system

Abstract

The system according to the embodiment aims to analyze a user's life log data and generate an AI agent that acts on behalf of the user in a virtual space. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a learning unit, a generation unit, and a behavior unit. The collection unit collects a user's life log data. The analysis unit analyzes the data collected by the collection unit. The learning unit learns the user's behavior patterns based on the analysis results obtained by the analysis unit. The generation unit generates an AI agent based on the behavior patterns learned by the learning unit. The behavior unit causes the AI ​​agent generated by the generation unit to act in a virtual space.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not yet been able to effectively utilize users' life log data to reproduce their behavior in a virtual space, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze a user's life log data and generate an AI agent that acts on behalf of the user in a virtual space. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a learning unit, a generation unit, and a behavior unit. The collection unit collects life log data of a user. The analysis unit analyzes the data collected by the collection unit. The learning unit learns the user's behavior patterns based on the analysis results obtained by the analysis unit. The generation unit generates an AI agent based on the behavior patterns learned by the learning unit. The behavior unit causes the AI ​​agent generated by the generation unit to act in a virtual space. [Effects of the Invention]

[0007] The system according to the embodiment can analyze a user's life log data and generate an AI agent that acts on behalf of the user in a virtual space. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The Mirror Life system, according to an embodiment of the present invention, is a system centered on AI technology that learns from individual lifelog data and acts on behalf of users in a virtual space that is identical to the real world. The Mirror Life system collects users' lifelog data, analyzes it using AI, and learns their behavioral patterns. Based on the learned behavioral patterns, an AI agent is generated that acts on their behalf in the virtual space. This AI agent performs various tasks on their behalf in a real-world environment. For example, it can perform daily tasks such as shopping, schedule management, replying to emails, and posting on social media within the virtual space. This allows users to save time and live more efficiently. For example, the Mirror Life system collects data from smartphones and wearable devices. AI analyzes the collected data to identify users' behavioral patterns. Based on the analysis results, the AI ​​learns the users' behavioral patterns and generates an AI agent. The generated AI agent performs tasks such as shopping, schedule management, replying to emails, and posting on social media within the virtual space. This allows users to perform daily tasks efficiently. This enables the Mirror Life system to efficiently collect, analyze, learn, generate, and act on users' life log data.

[0029] The Mirror Life system according to the embodiment includes a collection unit, an analysis unit, a learning unit, a generation unit, and a behavior unit. The collection unit collects a user's life log data. The life log data includes, but is not limited to, location information, activity history, and health data. The collection unit collects data from, for example, a smartphone or a wearable device. Smartphones and wearable devices include smartwatches, fitness trackers, and smart glasses. The analysis unit analyzes the collected data and identifies the user's behavioral patterns. The analysis may involve, for example, data mining, statistical analysis, or a machine learning algorithm. The learning unit learns the user's behavioral patterns based on the analysis results. The behavioral patterns include, for example, daily routines and the frequency and timing of specific behaviors. The generation unit generates an AI agent based on the learning results. The AI ​​agent includes, for example, interactive functions, task automation functions, and learning capabilities. The behavior unit controls the generated AI agent to perform tasks in a virtual space, such as shopping, schedule management, email replies, and social media posts. The virtual space includes, for example, a 3D virtual space, an AR / VR environment, an online platform, etc. This enables the Mirror Life system according to the embodiment to efficiently collect, analyze, learn, generate, and act on users' life log data.

[0030] The collection unit can collect data from a smartphone or a wearable device. The collection unit collects data from, for example, a smartphone or a wearable device. A smartphone may include a function for collecting location information and activity history through a smartphone app. A wearable device may include, for example, a smartwatch or a fitness tracker, from which health data such as heart rate and step count can be collected. For example, the collection unit may collect a user's heart rate data using a smartwatch. The collection unit may also collect a user's step count data using a fitness tracker. Furthermore, the collection unit may collect a user's visual information using smart glasses. This allows for the collection of data from smartphones and wearable devices to acquire a wider variety of data. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input data collected through a smartphone app into AI and have the AI ​​analyze the data.

[0031] The analysis unit can analyze the collected data and identify the user's behavioral patterns. The analysis unit, for example, analyzes the collected data and identifies the user's behavioral patterns. The behavioral patterns include, for example, daily routines and the frequency and timing of specific behaviors. For example, data mining technology is used for the analysis. Data mining technology is a technology for extracting useful information from large amounts of data and is used to identify the user's behavioral patterns. Statistical analysis technology is also used for the analysis. Statistical analysis technology is a technology for analyzing data distributions and correlations and is used to identify the user's behavioral patterns. Furthermore, machine learning algorithms are also used for the analysis. Machine learning algorithms are a technology for learning patterns from data and making predictions and classifications and are used to identify the user's behavioral patterns. For example, the analysis unit can identify the user's behavioral patterns using data mining technology. The analysis unit can also identify the user's behavioral patterns using statistical analysis technology. Furthermore, the analysis unit can also identify the user's behavioral patterns using machine learning algorithms. In this way, the user's behavioral patterns can be identified by analyzing the collected data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input collected data into AI and have the AI ​​identify behavioral patterns.

[0032] The learning unit can learn the user's behavioral patterns based on the analysis results. The learning unit, for example, learns the user's behavioral patterns based on the analysis results. The behavioral patterns include, for example, daily routines and the frequency and timing of specific behaviors. For example, machine learning technology is used for learning. Machine learning technology is a technology that learns patterns from data and performs predictions and classifications, and is used to learn the user's behavioral patterns. For example, the learning unit uses machine learning technology to learn the user's behavioral patterns. For example, the learning unit learns the user's daily routines and grasps the frequency and timing of specific behaviors. The learning unit can also learn the user's specific behavioral patterns and perform predictions and classifications. Furthermore, the learning unit can predict future behavior based on the user's behavioral patterns. In this way, by learning the user's behavioral patterns based on the analysis results, more accurate behavioral patterns can be obtained. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit may input the analysis results to AI and cause the AI ​​to learn the behavioral patterns.

[0033] The generation unit can generate an AI agent based on the learning results. The generation unit, for example, generates an AI agent based on the learning results. The AI ​​agent may include, for example, a dialogue function, a task automation function, and learning ability. For generation, for example, generative AI technology is used. Generative AI technology is a technology that generates new information from data and is used to generate an AI agent. For example, the generation unit generates an AI agent using generative AI technology. For example, the generation unit generates an AI agent with a dialogue function based on a user's behavior pattern. The generation unit can also generate an AI agent with a task automation function based on the user's behavior pattern. Furthermore, the generation unit can generate an AI agent with learning ability based on the user's behavior pattern. In this way, by generating an AI agent based on the learning results, an agent based on the user's behavior pattern can be created. Some or all of the above-mentioned 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 learning results into an AI and cause the AI ​​to generate an AI agent.

[0034] The behavior unit allows the generated AI agent to perform tasks such as shopping, schedule management, email replies, and social media posts within a virtual space. For example, the behavior unit allows the generated AI agent to perform tasks such as shopping, schedule management, email replies, and social media posts within a virtual space. Virtual spaces include, for example, 3D virtual spaces, AR / VR environments, and online platforms. For example, the behavior unit allows the AI ​​agent to shop on behalf of the user within the virtual space. The AI ​​agent selects the most appropriate product based on the user's preferences and past purchase history and completes the purchase process. The behavior unit also allows the AI ​​agent to manage the schedule within the virtual space. The AI ​​agent understands the user's schedule and suggests the optimal schedule. The behavior unit also allows the AI ​​agent to reply to emails within the virtual space. The AI ​​agent analyzes the content of the user's email and generates an appropriate reply. The behavior unit also allows the AI ​​agent to post to social media within the virtual space. The AI ​​agent generates appropriate social media posts based on the content of the user's posts. This allows the user to save time and live a more efficient life by performing various tasks within the virtual space. Some or all of the above-described processing in the behavior unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior unit may instruct an AI agent to perform a task and monitor the progress of the task.

[0035] The collection unit can analyze the user's past life log data and select the optimal data collection method. The collection unit, for example, analyzes the user's past life log data and selects the optimal data collection method. The optimal data collection method includes, for example, the type of data, collection frequency, and device selection criteria. For example, the collection unit prioritizes collecting data from devices that the user has frequently used in the past. The collection unit can also select the optimal collection time period based on the user's past activity patterns. Furthermore, the collection unit can analyze the user's past data collection history and suggest the most efficient collection method. In this way, the optimal data collection method can be selected by analyzing past data. Some or all of the above-mentioned 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 past life log data into AI and have the AI ​​select the optimal data collection method.

[0036] The collection unit can filter the life log data based on the user's current activity status and areas of interest when collecting the life log data. For example, the collection unit can filter the life log data based on the user's current activity status and areas of interest when collecting the life log data. The filtering can include, for example, a method for identifying areas of interest and criteria for evaluating the activity status. For example, when the user is exercising, the collection unit can prioritize collecting exercise-related data. Furthermore, when the user is working, the collection unit can prioritize collecting work-related data. Furthermore, when the user is immersed in a hobby, the collection unit can prioritize collecting data related to the hobby. By filtering the data based on the user's current activity status and areas of interest, more relevant data can be collected. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's activity status and areas of interest into AI and have the AI ​​perform data filtering.

[0037] The collection unit can prioritize collecting highly relevant data based on the user's geographical location information when collecting life log data. For example, the collection unit prioritizes collecting highly relevant data based on the user's geographical location information when collecting life log data. Geographical location information includes, for example, GPS data, location information services, etc. For example, when the user is in a specific location, the collection unit prioritizes collecting data related to that location. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to activities at home. In this way, by collecting data based on the geographical location information, more relevant data can be obtained. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's geographical location information to AI and cause the AI ​​to collect highly relevant data.

[0038] The collection unit can analyze the user's social media activities and collect related data when collecting life log data. For example, the collection unit analyzes the user's social media activities and collects related data when collecting life log data. Social media activities include, for example, analysis of post content and follower trends. For example, if the user posts about a specific topic on social media, the collection unit can collect data related to that topic. Furthermore, if the user participates in a specific event on social media, the collection unit can also collect data related to the event. Furthermore, if the user belongs to a specific group on social media, the collection unit can also collect data related to the group. This allows for efficient collection of related data by analyzing social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's social media activities into AI and have the AI ​​collect related data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. The importance of the data includes, for example, the frequency of use of the data and the degree of impact. For example, the analysis unit performs a detailed analysis of data with high importance. The analysis unit can also perform a simplified analysis of data with low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail for data with medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 the importance of the data into AI and have the AI ​​adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies different analysis algorithms depending on the data category during analysis. Data categories include, for example, text data, image data, and sensor data. For example, the analysis unit applies a health analysis algorithm to health data. The analysis unit can also apply a work analysis algorithm to work data. Furthermore, the analysis unit can apply a hobby analysis algorithm to hobby data. This enables more accurate analysis by applying different analysis algorithms depending on the data category. 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 the data category into AI and have the AI ​​apply the analysis algorithm.

[0041] The analysis unit can determine the analysis priority based on the time when the data was collected during analysis. For example, the analysis unit determines the analysis priority based on the time when the data was collected during analysis. The time when the data was collected includes, for example, the collection date and time, the collection frequency, etc. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also determine the analysis priority for past data based on its importance. Furthermore, the analysis unit can determine the analysis priority for data from a specific period based on the importance of that period. This enables efficient analysis by determining the analysis priority based on the time when the data was collected. Some or all of the above-described 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 the time when the data was collected into AI and have the AI ​​determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. Data relevance includes, for example, correlation analysis and co-occurrence network analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of data with low relevance. Furthermore, the analysis unit can analyze data with moderate relevance in an appropriate order. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. 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 the relevance of the data into AI and have the AI ​​adjust the order of analysis.

[0043] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit optimizes the learning algorithm by referring to past learning data during learning. Past learning data includes, for example, past training data sets and learning history. For example, the learning unit selects an optimal learning algorithm based on the past learning data. The learning unit can also analyze patterns of past learning data and adjust the learning algorithm. Furthermore, the learning unit can evaluate the results of past learning data and improve the learning algorithm. In this way, the learning algorithm can be optimized by referring to the past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into AI and cause the AI ​​to optimize the learning algorithm.

[0044] The learning unit can improve the accuracy of learning based on the interrelationships of data during learning. The learning unit, for example, improves the accuracy of learning based on the interrelationships of data during learning. Data interrelationships include, for example, correlation analysis and identification of causal relationships. For example, the learning unit analyzes the interrelationships of data and reflects them in the learning algorithm. The learning unit can also improve the accuracy of learning based on the interrelationships of data. Furthermore, the learning unit can evaluate the results of learning by taking the interrelationships of data into consideration. In this way, the accuracy of learning is improved by taking the interrelationships of data into consideration. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the interrelationships of data into AI and cause the AI ​​to improve the accuracy of learning.

[0045] The learning unit can weight the learning data based on the time when the data was collected during learning. For example, the learning unit weights the learning data based on the time when the data was collected during learning. The time when the data was collected includes, for example, the date and time of collection and the frequency of collection. For example, the learning unit weights the most recent data highly. The learning unit can also weight past data according to its importance. Furthermore, the learning unit can weight data from a specific period based on the importance of that period. This enables efficient learning by weighting based on the time when the data was collected. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the time when the data was collected into AI and have the AI ​​perform weighting of the learning data.

[0046] The learning unit can improve the accuracy of learning by referring to literature related to the data during learning. The learning unit, for example, improves the accuracy of learning by referring to literature related to the data during learning. Relevant literature includes, for example, academic papers, technical reports, etc. For example, the learning unit improves the learning algorithm based on the relevant literature. The learning unit can also incorporate knowledge from the relevant literature to improve the accuracy of learning. Furthermore, the learning unit can also evaluate the results of learning by referring to the relevant literature. In this way, the accuracy of learning is improved by referring to the related literature. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input relevant literature into the AI ​​and cause the AI ​​to improve the accuracy of learning.

[0047] The generation unit can adjust the level of detail of the AI ​​agent based on the importance of the learning result during generation. For example, the generation unit adjusts the level of detail of the AI ​​agent based on the importance of the learning result during generation. The importance of the learning result includes, for example, learning accuracy and influence. For example, the generation unit generates a detailed AI agent based on a learning result with high importance. The generation unit can also generate a simple AI agent based on a learning result with low importance. Furthermore, the generation unit can generate an AI agent with a moderate level of detail based on a learning result with medium importance. This enables efficient generation of AI agents by adjusting the level of detail based on the importance of the learning result. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, an AI. For example, the generation unit can input the importance of the learning result to the AI ​​and cause the AI ​​to adjust the level of detail of the AI ​​agent.

[0048] The generation unit can apply different generation algorithms depending on the user's behavioral patterns during generation. For example, the generation unit applies different generation algorithms depending on the user's behavioral patterns during generation. Examples of generation algorithms include neural networks and genetic algorithms. For example, the generation unit applies an efficient generation algorithm to tasks performed daily by the user. Furthermore, when the user participates in a specific event, the generation unit can apply a generation algorithm suitable for that event. Furthermore, when the user attempts a new task, the generation unit can apply a generation algorithm suitable for that task. By applying a generation algorithm depending on the user's behavioral patterns, a more appropriate AI agent can be generated. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the user's behavioral patterns into AI and have the AI ​​apply the generation algorithm.

[0049] The generation unit can generate an optimal AI agent based on the user's geographical location information at the time of generation. For example, the generation unit generates an optimal AI agent based on the user's geographical location information at the time of generation. Geographical location information includes, for example, GPS data, location information services, etc. For example, if the user is in a specific location, the generation unit generates an AI agent related to that location. Furthermore, if the user is traveling, the generation unit can generate an AI agent related to the travel destination. Furthermore, if the user is at home, the generation unit can generate an AI agent related to activities at home. By generating an optimal AI agent based on geographical location information, a more relevant agent can be generated. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may input the user's geographical location information into AI and cause the AI ​​to generate an optimal AI agent.

[0050] The generation unit can adjust the characteristics of the AI ​​agent by analyzing the user's social media activity at the time of generation. For example, the generation unit can adjust the characteristics of the AI ​​agent by analyzing the user's social media activity at the time of generation. Social media activity includes, for example, analysis of post content and follower trends. For example, if the user posts about a specific topic on social media, the generation unit can generate an AI agent related to that topic. Furthermore, if the user participates in a specific event on social media, the generation unit can also generate an AI agent related to that event. Furthermore, if the user belongs to a specific group on social media, the generation unit can generate an AI agent related to that group. In this way, a more appropriate AI agent can be generated by analyzing social media activity. Some or all of the above-described processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can input the user's social media activity into AI and cause the AI ​​to adjust the characteristics of the AI ​​agent.

[0051] The behavior unit can select the optimal behavior method by referring to the user's past behavior history when performing a behavior. For example, the behavior unit selects the optimal behavior method by referring to the user's past behavior history when performing a behavior. The past behavior history includes, for example, a behavior log and history data. For example, the behavior unit selects the optimal behavior method based on the user's past behavior patterns. The behavior unit can also select an efficient behavior method from the user's past behavior history. Furthermore, the behavior unit can analyze the user's past behavior history and select the most effective behavior method. In this way, the optimal behavior method can be selected by referring to the past behavior history. Some or all of the above-described processing in the behavior unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior unit can input the user's past behavior history into AI and have the AI ​​select the optimal behavior method.

[0052] The action unit can customize the action means based on the user's current living situation when taking action. The action unit, for example, customizes the action means based on the user's current living situation when taking action. The current living situation includes, for example, activity level, health status, etc. For example, if the user is busy, the action unit can suggest an efficient action means. Furthermore, if the user is relaxed, the action unit can also suggest an action means that will help the user relax. Furthermore, if the user is concentrating on a specific task, the action unit can also suggest an action means suitable for that task. This enables more appropriate action by customizing the action means based on the current living situation. Some or all of the above-mentioned processing in the action unit may be performed, for example, using AI, or may be performed without using AI. For example, the action unit can input the user's current living situation into AI and have the AI ​​customize the action means.

[0053] The action unit can select an optimal action method based on the user's geographical location information when taking action. For example, the action unit selects an optimal action method based on the user's geographical location information when taking action. Geographical location information includes, for example, GPS data, location information services, etc. For example, when the user is in a specific location, the action unit selects an action method related to that location. Furthermore, when the user is traveling, the action unit can select an action method related to the travel destination. Furthermore, when the user is at home, the action unit can select an action method related to activities at home. In this way, selecting an optimal action method based on the geographical location information enables more relevant actions. Some or all of the above-mentioned processing in the action unit may be performed using, for example, AI, or may be performed without using AI. For example, the action unit can input the user's geographical location information to AI and have the AI ​​select an optimal action method.

[0054] The action unit can analyze the user's social media activity and suggest action measures at the time of action. For example, the action unit analyzes the user's social media activity and suggests action measures at the time of action. Social media activity includes, for example, analysis of post content and follower trends. For example, if the user posts about a specific topic on social media, the action unit can suggest action measures related to that topic. Also, if the user is participating in a specific event on social media, the action unit can suggest action measures related to that event. Furthermore, if the user belongs to a specific group on social media, the action unit can suggest action measures related to that group. In this way, by analyzing social media activity, more appropriate action measures can be suggested. Some or all of the above-mentioned processing in the action unit may be performed using, for example, AI, or may be performed without using AI. For example, the action unit can input the user's social media activity into AI and have the AI ​​execute the suggestion of action measures.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The Mirror Life system can also be equipped with a health management unit that monitors the user's health. The health management unit collects biometric data such as the user's heart rate, blood pressure, and body temperature and sends it to the analysis unit. The analysis unit evaluates the user's health based on this data and can issue a warning if an abnormality is detected. For example, if the heart rate is abnormally high, the analysis unit can send a notification urging the user to rest. If the blood pressure is high, the system can also provide advice on diet and exercise. Furthermore, if the body temperature is high, the system can suggest the possibility of a fever and recommend that the user see a doctor. This allows the user's health to be monitored in real time and appropriate measures to be taken.

[0057] The collection unit can customize data collection based on the user's hobbies and interests. For example, if the user is interested in music, the collection unit will prioritize collecting music-related data. If the user is interested in sports, the collection unit can also collect sports-related data. Furthermore, if the user is interested in travel, the collection unit can also collect information on travel destinations and tourist spots. This makes it possible to collect data according to the user's hobbies and interests, allowing for the provision of more personalized services.

[0058] The Mirror Life system can further include an advice unit that provides advice to improve the user's lifestyle habits. The advice unit analyzes the user's lifestyle habits based on the collected life log data and suggests areas for improvement. For example, the advice unit can analyze the user's dietary data and suggest a nutritionally balanced meal plan. It can also suggest an appropriate exercise program based on exercise data. It can also analyze sleep data and provide advice on how to get good quality sleep. This can improve the user's lifestyle habits and support a healthy lifestyle.

[0059] The Mirror Life system can further include an entertainment unit that provides entertainment content customized based on the user's hobbies and interests. The entertainment unit identifies the user's hobbies and interests based on the user's life log data and provides entertainment content according to them. For example, if the user is interested in movies, the entertainment unit can provide the latest movie information and recommended movies. If the user is interested in music, the entertainment unit can provide the latest music information and recommended playlists. Furthermore, if the user is interested in games, the entertainment unit can provide the latest game information and recommended games. This makes it possible to provide entertainment content according to the user's hobbies and interests, thereby improving user satisfaction.

[0060] The Mirror Life system can also include an education module to support users' learning. The education module identifies the user's learning style and interests based on the user's life log data and provides learning content tailored to that. For example, if the user is interested in language learning, language learning materials and apps can be provided. If the user is interested in programming, programming materials and online courses can be provided. Furthermore, if the user is interested in history, historical documentaries and books can be provided. This supports the user's learning and promotes the improvement of their knowledge.

[0061] The Mirror Life system can further include a social section to strengthen the user's social connections. The social section identifies the user's social activities and interests based on the user's life log data and suggests corresponding social events and groups. For example, if the user has a particular hobby, the social section can suggest events and groups related to that hobby. If the user wants to make new friends, the social section can provide a place for the user to interact with people who share common interests. Furthermore, if the user is interested in volunteering, the social section can suggest appropriate volunteer opportunities. This strengthens the user's social connections and supports a fulfilling social life.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The collection unit collects the user's life log data. The life log data includes, for example, location information, activity history, and health data. The collection unit collects data from smartphones and wearable devices (smartwatches, fitness trackers, smart glasses, etc.). Step 2: The analysis unit analyzes the collected data and identifies user behavior patterns. This analysis uses data mining, statistical analysis, machine learning algorithms, etc. Step 3: The learning module learns the user's behavioral patterns based on the analysis results. These patterns include daily routines, the frequency and timing of specific actions, etc. Step 4: The generator generates an AI agent based on the learning results. The AI ​​agent includes features such as dialogue, task automation, and learning ability. Step 5: In the behavior section, the generated AI agent acts in a virtual space. Specifically, it performs tasks such as shopping, managing schedules, replying to emails, and posting on social media. Virtual spaces include 3D virtual spaces, AR / VR environments, and online platforms.

[0064] (Example 2) The Mirror Life system, according to an embodiment of the present invention, is a system centered on AI technology that learns from individual lifelog data and acts on behalf of users in a virtual space that is identical to the real world. The Mirror Life system collects users' lifelog data, analyzes it using AI, and learns their behavioral patterns. Based on the learned behavioral patterns, an AI agent is generated that acts on their behalf in the virtual space. This AI agent performs various tasks on their behalf in a real-world environment. For example, it can perform daily tasks such as shopping, schedule management, replying to emails, and posting on social media within the virtual space. This allows users to save time and live more efficiently. For example, the Mirror Life system collects data from smartphones and wearable devices. AI analyzes the collected data to identify users' behavioral patterns. Based on the analysis results, the AI ​​learns the users' behavioral patterns and generates an AI agent. The generated AI agent performs tasks such as shopping, schedule management, replying to emails, and posting on social media within the virtual space. This allows users to perform daily tasks efficiently. This enables the Mirror Life system to efficiently collect, analyze, learn, generate, and act on users' life log data.

[0065] The Mirror Life system according to the embodiment includes a collection unit, an analysis unit, a learning unit, a generation unit, and a behavior unit. The collection unit collects a user's life log data. The life log data includes, but is not limited to, location information, activity history, and health data. The collection unit collects data from, for example, a smartphone or a wearable device. Smartphones and wearable devices include smartwatches, fitness trackers, and smart glasses. The analysis unit analyzes the collected data and identifies the user's behavioral patterns. The analysis may involve, for example, data mining, statistical analysis, or a machine learning algorithm. The learning unit learns the user's behavioral patterns based on the analysis results. The behavioral patterns include, for example, daily routines and the frequency and timing of specific behaviors. The generation unit generates an AI agent based on the learning results. The AI ​​agent includes, for example, interactive functions, task automation functions, and learning capabilities. The behavior unit controls the generated AI agent to perform tasks in a virtual space, such as shopping, schedule management, email replies, and social media posts. The virtual space includes, for example, a 3D virtual space, an AR / VR environment, an online platform, etc. This enables the Mirror Life system according to the embodiment to efficiently collect, analyze, learn, generate, and act on users' life log data.

[0066] The collection unit can collect data from a smartphone or a wearable device. The collection unit collects data from, for example, a smartphone or a wearable device. A smartphone may include a function for collecting location information and activity history through a smartphone app. A wearable device may include, for example, a smartwatch or a fitness tracker, from which health data such as heart rate and step count can be collected. For example, the collection unit may collect a user's heart rate data using a smartwatch. The collection unit may also collect a user's step count data using a fitness tracker. Furthermore, the collection unit may collect a user's visual information using smart glasses. This allows for the collection of data from smartphones and wearable devices to acquire a wider variety of data. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input data collected through a smartphone app into AI and have the AI ​​analyze the data.

[0067] The analysis unit can analyze the collected data and identify the user's behavioral patterns. The analysis unit, for example, analyzes the collected data and identifies the user's behavioral patterns. The behavioral patterns include, for example, daily routines and the frequency and timing of specific behaviors. For example, data mining technology is used for the analysis. Data mining technology is a technology for extracting useful information from large amounts of data and is used to identify the user's behavioral patterns. Statistical analysis technology is also used for the analysis. Statistical analysis technology is a technology for analyzing data distributions and correlations and is used to identify the user's behavioral patterns. Furthermore, machine learning algorithms are also used for the analysis. Machine learning algorithms are a technology for learning patterns from data and making predictions and classifications and are used to identify the user's behavioral patterns. For example, the analysis unit can identify the user's behavioral patterns using data mining technology. The analysis unit can also identify the user's behavioral patterns using statistical analysis technology. Furthermore, the analysis unit can also identify the user's behavioral patterns using machine learning algorithms. In this way, the user's behavioral patterns can be identified by analyzing the collected data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input collected data into AI and have the AI ​​identify behavioral patterns.

[0068] The learning unit can learn the user's behavioral patterns based on the analysis results. The learning unit, for example, learns the user's behavioral patterns based on the analysis results. The behavioral patterns include, for example, daily routines and the frequency and timing of specific behaviors. For example, machine learning technology is used for learning. Machine learning technology is a technology that learns patterns from data and performs predictions and classifications, and is used to learn the user's behavioral patterns. For example, the learning unit uses machine learning technology to learn the user's behavioral patterns. For example, the learning unit learns the user's daily routines and grasps the frequency and timing of specific behaviors. The learning unit can also learn the user's specific behavioral patterns and perform predictions and classifications. Furthermore, the learning unit can predict future behavior based on the user's behavioral patterns. In this way, by learning the user's behavioral patterns based on the analysis results, more accurate behavioral patterns can be obtained. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit may input the analysis results to AI and cause the AI ​​to learn the behavioral patterns.

[0069] The generation unit can generate an AI agent based on the learning results. The generation unit, for example, generates an AI agent based on the learning results. The AI ​​agent may include, for example, a dialogue function, a task automation function, and learning ability. For generation, for example, generative AI technology is used. Generative AI technology is a technology that generates new information from data and is used to generate an AI agent. For example, the generation unit generates an AI agent using generative AI technology. For example, the generation unit generates an AI agent with a dialogue function based on a user's behavior pattern. The generation unit can also generate an AI agent with a task automation function based on the user's behavior pattern. Furthermore, the generation unit can generate an AI agent with learning ability based on the user's behavior pattern. In this way, by generating an AI agent based on the learning results, an agent based on the user's behavior pattern can be created. Some or all of the above-mentioned 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 learning results into an AI and cause the AI ​​to generate an AI agent.

[0070] The behavior unit allows the generated AI agent to perform tasks such as shopping, schedule management, email replies, and social media posts within a virtual space. For example, the behavior unit allows the generated AI agent to perform tasks such as shopping, schedule management, email replies, and social media posts within a virtual space. Virtual spaces include, for example, 3D virtual spaces, AR / VR environments, and online platforms. For example, the behavior unit allows the AI ​​agent to shop on behalf of the user within the virtual space. The AI ​​agent selects the most appropriate product based on the user's preferences and past purchase history and completes the purchase process. The behavior unit also allows the AI ​​agent to manage the schedule within the virtual space. The AI ​​agent understands the user's schedule and suggests the optimal schedule. The behavior unit also allows the AI ​​agent to reply to emails within the virtual space. The AI ​​agent analyzes the content of the user's email and generates an appropriate reply. The behavior unit also allows the AI ​​agent to post to social media within the virtual space. The AI ​​agent generates appropriate social media posts based on the content of the user's posts. This allows the user to save time and live a more efficient life by performing various tasks within the virtual space. Some or all of the above-described processing in the behavior unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior unit may instruct an AI agent to perform a task and monitor the progress of the task.

[0071] The collection unit can estimate the user's emotions and adjust the timing of collecting life log data based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of collecting life log data based on the estimated user emotions. For example, facial expression recognition technology is used to estimate emotions. Facial expression recognition technology is a technology that analyzes the user's facial expressions to estimate emotions, and is used to adjust the timing of collecting life log data. Voice analysis technology is also used to estimate emotions. Voice analysis technology is a technology that analyzes the tone and speed of the user's voice to estimate emotions, and is used to adjust the timing of collecting life log data. Furthermore, a biosensor is also used to estimate emotions. A biosensor is a technology that measures the user's heart rate and electrodermal activity to estimate emotions, and is used to adjust the timing of collecting life log data. For example, if the user is feeling stressed, the collection unit reduces the collection timing to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the collection timing to collect more detailed data. Furthermore, if the user is active, the collection unit can increase the collection timing to acquire data in real time. This allows for adjusting the collection timing based on the user's emotions, thereby reducing the burden on the user and collecting detailed data. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may 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 collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's emotion data into an AI and have the AI ​​adjust the collection timing.

[0072] The collection unit can analyze the user's past life log data and select the optimal data collection method. The collection unit, for example, analyzes the user's past life log data and selects the optimal data collection method. The optimal data collection method includes, for example, the type of data, collection frequency, and device selection criteria. For example, the collection unit prioritizes collecting data from devices that the user has frequently used in the past. The collection unit can also select the optimal collection time period based on the user's past activity patterns. Furthermore, the collection unit can analyze the user's past data collection history and suggest the most efficient collection method. In this way, the optimal data collection method can be selected by analyzing past data. Some or all of the above-mentioned 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 past life log data into AI and have the AI ​​select the optimal data collection method.

[0073] The collection unit can filter the life log data based on the user's current activity status and areas of interest when collecting the life log data. For example, the collection unit can filter the life log data based on the user's current activity status and areas of interest when collecting the life log data. The filtering can include, for example, a method for identifying areas of interest and criteria for evaluating the activity status. For example, when the user is exercising, the collection unit can prioritize collecting exercise-related data. Furthermore, when the user is working, the collection unit can prioritize collecting work-related data. Furthermore, when the user is immersed in a hobby, the collection unit can prioritize collecting data related to the hobby. By filtering the data based on the user's current activity status and areas of interest, more relevant data can be collected. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's activity status and areas of interest into AI and have the AI ​​perform data filtering.

[0074] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of data to be collected based on the estimated user's emotion. For example, facial expression recognition technology is used to estimate the emotion. Facial expression recognition technology is a technology that analyzes the user's facial expression to estimate the emotion, and is used to determine the priority of data to be collected. Voice analysis technology is also used to estimate the emotion. Voice analysis technology is a technology that analyzes the tone and speed of the user's voice to estimate the emotion, and is used to determine the priority of data to be collected. Furthermore, a biosensor is also used to estimate the emotion. A biosensor is a technology that measures the user's heart rate and electrodermal activity to estimate the emotion, and is used to determine the priority of data to be collected. For example, if the user is feeling stressed, the collection unit prioritizes collecting data related to stress reduction. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting data related to relaxation. Furthermore, if the user is excited, the collection unit can also prioritize collecting data related to the excitement. This allows data prioritization based on the user's emotions, allowing more important data to be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative 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 may input the user's emotion data into an AI and have the AI ​​determine the data priorities.

[0075] The collection unit can prioritize collecting highly relevant data based on the user's geographical location information when collecting life log data. For example, the collection unit prioritizes collecting highly relevant data based on the user's geographical location information when collecting life log data. Geographical location information includes, for example, GPS data, location information services, etc. For example, when the user is in a specific location, the collection unit prioritizes collecting data related to that location. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to activities at home. In this way, by collecting data based on the geographical location information, more relevant data can be obtained. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's geographical location information to AI and cause the AI ​​to collect highly relevant data.

[0076] The collection unit can analyze the user's social media activities and collect related data when collecting life log data. For example, the collection unit analyzes the user's social media activities and collects related data when collecting life log data. Social media activities include, for example, analysis of post content and follower trends. For example, if the user posts about a specific topic on social media, the collection unit can collect data related to that topic. Furthermore, if the user participates in a specific event on social media, the collection unit can also collect data related to the event. Furthermore, if the user belongs to a specific group on social media, the collection unit can also collect data related to the group. This allows for efficient collection of related data by analyzing social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's social media activities into AI and have the AI ​​collect related data.

[0077] The analysis unit can estimate the user's emotion and adjust the data analysis method based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the data analysis method based on the estimated user's emotion. For example, facial expression recognition technology is used to estimate the emotion. Facial expression recognition technology is a technology that analyzes the user's facial expression to estimate the emotion, and is used to adjust the data analysis method. Voice analysis technology is also used to estimate the emotion. Voice analysis technology is a technology that analyzes the tone and speed of the user's voice to estimate the emotion, and is used to adjust the data analysis method. Furthermore, a biosensor is also used to estimate the emotion. The biosensor is a technology that measures the user's heart rate and electrodermal activity to estimate the emotion, and is used to adjust the data analysis method. For example, if the user is feeling stressed, the analysis unit may prioritize analyzing data related to stress reduction. Furthermore, if the user is relaxed, the analysis unit may prioritize analyzing data related to relaxation. Furthermore, if the user is excited, the analysis unit may prioritize analyzing data related to the excitement. This allows for more appropriate analysis by adjusting the data analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative 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 analysis unit may be performed using, or without, AI. For example, the analysis unit may input the user's emotion data into AI and have the AI ​​adjust the data analysis method.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. The importance of the data includes, for example, the frequency of use of the data and the degree of impact. For example, the analysis unit performs a detailed analysis of data with high importance. The analysis unit can also perform a simplified analysis of data with low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail for data with medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 the importance of the data into AI and have the AI ​​adjust the level of detail of the analysis.

[0079] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies different analysis algorithms depending on the data category during analysis. Data categories include, for example, text data, image data, and sensor data. For example, the analysis unit applies a health analysis algorithm to health data. The analysis unit can also apply a work analysis algorithm to work data. Furthermore, the analysis unit can apply a hobby analysis algorithm to hobby data. This enables more accurate analysis by applying different analysis algorithms depending on the data category. 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 the data category into AI and have the AI ​​apply the analysis algorithm.

[0080] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis results based on the estimated user's emotion. For example, facial expression recognition technology is used to estimate the emotion. Facial expression recognition technology is a technology that analyzes the user's facial expression to estimate the emotion and is used to adjust the display method of the analysis results. Voice analysis technology is also used to estimate the emotion. Voice analysis technology is a technology that analyzes the tone and speed of the user's voice to estimate the emotion and is used to adjust the display method of the analysis results. Furthermore, biosensors are also used to estimate the emotion. Biosensors are a technology that measures the user's heart rate and electrodermal activity to estimate the emotion and is used to adjust the display method of the analysis results. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This allows for a more visible display by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit may input the user's emotion data into the AI ​​and have the AI ​​adjust the display method of the analysis results.

[0081] The analysis unit can determine the analysis priority based on the time when the data was collected during analysis. For example, the analysis unit determines the analysis priority based on the time when the data was collected during analysis. The time when the data was collected includes, for example, the collection date and time, the collection frequency, etc. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also determine the analysis priority for past data based on its importance. Furthermore, the analysis unit can determine the analysis priority for data from a specific period based on the importance of that period. This enables efficient analysis by determining the analysis priority based on the time when the data was collected. Some or all of the above-described 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 the time when the data was collected into AI and have the AI ​​determine the analysis priority.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. Data relevance includes, for example, correlation analysis and co-occurrence network analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of data with low relevance. Furthermore, the analysis unit can analyze data with moderate relevance in an appropriate order. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. 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 the relevance of the data into AI and have the AI ​​adjust the order of analysis.

[0083] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. The learning unit, for example, estimates the user's emotions and selects training data based on the estimated user emotions. For example, facial expression recognition technology is used to estimate emotions. Facial expression recognition technology is a technology that analyzes a user's facial expressions to estimate emotions and is used to select training data. Voice analysis technology is also used to estimate emotions. Voice analysis technology is a technology that analyzes the tone and speed of a user's voice to estimate emotions and is used to select training data. Furthermore, biosensors are also used to estimate emotions. Biosensors are a technology that measures a user's heart rate and electrodermal activity to estimate emotions and is used to select training data. For example, if the user is feeling stressed, the learning unit prioritizes learning data related to stress reduction. Furthermore, if the user is relaxed, the learning unit can also prioritize learning data related to relaxation. Furthermore, if the user is excited, the learning unit can also prioritize learning data related to excitement. In this way, by selecting training data based on the user's emotions, more appropriate data can be learned. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative 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 learning unit may be performed using, or without, an AI. For example, the learning unit may input user emotion data into the AI ​​and have the AI ​​select the learning data.

[0084] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit optimizes the learning algorithm by referring to past learning data during learning. Past learning data includes, for example, past training data sets and learning history. For example, the learning unit selects an optimal learning algorithm based on the past learning data. The learning unit can also analyze patterns of past learning data and adjust the learning algorithm. Furthermore, the learning unit can evaluate the results of past learning data and improve the learning algorithm. In this way, the learning algorithm can be optimized by referring to the past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into AI and cause the AI ​​to optimize the learning algorithm.

[0085] The learning unit can improve the accuracy of learning based on the interrelationships of data during learning. The learning unit, for example, improves the accuracy of learning based on the interrelationships of data during learning. Data interrelationships include, for example, correlation analysis and identification of causal relationships. For example, the learning unit analyzes the interrelationships of data and reflects them in the learning algorithm. The learning unit can also improve the accuracy of learning based on the interrelationships of data. Furthermore, the learning unit can evaluate the results of learning by taking the interrelationships of data into consideration. In this way, the accuracy of learning is improved by taking the interrelationships of data into consideration. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the interrelationships of data into AI and cause the AI ​​to improve the accuracy of learning.

[0086] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. The learning unit, for example, estimates the user's emotions and adjusts the frequency of learning based on the estimated user emotions. For example, facial expression recognition technology is used to estimate emotions. Facial expression recognition technology is a technology that analyzes the user's facial expressions to estimate emotions and is used to adjust the frequency of learning. Voice analysis technology is also used to estimate emotions. Voice analysis technology is a technology that analyzes the tone and speed of the user's voice to estimate emotions and is used to adjust the frequency of learning. Furthermore, biosensors are also used to estimate emotions. Biosensors are a technology that measures the user's heart rate and electrodermal activity to estimate emotions and is used to adjust the frequency of learning. For example, if the user is feeling stressed, the learning unit reduces the frequency of learning to reduce the user's burden. Furthermore, if the user is relaxed, the learning unit can increase the frequency of learning and learn more detailed data. Furthermore, if the user is active, the learning unit can increase the frequency of learning and learn data in real time. This allows the learning frequency to be adjusted based on the user's emotions, thereby reducing the burden on the user and enabling detailed data to be learned. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may 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-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit may input the user's emotion data into the AI ​​and have the AI ​​adjust the learning frequency.

[0087] The learning unit can weight the learning data based on the time when the data was collected during learning. For example, the learning unit weights the learning data based on the time when the data was collected during learning. The time when the data was collected includes, for example, the date and time of collection and the frequency of collection. For example, the learning unit weights the most recent data highly. The learning unit can also weight past data according to its importance. Furthermore, the learning unit can weight data from a specific period based on the importance of that period. This enables efficient learning by weighting based on the time when the data was collected. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the time when the data was collected into AI and have the AI ​​perform weighting of the learning data.

[0088] The learning unit can improve the accuracy of learning by referring to literature related to the data during learning. The learning unit, for example, improves the accuracy of learning by referring to literature related to the data during learning. Relevant literature includes, for example, academic papers, technical reports, etc. For example, the learning unit improves the learning algorithm based on the relevant literature. The learning unit can also incorporate knowledge from the relevant literature to improve the accuracy of learning. Furthermore, the learning unit can also evaluate the results of learning by referring to the relevant literature. In this way, the accuracy of learning is improved by referring to the related literature. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input relevant literature into the AI ​​and cause the AI ​​to improve the accuracy of learning.

[0089] The generation unit can estimate the user's emotions and adjust the AI ​​agent generation method based on the estimated user emotions. For example, the generation unit estimates the user's emotions and adjusts the AI ​​agent generation method based on the estimated user emotions. For example, facial expression recognition technology is used to estimate emotions. Facial expression recognition technology analyzes the user's facial expressions to estimate emotions and is used to adjust the AI ​​agent generation method. Voice analysis technology is also used to estimate emotions. Voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions and is used to adjust the AI ​​agent generation method. Furthermore, biosensors are also used to estimate emotions. Biosensors measure the user's heart rate and electrodermal activity to estimate emotions and are used to adjust the AI ​​agent generation method. For example, if the user is relaxed, the generation unit generates an AI agent that progresses at a leisurely pace. Also, if the user is in a hurry, the generation unit can generate an AI agent that completes tasks quickly. Furthermore, if the user is excited, the generation unit can generate an AI agent with visually stimulating effects. This allows for the generation of a more appropriate AI agent by adjusting the generation method based on the user's emotions. Emotion estimation is achieved, for example, by using an emotion estimation function with an emotion engine or 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 generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit may input user emotion data into the AI ​​and cause the AI ​​to adjust the generation method of the AI ​​agent.

[0090] The generation unit can adjust the level of detail of the AI ​​agent based on the importance of the learning result during generation. For example, the generation unit adjusts the level of detail of the AI ​​agent based on the importance of the learning result during generation. The importance of the learning result includes, for example, learning accuracy and influence. For example, the generation unit generates a detailed AI agent based on a learning result with high importance. The generation unit can also generate a simple AI agent based on a learning result with low importance. Furthermore, the generation unit can generate an AI agent with a moderate level of detail based on a learning result with medium importance. This enables efficient generation of AI agents by adjusting the level of detail based on the importance of the learning result. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, an AI. For example, the generation unit can input the importance of the learning result to the AI ​​and cause the AI ​​to adjust the level of detail of the AI ​​agent.

[0091] The generation unit can apply different generation algorithms depending on the user's behavioral patterns during generation. For example, the generation unit applies different generation algorithms depending on the user's behavioral patterns during generation. Examples of generation algorithms include neural networks and genetic algorithms. For example, the generation unit applies an efficient generation algorithm to tasks performed daily by the user. Furthermore, when the user participates in a specific event, the generation unit can apply a generation algorithm suitable for that event. Furthermore, when the user attempts a new task, the generation unit can apply a generation algorithm suitable for that task. By applying a generation algorithm depending on the user's behavioral patterns, a more appropriate AI agent can be generated. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the user's behavioral patterns into AI and have the AI ​​apply the generation algorithm.

[0092] The generation unit can estimate the user's emotions and determine the priority of AI agents to be generated based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and determines the priority of AI agents to be generated based on the estimated user emotions. For example, facial expression recognition technology is used to estimate emotions. Facial expression recognition technology is a technology that analyzes the user's facial expressions to estimate emotions and is used to determine the priority of AI agents to be generated. Voice analysis technology is also used to estimate emotions. Voice analysis technology is a technology that analyzes the tone and speed of the user's voice to estimate emotions and is used to determine the priority of AI agents to be generated. Furthermore, biosensors are also used to estimate emotions. Biosensors are a technology that measures the user's heart rate and electrodermal activity to estimate emotions and is used to determine the priority of AI agents to be generated. For example, if the user is feeling stressed, the generation unit can prioritize generating AI agents related to stress reduction. Furthermore, if the user is relaxed, the generation unit can prioritize generating AI agents related to relaxation. Furthermore, if the user is excited, the generation unit can also preferentially generate AI agents related to that excitement. This allows for determining priorities based on the user's emotions, thereby preferentially generating more important AI agents. Emotion estimation is achieved using an emotion estimation function, for example, with 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 generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit may input the user's emotion data into the AI ​​and have the AI ​​determine the priority of the AI ​​agents.

[0093] The generation unit can generate an optimal AI agent based on the user's geographical location information at the time of generation. For example, the generation unit generates an optimal AI agent based on the user's geographical location information at the time of generation. Geographical location information includes, for example, GPS data, location information services, etc. For example, if the user is in a specific location, the generation unit generates an AI agent related to that location. Furthermore, if the user is traveling, the generation unit can generate an AI agent related to the travel destination. Furthermore, if the user is at home, the generation unit can generate an AI agent related to activities at home. By generating an optimal AI agent based on geographical location information, a more relevant agent can be generated. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may input the user's geographical location information into AI and cause the AI ​​to generate an optimal AI agent.

[0094] The generation unit can adjust the characteristics of the AI ​​agent by analyzing the user's social media activity at the time of generation. For example, the generation unit can adjust the characteristics of the AI ​​agent by analyzing the user's social media activity at the time of generation. Social media activity includes, for example, analysis of post content and follower trends. For example, if the user posts about a specific topic on social media, the generation unit can generate an AI agent related to that topic. Furthermore, if the user participates in a specific event on social media, the generation unit can also generate an AI agent related to that event. Furthermore, if the user belongs to a specific group on social media, the generation unit can generate an AI agent related to that group. In this way, a more appropriate AI agent can be generated by analyzing social media activity. Some or all of the above-described processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can input the user's social media activity into AI and cause the AI ​​to adjust the characteristics of the AI ​​agent.

[0095] The behavior unit can estimate the user's emotions and adjust the behavior of the AI ​​agent based on the estimated user emotions. For example, the behavior unit can estimate the user's emotions and adjust the behavior of the AI ​​agent based on the estimated user emotions. For example, facial expression recognition technology is used to estimate emotions. Facial expression recognition technology analyzes the user's facial expressions to infer emotions and is used to adjust the behavior of the AI ​​agent. Voice analysis technology can also be used to estimate emotions. Voice analysis technology analyzes the tone and speed of the user's voice to infer emotions and is used to adjust the behavior of the AI ​​agent. Furthermore, biosensors can also be used to estimate emotions. Biosensors measure the user's heart rate and electrodermal activity to infer emotions and are used to adjust the behavior of the AI ​​agent. For example, if the user is relaxed, the behavior unit can generate an AI agent that acts at a leisurely pace. If the user is in a hurry, the behavior unit can also generate an AI agent that completes tasks quickly. Furthermore, if the user is excited, the behavior unit can generate an AI agent with visually stimulating effects. This allows for more appropriate behavior by adjusting the behavioral method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative 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 behavior unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the behavior unit may input user emotion data into the AI ​​and have the AI ​​adjust the behavioral method of the AI ​​agent.

[0096] The behavior unit can select the optimal behavior method by referring to the user's past behavior history when performing a behavior. For example, the behavior unit selects the optimal behavior method by referring to the user's past behavior history when performing a behavior. The past behavior history includes, for example, a behavior log and history data. For example, the behavior unit selects the optimal behavior method based on the user's past behavior patterns. The behavior unit can also select an efficient behavior method from the user's past behavior history. Furthermore, the behavior unit can analyze the user's past behavior history and select the most effective behavior method. In this way, the optimal behavior method can be selected by referring to the past behavior history. Some or all of the above-described processing in the behavior unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior unit can input the user's past behavior history into AI and have the AI ​​select the optimal behavior method.

[0097] The action unit can customize the action means based on the user's current living situation when taking action. The action unit, for example, customizes the action means based on the user's current living situation when taking action. The current living situation includes, for example, activity level, health status, etc. For example, if the user is busy, the action unit can suggest an efficient action means. Furthermore, if the user is relaxed, the action unit can also suggest an action means that will help the user relax. Furthermore, if the user is concentrating on a specific task, the action unit can also suggest an action means suitable for that task. This enables more appropriate action by customizing the action means based on the current living situation. Some or all of the above-mentioned processing in the action unit may be performed, for example, using AI, or may be performed without using AI. For example, the action unit can input the user's current living situation into AI and have the AI ​​customize the action means.

[0098] The behavior unit can estimate the user's emotions and determine the action priorities of the AI ​​agent based on the estimated user emotions. The behavior unit, for example, estimates the user's emotions and determines the action priorities of the AI ​​agent based on the estimated user emotions. For example, facial expression recognition technology is used to estimate emotions. Facial expression recognition technology is a technology that analyzes the user's facial expressions to estimate emotions and is used to determine the action priorities of the AI ​​agent. Voice analysis technology is also used to estimate emotions. Voice analysis technology is a technology that analyzes the tone and speed of the user's voice to estimate emotions and is used to determine the action priorities of the AI ​​agent. Furthermore, biosensors are also used to estimate emotions. Biosensors are a technology that measures the user's heart rate and electrodermal activity to estimate emotions and is used to determine the action priorities of the AI ​​agent. For example, if the user is feeling stressed, the behavior unit can prioritize actions related to stress reduction. Furthermore, if the user is relaxed, the behavior unit can prioritize actions related to relaxation. Furthermore, if the user is excited, the behavior unit can prioritize actions related to the excitement. This allows for determining action priorities based on the user's emotions, thereby prioritizing more important actions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative 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 behavior unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the behavior unit may input user emotion data into the AI ​​and have the AI ​​determine the action priorities of the AI ​​agent.

[0099] The action unit can select an optimal action method based on the user's geographical location information when taking action. For example, the action unit selects an optimal action method based on the user's geographical location information when taking action. Geographical location information includes, for example, GPS data, location information services, etc. For example, when the user is in a specific location, the action unit selects an action method related to that location. Furthermore, when the user is traveling, the action unit can select an action method related to the travel destination. Furthermore, when the user is at home, the action unit can select an action method related to activities at home. In this way, selecting an optimal action method based on the geographical location information enables more relevant actions. Some or all of the above-mentioned processing in the action unit may be performed using, for example, AI, or may be performed without using AI. For example, the action unit can input the user's geographical location information to AI and have the AI ​​select an optimal action method.

[0100] The action unit can analyze the user's social media activity and suggest action measures at the time of action. For example, the action unit analyzes the user's social media activity and suggests action measures at the time of action. Social media activity includes, for example, analysis of post content and follower trends. For example, if the user posts about a specific topic on social media, the action unit can suggest action measures related to that topic. Also, if the user is participating in a specific event on social media, the action unit can suggest action measures related to that event. Furthermore, if the user belongs to a specific group on social media, the action unit can suggest action measures related to that group. In this way, by analyzing social media activity, more appropriate action measures can be suggested. Some or all of the above-mentioned processing in the action unit may be performed using, for example, AI, or may be performed without using AI. For example, the action unit can input the user's social media activity into AI and have the AI ​​execute the suggestion of action measures. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, learning unit, generation unit, and behavior unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's life log data using the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing device 12 and identifies the user's behavior pattern. The learning unit learns the user's behavior pattern based on the analysis result by the specific processing unit 290 of the data processing device 12. The generation unit generates an AI agent based on the learning result by the specific processing unit 290 of the data processing device 12. The behavior unit controls the AI ​​agent generated by the control unit 46A of the smart device 14 to perform a task in a virtual space. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, learning unit, generation unit, and behavior unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's life log data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing device 12 and identifies the user's behavior pattern. The learning unit learns the user's behavior pattern based on the analysis results by the specific processing unit 290 of the data processing device 12. The generation unit generates an AI agent based on the learning results by the specific processing unit 290 of the data processing device 12. The behavior unit controls the AI ​​agent generated by the control unit 46A of the smart glasses 214 to perform tasks in a virtual space. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, learning unit, generation unit, and behavior unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects the user's life log data using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit analyzes the collected data by the identification processing unit 290 of the data processing device 12 and identifies the user's behavior pattern. The learning unit learns the user's behavior pattern based on the analysis results by the identification processing unit 290 of the data processing device 12. The generation unit generates an AI agent based on the learning results by the identification processing unit 290 of the data processing device 12. The behavior unit controls the AI ​​agent generated by the control unit 46A of the headset type terminal 314 to perform tasks in a virtual space. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, learning unit, generation unit, and behavior unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's life log data using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing device 12 and identifies the user's behavior pattern. The learning unit learns the user's behavior pattern based on the analysis results by the specific processing unit 290 of the data processing device 12. The generation unit generates an AI agent based on the learning results by the specific processing unit 290 of the data processing device 12. The behavior unit controls the AI ​​agent generated by the control unit 46A of the robot 414 to perform tasks in a virtual space.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The Mirror Life system can also be equipped with a health management unit that monitors the user's health. The health management unit collects biometric data such as the user's heart rate, blood pressure, and body temperature and sends it to the analysis unit. The analysis unit evaluates the user's health based on this data and can issue a warning if an abnormality is detected. For example, if the heart rate is abnormally high, the analysis unit can send a notification urging the user to rest. If the blood pressure is high, the system can also provide advice on diet and exercise. Furthermore, if the body temperature is high, the system can suggest the possibility of a fever and recommend that the user see a doctor. This allows the user's health to be monitored in real time and appropriate measures to be taken.

[0103] The collection unit can customize data collection based on the user's hobbies and interests. For example, if the user is interested in music, the collection unit will prioritize collecting music-related data. If the user is interested in sports, the collection unit can also collect sports-related data. Furthermore, if the user is interested in travel, the collection unit can also collect information on travel destinations and tourist spots. This makes it possible to collect data according to the user's hobbies and interests, allowing for the provision of more personalized services.

[0104] The analysis unit can estimate the user's emotions and adjust the priority of data analysis based on the estimated user's emotions. For example, if the user is feeling stressed, data related to stress reduction can be analyzed with priority. Also, if the user is relaxed, data related to relaxation can be analyzed with priority. Furthermore, if the user is excited, data related to the excitement can be analyzed with priority. In this way, adjusting the priority of data analysis based on the user's emotions enables more appropriate analysis.

[0105] The learning unit can estimate the user's emotions and select learning data based on the estimated user's emotions. For example, if the user is feeling stressed, data related to stress reduction can be learned preferentially. Also, if the user is relaxed, data related to relaxation can be learned preferentially. Furthermore, if the user is excited, data related to excitement can be learned preferentially. In this way, by selecting learning data based on the user's emotions, more appropriate data can be learned.

[0106] The generation unit can estimate the user's emotions and adjust the generation method of the AI ​​agent based on the estimated user emotions. For example, if the user is relaxed, it can generate an AI agent that proceeds at a leisurely pace. If the user is in a hurry, it can also generate an AI agent that completes tasks quickly. Furthermore, if the user is excited, it can generate an AI agent with visually stimulating effects. In this way, by adjusting the generation method based on the user's emotions, it is possible to generate a more appropriate AI agent.

[0107] The behavior unit can estimate the user's emotions and adjust the behavior of the AI ​​agent based on the estimated user emotions. For example, if the user is relaxed, it can generate an AI agent that acts at a leisurely pace. If the user is in a hurry, it can generate an AI agent that completes tasks quickly. Furthermore, if the user is excited, it can generate an AI agent that adds visually stimulating effects. This allows for more appropriate behavior by adjusting the behavior based on the user's emotions.

[0108] The Mirror Life system can further include an advice unit that provides advice to improve the user's lifestyle habits. The advice unit analyzes the user's lifestyle habits based on the collected life log data and suggests areas for improvement. For example, the advice unit can analyze the user's dietary data and suggest a nutritionally balanced meal plan. It can also suggest an appropriate exercise program based on exercise data. It can also analyze sleep data and provide advice on how to get good quality sleep. This can improve the user's lifestyle habits and support a healthy lifestyle.

[0109] The Mirror Life system can further include an entertainment unit that provides entertainment content customized based on the user's hobbies and interests. The entertainment unit identifies the user's hobbies and interests based on the user's life log data and provides entertainment content according to them. For example, if the user is interested in movies, the entertainment unit can provide the latest movie information and recommended movies. If the user is interested in music, the entertainment unit can provide the latest music information and recommended playlists. Furthermore, if the user is interested in games, the entertainment unit can provide the latest game information and recommended games. This makes it possible to provide entertainment content according to the user's hobbies and interests, thereby improving user satisfaction.

[0110] The Mirror Life system can also include an education module to support users' learning. The education module identifies the user's learning style and interests based on the user's life log data and provides learning content tailored to that. For example, if the user is interested in language learning, language learning materials and apps can be provided. If the user is interested in programming, programming materials and online courses can be provided. Furthermore, if the user is interested in history, historical documentaries and books can be provided. This supports the user's learning and promotes the improvement of their knowledge.

[0111] The Mirror Life system can further include a social section to strengthen the user's social connections. The social section identifies the user's social activities and interests based on the user's life log data and suggests corresponding social events and groups. For example, if the user has a particular hobby, the social section can suggest events and groups related to that hobby. If the user wants to make new friends, the social section can provide a place for the user to interact with people who share common interests. Furthermore, if the user is interested in volunteering, the social section can suggest appropriate volunteer opportunities. This strengthens the user's social connections and supports a fulfilling social life.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The collection unit collects the user's life log data. The life log data includes, for example, location information, activity history, and health data. The collection unit collects data from smartphones and wearable devices (smartwatches, fitness trackers, smart glasses, etc.). Step 2: The analysis unit analyzes the collected data and identifies user behavior patterns. This analysis uses data mining, statistical analysis, machine learning algorithms, etc. Step 3: The learning module learns the user's behavioral patterns based on the analysis results. These patterns include daily routines, the frequency and timing of specific actions, etc. Step 4: The generator generates an AI agent based on the learning results. The AI ​​agent includes features such as dialogue, task automation, and learning ability. Step 5: In the behavior section, the generated AI agent acts in a virtual space. Specifically, it performs tasks such as shopping, managing schedules, replying to emails, and posting on social media. Virtual spaces include 3D virtual spaces, AR / VR environments, and online platforms.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 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 life log data of a user; an analysis unit that analyzes the data collected by the collection unit; a learning unit that learns a behavioral pattern of a user based on the analysis result obtained by the analysis unit; a generation unit that generates an AI agent based on the behavioral patterns learned by the learning unit; and a behavior unit in which the AI ​​agent generated by the generation unit acts in a virtual space. A system characterized by:

2. The collecting unit Collect data from smartphones or wearable devices The system of claim 1 .

3. The analysis unit Analyzing collected data to identify user behavior patterns The system of claim 1 .

4. The learning unit Learn user behavior patterns based on analysis results The system of claim 1 .

5. The generation unit Generate an AI agent based on the learning results The system of claim 1 .

6. The behavior unit: The generated AI agent performs tasks such as shopping, schedule management, replying to emails, and posting on social media within the virtual space. The system of claim 1 .

7. The collecting unit The system estimates the user's emotions and adjusts the timing of collecting life log data based on the estimated user emotions. The system of claim 1 .

8. The collecting unit Analyze users' past life log data and select the optimal data collection method The system of claim 1 .

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