavioral patterns. For example, the generation unit simulates the user's future behavior based on the user's past behavioral data. The generation unit can also generate a virtual twin that reflects the user's emotions based on the user's emotional data. The provision unit allows the virtual twin generated by the generation unit to present options and advice to the user. The provision unit can present options to the user via a text message, for example. The provision unit can also provide advice to the user via a voice assistant. The provision unit can also present options to the user via a pop-up notification.For example, if a user is considering an investment, the providing unit recommends optimal investment destinations. Furthermore, if a user is planning a trip, the providing unit can also suggest optimal travel plans. The encryption unit encrypts the information collected by the collecting unit. The encryption unit encrypts the information using, for example, AES (Advanced Encryption Standard). Furthermore, the encryption unit can also encrypt the information using RSA (Rivest-Shamir-Adleman). Furthermore, the encryption unit can adjust the strength of encryption depending on the importance of the information. For example, the encryption unit applies strong encryption to highly important information. Furthermore, the encryption unit can also apply simplified encryption to less important information. The reliability evaluation unit evaluates the reliability of options and advice provided by the providing unit. The reliability evaluation unit evaluates the reliability based on, for example, user feedback. Furthermore, the reliability evaluation unit can also evaluate the reliability using statistical analysis. Furthermore, the reliability evaluation unit can adjust the level of detail of the evaluation depending on the importance of the options and advice. For example, the reliability evaluation unit performs a detailed reliability evaluation for highly important options and advice. The reliability evaluation unit can also perform a simplified reliability evaluation for less important options and advice. As a result, the virtual twin generation system according to the embodiment can provide highly reliable services to users by collecting, analyzing, generating, providing, encrypting, and evaluating the reliability of user information.

[0030] The collection unit can analyze the user's past behavioral history and select an appropriate information collection method. For example, the collection unit prioritizes collection of information sources that the user frequently accessed in the past. The collection unit can also collect information during specific time periods based on the user's past behavioral patterns. The collection unit can also select the optimal information collection method based on devices and applications the user has used in the past. This enables efficient information collection by selecting the optimal information collection method based on the user's past behavioral history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's behavioral history data into a generation AI and have the generation AI select the optimal information collection method.

[0031] When collecting information, the collection unit can filter the information based on the user's current living situation or areas of interest. For example, the collection unit prioritizes collecting information related to topics in which the user is currently interested. The collection unit can also filter appropriate information depending on the user's living situation (e.g., at work, on vacation). The collection unit can also collect health-related information based on the user's current health condition. This allows more relevant information to be collected by filtering information based on the user's living situation or areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's living situation data into a generation AI and have the generation AI perform filtering.

[0032] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the collection unit can collect news and event information related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting tourist information and traffic information for the travel destination. Furthermore, when the user is at home, the collection unit can collect information about nearby stores and services. In this way, by collecting highly relevant information based on the user's geographical location information, more appropriate information can be provided. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect highly relevant information.

[0033] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can collect information related to topics in which the user has shown interest on social media. The collection unit can also prioritize the collection of information shared by the user's followers and friends. The collection unit can also collect information related to groups and communities in which the user participates. This allows for the collection of related information based on the user's social media activities, thereby providing more appropriate information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0034] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also perform an analysis with an appropriate level of detail on information of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the collected information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0035] The analysis unit can apply different analysis algorithms based on the category of information during analysis. For example, the analysis unit can apply a medical data analysis algorithm to health information. The analysis unit can also apply a financial data analysis algorithm to financial information. The analysis unit can also apply a social data analysis algorithm to social media information. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input information category data to the generation AI and have the generation AI apply the analysis algorithm.

[0036] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was submitted. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also postpone analysis of information that was submitted recently. The analysis unit can also analyze information that was submitted recently with a moderate priority. In this way, efficient analysis is possible by determining the priority of analysis based on the time when the information was submitted. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the information was submitted to the generation AI and have the generation AI determine the priority of analysis.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of information with high relevance. The analysis unit can also postpone analysis of information with low relevance. The analysis unit can also analyze information with moderate relevance in an appropriate order. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0038] The generation unit can adjust the level of detail of the virtual twins based on the importance of the analysis results during generation. For example, the generation unit can generate detailed virtual twins based on analysis results with high importance. The generation unit can also generate simplified virtual twins based on analysis results with low importance. The generation unit can also generate virtual twins with a moderate level of detail based on analysis results with medium importance. This enables efficient generation by adjusting the level of detail of the virtual twins based on the importance of the analysis results. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input importance data of the analysis results into the generation AI and cause the generation AI to adjust the level of detail.

[0039] The generation unit can apply different generation algorithms based on the user's behavioral patterns during generation. For example, the generation unit applies a specific generation algorithm based on the user's frequent behavior. The generation unit can also apply a new generation algorithm if the user's behavioral pattern changes. The generation unit can also continue to apply an existing generation algorithm if the user's behavioral pattern is stable. This improves the accuracy of the virtual twin by applying an appropriate generation algorithm according to the user's behavioral pattern. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's behavioral pattern data into the generation AI and have the generation AI apply the generation algorithm.

[0040] At the time of generation, the generation unit can determine the priority of the virtual twins based on the user's past behavioral history. The generation unit can determine the priority of the virtual twins based on, for example, the user's frequent past behaviors. The generation unit can also determine the priority of the virtual twins based on important behaviors from the user's past behavioral history. The generation unit can also analyze the user's past behavioral history and determine the most efficient priority. This enables efficient generation by determining the priority of the virtual twins based on the user's past behavioral history. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's behavioral history data into the generation AI and have the generation AI determine the priority.

[0041] The generation unit can improve the accuracy of the virtual twins by referring to the user's related information during generation. The generation unit can improve the accuracy of the virtual twins by referring to the user's related information (e.g., health data), for example. The generation unit can also improve the accuracy of the virtual twins by referring to the user's related information (e.g., purchase history). The generation unit can also improve the accuracy of the virtual twins by referring to the user's related information (e.g., behavioral history). In this way, by referring to the user's related information, the accuracy of the virtual twins is improved. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's related information data into the generation AI and cause the generation AI to improve the accuracy.

[0042] The providing unit can adjust the level of detail of the presentation based on the importance of the option when providing the options. For example, the providing unit presents options with detailed information for options with high importance. The providing unit can also present options with simplified information for options with low importance. The providing unit can also present options with an appropriate level of detail for options with medium importance. This enables efficient presentation by adjusting the level of detail of the presentation according to the importance of the option. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the options to the generating AI and cause the generating AI to adjust the level of detail of the presentation.

[0043] The providing unit can apply different presentation algorithms depending on the category of the option when providing the options. For example, the providing unit can apply a presentation algorithm including a risk assessment to finance-related options. The providing unit can also apply a presentation algorithm based on medical data to health-related options. The providing unit can also apply a presentation algorithm including a travel plan to travel-related options. This improves the accuracy of presentation by applying an appropriate presentation algorithm depending on the category of the option. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without using AI. For example, the providing unit can input category data of the option to the generation AI and cause the generation AI to apply the presentation algorithm.

[0044] The providing unit can determine the priority of presentation based on the submission time of the options when providing them. For example, the providing unit prioritizes presenting the most recent options. The providing unit can also present options that were submitted earlier later. The providing unit can also present options that were submitted more recently with a moderate priority. In this way, efficient presentation is possible by determining the priority of presentation based on the submission time of the options. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the submission time data of the options into the generation AI and cause the generation AI to determine the presentation priority.

[0045] The providing unit can adjust the order of presentation based on the relevance of the options when providing them. For example, the providing unit prioritizes the presentation of options with high relevance. The providing unit can also present options with low relevance later. The providing unit can also present options with medium relevance in an appropriate order. In this way, adjusting the order of presentation based on the relevance of the options enables efficient presentation. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of the options to a generating AI and cause the generating AI to adjust the order of presentation.

[0046] The encryption unit can adjust the level of detail of encryption based on the importance of the information during encryption. For example, the encryption unit performs detailed encryption on highly important information. The encryption unit can also perform simplified encryption on less important information. The encryption unit can also perform encryption with an appropriate level of detail on medium important information. This enables efficient encryption by adjusting the level of detail of encryption according to the importance of the information. Some or all of the above-described processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of encryption.

[0047] The encryption unit can apply different encryption algorithms depending on the category of information during encryption. For example, the encryption unit can apply an encryption algorithm specialized for financial data to financial information. The encryption unit can also apply an encryption algorithm specialized for medical data to health information. The encryption unit can also apply an encryption algorithm specialized for social data to social media information. This improves the accuracy of encryption by applying an appropriate encryption algorithm depending on the category of information. Some or all of the above-mentioned processing in the encryption unit can be performed using, for example, AI, or can be performed without using AI. For example, the encryption unit can input information category data to the generation AI and have the generation AI apply the encryption algorithm.

[0048] During encryption, the encryption unit can adjust the encryption order based on the time when the information was submitted. For example, the encryption unit prioritizes encrypting the most recent information. The encryption unit can also encrypt information that was submitted recently later. The encryption unit can also encrypt information that was submitted recently in an appropriate order. This enables efficient encryption by adjusting the encryption order based on the time when the information was submitted. Some or all of the above-mentioned processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can input information submission time data to the generation AI and have the generation AI adjust the encryption order.

[0049] The encryption unit can improve the accuracy of encryption based on the relevance of information during encryption. For example, the encryption unit prioritizes encrypting information with high relevance. The encryption unit can also postpone encrypting information with low relevance. The encryption unit can also encrypt information with moderate relevance with moderate accuracy. This enables efficient encryption by improving the accuracy of encryption based on the relevance of information. Some or all of the above-mentioned processing in the encryption unit may be performed using AI, for example, or may be performed without using AI. For example, the encryption unit can input information relevance data to the generation AI and cause the generation AI to improve the accuracy of encryption.

[0050] The reliability evaluation unit can adjust the level of detail of the evaluation based on the importance of the options and advice provided during the reliability evaluation. For example, the reliability evaluation unit performs a detailed reliability evaluation for options and advice with high importance. The reliability evaluation unit can also perform a simplified reliability evaluation for options and advice with low importance. The reliability evaluation unit can also perform a reliability evaluation with an appropriate level of detail for options and advice with medium importance. This enables efficient reliability evaluation by adjusting the level of detail of the evaluation according to the importance of the options and advice. Some or all of the above-mentioned processing in the reliability evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reliability evaluation unit can input importance data of the options and advice to the generation AI and cause the generation AI to adjust the level of detail of the evaluation.

[0051] The reliability evaluation unit can apply different evaluation algorithms depending on the category of the options or advice when evaluating reliability. For example, the reliability evaluation unit can apply an evaluation algorithm specialized for financial data to finance-related options or advice. The reliability evaluation unit can also apply an evaluation algorithm specialized for medical data to health-related options or advice. The reliability evaluation unit can also apply an evaluation algorithm specialized for travel data to travel-related options or advice. This improves the accuracy of the reliability evaluation by applying an appropriate evaluation algorithm depending on the category of the options or advice. Some or all of the above-mentioned processing in the reliability evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reliability evaluation unit can input category data of the options or advice into the generation AI and cause the generation AI to apply the evaluation algorithm.

[0052] The reliability evaluation unit can determine the priority of evaluation based on the submission time of options and advice during reliability evaluation. For example, the reliability evaluation unit prioritizes evaluation of the most recent options and advice. The reliability evaluation unit can also postpone evaluation of options and advice that were submitted recently. The reliability evaluation unit can also evaluate options and advice that were submitted recently with a moderate priority. In this way, efficient reliability evaluation is possible by determining the priority of evaluation based on the submission time of options and advice. Some or all of the above-mentioned processing in the reliability evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reliability evaluation unit can input data on the submission time of options and advice into the generation AI and have the generation AI determine the priority of evaluation.

[0053] The reliability evaluation unit can determine the evaluation order based on the relevance of options or advice during reliability evaluation. For example, the reliability evaluation unit prioritizes evaluation of options or advice with high relevance. The reliability evaluation unit can also postpone evaluation of options or advice with low relevance. The reliability evaluation unit can also evaluate options or advice with medium relevance in an appropriate order. This enables efficient reliability evaluation by adjusting the evaluation order based on the relevance of options and advice. Some or all of the above-mentioned processing in the reliability evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the reliability evaluation unit can input relevance data of options and advice to the generation AI and cause the generation AI to adjust the evaluation order.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The collection unit can collect the user's biometric data in real time and detect changes in the user's health condition. For example, the collection unit can collect biometric data such as the user's heart rate, blood pressure, and body temperature using sensors. The collection unit can also monitor the user's sleep patterns and evaluate the quality of their sleep. Furthermore, the collection unit can track the user's exercise volume and understand their daily activity level. This allows the user's health condition to be monitored in real time and appropriate advice to be provided as needed.

[0056] The generation unit can generate behavioral scenarios for the virtual twins based on the user's past behavioral history. For example, the generation unit can create behavioral scenarios for the virtual twins based on places the user has frequently visited in the past. The generation unit can also simulate the purchasing behavior of the virtual twins based on the user's past purchasing history. Furthermore, the generation unit can predict the virtual twins' online behavior based on the user's past social media activity. This makes it possible to generate more realistic virtual twins based on the user's past behavioral history.

[0057] The reliability evaluation unit can evaluate the reliability of options and advice provided based on past performance. For example, the reliability evaluation unit can highly evaluate options and advice that have shown a high success rate in the past. The reliability evaluation unit can also lowly evaluate options and advice that have shown a low success rate in the past. Furthermore, the reliability evaluation unit can moderately evaluate options and advice that have shown a medium past performance. In this way, by evaluating reliability based on past performance, it is possible to provide more reliable options and advice.

[0058] The collection unit can select information collection targets based on the user's hobbies and preferences. For example, if the user is interested in music, the collection unit can prioritize collecting music-related information. Also, if the user is interested in sports, the collection unit can prioritize collecting sports-related information. Furthermore, if the user is interested in cooking, the collection unit can prioritize collecting cooking-related information. In this way, by selecting information collection targets based on the user's hobbies and preferences, more relevant information can be provided.

[0059] The generation unit can generate behavioral scenarios for the virtual twins based on the user's past behavioral history. For example, the generation unit can create behavioral scenarios for the virtual twins based on places the user has frequently visited in the past. The generation unit can also simulate the purchasing behavior of the virtual twins based on the user's past purchasing history. Furthermore, the generation unit can predict the virtual twins' online behavior based on the user's past social media activity. This makes it possible to generate more realistic virtual twins based on the user's past behavioral history.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects user information. User information includes personal information, behavioral history, and emotional data. The collection unit uses sensors to collect user behavioral data and can also collect user opinions and emotions through questionnaires. It can also analyze log data to identify user behavioral patterns. For example, it can collect GPS data from the user's smartphone to understand their movement history. It can also collect web browsing history to identify the user's interests. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze user behavior patterns using data mining technology and infer user emotions using machine learning algorithms. It can also analyze user text data using natural language processing technology. For example, it can analyze a user's social media posts to infer emotions. It can also analyze a user's purchase history to identify purchasing trends. Step 3: The generation unit generates a virtual twin based on the information analyzed by the analysis unit. The generation unit uses a generation AI to generate a digital avatar of the user and can predict the user's future behavior using a simulation model. It can also generate a virtual twin based on the user's behavioral patterns. For example, it can simulate future behavior based on past behavioral data and generate a virtual twin that reflects emotions based on emotional data. Step 4: The provider uses the virtual twin generated by the generator to present options and advice to the user. The provider can present options to the user via text messages and provide advice via a voice assistant. It can also present options through pop-up notifications. For example, if the user is considering investing, it can recommend the best investment options, or if the user is planning a trip, it can suggest the best travel plans. Step 5: The encryption unit encrypts the information collected by the collection unit. The encryption unit can encrypt the information using AES (Advanced Encryption Standard) or RSA (Rivest-Shamir-Adleman). Furthermore, the encryption strength can be adjusted depending on the importance of the information. For example, strong encryption can be applied to highly important information, and simplified encryption can be applied to less important information. Step 6: The reliability evaluation unit evaluates the reliability of the options and advice provided by the providing unit. The reliability evaluation unit can evaluate reliability based on user feedback and can evaluate reliability using statistical analysis. Furthermore, the level of detail of the evaluation can be adjusted depending on the importance of the options and advice. For example, a detailed reliability evaluation can be performed for options and advice with high importance, and a simplified reliability evaluation can be performed for options and advice with low importance.

[0062] (Example 2) A virtual twin generation system according to an embodiment of the present invention generates virtual twins using all personal information as training data, and provides services such as suggesting options, providing advice, and predicting the future to users. This virtual twin generation system begins when a user enters all information related to themselves, including their parents, relatives, and ancestors (summary, photos, behavioral history, and purchase history) as training data on their personal page. Next, a generation AI analyzes this information and generates the user's virtual twin. This virtual twin provides services such as suggesting options, providing advice (recommending options, presenting risk levels), and predicting the future when the user engages in an activity. For example, if a user is considering investing, the virtual twin can recommend optimal investments and present risk levels based on past data. Similarly, if a user is planning a trip, the virtual twin can suggest optimal travel plans and provide advice on avoiding risks. In this way, users can utilize their own virtual twins to make better decisions and avoid risks. Furthermore, the system includes a collection unit, analysis unit, generation unit, provision unit, encryption unit, and reliability evaluation unit, and these units work together to safely manage user information and provide highly reliable services. As a result, the virtual twin generation system can provide highly reliable services to users by collecting, analyzing, generating, providing, encrypting, and evaluating the reliability of user information.

[0063] A virtual twin generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a provision unit, an encryption unit, and a reliability evaluation unit. The collection unit collects user information. The user information includes, but is not limited to, personal information, behavioral history, and emotional data. The collection unit collects user behavioral data using, for example, a sensor. The collection unit can also collect user opinions and emotions through questionnaires. The collection unit can also analyze log data to identify user behavioral patterns. For example, the collection unit collects GPS data from the user's smartphone to understand the user's movement history. The collection unit can also collect the user's web browsing history to identify the user's interests. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the user's behavioral patterns using data mining technology. The analysis unit can also estimate the user's emotions using a machine learning algorithm. The analysis unit can also analyze the user's text data using natural language processing technology. For example, the analysis unit analyzes a user's social media posts to estimate the user's emotions. The analysis unit can also analyze the user's purchasing history to identify the user's purchasing trends. The generation unit generates a virtual twin based on the information analyzed by the analysis unit. The generation unit, for example, generates a digital avatar of the user using a generation AI. The generation unit can also predict the user's future behavior using a simulation model. The generation unit can also generate a virtual twin based on the user's behavioral patterns. For example, the generation unit simulates the user's future behavior based on the user's past behavioral data. The generation unit can also generate a virtual twin that reflects the user's emotions based on the user's emotional data. The provision unit allows the virtual twin generated by the generation unit to present options and advice to the user. The provision unit can present options to the user via a text message, for example. The provision unit can also provide advice to the user via a voice assistant. The provision unit can also present options to the user via a pop-up notification.For example, if a user is considering an investment, the providing unit recommends optimal investment destinations. Furthermore, if a user is planning a trip, the providing unit can also suggest optimal travel plans. The encryption unit encrypts the information collected by the collecting unit. The encryption unit encrypts the information using, for example, AES (Advanced Encryption Standard). Furthermore, the encryption unit can also encrypt the information using RSA (Rivest-Shamir-Adleman). Furthermore, the encryption unit can adjust the strength of encryption depending on the importance of the information. For example, the encryption unit applies strong encryption to highly important information. Furthermore, the encryption unit can also apply simplified encryption to less important information. The reliability evaluation unit evaluates the reliability of options and advice provided by the providing unit. The reliability evaluation unit evaluates the reliability based on, for example, user feedback. Furthermore, the reliability evaluation unit can also evaluate the reliability using statistical analysis. Furthermore, the reliability evaluation unit can adjust the level of detail of the evaluation depending on the importance of the options and advice. For example, the reliability evaluation unit performs a detailed reliability evaluation for highly important options and advice. The reliability evaluation unit can also perform a simplified reliability evaluation for less important options and advice. As a result, the virtual twin generation system according to the embodiment can provide highly reliable services to users by collecting, analyzing, generating, providing, encrypting, and evaluating the reliability of user information.

[0064] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of information collection and collects information when the user is relaxed. Furthermore, if the user is excited, the collection unit can collect information in real time and immediately reflect the information. Furthermore, if the user is tired, the collection unit can temporarily stop information collection and resume it after the user has rested. This allows for more appropriate information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0065] The collection unit can analyze the user's past behavioral history and select an appropriate information collection method. For example, the collection unit prioritizes collection of information sources that the user frequently accessed in the past. The collection unit can also collect information during specific time periods based on the user's past behavioral patterns. The collection unit can also select the optimal information collection method based on devices and applications the user has used in the past. This enables efficient information collection by selecting the optimal information collection method based on the user's past behavioral history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's behavioral history data into a generation AI and have the generation AI select the optimal information collection method.

[0066] When collecting information, the collection unit can filter the information based on the user's current living situation or areas of interest. For example, the collection unit prioritizes collecting information related to topics in which the user is currently interested. The collection unit can also filter appropriate information depending on the user's living situation (e.g., at work, on vacation). The collection unit can also collect health-related information based on the user's current health condition. This allows more relevant information to be collected by filtering information based on the user's living situation or areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's living situation data into a generation AI and have the generation AI perform filtering.

[0067] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit can prioritize collecting information that provides a sense of security. Furthermore, if the user is excited, the collection unit can prioritize collecting information that piques the user's interest. Furthermore, if the user is relaxed, the collection unit can prioritize collecting information that helps the user maintain relaxation. This allows for more appropriate information to be collected by determining the priority of information according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0068] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the collection unit can collect news and event information related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting tourist information and traffic information for the travel destination. Furthermore, when the user is at home, the collection unit can collect information about nearby stores and services. In this way, by collecting highly relevant information based on the user's geographical location information, more appropriate information can be provided. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect highly relevant information.

[0069] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can collect information related to topics in which the user has shown interest on social media. The collection unit can also prioritize the collection of information shared by the user's followers and friends. The collection unit can also collect information related to groups and communities in which the user participates. This allows for the collection of related information based on the user's social media activities, thereby providing more appropriate information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0070] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis to improve accuracy. If the user is in a hurry, the analysis unit can also perform a quick analysis and provide results immediately. If the user is feeling anxious, the analysis unit can also carefully present the analysis results to provide a sense of security. This allows for adjusting the accuracy of the analysis according to the user's emotions and providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis accuracy.

[0071] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also perform an analysis with an appropriate level of detail on information of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the collected information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms based on the category of information during analysis. For example, the analysis unit can apply a medical data analysis algorithm to health information. The analysis unit can also apply a financial data analysis algorithm to financial information. The analysis unit can also apply a social data analysis algorithm to social media information. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input information category data to the generation AI and have the generation AI apply the analysis algorithm.

[0073] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for more appropriate display by adjusting the display method of the analysis results according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0074] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was submitted. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also postpone analysis of information that was submitted recently. The analysis unit can also analyze information that was submitted recently with a moderate priority. In this way, efficient analysis is possible by determining the priority of analysis based on the time when the information was submitted. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the information was submitted to the generation AI and have the generation AI determine the priority of analysis.

[0075] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of information with high relevance. The analysis unit can also postpone analysis of information with low relevance. The analysis unit can also analyze information with moderate relevance in an appropriate order. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0076] The generation unit can estimate the user's emotions and adjust the virtual twin generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate detailed virtual twins. If the user is in a hurry, the generation unit can also generate simplified virtual twins. If the user is excited, the generation unit can also generate visually stimulating virtual twins. This allows for the generation of more appropriate virtual twins by adjusting the virtual twin generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the generation method.

[0077] The generation unit can adjust the level of detail of the virtual twins based on the importance of the analysis results during generation. For example, the generation unit can generate detailed virtual twins based on analysis results with high importance. The generation unit can also generate simplified virtual twins based on analysis results with low importance. The generation unit can also generate virtual twins with a moderate level of detail based on analysis results with medium importance. This enables efficient generation by adjusting the level of detail of the virtual twins based on the importance of the analysis results. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input importance data of the analysis results into the generation AI and cause the generation AI to adjust the level of detail.

[0078] The generation unit can apply different generation algorithms based on the user's behavioral patterns during generation. For example, the generation unit applies a specific generation algorithm based on the user's frequent behavior. The generation unit can also apply a new generation algorithm if the user's behavioral pattern changes. The generation unit can also continue to apply an existing generation algorithm if the user's behavioral pattern is stable. This improves the accuracy of the virtual twin by applying an appropriate generation algorithm according to the user's behavioral pattern. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's behavioral pattern data into the generation AI and have the generation AI apply the generation algorithm.

[0079] The generation unit can estimate the user's emotions and adjust the display method of the virtual twin based on the estimated user emotions. For example, if the user is nervous, the generation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the generation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can provide a display method that focuses on the main points. This allows for more appropriate display by adjusting the display method of the virtual twin according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0080] At the time of generation, the generation unit can determine the priority of the virtual twins based on the user's past behavioral history. The generation unit can determine the priority of the virtual twins based on, for example, the user's frequent past behaviors. The generation unit can also determine the priority of the virtual twins based on important behaviors from the user's past behavioral history. The generation unit can also analyze the user's past behavioral history and determine the most efficient priority. This enables efficient generation by determining the priority of the virtual twins based on the user's past behavioral history. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's behavioral history data into the generation AI and have the generation AI determine the priority.

[0081] The generation unit can improve the accuracy of the virtual twins by referring to the user's related information during generation. The generation unit can improve the accuracy of the virtual twins by referring to the user's related information (e.g., health data), for example. The generation unit can also improve the accuracy of the virtual twins by referring to the user's related information (e.g., purchase history). The generation unit can also improve the accuracy of the virtual twins by referring to the user's related information (e.g., behavioral history). In this way, by referring to the user's related information, the accuracy of the virtual twins is improved. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's related information data into the generation AI and cause the generation AI to improve the accuracy.

[0082] The providing unit can estimate the user's emotions and adjust the way options are presented based on the estimated user's emotions. For example, if the user is nervous, the providing unit presents simple, highly visible options. Furthermore, if the user is relaxed, the providing unit can present options that include detailed information. Furthermore, if the user is in a hurry, the providing unit can present options that focus on the main points. This allows for adjusting the way options are presented according to the user's emotions, thereby providing more appropriate options. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation method.

[0083] The providing unit can adjust the level of detail of the presentation based on the importance of the option when providing the options. For example, the providing unit presents options with detailed information for options with high importance. The providing unit can also present options with simplified information for options with low importance. The providing unit can also present options with an appropriate level of detail for options with medium importance. This enables efficient presentation by adjusting the level of detail of the presentation according to the importance of the option. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the options to the generating AI and cause the generating AI to adjust the level of detail of the presentation.

[0084] The providing unit can apply different presentation algorithms depending on the category of the option when providing the options. For example, the providing unit can apply a presentation algorithm including a risk assessment to finance-related options. The providing unit can also apply a presentation algorithm based on medical data to health-related options. The providing unit can also apply a presentation algorithm including a travel plan to travel-related options. This improves the accuracy of presentation by applying an appropriate presentation algorithm depending on the category of the option. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without using AI. For example, the providing unit can input category data of the option to the generation AI and cause the generation AI to apply the presentation algorithm.

[0085] The providing unit can estimate the user's emotions and adjust the display method of the options based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This allows for more appropriate display by adjusting the display method of the options according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method.

[0086] The providing unit can determine the priority of presentation based on the submission time of the options when providing them. For example, the providing unit prioritizes presenting the most recent options. The providing unit can also present options that were submitted earlier later. The providing unit can also present options that were submitted more recently with a moderate priority. In this way, efficient presentation is possible by determining the priority of presentation based on the submission time of the options. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the submission time data of the options into the generation AI and cause the generation AI to determine the presentation priority.

[0087] The providing unit can adjust the order of presentation based on the relevance of the options when providing them. For example, the providing unit prioritizes the presentation of options with high relevance. The providing unit can also present options with low relevance later. The providing unit can also present options with medium relevance in an appropriate order. In this way, adjusting the order of presentation based on the relevance of the options enables efficient presentation. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of the options to a generating AI and cause the generating AI to adjust the order of presentation.

[0088] The encryption unit can estimate the user's emotions and adjust the encryption strength based on the estimated user emotions. For example, if the user feels anxious, the encryption unit can apply high-strength encryption. Furthermore, if the user feels relaxed, the encryption unit can also apply standard encryption. Furthermore, if the user is in a hurry, the encryption unit can also apply quick encryption. This allows for more appropriate encryption by adjusting the encryption strength according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the encryption unit can be performed using, for example, AI, or without AI. For example, the encryption unit can input the user's emotion data into the generation AI and have the generation AI adjust the encryption strength.

[0089] The encryption unit can adjust the level of detail of encryption based on the importance of the information during encryption. For example, the encryption unit performs detailed encryption on highly important information. The encryption unit can also perform simplified encryption on less important information. The encryption unit can also perform encryption with an appropriate level of detail on medium important information. This enables efficient encryption by adjusting the level of detail of encryption according to the importance of the information. Some or all of the above-described processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of encryption.

[0090] The encryption unit can apply different encryption algorithms depending on the category of information during encryption. For example, the encryption unit can apply an encryption algorithm specialized for financial data to financial information. The encryption unit can also apply an encryption algorithm specialized for medical data to health information. The encryption unit can also apply an encryption algorithm specialized for social data to social media information. This improves the accuracy of encryption by applying an appropriate encryption algorithm depending on the category of information. Some or all of the above-mentioned processing in the encryption unit can be performed using, for example, AI, or can be performed without using AI. For example, the encryption unit can input information category data to the generation AI and have the generation AI apply the encryption algorithm.

[0091] The encryption unit can estimate the user's emotions and determine encryption priorities based on the estimated user emotions. For example, if the user is feeling anxious, the encryption unit prioritizes encryption of important information. The encryption unit can also perform encryption with a standard priority if the user is relaxed. The encryption unit can also perform encryption quickly if the user is in a hurry. This enables efficient encryption by determining encryption priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the encryption unit can be performed using, for example, an AI, or without an AI. For example, the encryption unit can input the user's emotion data into the generation AI and have the generation AI determine the encryption priorities.

[0092] During encryption, the encryption unit can adjust the encryption order based on the time when the information was submitted. For example, the encryption unit prioritizes encrypting the most recent information. The encryption unit can also encrypt information that was submitted recently later. The encryption unit can also encrypt information that was submitted recently in an appropriate order. This enables efficient encryption by adjusting the encryption order based on the time when the information was submitted. Some or all of the above-mentioned processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can input information submission time data to the generation AI and have the generation AI adjust the encryption order.

[0093] The encryption unit can improve the accuracy of encryption based on the relevance of information during encryption. For example, the encryption unit prioritizes encrypting information with high relevance. The encryption unit can also postpone encrypting information with low relevance. The encryption unit can also encrypt information with moderate relevance with moderate accuracy. This enables efficient encryption by improving the accuracy of encryption based on the relevance of information. Some or all of the above-mentioned processing in the encryption unit may be performed using AI, for example, or may be performed without using AI. For example, the encryption unit can input information relevance data to the generation AI and cause the generation AI to improve the accuracy of encryption.

[0094] The trustworthiness evaluation unit can estimate the user's emotions and adjust the trustworthiness evaluation criteria based on the estimated user emotions. For example, if the user is feeling anxious, the trustworthiness evaluation unit can apply strict trustworthiness evaluation criteria. Furthermore, if the user is relaxed, the trustworthiness evaluation unit can also apply standard trustworthiness evaluation criteria. Furthermore, if the user is in a hurry, the trustworthiness evaluation unit can also apply rapid trustworthiness evaluation criteria. This allows for more appropriate trustworthiness evaluation by adjusting the trustworthiness evaluation criteria according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the trustworthiness evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the trustworthiness evaluation unit can input the user's emotion data into the generation AI and have the generation AI adjust the trustworthiness evaluation criteria.

[0095] The reliability evaluation unit can adjust the level of detail of the evaluation based on the importance of the options and advice provided during the reliability evaluation. For example, the reliability evaluation unit performs a detailed reliability evaluation for options and advice with high importance. The reliability evaluation unit can also perform a simplified reliability evaluation for options and advice with low importance. The reliability evaluation unit can also perform a reliability evaluation with an appropriate level of detail for options and advice with medium importance. This enables efficient reliability evaluation by adjusting the level of detail of the evaluation according to the importance of the options and advice. Some or all of the above-mentioned processing in the reliability evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reliability evaluation unit can input importance data of the options and advice to the generation AI and cause the generation AI to adjust the level of detail of the evaluation.

[0096] The reliability evaluation unit can apply different evaluation algorithms depending on the category of the options or advice when evaluating reliability. For example, the reliability evaluation unit can apply an evaluation algorithm specialized for financial data to finance-related options or advice. The reliability evaluation unit can also apply an evaluation algorithm specialized for medical data to health-related options or advice. The reliability evaluation unit can also apply an evaluation algorithm specialized for travel data to travel-related options or advice. This improves the accuracy of the reliability evaluation by applying an appropriate evaluation algorithm depending on the category of the options or advice. Some or all of the above-mentioned processing in the reliability evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reliability evaluation unit can input category data of the options or advice into the generation AI and cause the generation AI to apply the evaluation algorithm.

[0097] The trustworthiness evaluation unit can estimate the user's emotions and adjust the trustworthiness evaluation display method based on the estimated user emotions. For example, if the user is nervous, the trustworthiness evaluation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the trustworthiness evaluation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the trustworthiness evaluation unit can provide a display method that focuses on the main points. This allows for a more appropriate display by adjusting the trustworthiness evaluation display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the trustworthiness evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the trustworthiness evaluation unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0098] The reliability evaluation unit can determine the priority of evaluation based on the submission time of options and advice during reliability evaluation. For example, the reliability evaluation unit prioritizes evaluation of the most recent options and advice. The reliability evaluation unit can also postpone evaluation of options and advice that were submitted recently. The reliability evaluation unit can also evaluate options and advice that were submitted recently with a moderate priority. In this way, efficient reliability evaluation is possible by determining the priority of evaluation based on the submission time of options and advice. Some or all of the above-mentioned processing in the reliability evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reliability evaluation unit can input data on the submission time of options and advice into the generation AI and have the generation AI determine the priority of evaluation.

[0099] The reliability evaluation unit can determine the evaluation order based on the relevance of options or advice during reliability evaluation. For example, the reliability evaluation unit prioritizes evaluation of options or advice with high relevance. The reliability evaluation unit can also postpone evaluation of options or advice with low relevance. The reliability evaluation unit can also evaluate options or advice with medium relevance in an appropriate order. This enables efficient reliability evaluation by adjusting the evaluation order based on the relevance of options and advice. Some or all of the above-mentioned processing in the reliability evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the reliability evaluation unit can input relevance data of options and advice to the generation AI and cause the generation AI to adjust the evaluation order. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, provision unit, encryption unit, and reliability evaluation unit described above is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user information using sensors or a questionnaire function of the smart device 14. The analysis unit analyzes the collected information by the specific processing unit 290 of the data processing device 12. The generation unit generates a virtual twin by the specific processing unit 290 of the data processing device 12. The provision unit provides options and advice to the user through the control unit 46A of the smart device 14. The encryption unit encrypts information by the specific processing unit 290 of the data processing device 12. The reliability evaluation unit evaluates the reliability of the options and advice provided by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, provision unit, encryption unit, and reliability evaluation unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user information using sensors or a questionnaire function of the smart glasses 214. The analysis unit analyzes the collected information by the specific processing unit 290 of the data processing device 12. The generation unit generates a virtual twin by the specific processing unit 290 of the data processing device 12. The provision unit provides options and advice to the user through the control unit 46A of the smart glasses 214. The encryption unit encrypts information by the specific processing unit 290 of the data processing device 12. The reliability evaluation unit evaluates the reliability of the options and advice provided by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, provision unit, encryption unit, and reliability evaluation unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects user information using sensors or a questionnaire function of the headset type terminal 314. The analysis unit analyzes the collected information by the specific processing unit 290 of the data processing device 12. The generation unit generates a virtual twin by the specific processing unit 290 of the data processing device 12. The provision unit provides options and advice to the user through the control unit 46A of the headset type terminal 314. The encryption unit encrypts information by the specific processing unit 290 of the data processing device 12. The reliability evaluation unit evaluates the reliability of the options and advice provided by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, provision unit, encryption unit, and reliability evaluation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user information using sensors or a questionnaire function of the robot 414. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12. The generation unit generates a virtual twin by the specific processing unit 290 of the data processing device 12. The provision unit provides options and advice to the user through the control unit 46A of the robot 414. The encryption unit encrypts information by the specific processing unit 290 of the data processing device 12. The reliability evaluation unit evaluates the reliability of the options and advice provided by the specific processing unit 290 of the data processing device 12.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The collection unit can collect the user's biometric data in real time and detect changes in the user's health condition. For example, the collection unit can collect biometric data such as the user's heart rate, blood pressure, and body temperature using sensors. The collection unit can also monitor the user's sleep patterns and evaluate the quality of their sleep. Furthermore, the collection unit can track the user's exercise volume and understand their daily activity level. This allows the user's health condition to be monitored in real time and appropriate advice to be provided as needed.

[0102] The analysis unit can estimate the user's emotions and evaluate the reliability of the analysis results based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can evaluate the reliability of the analysis results as low. Also, if the user is relaxed, the analysis unit can evaluate the reliability of the analysis results as high. Furthermore, if the user is excited, the analysis unit can evaluate the reliability of the analysis results as medium. In this way, by evaluating the reliability of the analysis results according to the user's emotions, it is possible to provide more appropriate analysis results.

[0103] The generation unit can generate behavioral scenarios for the virtual twins based on the user's past behavioral history. For example, the generation unit can create behavioral scenarios for the virtual twins based on places the user has frequently visited in the past. The generation unit can also simulate the purchasing behavior of the virtual twins based on the user's past purchasing history. Furthermore, the generation unit can predict the virtual twins' online behavior based on the user's past social media activity. This makes it possible to generate more realistic virtual twins based on the user's past behavioral history.

[0104] The providing unit can estimate the user's emotions and adjust the content of the advice based on the estimated user's emotions. For example, if the user feels anxious, the providing unit can provide advice that gives a sense of security. Furthermore, if the user feels relaxed, the providing unit can also provide advice that includes detailed information. Furthermore, if the user feels excited, the providing unit can also provide interesting advice. In this way, by adjusting the content of the advice according to the user's emotions, more appropriate advice can be provided.

[0105] The encryption unit can estimate the user's emotions and select an encryption method based on the estimated user's emotions. For example, if the user feels anxious, the encryption unit can select a strong encryption method. If the user feels relaxed, the encryption unit can also select a standard encryption method. Furthermore, if the user is in a hurry, the encryption unit can also select a quick encryption method. In this way, by selecting an encryption method according to the user's emotions, more appropriate encryption can be achieved.

[0106] The reliability evaluation unit can evaluate the reliability of options and advice provided based on past performance. For example, the reliability evaluation unit can highly evaluate options and advice that have shown a high success rate in the past. The reliability evaluation unit can also lowly evaluate options and advice that have shown a low success rate in the past. Furthermore, the reliability evaluation unit can moderately evaluate options and advice that have shown a medium past performance. In this way, by evaluating reliability based on past performance, it is possible to provide more reliable options and advice.

[0107] The collection unit can select information collection targets based on the user's hobbies and preferences. For example, if the user is interested in music, the collection unit can prioritize collecting music-related information. Also, if the user is interested in sports, the collection unit can prioritize collecting sports-related information. Furthermore, if the user is interested in cooking, the collection unit can prioritize collecting cooking-related information. In this way, by selecting information collection targets based on the user's hobbies and preferences, more relevant information can be provided.

[0108] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible presentation method. If the user is relaxed, the analysis unit can also provide a presentation method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a presentation method that focuses on the main points. This allows for more appropriate presentation by adjusting the presentation method of the analysis results according to the user's emotions.

[0109] The generation unit can generate behavioral scenarios for the virtual twins based on the user's past behavioral history. For example, the generation unit can create behavioral scenarios for the virtual twins based on places the user has frequently visited in the past. The generation unit can also simulate the purchasing behavior of the virtual twins based on the user's past purchasing history. Furthermore, the generation unit can predict the virtual twins' online behavior based on the user's past social media activity. This makes it possible to generate more realistic virtual twins based on the user's past behavioral history.

[0110] The providing unit can estimate the user's emotions and adjust the way options are presented based on the estimated user's emotions. For example, if the user is nervous, the providing unit can present simple, highly visible options. If the user is relaxed, the providing unit can also present options that include detailed information. Furthermore, if the user is in a hurry, the providing unit can also present options that focus on the main points. In this way, by adjusting the way options are presented according to the user's emotions, more appropriate options can be provided.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The collection unit collects user information. User information includes personal information, behavioral history, and emotional data. The collection unit uses sensors to collect user behavioral data and can also collect user opinions and emotions through questionnaires. It can also analyze log data to identify user behavioral patterns. For example, it can collect GPS data from the user's smartphone to understand their movement history. It can also collect web browsing history to identify the user's interests. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze user behavior patterns using data mining technology and infer user emotions using machine learning algorithms. It can also analyze user text data using natural language processing technology. For example, it can analyze a user's social media posts to infer emotions. It can also analyze a user's purchase history to identify purchasing trends. Step 3: The generation unit generates a virtual twin based on the information analyzed by the analysis unit. The generation unit uses a generation AI to generate a digital avatar of the user and can predict the user's future behavior using a simulation model. It can also generate a virtual twin based on the user's behavioral patterns. For example, it can simulate future behavior based on past behavioral data and generate a virtual twin that reflects emotions based on emotional data. Step 4: The provider uses the virtual twin generated by the generator to present options and advice to the user. The provider can present options to the user via text messages and provide advice via a voice assistant. It can also present options through pop-up notifications. For example, if the user is considering investing, it can recommend the best investment options, or if the user is planning a trip, it can suggest the best travel plans. Step 5: The encryption unit encrypts the information collected by the collection unit. The encryption unit can encrypt the information using AES (Advanced Encryption Standard) or RSA (Rivest-Shamir-Adleman). Furthermore, the encryption strength can be adjusted depending on the importance of the information. For example, strong encryption can be applied to highly important information, and simplified encryption can be applied to less important information. Step 6: The reliability evaluation unit evaluates the reliability of the options and advice provided by the providing unit. The reliability evaluation unit can evaluate reliability based on user feedback and can evaluate reliability using statistical analysis. Furthermore, the level of detail of the evaluation can be adjusted depending on the importance of the options and advice. For example, a detailed reliability evaluation can be performed for options and advice with high importance, and a simplified reliability evaluation can be performed for options and advice with low importance.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user information; an analysis unit that analyzes the information collected by the collection unit; a generation unit that generates a virtual twin based on the information analyzed by the analysis unit; a providing unit that allows the virtual twin generated by the generating unit to present options or give advice to the user; an encryption unit that encrypts the information collected by the collection unit; a reliability evaluation unit that evaluates the reliability of the options or advice provided by the providing unit; Equipped with A system characterized by:

2. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

3. The collecting unit Analyze users' past behavioral history and select the appropriate information collection method 2. The system of claim 1.

4. The collecting unit When collecting information, filter it based on the user's current life situation or interests 2. The system of claim 1.

5. The collecting unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions.

2. The system of claim 1.

6. The collecting unit When collecting information, prioritize collection of highly relevant information based on the user's geographic location information.

2. The system of claim 1.

7. The collecting unit When collecting information, we analyze your social media activity and collect relevant information.

2. The system of claim 1.

8. The analysis unit Estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions.

2. The system of claim 1.

Images