System

Abstract

To provide a system according to an embodiment which can efficiently control an interaction on a digital device by using personal data of a user.SOLUTION: The system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a control unit. The collection unit collects personal data of a user. The analysis unit analyzes the personal data collected by the collection unit. The generation unit generates a digital avatar on the basis of the data analyzed by the analysis unit. The control unit controls an interaction on a digital device by the digital avatar generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not yet fully utilized users' personal data to efficiently control interactions on digital devices, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently control interactions on digital devices by utilizing personal data of users. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a control unit. The collection unit collects personal data of a user. The analysis unit analyzes the personal data collected by the collection unit. The generation unit generates a digital avatar based on the data analyzed by the analysis unit. The control unit controls an interaction on a digital device by the digital avatar generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can utilize personal data of a user to efficiently control interactions on a digital device. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention is a system that generates a digital avatar "I B" based on the user's personal data and has the user take charge of interactions on a digital device. This system collects and analyzes the user's personal data, and generates a digital avatar based on the data. The generated digital avatar uses data specialized for various scenes, such as business mode, private mode, child-rearing mode, and nursing care mode, to interact on the digital device on behalf of the user. For example, there is a collection unit that collects the user's personal data, and this collection unit collects various data such as the user's work style, schedule, friendships, hobbies, child's schedule, and information on nursing care. For example, the content and schedule of the user's email, SNS message history, and the like are included. Next, there is an analysis unit that analyzes the collected personal data, and this analysis unit analyzes the collected data and grasps the user's behavioral patterns and preferences. For example, it analyzes what time of day the user replies to emails, what kind of messages the user sends, and the like. There is a generation unit that generates a digital avatar based on the analyzed data, and this generation unit generates a digital avatar that resembles the user based on the analyzed data. For example, a digital avatar that mimics the user's tone of voice and manner of expression is generated. The generated digital avatar is controlled by a control unit that controls interactions on a digital device. This control unit controls the operation of the digital avatar according to various scenes, such as business mode, private mode, child-rearing mode, and nursing care mode. For example, in business mode, the system responds to emails and adjusts meeting schedules based on the user's work style and schedule. In private mode, the system exchanges messages with friends and collects information about hobbies. In child-rearing mode, the system manages children's schedules and provides information about child-rearing. In nursing care mode, the system provides advice and schedule management regarding nursing care. In this way, the digital avatar "I B" generated based on the user's personal data is responsible for interactions on the digital device, reducing the burden on the user.This allows the system to generate a digital avatar based on the user's personal data and have it handle interactions on digital devices.

[0029] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a control unit. The collection unit collects personal data of a user. The personal data of a user includes, for example, a name, an address, an age, a hobby, a behavior history, and the like, but is not limited to such examples. The collection unit can collect, for example, the contents of the user's emails, schedules, and SNS message histories. The analysis unit analyzes the personal data collected by the collection unit. The analysis is performed, for example, by a method such as data mining, statistical analysis, and machine learning algorithms, but is not limited to such examples. The analysis unit analyzes, for example, what time of day the user replies to emails, what kind of messages the user sends, and the like. The generation unit generates a digital avatar based on the data analyzed by the analysis unit. The digital avatar imitates, for example, the user's tone of speech and manner of expression, and includes, for example, appearance, movement, and manner of interaction. The generation unit generates, for example, a digital avatar imitating, for example, the user's tone of speech and manner of expression. The control unit controls the interaction on a digital device by the digital avatar generated by the generation unit. The interaction may be performed by, but is not limited to, voice dialogue, text messages, gestures, etc. The control unit controls the operation of the digital avatar according to various scenes, such as a business mode, a private mode, a child-rearing mode, and a care mode. In this way, the system according to the embodiment generates a digital avatar based on the user's personal data and has the digital avatar take charge of interactions on the digital device.

[0030] The collection unit collects personal data of the user. The personal data of the user includes, but is not limited to, for example, a name, an address, an age, a hobby, and a behavior history. The collection unit can collect, for example, the contents of the user's email, schedule, and SNS message history. Specifically, the collection unit uses an API or a database connection to acquire data from the user's device or cloud service. For example, when collecting the contents of an email, data from the inbox and outbox is acquired through the API of the email service used by the user. For schedule information, the API of a calendar application is used to collect details of the event and reminder information. For SNS message history, the user's posted content and message exchanges are acquired through the API of each SNS platform. These data are managed centrally by the collection unit and stored in a database with security measures. The collection unit can adjust the frequency and scope of data collection according to the user's settings, and allows the user to select the type and scope of data to be collected from the perspective of privacy protection. As a result, the collection unit can efficiently and safely collect various personal data of the user and provide data that serves as the basis for the entire system.

[0031] The analysis unit analyzes the personal data collected by the collection unit. The analysis is performed by, for example, data mining, statistical analysis, machine learning algorithms, and the like, but is not limited to such examples. Specifically, the analysis unit uses data mining technology to extract useful information from a large amount of data in order to clarify the user's behavioral patterns and preferences. For example, in order to analyze what time of day a user replies to emails and what kind of messages they send, the analysis unit performs topic analysis of email sending frequency by time period and message contents. In statistical analysis, behavioral tendencies based on the user's age and hobbies are clarified and characteristics common to a specific group are extracted. By using machine learning algorithms, a model that predicts future behavior from the user's behavior history can be constructed. For example, a model that has learned past email reply patterns is used to predict the time period when the user is most likely to reply next. It is also possible to use natural language processing technology to analyze the content of the user's message and understand emotions and intentions. As a result, the analysis unit can analyze the collected data from various angles and grasp the user's behavior and preferences in detail. Furthermore, the analysis results are fed back to the generation unit and control unit, and are used to improve the accuracy and performance of the entire system.

[0032] The generation unit generates a digital avatar based on the data analyzed by the analysis unit. The digital avatar imitates, for example, the user's tone of speech and manner of expression, including appearance, movement, and manner of interaction, but is not limited to such examples. Specifically, the generation unit uses natural language generation technology to imitate the user's tone of speech and manner of expression. For example, the generation unit learns the style and phraseology of messages sent by the user in the past, and generates a new message based on the learned style and phrase. In addition, 3D modeling technology and animation technology are used to imitate the user's appearance and movement. A digital avatar with a realistic appearance is created based on the user's photos and videos, and the user's characteristic movements and gestures are reproduced. Furthermore, the optimal communication method is selected for the interaction method based on the user's preferences and behavior patterns. For example, a formal tone of speech is used in a business scene, and a casual tone of speech is used in a private scene, allowing for flexible response according to the scene. By combining these technologies, the generation unit can generate a digital avatar that is natural and familiar to the user, and can smoothly proceed with interaction with the user.

[0033] The control unit controls the interaction of the digital avatar generated by the generation unit on the digital device. The interaction is performed by, for example, voice dialogue, text messages, gestures, and the like, but is not limited to such examples. Specifically, the control unit uses voice recognition technology and voice synthesis technology for the digital avatar to have a natural dialogue with the user. For example, when a user speaks to the digital avatar, the control unit recognizes the voice, generates an appropriate response, and replies by voice synthesis. In addition, in the exchange of text messages, a response is generated in real time to the user's input to maintain a natural conversation. For gestures, the digital avatar imitates the user's movements and provides visual feedback. The control unit controls the operation of the digital avatar according to various scenes, such as a business mode, a private mode, a child-rearing mode, and a caregiving mode. For example, in the business mode, the digital avatar uses formal language and polite behavior, and in the private mode, the digital avatar uses a relaxed tone of voice and casual behavior. In the child-rearing mode, educational content and expressions for children are used, and in the caregiving mode, the digital avatar uses a gentle tone of voice and behavior that gives a sense of security. This enables the control unit to provide optimal interactions according to the user's needs and the scene, making the digital avatar a useful and familiar presence for the user.

[0034] In the business mode, the control unit can cause the digital avatar to reply to emails or adjust the schedule of a meeting based on the user's work style or schedule. The control unit causes the digital avatar to reply to emails based on the user's work style, for example. For example, if the user is working remotely, the digital avatar can reply to emails in a manner suitable for remote work. The control unit can also cause the digital avatar to adjust the schedule of a meeting based on the user's schedule. For example, if the user is using flextime, the digital avatar can adjust the schedule of a meeting in a manner suitable for flextime. This makes it possible to control the operation of the digital avatar in the business mode.

[0035] In the private mode, the control unit can cause the digital avatar to exchange messages with the user's friends or collect information related to hobbies. The control unit, for example, exchanges messages with the user's friends. For example, the digital avatar can exchange text messages with the user's friends. The control unit can also collect information related to the user's hobbies. For example, the digital avatar can collect information related to the user's hobbies from the Internet and provide it to the user. This makes it possible to control the operation of the digital avatar in the private mode.

[0036] In the parenting mode, the control unit can use the digital avatar to manage the user's child's schedule and provide information about childcare. The control unit, for example, manages the user's child's schedule. For example, the digital avatar can manage the user's child's school timetable and extracurricular activities. The control unit can also provide information about childcare. For example, the digital avatar can collect information about childcare from parenting books and online forums and provide the information to the user. This makes it possible to control the operation of the digital avatar in the parenting mode.

[0037] In the care mode, the control unit can provide care advice and schedule management through the digital avatar. The control unit, for example, provides care advice. For example, the digital avatar can provide medical information and rehabilitation methods. The control unit can also manage the care schedule. For example, the digital avatar can manage care plans and medication times. This makes it possible to control the operation of the digital avatar in the care mode.

[0038] The collection unit can analyze the user's past data collection history and select an appropriate collection method. For example, the collection unit preferentially selects a data collection method that the user has frequently used in the past. The collection unit can also suggest the most efficient collection method from the user's past data collection history. Furthermore, the collection unit can analyze the user's past data collection history and customize the collection method. This makes it possible to select the optimal collection method based on the past data collection history.

[0039] When collecting personal data, the collection unit can filter the data based on the user's current activity status and areas of interest. For example, when the user is at work, the collection unit preferentially collects data related to the user's work. In addition, when the user is engaged in a hobby-related activity, the collection unit can also collect data related to the hobby. Furthermore, when the user is raising a child, the collection unit can also collect data related to child-rearing. This makes it possible to filter data based on the user's activity status and areas of interest.

[0040] When collecting personal data, the collection unit can preferentially collect highly relevant data in consideration of the user's geographical location information. For example, when the user is in a specific location, the collection unit preferentially collects data related to the location. In addition, when the user is traveling, the collection unit can also collect data related to the travel destination. Furthermore, when the user is at home, the collection unit can also collect data related to the home. In this way, highly relevant data can be collected based on the user's geographical location information.

[0041] When collecting personal data, the collection unit can analyze the user's social media activities and collect related data. For example, if the user frequently posts about a specific topic, the collection unit collects data related to the topic. In addition, if the user participates in a specific group, the collection unit can also collect data related to the group. Furthermore, if the user participates in a specific event, the collection unit can also collect data related to the event. In this way, it is possible to collect related data based on the user's social media activities.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority order of the analysis according to the importance of the data. This makes it possible to adjust the level of detail of the analysis based on the importance of the data.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image analysis algorithm to image data. Furthermore, the analysis unit can also apply a voice analysis algorithm to voice data. This makes it possible to apply an appropriate analysis algorithm depending on the category of data.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit gives priority to analyzing the most recent data. The analysis unit can also determine the analysis priority for past data according to its importance. Furthermore, the analysis unit can adjust the analysis schedule based on the time when the data was collected. This makes it possible to determine the analysis priority based on the time when the data was collected.

[0045] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the analysis schedule based on the relevance of data. This makes it possible to adjust the order of analysis based on the relevance of data.

[0046] At the time of generation, the generation unit can generate an optimal digital avatar by analyzing the user's past behavioral patterns. The generation unit generates the digital avatar based on, for example, an expression method frequently used by the user in the past. The generation unit can also generate a digital avatar that can respond optimally from the user's past behavioral patterns. Furthermore, the generation unit can analyze the user's past behavioral patterns and generate a customized digital avatar. This makes it possible to generate an optimal digital avatar based on the user's past behavioral patterns.

[0047] The generation unit can customize the characteristics of the digital avatar based on the user's current living situation at the time of generation. For example, when the user is at work, the generation unit generates a digital avatar having work-related characteristics. In addition, when the user is spending private time, the generation unit can also generate a digital avatar having private-related characteristics. Furthermore, when the user is raising a child, the generation unit can also generate a digital avatar having child-rearing-related characteristics. This makes it possible to customize the characteristics of the digital avatar based on the user's current living situation.

[0048] The generation unit can generate an appropriate digital avatar in consideration of the geographical location information of the user at the time of generation. For example, when the user is in a specific location, the generation unit generates a digital avatar having characteristics related to the location. In addition, when the user is traveling, the generation unit can also generate a digital avatar having characteristics related to the travel destination. Furthermore, when the user is at home, the generation unit can also generate a digital avatar having characteristics related to the home. In this way, an optimal digital avatar can be generated based on the geographical location information of the user.

[0049] The generation unit can adjust the characteristics of the digital avatar at the time of generation by analyzing the social media activity of the user. For example, if the user frequently posts about a specific topic, the generation unit can generate a digital avatar having characteristics related to the topic. In addition, if the user participates in a specific group, the generation unit can also generate a digital avatar having characteristics related to the group. Furthermore, if the user participates in a specific event, the generation unit can also generate a digital avatar having characteristics related to the event. In this way, the characteristics of the digital avatar can be adjusted based on the social media activity of the user.

[0050] During control, the control unit can select an appropriate action by referring to the user's past interaction history. For example, the control unit selects an action for the digital avatar based on the user's preferred response method in the past. The control unit can also select the most effective action from the user's past interaction history. Furthermore, the control unit can analyze the user's past interaction history and select a customized action. This makes it possible to select an optimal action based on the user's past interaction history.

[0051] During control, the control unit can customize the behavior of the digital avatar based on the current activity status of the user. For example, when the user is at work, the control unit controls the digital avatar to perform a work-related action. In addition, when the user is spending private time, the control unit can also control the digital avatar to perform a private-related action. Furthermore, when the user is raising a child, the control unit can also control the digital avatar to perform a child-rearing-related action. This allows the behavior of the digital avatar to be customized based on the current activity status of the user.

[0052] During control, the control unit can select an optimal action by taking into consideration the geographical location information of the user. For example, when the user is in a specific location, the control unit controls a digital avatar that performs an action related to the location. In addition, when the user is traveling, the control unit can also control a digital avatar that performs an action related to the travel destination. Furthermore, when the user is at home, the control unit can also control a digital avatar that performs an action related to the home. In this way, it is possible to select an optimal action based on the geographical location information of the user.

[0053] During control, the control unit can adjust the behavior of the digital avatar by analyzing the social media activity of the user. For example, if the user frequently posts about a particular topic, the control unit can control the digital avatar to perform an action related to the topic. In addition, if the user participates in a particular group, the control unit can also control the digital avatar to perform an action related to the group. Furthermore, if the user participates in a particular event, the control unit can also control the digital avatar to perform an action related to the event. This makes it possible to adjust the behavior of the digital avatar based on the social media activity of the user.

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

[0055] The collection unit can analyze the user's past data collection history and select an appropriate collection method. For example, the collection unit preferentially selects a data collection method that the user has frequently used in the past. The collection unit can also suggest the most efficient collection method based on the user's past data collection history. Furthermore, the collection unit can analyze the user's past data collection history and customize the collection method. This makes it possible to select the optimal collection method based on the past data collection history.

[0056] When collecting personal data, the collection unit can filter the data based on the user's current activity status and areas of interest. For example, when the user is at work, data related to the work is preferentially collected. In addition, when the user is engaged in a hobby-related activity, the collection unit can also collect data related to the hobby. Furthermore, when the user is raising a child, the collection unit can also collect data related to the child-rearing. This makes it possible to filter data based on the user's activity status and areas of interest.

[0057] When collecting personal data, the collection unit can prioritize collecting highly relevant data in consideration of the user's geographical location information. For example, when the user is in a specific location, data related to the location is prioritized. In addition, when the user is traveling, the collection unit can also collect data related to the travel destination. Furthermore, when the user is at home, the collection unit can also collect data related to the home. In this way, highly relevant data can be collected based on the user's geographical location information.

[0058] When collecting personal data, the collection unit can analyze the user's social media activities and collect related data. For example, if the user frequently posts about a specific topic, the collection unit can collect data related to the topic. In addition, if the user participates in a specific group, the collection unit can also collect data related to the group. Furthermore, if the user participates in a specific event, the collection unit can also collect data related to the event. In this way, it is possible to collect related data based on the user's social media activities.

[0059] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, important data is analyzed in detail. The analysis unit can also perform a simplified analysis of less important data. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. This makes it possible to adjust the level of detail of the analysis based on the importance of the data.

[0060] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, a natural language processing algorithm is applied to text data. The analysis unit can also apply an image analysis algorithm to image data. Furthermore, the analysis unit can also apply a voice analysis algorithm to voice data. This makes it possible to apply an appropriate analysis algorithm depending on the category of data.

[0061] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the most recent data is given priority for analysis. The analysis unit can also determine the analysis priority for past data according to its importance. Furthermore, the analysis unit can adjust the analysis schedule based on the time when the data was collected. This makes it possible to determine the analysis priority based on the time when the data was collected.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The collection unit collects the user's personal data. The user's personal data includes the user's name, address, age, hobbies, behavioral history, etc. In addition, the collection unit can collect the user's email content, schedule, SNS message history, etc. Step 2: The analysis unit analyzes the personal data collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, it analyzes what time of day a user replies to emails, and what kind of messages they send. Step 3: The generation unit generates a digital avatar based on the data analyzed by the analysis unit. The digital avatar imitates the user's tone of speech and manner of expression, including appearance, movement, and manner of interaction. For example, a digital avatar that imitates the user's tone of speech and manner of expression is generated. Step 4: The control unit controls the interaction on the digital device of the digital avatar generated by the generation unit. The interaction is performed by voice dialogue, text message, gesture, etc. The control unit controls the behavior of the digital avatar according to various scenes, such as business mode, private mode, childcare mode, and nursing care mode.

[0064] (Example 2) The system according to the embodiment of the present invention is a system that generates a digital avatar "I B" based on the user's personal data and has the user take charge of interactions on a digital device. This system collects and analyzes the user's personal data, and generates a digital avatar based on the data. The generated digital avatar uses data specialized for various scenes, such as business mode, private mode, child-rearing mode, and nursing care mode, to interact on the digital device on behalf of the user. For example, there is a collection unit that collects the user's personal data, and this collection unit collects various data such as the user's work style, schedule, friendships, hobbies, child's schedule, and information on nursing care. For example, the content and schedule of the user's email, SNS message history, and the like are included. Next, there is an analysis unit that analyzes the collected personal data, and this analysis unit analyzes the collected data and grasps the user's behavioral patterns and preferences. For example, it analyzes what time of day the user replies to emails, what kind of messages the user sends, and the like. There is a generation unit that generates a digital avatar based on the analyzed data, and this generation unit generates a digital avatar that resembles the user based on the analyzed data. For example, a digital avatar that mimics the user's tone of voice and manner of expression is generated. The generated digital avatar is controlled by a control unit that controls interactions on a digital device. This control unit controls the operation of the digital avatar according to various scenes, such as business mode, private mode, child-rearing mode, and nursing care mode. For example, in business mode, the system responds to emails and adjusts meeting schedules based on the user's work style and schedule. In private mode, the system exchanges messages with friends and collects information about hobbies. In child-rearing mode, the system manages children's schedules and provides information about child-rearing. In nursing care mode, the system provides advice and schedule management regarding nursing care. In this way, the digital avatar "I B" generated based on the user's personal data is responsible for interactions on the digital device, reducing the burden on the user.This allows the system to generate a digital avatar based on the user's personal data and have it handle interactions on digital devices.

[0065] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a control unit. The collection unit collects personal data of a user. The personal data of a user includes, for example, a name, an address, an age, a hobby, a behavior history, and the like, but is not limited to such examples. The collection unit can collect, for example, the contents of the user's emails, schedules, and SNS message histories. The analysis unit analyzes the personal data collected by the collection unit. The analysis is performed, for example, by a method such as data mining, statistical analysis, and machine learning algorithms, but is not limited to such examples. The analysis unit analyzes, for example, what time of day the user replies to emails, what kind of messages the user sends, and the like. The generation unit generates a digital avatar based on the data analyzed by the analysis unit. The digital avatar imitates, for example, the user's tone of speech and manner of expression, and includes, for example, appearance, movement, and manner of interaction. The generation unit generates, for example, a digital avatar imitating, for example, the user's tone of speech and manner of expression. The control unit controls the interaction on a digital device by the digital avatar generated by the generation unit. The interaction may be performed by, but is not limited to, voice dialogue, text messages, gestures, etc. The control unit controls the operation of the digital avatar according to various scenes, such as a business mode, a private mode, a child-rearing mode, and a care mode. In this way, the system according to the embodiment generates a digital avatar based on the user's personal data and has the digital avatar take charge of interactions on the digital device.

[0066] The collection unit collects personal data of the user. The personal data of the user includes, but is not limited to, for example, a name, an address, an age, a hobby, and a behavior history. The collection unit can collect, for example, the contents of the user's email, schedule, and SNS message history. Specifically, the collection unit uses an API or a database connection to acquire data from the user's device or cloud service. For example, when collecting the contents of an email, data from the inbox and outbox is acquired through the API of the email service used by the user. For schedule information, the API of a calendar application is used to collect details of the event and reminder information. For SNS message history, the user's posted content and message exchanges are acquired through the API of each SNS platform. These data are managed centrally by the collection unit and stored in a database with security measures. The collection unit can adjust the frequency and scope of data collection according to the user's settings, and allows the user to select the type and scope of data to be collected from the perspective of privacy protection. As a result, the collection unit can efficiently and safely collect various personal data of the user and provide data that serves as the basis for the entire system.

[0067] The analysis unit analyzes the personal data collected by the collection unit. The analysis is performed by, for example, data mining, statistical analysis, machine learning algorithms, and the like, but is not limited to such examples. Specifically, the analysis unit uses data mining technology to extract useful information from a large amount of data in order to clarify the user's behavioral patterns and preferences. For example, in order to analyze what time of day a user replies to emails and what kind of messages they send, the analysis unit performs topic analysis of email sending frequency by time period and message contents. In statistical analysis, behavioral tendencies based on the user's age and hobbies are clarified and characteristics common to a specific group are extracted. By using machine learning algorithms, a model that predicts future behavior from the user's behavior history can be constructed. For example, a model that has learned past email reply patterns is used to predict the time period when the user is most likely to reply next. It is also possible to use natural language processing technology to analyze the content of the user's message and understand emotions and intentions. As a result, the analysis unit can analyze the collected data from various angles and grasp the user's behavior and preferences in detail. Furthermore, the analysis results are fed back to the generation unit and control unit, and are used to improve the accuracy and performance of the entire system.

[0068] The generation unit generates a digital avatar based on the data analyzed by the analysis unit. The digital avatar imitates, for example, the user's tone of speech and manner of expression, including appearance, movement, and manner of interaction, but is not limited to such examples. Specifically, the generation unit uses natural language generation technology to imitate the user's tone of speech and manner of expression. For example, the generation unit learns the style and phraseology of messages sent by the user in the past, and generates a new message based on the learned style and phrase. In addition, 3D modeling technology and animation technology are used to imitate the user's appearance and movement. A digital avatar with a realistic appearance is created based on the user's photos and videos, and the user's characteristic movements and gestures are reproduced. Furthermore, the optimal communication method is selected for the interaction method based on the user's preferences and behavior patterns. For example, a formal tone of speech is used in a business scene, and a casual tone of speech is used in a private scene, allowing for flexible response according to the scene. By combining these technologies, the generation unit can generate a digital avatar that is natural and familiar to the user, and can smoothly proceed with interaction with the user.

[0069] The control unit controls the interaction of the digital avatar generated by the generation unit on the digital device. The interaction is performed by, for example, voice dialogue, text messages, gestures, and the like, but is not limited to such examples. Specifically, the control unit uses voice recognition technology and voice synthesis technology for the digital avatar to have a natural dialogue with the user. For example, when a user speaks to the digital avatar, the control unit recognizes the voice, generates an appropriate response, and replies by voice synthesis. In addition, in the exchange of text messages, a response is generated in real time to the user's input to maintain a natural conversation. For gestures, the digital avatar imitates the user's movements and provides visual feedback. The control unit controls the operation of the digital avatar according to various scenes, such as a business mode, a private mode, a child-rearing mode, and a caregiving mode. For example, in the business mode, the digital avatar uses formal language and polite behavior, and in the private mode, the digital avatar uses a relaxed tone of voice and casual behavior. In the child-rearing mode, educational content and expressions for children are used, and in the caregiving mode, the digital avatar uses a gentle tone of voice and behavior that gives a sense of security. This enables the control unit to provide optimal interactions according to the user's needs and the scene, making the digital avatar a useful and familiar presence for the user.

[0070] In the business mode, the control unit can cause the digital avatar to reply to emails or adjust the schedule of a meeting based on the user's work style or schedule. The control unit causes the digital avatar to reply to emails based on the user's work style, for example. For example, if the user is working remotely, the digital avatar can reply to emails in a manner suitable for remote work. The control unit can also cause the digital avatar to adjust the schedule of a meeting based on the user's schedule. For example, if the user is using flextime, the digital avatar can adjust the schedule of a meeting in a manner suitable for flextime. This makes it possible to control the operation of the digital avatar in the business mode.

[0071] In the private mode, the control unit can cause the digital avatar to exchange messages with the user's friends or collect information related to hobbies. The control unit, for example, exchanges messages with the user's friends. For example, the digital avatar can exchange text messages with the user's friends. The control unit can also collect information related to the user's hobbies. For example, the digital avatar can collect information related to the user's hobbies from the Internet and provide it to the user. This makes it possible to control the operation of the digital avatar in the private mode.

[0072] In the parenting mode, the control unit can use the digital avatar to manage the user's child's schedule and provide information about childcare. The control unit, for example, manages the user's child's schedule. For example, the digital avatar can manage the user's child's school timetable and extracurricular activities. The control unit can also provide information about childcare. For example, the digital avatar can collect information about childcare from parenting books and online forums and provide the information to the user. This makes it possible to control the operation of the digital avatar in the parenting mode.

[0073] In the care mode, the control unit can provide care advice and schedule management through the digital avatar. The control unit, for example, provides care advice. For example, the digital avatar can provide medical information and rehabilitation methods. The control unit can also manage the care schedule. For example, the digital avatar can manage care plans and medication times. This makes it possible to control the operation of the digital avatar in the care mode.

[0074] The collection unit can estimate the user's emotion and adjust the timing of collecting personal data based on the estimated emotion of the user. For example, when the user is stressed, the collection unit delays the collection timing to collect data when the user is relaxed. Also, when the user is relaxed, the collection unit can advance the collection timing to collect more data. Furthermore, when the user is busy, the collection unit can adjust the collection timing to reduce the burden on the user. This makes it possible to adjust the timing of data collection according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0075] The collection unit can analyze the user's past data collection history and select an appropriate collection method. For example, the collection unit preferentially selects a data collection method that the user has frequently used in the past. The collection unit can also suggest the most efficient collection method from the user's past data collection history. Furthermore, the collection unit can analyze the user's past data collection history and customize the collection method. This makes it possible to select the optimal collection method based on the past data collection history.

[0076] When collecting personal data, the collection unit can filter the data based on the user's current activity status and areas of interest. For example, when the user is at work, the collection unit preferentially collects data related to the user's work. In addition, when the user is engaged in a hobby-related activity, the collection unit can also collect data related to the hobby. Furthermore, when the user is raising a child, the collection unit can also collect data related to child-rearing. This makes it possible to filter data based on the user's activity status and areas of interest.

[0077] 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. For example, when the user is feeling stressed, the collection unit preferentially collects data that is useful for reducing stress. In addition, when the user is relaxed, the collection unit can also preferentially collect data related to relaxation. Furthermore, when the user is busy, the collection unit can also preferentially collect important data. This makes it possible to determine the priority of data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0078] When collecting personal data, the collection unit can preferentially collect highly relevant data in consideration of the user's geographical location information. For example, when the user is in a specific location, the collection unit preferentially collects data related to the location. In addition, when the user is traveling, the collection unit can also collect data related to the travel destination. Furthermore, when the user is at home, the collection unit can also collect data related to the home. In this way, highly relevant data can be collected based on the user's geographical location information.

[0079] When collecting personal data, the collection unit can analyze the user's social media activities and collect related data. For example, if the user frequently posts about a specific topic, the collection unit collects data related to the topic. In addition, if the user participates in a specific group, the collection unit can also collect data related to the group. Furthermore, if the user participates in a specific event, the collection unit can also collect data related to the event. In this way, it is possible to collect related data based on the user's social media activities.

[0080] The analysis unit can estimate the user's emotion and adjust the method of data analysis based on the estimated user's emotion. For example, when the user is feeling stressed, the analysis unit preferentially analyzes data that is useful for stress reduction. In addition, when the user is relaxed, the analysis unit can also preferentially analyze data related to relaxation. Furthermore, when the user is busy, the analysis unit can also preferentially analyze important data. This makes it possible to adjust the method of data analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority order of the analysis according to the importance of the data. This makes it possible to adjust the level of detail of the analysis based on the importance of the data.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image analysis algorithm to image data. Furthermore, the analysis unit can also apply a voice analysis algorithm to voice data. This makes it possible to apply an appropriate analysis algorithm depending on the category of data.

[0083] The analysis unit can estimate the user's emotion and determine the analysis priority based on the estimated user's emotion. For example, when the user is feeling stressed, the analysis unit prioritizes the analysis of data that is useful for stress reduction. In addition, when the user is relaxed, the analysis unit can also prioritize the analysis of data related to relaxation. Furthermore, when the user is busy, the analysis unit can also prioritize the analysis of important data. This makes it possible to determine the analysis priority according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0084] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit gives priority to analyzing the most recent data. The analysis unit can also determine the analysis priority for past data according to its importance. Furthermore, the analysis unit can adjust the analysis schedule based on the time when the data was collected. This makes it possible to determine the analysis priority based on the time when the data was collected.

[0085] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the analysis schedule based on the relevance of data. This makes it possible to adjust the order of analysis based on the relevance of data.

[0086] The generation unit can estimate the user's emotion and adjust the generation method of the digital avatar based on the estimated emotion of the user. For example, when the user is relaxed, the generation unit generates a digital avatar with a relaxed facial expression and tone of voice. In addition, when the user is stressed, the generation unit can also generate a digital avatar with a facial expression and tone of voice that helps reduce stress. Furthermore, when the user is busy, the generation unit can also generate a digital avatar that can respond efficiently. This makes it possible to adjust the generation method of the digital avatar according to the emotion of the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0087] At the time of generation, the generation unit can generate an optimal digital avatar by analyzing the user's past behavioral patterns. The generation unit generates the digital avatar based on, for example, an expression method frequently used by the user in the past. The generation unit can also generate a digital avatar that can respond optimally from the user's past behavioral patterns. Furthermore, the generation unit can analyze the user's past behavioral patterns and generate a customized digital avatar. This makes it possible to generate an optimal digital avatar based on the user's past behavioral patterns.

[0088] The generation unit can customize the characteristics of the digital avatar based on the user's current living situation at the time of generation. For example, when the user is at work, the generation unit generates a digital avatar having work-related characteristics. In addition, when the user is spending private time, the generation unit can also generate a digital avatar having private-related characteristics. Furthermore, when the user is raising a child, the generation unit can also generate a digital avatar having child-rearing-related characteristics. This makes it possible to customize the characteristics of the digital avatar based on the user's current living situation.

[0089] The generation unit can estimate the user's emotion and determine the priority of the digital avatar to be generated based on the estimated user's emotion. For example, when the user is feeling stressed, the generation unit preferentially generates a digital avatar that helps reduce stress. In addition, when the user is relaxed, the generation unit can also preferentially generate a digital avatar related to relaxation. Furthermore, when the user is busy, the generation unit can also preferentially generate a digital avatar that can respond efficiently. This makes it possible to determine the priority of generating digital avatars according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0090] The generation unit can generate an appropriate digital avatar in consideration of the geographical location information of the user at the time of generation. For example, when the user is in a specific location, the generation unit generates a digital avatar having characteristics related to the location. In addition, when the user is traveling, the generation unit can also generate a digital avatar having characteristics related to the travel destination. Furthermore, when the user is at home, the generation unit can also generate a digital avatar having characteristics related to the home. In this way, an optimal digital avatar can be generated based on the geographical location information of the user.

[0091] The generation unit can adjust the characteristics of the digital avatar at the time of generation by analyzing the social media activity of the user. For example, if the user frequently posts about a specific topic, the generation unit can generate a digital avatar having characteristics related to the topic. In addition, if the user participates in a specific group, the generation unit can also generate a digital avatar having characteristics related to the group. Furthermore, if the user participates in a specific event, the generation unit can also generate a digital avatar having characteristics related to the event. In this way, the characteristics of the digital avatar can be adjusted based on the social media activity of the user.

[0092] The control unit can estimate the user's emotion and adjust the behavior of the digital avatar based on the estimated user's emotion. For example, when the user is stressed, the control unit can calm the digital avatar's behavior and take measures to help reduce stress. In addition, when the user is relaxed, the control unit can also make the digital avatar's behavior relaxing and take measures to maintain relaxation. Furthermore, when the user is busy, the control unit can make the digital avatar's behavior efficient and take quick measures. This makes it possible to adjust the digital avatar's behavior according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0093] During control, the control unit can select an appropriate action by referring to the user's past interaction history. For example, the control unit selects an action for the digital avatar based on the user's preferred response method in the past. The control unit can also select the most effective action from the user's past interaction history. Furthermore, the control unit can analyze the user's past interaction history and select a customized action. This makes it possible to select an optimal action based on the user's past interaction history.

[0094] During control, the control unit can customize the behavior of the digital avatar based on the current activity status of the user. For example, when the user is at work, the control unit controls the digital avatar to perform a work-related action. In addition, when the user is spending private time, the control unit can also control the digital avatar to perform a private-related action. Furthermore, when the user is raising a child, the control unit can also control the digital avatar to perform a child-rearing-related action. This allows the behavior of the digital avatar to be customized based on the current activity status of the user.

[0095] The control unit can estimate the user's emotion and determine the priority of the digital avatar's actions based on the estimated user's emotion. For example, when the user is feeling stressed, the control unit can prioritize actions that help reduce stress. Also, when the user is relaxed, the control unit can prioritize actions that help maintain relaxation. Furthermore, when the user is busy, the control unit can prioritize actions that allow efficient responses. This makes it possible to determine the priority of the digital avatar's actions according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] During control, the control unit can select an optimal action by taking into consideration the geographical location information of the user. For example, when the user is in a specific location, the control unit controls a digital avatar that performs an action related to the location. In addition, when the user is traveling, the control unit can also control a digital avatar that performs an action related to the travel destination. Furthermore, when the user is at home, the control unit can also control a digital avatar that performs an action related to the home. In this way, it is possible to select an optimal action based on the geographical location information of the user.

[0097] During control, the control unit can adjust the behavior of the digital avatar by analyzing the social media activity of the user. For example, if the user frequently posts about a particular topic, the control unit can control the digital avatar to perform an action related to the topic. In addition, if the user participates in a particular group, the control unit can also control the digital avatar to perform an action related to the group. Furthermore, if the user participates in a particular event, the control unit can also control the digital avatar to perform an action related to the event. This makes it possible to adjust the behavior of the digital avatar based on the social media activity of the user.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The collection unit can analyze the user's past data collection history and select an appropriate collection method. For example, the collection unit preferentially selects a data collection method that the user has frequently used in the past. The collection unit can also suggest the most efficient collection method based on the user's past data collection history. Furthermore, the collection unit can analyze the user's past data collection history and customize the collection method. This makes it possible to select the optimal collection method based on the past data collection history.

[0100] When collecting personal data, the collection unit can filter the data based on the user's current activity status and areas of interest. For example, when the user is at work, data related to the work is preferentially collected. In addition, when the user is engaged in a hobby-related activity, the collection unit can also collect data related to the hobby. Furthermore, when the user is raising a child, the collection unit can also collect data related to the child-rearing. This makes it possible to filter data based on the user's activity status and areas of interest.

[0101] 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. For example, when the user is feeling stressed, data useful for stress reduction is preferentially collected. In addition, when the user is relaxed, the collection unit can also preferentially collect data related to relaxation. Furthermore, when the user is busy, the collection unit can also preferentially collect important data. This makes it possible to determine the priority of data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0102] When collecting personal data, the collection unit can prioritize collecting highly relevant data in consideration of the user's geographical location information. For example, when the user is in a specific location, data related to the location is prioritized. In addition, when the user is traveling, the collection unit can also collect data related to the travel destination. Furthermore, when the user is at home, the collection unit can also collect data related to the home. In this way, highly relevant data can be collected based on the user's geographical location information.

[0103] When collecting personal data, the collection unit can analyze the user's social media activities and collect related data. For example, if the user frequently posts about a specific topic, the collection unit can collect data related to the topic. In addition, if the user participates in a specific group, the collection unit can also collect data related to the group. Furthermore, if the user participates in a specific event, the collection unit can also collect data related to the event. In this way, it is possible to collect related data based on the user's social media activities.

[0104] The analysis unit can estimate the user's emotion and adjust the method of data analysis based on the estimated user's emotion. For example, when the user is feeling stressed, data that is useful for stress reduction is preferentially analyzed. In addition, when the user is relaxed, the analysis unit can also preferentially analyze data related to relaxation. Furthermore, when the user is busy, the analysis unit can also preferentially analyze important data. This makes it possible to adjust the method of data analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0105] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, important data is analyzed in detail. The analysis unit can also perform a simplified analysis of less important data. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. This makes it possible to adjust the level of detail of the analysis based on the importance of the data.

[0106] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, a natural language processing algorithm is applied to text data. The analysis unit can also apply an image analysis algorithm to image data. Furthermore, the analysis unit can also apply a voice analysis algorithm to voice data. This makes it possible to apply an appropriate analysis algorithm depending on the category of data.

[0107] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, when the user is feeling stressed, data that is useful for stress reduction is preferentially analyzed. In addition, when the user is relaxed, the analysis unit can also preferentially analyze data related to relaxation. Furthermore, when the user is busy, the analysis unit can also preferentially analyze important data. This makes it possible to determine the priority of analysis according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0108] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the most recent data is given priority for analysis. The analysis unit can also determine the analysis priority for past data according to its importance. Furthermore, the analysis unit can adjust the analysis schedule based on the time when the data was collected. This makes it possible to determine the analysis priority based on the time when the data was collected.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The collection unit collects the user's personal data. The user's personal data includes the user's name, address, age, hobbies, behavioral history, etc. In addition, the collection unit can collect the user's email content, schedule, SNS message history, etc. Step 2: The analysis unit analyzes the personal data collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, it analyzes what time of day a user replies to emails, and what kind of messages they send. Step 3: The generation unit generates a digital avatar based on the data analyzed by the analysis unit. The digital avatar imitates the user's tone of speech and manner of expression, including appearance, movement, and manner of interaction. For example, a digital avatar that imitates the user's tone of speech and manner of expression is generated. Step 4: The control unit controls the interaction on the digital device of the digital avatar generated by the generation unit. The interaction is performed by voice dialogue, text message, gesture, etc. The control unit controls the behavior of the digital avatar according to various scenes, such as business mode, private mode, childcare mode, and nursing care mode.

[0111] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0112] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] For example, the collection unit can collect personal data of the user using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a digital avatar based on the analyzed data. The control unit is realized, for example, by the control unit 46A of the smart device 14, and controls the operation of the generated digital avatar. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] 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, the RAM 30, and the 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.

[0118] 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.

[0119] 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 the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] 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 performed in a secure state.

[0122] Fig. 4 shows an example of 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.

[0123] 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.

[0124] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0128] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] For example, the collection unit can collect personal data of the user using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a digital avatar based on the analyzed data. The control unit is realized, for example, by the control unit 46A of the smart glasses 214, and controls the operation of the generated digital avatar. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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, the RAM 30, and the 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.

[0134] 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.

[0135] 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 the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] 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 performed in a secure state.

[0138] 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.

[0139] 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.

[0140] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0142] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] 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 voice indicating a user input for 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.

[0144] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] For example, the collection unit can collect personal data of the user using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a digital avatar based on the analyzed data. The control unit is realized, for example, by the control unit 46A of the headset type terminal 314, and controls the operation of the generated digital avatar. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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, the RAM 30, and the 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.

[0150] 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.

[0151] 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 the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] 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 performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0155] 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.

[0156] 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.

[0157] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0159] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] 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 a voice indicating a user input for 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.

[0161] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0163] For example, the collection unit can collect personal data of the user using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a digital avatar based on the analyzed data. The control unit is realized, for example, by the control unit 46A of the robot 414, and controls the operation of the generated digital avatar. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0171] 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.

[0172] 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 Universal Serial Bus (USB) 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.

[0173] In addition, 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 upon request from the data processing device 12.

[0174] 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.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0177] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) A collection unit that collects personal data of users; an analysis unit that analyzes the personal data collected by the collection unit; a generation unit that generates a digital avatar based on the data analyzed by the analysis unit; A control unit that controls an interaction on a digital device with the digital avatar generated by the generation unit. A system characterized by: (Appendix 2) The control unit is In a business mode, the digital avatar responds to emails or schedules meetings based on the user's work style or schedule. 2. The system of claim 1, (Appendix 3) The control unit is In private mode, the digital avatar exchanges messages with the user's friends or collects information about the user's hobbies. 2. The system of claim 1, (Appendix 4) The control unit is In a child-rearing mode, the digital avatar manages the user's child's schedule and provides information about child-rearing. 2. The system of claim 1, (Appendix 5) The control unit is In the care mode, the digital avatar provides advice on care and manages schedules. 2. The system of claim 1, (Appendix 6) The collecting unit includes: The system estimates a user's emotions and adjusts the timing of personal data collection according to the estimated user emotions. 2. The system of claim 1, (Appendix 7) The collecting unit includes: Analyze the user's past data collection history and select the appropriate collection method 2. The system of claim 1, (Appendix 8) The collecting unit includes: Filtering personal data at the time of collection based on the user's current activity and interests 2. The system of claim 1, (Appendix 9) The collecting unit includes: Estimate user emotions and prioritize data to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting personal data, prioritize collection of relevant data by taking into account the user's geographic location information 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting personal data, analyze your social media activity and collect related data 2. The system of claim 1, (Appendix 12) The analysis unit is Infer user emotions and adjust data analysis methods according to the inferred user emotions 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, adjust the level of detail based on the importance of the data 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 15) The analysis unit is Estimate the user's emotions and determine the priority of analysis according to the estimated user emotions. 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, prioritize analysis based on when the data was collected 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 18) The generation unit is Estimate the user's emotions and adjust the way the digital avatar is generated according to the estimated user emotions. 2. The system of claim 1, (Appendix 19) The generation unit is At the time of generation, the system analyzes the user's past behavioral patterns to generate an appropriate digital avatar. 2. The system of claim 1, (Appendix 20) The generation unit is At the time of creation, customize the characteristics of the digital avatar based on the user's current life situation 2. The system of claim 1, (Appendix 21) The generation unit is Estimate the user's emotions and determine the priority of the digital avatar to be generated according to the estimated user's emotions. 2. The system of claim 1, (Appendix 22) The generation unit is At the time of generation, the appropriate digital avatar is generated taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 23) The generation unit is At the time of creation, the characteristics of the digital avatar are adjusted by analyzing the user's social media activity. 2. The system of claim 1, (Appendix 24) The control unit is Estimating a user's emotions and adjusting the behavior of a digital avatar according to the estimated user's emotions 2. The system of claim 1, (Appendix 25) The control unit is When controlling the system, the system selects the appropriate action by referring to the user's past interaction history. 2. The system of claim 1, (Appendix 26) The control unit is When controlled, customize the behavior of the digital avatar based on the user's current activity. 2. The system of claim 1, (Appendix 27) The control unit is Estimate the user's emotions and prioritize the actions of a digital avatar according to the estimated user's emotions. 2. The system of claim 1, (Appendix 28) The control unit is When controlling the device, the optimal action is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 29) The control unit is When in control, the user's social media activity is analyzed to adjust the behavior of the digital avatar 2. The system of claim 1, [Explanation of symbols]

[0183] 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 personal data of users; an analysis unit that analyzes the personal data collected by the collection unit; a generation unit that generates a digital avatar based on the data analyzed by the analysis unit; A control unit that controls an interaction on a digital device with the digital avatar generated by the generation unit. A system characterized by:

2. The control unit is In a business mode, the digital avatar responds to emails or schedules meetings based on the user's work style or schedule.

2. The system of claim 1 .

3. The control unit is In private mode, the digital avatar exchanges messages with the user's friends or collects information about the user's hobbies.

2. The system of claim 1 .

4. The control unit is In a child-rearing mode, the digital avatar manages the user's child's schedule and provides information about child-rearing.

2. The system of claim 1 .

5. The control unit is In the care mode, the digital avatar provides advice on care and manages schedules.

2. The system of claim 1 .

6. The collecting unit includes: The user's feelings are estimated, and a timing for collecting personal data is adjusted according to the estimated feelings of the user.

2. The system of claim 1 .

7. The collecting unit includes: Analyzing the user's past data collection history and selecting an appropriate collection method 2. The system of claim 1 .

8. The collecting unit includes: When collecting personal data, filtering is performed based on the user's current activities and interests.

2. The system of claim 1 .

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