system

Abstract

The system according to the embodiment aims to generate a digital clone based on user data and make future predictions based on the current situation. [Solution] A system according to an embodiment includes a learning unit, a generation unit, an execution unit, an input unit, a calculation unit, and an output unit. The learning unit learns user data. The generation unit generates a digital clone based on the data learned by the learning unit. The execution unit causes the digital clone generated by the generation unit to execute a task. The input unit inputs a current situation. The calculation unit calculates a result based on the situation input by the input unit. The output unit outputs the result calculated by the calculation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately generate digital clones based on user data or predict the future based on current conditions, so there is room for improvement.

[0005] The system according to the embodiment aims to generate a digital clone based on user data and make future predictions based on the current situation. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning unit, a generation unit, an execution unit, an input unit, a calculation unit, and an output unit. The learning unit learns user data. The generation unit generates a digital clone based on the data learned by the learning unit. The execution unit causes the digital clone generated by the generation unit to execute a task. The input unit inputs a current situation. The calculation unit calculates a result based on the situation input by the input unit. The output unit outputs the result calculated by the calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment generates a digital clone based on the user's data and can make future predictions based on the current situation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A digital clone generation system according to an embodiment of the present invention learns user data and generates an online digital clone. In this digital clone generation system, a user inputs their own data into a generation AI, which then learns the data to generate a digital clone. The generated digital clone can perform various tasks on behalf of the user, such as replying to emails, managing schedules, and recommending content. Furthermore, by inputting the current situation for which future predictions are desired, the generation AI analyzes the situation and calculates the likely outcome. The calculation results include outcomes and critical turning points. For example, in business decision-making, the system can predict the outcomes of different options and suggest the optimal option. This mechanism allows users to utilize their digital clones to efficiently complete tasks and predict future scenarios, potentially saving time and improving the quality of decision-making. For example, by having the user train the digital clone to learn their own behavioral patterns and thought processes, a more accurate digital clone can be generated. The generated digital clone can also perform tasks on behalf of the user, such as replying to emails, managing schedules, and recommending content. Furthermore, by inputting the current situation for which future predictions are desired, the generation AI can analyze the situation and calculate what results can be derived. This allows users to utilize their digital clones to efficiently complete tasks and predict future scenarios. This allows the digital clone generation system to learn the user's data, generate a digital clone, perform tasks, input the current situation, calculate the results, and output them.

[0029] A digital clone generation system according to an embodiment includes a learning unit, a generation unit, an execution unit, an input unit, a calculation unit, and an output unit. The learning unit learns user data. The user data includes, but is not limited to, behavioral data, emotional data, and historical data. The learning unit learns the user data using, for example, machine learning or deep learning algorithms. The learning unit can also learn the user's behavioral patterns and thought processes. For example, the learning unit can learn the user's daily behavior and behavior during specific events. The learning unit can also learn the user's decision-making process and problem-solving procedures. The generation unit generates a digital clone based on the data learned by the learning unit. Examples of digital clones include, but are not limited to, virtual avatars and AI agents. The generation unit generates the digital clone based on, for example, feature quantities and model parameters of the learned data. The generation unit can also estimate the user's emotions and adjust the digital clone generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a digital clone that moves at a leisurely pace. The generation unit can also generate a digital clone that can quickly complete a task when the user is in a hurry. The execution unit causes the digital clone generated by the generation unit to execute a task. Examples of tasks include, but are not limited to, replying to emails, managing a schedule, and recommending content. The execution unit can, for example, estimate a user's emotions and adjust a task execution method based on the estimated user emotions. The execution unit can also select an optimal execution method by referring to the user's past task history. The input unit inputs a current situation for which future prediction is desired. Examples of the current situation include, but are not limited to, the current time, location, and user state. The input unit can, for example, estimate a user's emotions and select input data based on the estimated user emotions. The input unit can also select an optimal input method by referring to the user's past input history. The calculation unit calculates a result based on the situation input by the input unit.The calculation may include, but is not limited to, numerical calculation, statistical analysis, simulation, etc. For example, the calculation unit may estimate a user's emotion and adjust the calculation method based on the estimated user's emotion. The calculation unit may also select an optimal calculation algorithm by referring to the user's past data. The output unit outputs the results calculated by the calculation unit. For example, the results may include, but are not limited to, text, graphs, reports, etc. For example, the output unit may estimate a user's emotion and adjust the output method based on the estimated user's emotion. The output unit may also select an optimal output method by referring to the user's past output history. In this way, the digital clone generation system according to the embodiment can learn user data, generate a digital clone, execute tasks, input the current situation, calculate results, and output results.

[0030] The learning unit can learn the user's behavioral patterns or thought processes. The learning unit, for example, learns the user's daily behavior. For example, the learning unit can collect and learn data on the user's daily routines and actions during specific events. The learning unit can also learn the user's decision-making process. For example, the learning unit can collect and learn data on how the user solves problems and the steps they take to make decisions. Furthermore, the learning unit can learn the user's thought processes. For example, the learning unit can collect and learn data on how the user processes information and the criteria they use to make decisions. In this way, the learning unit can learn the user's behavioral patterns and thought processes, thereby generating a more accurate digital clone.

[0031] The generation unit can generate a digital clone based on the learned data. The generation unit can generate the digital clone based on, for example, feature quantities of the learned data. For example, the generation unit can extract feature quantities of a user's behavioral patterns or thought processes and generate a digital clone based on the extracted feature quantities. The generation unit can also generate a digital clone based on learned model parameters. For example, the generation unit can generate a digital clone using model parameters from machine learning or deep learning. Furthermore, the generation unit can estimate a user's emotions and adjust a digital clone generation method based on the estimated user's emotions. For example, if a user is relaxed, the generation unit can generate a digital clone that progresses at a leisurely pace. If a user is in a hurry, the generation unit can generate a digital clone that completes tasks quickly. In this way, the generation unit can perform various tasks on behalf of a user by generating a digital clone based on the learned data.

[0032] The execution unit can execute tasks such as replying to emails, managing schedules, and recommending content. For example, the execution unit replies to emails on behalf of the user. For example, the execution unit can analyze emails received by the user and generate an appropriate reply. The execution unit can also manage the user's schedule. For example, the execution unit can register the user's events in a calendar and set reminders. Furthermore, the execution unit can recommend content to the user. For example, the execution unit can recommend content such as movies, music, and articles based on the user's preferences. In this way, the execution unit can reduce the burden on the user by executing tasks such as replying to emails, managing schedules, and recommending content.

[0033] The input unit can input a current situation for which a future prediction is desired. The input unit, for example, provides an interface for a user to input the current situation for which a future prediction is desired. For example, the input unit can display a form or dialog for the user to input information such as the current time, location, and state. The input unit can also estimate the user's emotions and select input data based on the estimated user emotions. For example, if the user is feeling stressed, the input unit can preferentially input data that will help the user relax. The input unit can also select the optimal input method by referring to the user's past input history. For example, the input unit can automatically display data that the user has frequently input in the past as candidates. In this way, the input unit can predict a future scenario by inputting the current situation for which a future prediction is desired.

[0034] The calculation unit can analyze the input situation and calculate what kind of result can be derived. The calculation unit, for example, executes an algorithm for analyzing the input situation and calculating what kind of result can be derived. For example, the calculation unit can analyze the input situation using techniques such as numerical calculation, statistical analysis, and simulation. The calculation unit can also estimate the user's emotions and adjust the calculation method based on the estimated user emotions. For example, if the user is feeling stressed, the calculation unit can perform calculations in a way that helps the user relax. The calculation unit can also select an optimal calculation algorithm by referring to the user's past data. For example, the calculation unit can select an optimal calculation algorithm based on data that the user has frequently used in the past. In this way, the calculation unit can analyze the input situation and calculate what kind of result can be derived, thereby suggesting optimal options.

[0035] The output unit can output the calculated results. For example, the output unit outputs the calculated results in the form of text, graphs, reports, or the like. For example, the output unit can provide an interface for displaying the results in a format that is easy for the user to understand. The output unit can also estimate the user's emotions and adjust the output method based on the estimated user emotions. For example, if the user is feeling stressed, the output unit can display the results in a way that helps the user relax. The output unit can also select the optimal output method by referring to the user's past output history. For example, the output unit can select the optimal output method based on output methods that the user has frequently used in the past. In this way, the output unit outputs the calculated results, allowing the user to check the results and use them for decision-making.

[0036] The learning unit can analyze the user's past behavioral history and select an appropriate learning algorithm. The learning unit, for example, collects log data and history data to analyze the user's past behavioral history. For example, the learning unit can select an optimal learning algorithm based on data on apps and websites that the user has frequently used in the past. The learning unit can also analyze the user's past search history and select an algorithm that prioritizes learning related information. Furthermore, the learning unit can select an algorithm that learns purchasing patterns based on the user's past purchasing history. In this way, the learning unit can select an optimal learning algorithm by analyzing the user's past behavioral history.

[0037] The learning unit can adjust the timing of studying based on the user's lifestyle patterns during studying. The learning unit, for example, collects daily behavioral data to study the user's lifestyle patterns. For example, the learning unit can adjust the timing so that the user studies during their morning commute. The learning unit can also adjust the timing so that the user studies during their evening relaxation time. Furthermore, the learning unit can also adjust the timing so that the user studies intensively on weekends. In this way, the learning unit can adjust the timing of studying based on the user's lifestyle patterns, thereby enabling efficient studying.

[0038] During learning, the learning unit can prioritize learning highly relevant data by taking into account the user's geographical location information. The learning unit, for example, uses GPS data or location information services to acquire the user's geographical location information. For example, if the user is in a specific area, the learning unit can prioritize learning information related to that area. Also, if the user is traveling, the learning unit can prioritize learning tourist information about the travel destination. Furthermore, if the user is participating in a specific event, the learning unit can prioritize learning information related to that event. In this way, the learning unit can learn more appropriate data by prioritizing learning highly relevant data by taking into account the user's geographical location information.

[0039] During learning, the learning unit can analyze the user's social media activity and learn related data. For example, the learning unit collects data such as the content of posts, the number of likes, and comments in order to analyze the user's social media activity. For example, the learning unit can learn related information based on the content the user shared on social media. The learning unit can also analyze the content posted by accounts the user follows and learn related information. Furthermore, the learning unit can learn related information based on the activities of groups and communities in which the user participates. In this way, the learning unit can learn related data by analyzing the user's social media activity.

[0040] At the time of generation, the generation unit can generate an appropriate clone by referring to the user's past behavioral patterns. The generation unit, for example, collects log data and history data to refer to the user's past behavioral patterns. For example, the generation unit can generate an optimal clone based on tasks that the user frequently performed in the past. The generation unit can also generate a clone that efficiently completes tasks by referring to the user's past schedule. Furthermore, the generation unit can generate an optimal clone based on the user's past email reply patterns. In this way, the generation unit can generate an optimal clone by referring to the user's past behavioral patterns.

[0041] The generation unit can customize the characteristics of the clone based on the user's current living situation at the time of generation. The generation unit collects information such as time of day, location, and user status to obtain the user's current living situation. For example, if the user is busy, the generation unit can generate a clone that completes tasks quickly. Also, if the user is relaxed, the generation unit can generate a clone that completes tasks at a leisurely pace. Furthermore, if the user is traveling, the generation unit can generate a clone that prioritizes processing information about the travel destination. In this way, the generation unit can generate a more appropriate clone by customizing the characteristics of the clone based on the user's current living situation.

[0042] The generation unit can generate an appropriate clone by taking into account the user's geographical location information. The generation unit, for example, uses GPS data or a location information service to obtain the user's geographical location information. For example, if the user is in a specific area, the generation unit can generate a clone that prioritizes processing information related to that area. Also, if the user is traveling, the generation unit can generate a clone that prioritizes processing information about the travel destination. Furthermore, if the user is participating in a specific event, the generation unit can generate a clone that prioritizes processing information related to the event. In this way, the generation unit can generate a more appropriate clone by taking into account the user's geographical location information.

[0043] At the time of generation, the generation unit can analyze the user's social media activity and generate related clones. For example, the generation unit collects data such as the content of posts, the number of likes, and comments to analyze the user's social media activity. For example, the generation unit can generate a clone that processes related information based on content shared by the user on social media. The generation unit can also analyze the content posted by accounts the user follows and generate a clone that processes related information. Furthermore, the generation unit can generate a clone that processes related information based on the activities of groups and communities in which the user participates. In this way, the generation unit can generate related clones by analyzing the user's social media activity.

[0044] During execution, the execution unit can select the optimal execution method by referring to the user's past task history. The execution unit, for example, collects log data and history data to refer to the user's past task history. For example, the execution unit can select the optimal execution method based on tasks that the user frequently performed in the past. The execution unit can also select a method for efficiently executing tasks by referring to the user's past schedule. Furthermore, the execution unit can select the optimal execution method based on the user's past email reply patterns. In this way, the execution unit can select the optimal execution method by referring to the user's past task history.

[0045] During execution, the execution unit can adjust task priorities based on the user's current lifestyle. The execution unit, for example, collects information such as time of day, location, and user status to obtain the user's current lifestyle. For example, if the user is busy, the execution unit can prioritize executing important tasks. Also, if the user is relaxed, the execution unit can prioritize executing relaxing tasks. Furthermore, if the user is traveling, the execution unit can prioritize executing a task that processes information about the travel destination. In this way, the execution unit can prioritize executing more appropriate tasks by adjusting task priorities based on the user's current lifestyle.

[0046] The execution unit can execute the optimal task during execution, taking into account the user's geographical location information. The execution unit, for example, uses GPS data or location information services to obtain the user's geographical location information. For example, if the user is in a specific area, the execution unit can execute a task that prioritizes processing information related to that area. Also, if the user is traveling, the execution unit can execute a task that prioritizes processing information about the travel destination. Furthermore, if the user is participating in a specific event, the execution unit can execute a task that prioritizes processing information related to the event. In this way, the execution unit can execute a more appropriate task by executing the optimal task taking into account the user's geographical location information.

[0047] During execution, the execution unit can analyze a user's social media activity and perform related tasks. For example, the execution unit collects data such as post content, number of likes, and comments to analyze the user's social media activity. For example, the execution unit can execute a task of processing related information based on content shared by the user on social media. The execution unit can also execute a task of analyzing posts from accounts the user follows and processing related information. Furthermore, the execution unit can execute a task of processing related information based on the activities of groups and communities in which the user participates. In this way, the execution unit can execute related tasks by analyzing the user's social media activity.

[0048] The input unit can select the optimal input method by referring to the user's past input history when inputting data. The input unit, for example, collects log data and history data in order to refer to the user's past input history. For example, the input unit can automatically display data that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest data that will be used in a specific time period from the user's past input history. In this way, the input unit can select the optimal input method by referring to the user's past input history.

[0049] The input unit can filter input data based on the user's current living situation when inputting the data. The input unit collects information such as time of day, location, and user status to obtain the user's current living situation. For example, if the user is busy, the input unit can prioritize inputting important data. Also, if the user is relaxed, the input unit can prioritize inputting data that will help the user relax. Furthermore, if the user is traveling, the input unit can prioritize inputting information about the travel destination. In this way, the input unit can input more appropriate data by filtering the input data based on the user's current living situation.

[0050] During input, the input unit can prioritize inputting highly relevant data in consideration of the user's geographical location information. The input unit, for example, uses GPS data or a location information service to acquire the user's geographical location information. For example, when the user is in a specific area, the input unit can prioritize inputting information related to that area. Furthermore, when the user is traveling, the input unit can prioritize inputting information about the travel destination. Furthermore, when the user is participating in a specific event, the input unit can prioritize inputting information related to the event. In this way, the input unit can input more appropriate data by prioritized inputting highly relevant data in consideration of the user's geographical location information.

[0051] The input unit can analyze the user's social media activity and input related data at the time of input. The input unit, for example, collects data such as the content of posts, the number of likes, and comments in order to analyze the user's social media activity. For example, the input unit can input related information based on content shared by the user on social media. The input unit can also analyze the content posted by accounts the user follows and input related information. Furthermore, the input unit can input related information based on the activities of groups and communities in which the user participates. In this way, the input unit can input related data by analyzing the user's social media activity.

[0052] During calculation, the calculation unit can select the optimal calculation algorithm by referring to the user's past data. The calculation unit, for example, collects log data and history data to refer to the user's past data. For example, the calculation unit can select the optimal calculation algorithm based on data that the user frequently used in the past. The calculation unit can also analyze the user's past search history and select an algorithm that prioritizes calculation of related information. Furthermore, the calculation unit can select an algorithm that calculates a purchasing pattern by referring to the user's past purchase history. In this way, the calculation unit can select the optimal calculation algorithm by referring to the user's past data.

[0053] The calculation unit can adjust the priority of calculations based on the user's current living situation during calculations. The calculation unit, for example, collects information such as time of day, location, and user status to obtain the user's current living situation. For example, if the user is busy, the calculation unit can prioritize calculation of important data. Also, if the user is relaxed, the calculation unit can prioritize calculation of relaxing data. Furthermore, if the user is traveling, the calculation unit can prioritize calculation of travel destination information. In this way, the calculation unit can prioritize calculation of more appropriate data by adjusting the priority of calculations based on the user's current living situation.

[0054] During calculation, the calculation unit can generate optimal calculation results by taking into account the user's geographical location information. The calculation unit, for example, uses GPS data or location information services to acquire the user's geographical location information. For example, if the user is in a specific area, the calculation unit can prioritize calculation of information related to that area. Furthermore, if the user is traveling, the calculation unit can prioritize calculation of information about the travel destination. Furthermore, if the user is participating in a specific event, the calculation unit can prioritize calculation of information related to the event. In this way, the calculation unit can generate more appropriate calculation results by generating optimal calculation results by taking into account the user's geographical location information.

[0055] During the calculation, the calculation unit can analyze the user's social media activity and generate related calculation results. For example, the calculation unit collects data such as the content of posts, the number of likes, and comments to analyze the user's social media activity. For example, the calculation unit can calculate related information based on the content shared by the user on social media. The calculation unit can also analyze the content posted by accounts the user follows and calculate related information. Furthermore, the calculation unit can calculate related information based on the activities of groups and communities in which the user participates. In this way, the calculation unit can generate related calculation results by analyzing the user's social media activity.

[0056] At the time of output, the output unit can select the optimal output method by referring to the user's past output history. The output unit, for example, collects log data and history data to refer to the user's past output history. For example, the output unit can select the optimal output method based on output methods that the user has frequently used in the past. The output unit can also analyze the user's past output history and select a method to prioritize output of related information. Furthermore, the output unit can also select the optimal output method by referring to the user's past output history. In this way, the output unit can select the optimal output method by referring to the user's past output history.

[0057] The output unit can customize output data based on the user's current living situation at the time of output. The output unit, for example, collects information such as time of day, location, and user status to obtain the user's current living situation. For example, if the user is busy, the output unit can prioritize outputting important data. Also, if the user is relaxed, the output unit can prioritize outputting data that will help the user relax. Furthermore, if the user is traveling, the output unit can prioritize outputting information about the travel destination. In this way, the output unit can output more appropriate data by customizing the output data based on the user's current living situation.

[0058] The output unit can select the optimal output method at the time of output, taking into consideration the user's geographical location information. The output unit, for example, uses GPS data or location information services to acquire the user's geographical location information. For example, if the user is in a specific area, the output unit can prioritize outputting information related to that area. Furthermore, if the user is traveling, the output unit can prioritize outputting information about the travel destination. Furthermore, if the user is participating in a specific event, the output unit can prioritize outputting information related to the event. In this way, the output unit can select a more appropriate output method by selecting the optimal output method taking into consideration the user's geographical location information.

[0059] At the time of output, the output unit can analyze the user's social media activity and output related data. For example, the output unit collects data such as the content of posts, the number of likes, and comments to analyze the user's social media activity. For example, the output unit can output related information based on the content the user shared on social media. The output unit can also analyze the content of posts from accounts the user follows and output related information. Furthermore, the output unit can output related information based on the activities of groups and communities in which the user participates. In this way, the output unit can output related data by analyzing the user's social media activity.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The digital cloning system may further include a health management unit that collects the user's health data and adjusts task priorities based on the user's health condition. For example, the health management unit may collect the user's heart rate and sleep data and prioritize tasks that will help the user relax if the user is tired. The health management unit may also collect the user's dietary data and suggest nutritionally balanced meals. The health management unit may also collect the user's exercise data and suggest appropriate exercise plans. In this way, the digital cloning system can support a healthier lifestyle by adjusting task priorities based on the user's health condition.

[0062] The learning unit can select learning data based on the user's hobbies and interests. For example, the learning unit can collect data on movies the user has watched or books the user has read in the past and select related content as learning data. The learning unit can also collect data on events the user has attended or travel destinations and select related information as learning data. Furthermore, the learning unit can collect data on social media accounts the user follows and select related information as learning data. In this way, the learning unit can generate a more personalized digital clone by selecting learning data based on the user's hobbies and interests.

[0063] The output unit can customize the display method of the output data based on the user's visual preferences. For example, the output unit can display data using the user's preferred colors and fonts. The output unit can also adjust the display method to reduce the user's visual fatigue. Furthermore, the output unit can refer to the user's past display history and select the optimal display method. In this way, the output unit can customize the display method of the output data based on the user's visual preferences, thereby achieving a more comfortable data display.

[0064] The learning unit can collect social network data of the user and select learning data based on the user's friendships. For example, the learning unit can collect data on friends and family with whom the user frequently interacts and select related information as learning data. The learning unit can also collect data on groups and communities in which the user participates and select related information as learning data. Furthermore, the learning unit can analyze the user's activity history on social networks and select related information as learning data. In this way, the learning unit can generate a more personalized digital clone by selecting learning data based on the user's friendships.

[0065] The generation unit can adjust the characteristics of the digital clone based on the user's life events. For example, when the user experiences an important life event such as marriage or childbirth, the generation unit can generate a digital clone with characteristics corresponding to the event. In addition, when the user experiences a life event such as changing jobs or moving, the generation unit can generate a digital clone with characteristics corresponding to the event. Furthermore, when the user begins to develop a new hobby or interest, the generation unit can generate a digital clone with characteristics corresponding to the hobby or interest. In this way, the generation unit can generate a more appropriate clone by adjusting the characteristics of the digital clone based on the user's life events.

[0066] The execution unit can receive real-time feedback from the user and instantly adjust the task execution method. For example, the execution unit can be provided with an interface that allows the user to provide real-time feedback on the progress of the task. The execution unit can also instantly adjust the task execution method based on the user's feedback. Furthermore, the execution unit can refer to the user's feedback history and select the optimal execution method. In this way, the execution unit can instantly adjust the task execution method by receiving real-time feedback from the user.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The learning unit learns the user's data. The user's data includes behavioral data, emotional data, and historical data. The learning unit uses machine learning and deep learning algorithms to learn the user's behavioral patterns and thought processes. Step 2: The generation unit generates a digital clone based on the data learned by the learning unit. Digital clones include virtual avatars and AI agents. The generation unit generates the digital clone based on the features and model parameters of the learned data, and can also estimate the user's emotions and adjust the generation method. Step 3: The execution unit executes tasks using the digital clone generated by the generation unit. Tasks include replying to emails, managing schedules, and recommending content. The execution unit can estimate the user's emotions and adjust the way the task is performed based on the estimated emotions. Step 4: The input unit inputs the current situation for which future prediction is desired. The current situation includes the current time, location, and user state. The input unit can estimate the user's emotions and select input data based on the estimated emotions. Step 5: The calculation unit calculates the result based on the situation input by the input unit. The calculation may include numerical calculation, statistical analysis, simulation, etc. The calculation unit may estimate the user's emotion and adjust the calculation method based on the estimated emotion. Step 6: The output unit outputs the results calculated by the calculation unit. The results may include text, graphs, reports, etc. The output unit can estimate the user's emotions and adjust the output method based on the estimated emotions.

[0069] (Example 2) A digital clone generation system according to an embodiment of the present invention learns user data and generates an online digital clone. In this digital clone generation system, a user inputs their own data into a generation AI, which then learns the data to generate a digital clone. The generated digital clone can perform various tasks on behalf of the user, such as replying to emails, managing schedules, and recommending content. Furthermore, by inputting the current situation for which future predictions are desired, the generation AI analyzes the situation and calculates the likely outcome. The calculation results include outcomes and critical turning points. For example, in business decision-making, the system can predict the outcomes of different options and suggest the optimal option. This mechanism allows users to utilize their digital clones to efficiently complete tasks and predict future scenarios, potentially saving time and improving the quality of decision-making. For example, by having the user train the digital clone to learn their own behavioral patterns and thought processes, a more accurate digital clone can be generated. The generated digital clone can also perform tasks on behalf of the user, such as replying to emails, managing schedules, and recommending content. Furthermore, by inputting the current situation for which future predictions are desired, the generation AI can analyze the situation and calculate what results can be derived. This allows users to utilize their digital clones to efficiently complete tasks and predict future scenarios. This allows the digital clone generation system to learn the user's data, generate a digital clone, perform tasks, input the current situation, calculate the results, and output them.

[0070] A digital clone generation system according to an embodiment includes a learning unit, a generation unit, an execution unit, an input unit, a calculation unit, and an output unit. The learning unit learns user data. The user data includes, but is not limited to, behavioral data, emotional data, and historical data. The learning unit learns the user data using, for example, machine learning or deep learning algorithms. The learning unit can also learn the user's behavioral patterns and thought processes. For example, the learning unit can learn the user's daily behavior and behavior during specific events. The learning unit can also learn the user's decision-making process and problem-solving procedures. The generation unit generates a digital clone based on the data learned by the learning unit. Examples of digital clones include, but are not limited to, virtual avatars and AI agents. The generation unit generates the digital clone based on, for example, feature quantities and model parameters of the learned data. The generation unit can also estimate the user's emotions and adjust the digital clone generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a digital clone that moves at a leisurely pace. The generation unit can also generate a digital clone that can quickly complete a task when the user is in a hurry. The execution unit causes the digital clone generated by the generation unit to execute a task. Examples of tasks include, but are not limited to, replying to emails, managing a schedule, and recommending content. The execution unit can, for example, estimate a user's emotions and adjust a task execution method based on the estimated user emotions. The execution unit can also select an optimal execution method by referring to the user's past task history. The input unit inputs a current situation for which future prediction is desired. Examples of the current situation include, but are not limited to, the current time, location, and user state. The input unit can, for example, estimate a user's emotions and select input data based on the estimated user emotions. The input unit can also select an optimal input method by referring to the user's past input history. The calculation unit calculates a result based on the situation input by the input unit.The calculation may include, but is not limited to, numerical calculation, statistical analysis, simulation, etc. For example, the calculation unit may estimate a user's emotion and adjust the calculation method based on the estimated user's emotion. The calculation unit may also select an optimal calculation algorithm by referring to the user's past data. The output unit outputs the results calculated by the calculation unit. For example, the results may include, but are not limited to, text, graphs, reports, etc. For example, the output unit may estimate a user's emotion and adjust the output method based on the estimated user's emotion. The output unit may also select an optimal output method by referring to the user's past output history. In this way, the digital clone generation system according to the embodiment can learn user data, generate a digital clone, execute tasks, input the current situation, calculate results, and output results.

[0071] The learning unit can learn the user's behavioral patterns or thought processes. The learning unit, for example, learns the user's daily behavior. For example, the learning unit can collect and learn data on the user's daily routines and actions during specific events. The learning unit can also learn the user's decision-making process. For example, the learning unit can collect and learn data on how the user solves problems and the steps they take to make decisions. Furthermore, the learning unit can learn the user's thought processes. For example, the learning unit can collect and learn data on how the user processes information and the criteria they use to make decisions. In this way, the learning unit can learn the user's behavioral patterns and thought processes, thereby generating a more accurate digital clone.

[0072] The generation unit can generate a digital clone based on the learned data. The generation unit can generate the digital clone based on, for example, feature quantities of the learned data. For example, the generation unit can extract feature quantities of a user's behavioral patterns or thought processes and generate a digital clone based on the extracted feature quantities. The generation unit can also generate a digital clone based on learned model parameters. For example, the generation unit can generate a digital clone using model parameters from machine learning or deep learning. Furthermore, the generation unit can estimate a user's emotions and adjust a digital clone generation method based on the estimated user's emotions. For example, if a user is relaxed, the generation unit can generate a digital clone that progresses at a leisurely pace. If a user is in a hurry, the generation unit can generate a digital clone that completes tasks quickly. In this way, the generation unit can perform various tasks on behalf of a user by generating a digital clone based on the learned data.

[0073] The execution unit can execute tasks such as replying to emails, managing schedules, and recommending content. For example, the execution unit replies to emails on behalf of the user. For example, the execution unit can analyze emails received by the user and generate an appropriate reply. The execution unit can also manage the user's schedule. For example, the execution unit can register the user's events in a calendar and set reminders. Furthermore, the execution unit can recommend content to the user. For example, the execution unit can recommend content such as movies, music, and articles based on the user's preferences. In this way, the execution unit can reduce the burden on the user by executing tasks such as replying to emails, managing schedules, and recommending content.

[0074] The input unit can input a current situation for which a future prediction is desired. The input unit, for example, provides an interface for a user to input the current situation for which a future prediction is desired. For example, the input unit can display a form or dialog for the user to input information such as the current time, location, and state. The input unit can also estimate the user's emotions and select input data based on the estimated user emotions. For example, if the user is feeling stressed, the input unit can preferentially input data that will help the user relax. The input unit can also select the optimal input method by referring to the user's past input history. For example, the input unit can automatically display data that the user has frequently input in the past as candidates. In this way, the input unit can predict a future scenario by inputting the current situation for which a future prediction is desired.

[0075] The calculation unit can analyze the input situation and calculate what kind of result can be derived. The calculation unit, for example, executes an algorithm for analyzing the input situation and calculating what kind of result can be derived. For example, the calculation unit can analyze the input situation using techniques such as numerical calculation, statistical analysis, and simulation. The calculation unit can also estimate the user's emotions and adjust the calculation method based on the estimated user emotions. For example, if the user is feeling stressed, the calculation unit can perform calculations in a way that helps the user relax. The calculation unit can also select an optimal calculation algorithm by referring to the user's past data. For example, the calculation unit can select an optimal calculation algorithm based on data that the user has frequently used in the past. In this way, the calculation unit can analyze the input situation and calculate what kind of result can be derived, thereby suggesting optimal options.

[0076] The output unit can output the calculated results. For example, the output unit outputs the calculated results in the form of text, graphs, reports, or the like. For example, the output unit can provide an interface for displaying the results in a format that is easy for the user to understand. The output unit can also estimate the user's emotions and adjust the output method based on the estimated user emotions. For example, if the user is feeling stressed, the output unit can display the results in a way that helps the user relax. The output unit can also select the optimal output method by referring to the user's past output history. For example, the output unit can select the optimal output method based on output methods that the user has frequently used in the past. In this way, the output unit outputs the calculated results, allowing the user to check the results and use them for decision-making.

[0077] The learning unit can estimate the user's emotions and select training data based on the estimated user's emotions. The learning unit, for example, uses technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the learning unit can analyze the user's facial expression data and voice data to estimate the emotions. The learning unit can also select training data based on the estimated user's emotions. For example, if the user is feeling stressed, the learning unit can preferentially select relaxing content as training data. Furthermore, if the user is excited, the learning unit can also select stimulating content as training data. Furthermore, if the user is tired, the learning unit can select relaxing music or videos as training data. In this way, the learning unit can learn more appropriate data by selecting training data based on the user's emotions.

[0078] The learning unit can analyze the user's past behavioral history and select an appropriate learning algorithm. The learning unit, for example, collects log data and history data to analyze the user's past behavioral history. For example, the learning unit can select an optimal learning algorithm based on data on apps and websites that the user has frequently used in the past. The learning unit can also analyze the user's past search history and select an algorithm that prioritizes learning related information. Furthermore, the learning unit can select an algorithm that learns purchasing patterns based on the user's past purchasing history. In this way, the learning unit can select an optimal learning algorithm by analyzing the user's past behavioral history.

[0079] The learning unit can adjust the timing of studying based on the user's lifestyle patterns during studying. The learning unit, for example, collects daily behavioral data to study the user's lifestyle patterns. For example, the learning unit can adjust the timing so that the user studies during their morning commute. The learning unit can also adjust the timing so that the user studies during their evening relaxation time. Furthermore, the learning unit can also adjust the timing so that the user studies intensively on weekends. In this way, the learning unit can adjust the timing of studying based on the user's lifestyle patterns, thereby enabling efficient studying.

[0080] The learning unit can estimate the user's emotions and determine learning priorities based on the estimated user's emotions. The learning unit uses, for example, techniques such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the learning unit can analyze the user's facial expression data and voice data to estimate the emotions. The learning unit can also determine learning priorities based on the estimated user's emotions. For example, if the user is feeling stressed, the learning unit can prioritize learning relaxing content. Furthermore, if the user is excited, the learning unit can prioritize learning stimulating content. Furthermore, if the user is tired, the learning unit can prioritize learning relaxing music and videos. In this way, the learning unit can prioritize learning more appropriate data by determining learning priorities based on the user's emotions.

[0081] During learning, the learning unit can prioritize learning highly relevant data by taking into account the user's geographical location information. The learning unit, for example, uses GPS data or location information services to acquire the user's geographical location information. For example, if the user is in a specific area, the learning unit can prioritize learning information related to that area. Also, if the user is traveling, the learning unit can prioritize learning tourist information about the travel destination. Furthermore, if the user is participating in a specific event, the learning unit can prioritize learning information related to that event. In this way, the learning unit can learn more appropriate data by prioritizing learning highly relevant data by taking into account the user's geographical location information.

[0082] During learning, the learning unit can analyze the user's social media activity and learn related data. For example, the learning unit collects data such as the content of posts, the number of likes, and comments in order to analyze the user's social media activity. For example, the learning unit can learn related information based on the content the user shared on social media. The learning unit can also analyze the content posted by accounts the user follows and learn related information. Furthermore, the learning unit can learn related information based on the activities of groups and communities in which the user participates. In this way, the learning unit can learn related data by analyzing the user's social media activity.

[0083] The generation unit can estimate the user's emotions and adjust the digital clone generation method based on the estimated user's emotions. The generation unit uses, for example, technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the generation unit can analyze the user's facial expression data and voice data to estimate the emotions. The generation unit can also adjust the digital clone generation method based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a digital clone that progresses at a leisurely pace. If the user is in a hurry, the generation unit can generate a digital clone that quickly completes tasks. Furthermore, if the user is excited, the generation unit can generate a digital clone that actively completes tasks. In this way, the generation unit can generate a more appropriate clone by adjusting the digital clone generation method based on the user's emotions.

[0084] At the time of generation, the generation unit can generate an appropriate clone by referring to the user's past behavioral patterns. The generation unit, for example, collects log data and history data to refer to the user's past behavioral patterns. For example, the generation unit can generate an optimal clone based on tasks that the user frequently performed in the past. The generation unit can also generate a clone that efficiently completes tasks by referring to the user's past schedule. Furthermore, the generation unit can generate an optimal clone based on the user's past email reply patterns. In this way, the generation unit can generate an optimal clone by referring to the user's past behavioral patterns.

[0085] The generation unit can customize the characteristics of the clone based on the user's current living situation at the time of generation. The generation unit collects information such as time of day, location, and user status to obtain the user's current living situation. For example, if the user is busy, the generation unit can generate a clone that completes tasks quickly. Also, if the user is relaxed, the generation unit can generate a clone that completes tasks at a leisurely pace. Furthermore, if the user is traveling, the generation unit can generate a clone that prioritizes processing information about the travel destination. In this way, the generation unit can generate a more appropriate clone by customizing the characteristics of the clone based on the user's current living situation.

[0086] The generation unit can estimate the user's emotions and determine the priority of clones to be generated based on the estimated user's emotions. The generation unit uses, for example, techniques such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the generation unit can analyze the user's facial expression data and voice data to estimate the emotions. The generation unit can also determine the priority of clones to be generated based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can generate a clone that prioritizes relaxing tasks. If the user is excited, the generation unit can also generate a clone that prioritizes stimulating tasks. Furthermore, if the user is tired, the generation unit can generate a clone that prioritizes relaxing tasks. In this way, the generation unit can generate more appropriate clones by determining the priority of clones based on the user's emotions.

[0087] The generation unit can generate an appropriate clone by taking into account the user's geographical location information. The generation unit, for example, uses GPS data or a location information service to obtain the user's geographical location information. For example, if the user is in a specific area, the generation unit can generate a clone that prioritizes processing information related to that area. Also, if the user is traveling, the generation unit can generate a clone that prioritizes processing information about the travel destination. Furthermore, if the user is participating in a specific event, the generation unit can generate a clone that prioritizes processing information related to the event. In this way, the generation unit can generate a more appropriate clone by taking into account the user's geographical location information.

[0088] At the time of generation, the generation unit can analyze the user's social media activity and generate related clones. For example, the generation unit collects data such as the content of posts, the number of likes, and comments to analyze the user's social media activity. For example, the generation unit can generate a clone that processes related information based on content shared by the user on social media. The generation unit can also analyze the content posted by accounts the user follows and generate a clone that processes related information. Furthermore, the generation unit can generate a clone that processes related information based on the activities of groups and communities in which the user participates. In this way, the generation unit can generate related clones by analyzing the user's social media activity.

[0089] The execution unit can estimate the user's emotion and adjust the task execution method based on the estimated user's emotion. The execution unit uses, for example, technologies such as facial expression recognition and voice analysis to estimate the user's emotion. For example, the execution unit can analyze the user's facial expression data and voice data to estimate the emotion. The execution unit can also adjust the task execution method based on the estimated user's emotion. For example, if the user is feeling stressed, the execution unit can execute the task in a relaxing manner. If the user is excited, the execution unit can also execute the task proactively. Furthermore, if the user is tired, the execution unit can also execute the task efficiently. In this way, the execution unit can execute the task in a more appropriate manner by adjusting the task execution method based on the user's emotion.

[0090] During execution, the execution unit can select the optimal execution method by referring to the user's past task history. The execution unit, for example, collects log data and history data to refer to the user's past task history. For example, the execution unit can select the optimal execution method based on tasks that the user frequently performed in the past. The execution unit can also select a method for efficiently executing tasks by referring to the user's past schedule. Furthermore, the execution unit can select the optimal execution method based on the user's past email reply patterns. In this way, the execution unit can select the optimal execution method by referring to the user's past task history.

[0091] During execution, the execution unit can adjust task priorities based on the user's current lifestyle. The execution unit, for example, collects information such as time of day, location, and user status to obtain the user's current lifestyle. For example, if the user is busy, the execution unit can prioritize executing important tasks. Also, if the user is relaxed, the execution unit can prioritize executing relaxing tasks. Furthermore, if the user is traveling, the execution unit can prioritize executing a task that processes information about the travel destination. In this way, the execution unit can prioritize executing more appropriate tasks by adjusting task priorities based on the user's current lifestyle.

[0092] The execution unit can estimate the user's emotions and determine the order of task execution based on the estimated user's emotions. The execution unit uses, for example, technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the execution unit can analyze the user's facial expression data and voice data to estimate the emotions. The execution unit can also determine the order of task execution based on the estimated user's emotions. For example, if the user is feeling stressed, the execution unit can prioritize executing a relaxing task. Furthermore, if the user is excited, the execution unit can prioritize executing a stimulating task. Furthermore, if the user is tired, the execution unit can prioritize executing a relaxing task. In this way, the execution unit can execute tasks in a more appropriate order by determining the order of task execution based on the user's emotions.

[0093] The execution unit can execute the optimal task during execution, taking into account the user's geographical location information. The execution unit, for example, uses GPS data or location information services to obtain the user's geographical location information. For example, if the user is in a specific area, the execution unit can execute a task that prioritizes processing information related to that area. Also, if the user is traveling, the execution unit can execute a task that prioritizes processing information about the travel destination. Furthermore, if the user is participating in a specific event, the execution unit can execute a task that prioritizes processing information related to the event. In this way, the execution unit can execute a more appropriate task by executing the optimal task taking into account the user's geographical location information.

[0094] During execution, the execution unit can analyze a user's social media activity and perform related tasks. For example, the execution unit collects data such as post content, number of likes, and comments to analyze the user's social media activity. For example, the execution unit can execute a task of processing related information based on content shared by the user on social media. The execution unit can also execute a task of analyzing posts from accounts the user follows and processing related information. Furthermore, the execution unit can execute a task of processing related information based on the activities of groups and communities in which the user participates. In this way, the execution unit can execute related tasks by analyzing the user's social media activity.

[0095] The input unit can estimate the user's emotion and select input data based on the estimated user's emotion. The input unit uses, for example, techniques such as facial expression recognition and voice analysis to estimate the user's emotion. For example, the input unit can analyze the user's facial expression data and voice data to estimate the emotion. The input unit can also select input data based on the estimated user's emotion. For example, the input unit can preferentially input relaxing data when the user is feeling stressed. Furthermore, the input unit can preferentially input stimulating data when the user is excited. Furthermore, the input unit can preferentially input relaxing music or video when the user is tired. In this way, the input unit can input more appropriate data by selecting input data based on the user's emotion.

[0096] The input unit can select the optimal input method by referring to the user's past input history when inputting data. The input unit, for example, collects log data and history data in order to refer to the user's past input history. For example, the input unit can automatically display data that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest data that will be used in a specific time period from the user's past input history. In this way, the input unit can select the optimal input method by referring to the user's past input history.

[0097] The input unit can filter input data based on the user's current living situation when inputting the data. The input unit collects information such as time of day, location, and user status to obtain the user's current living situation. For example, if the user is busy, the input unit can prioritize inputting important data. Also, if the user is relaxed, the input unit can prioritize inputting data that will help the user relax. Furthermore, if the user is traveling, the input unit can prioritize inputting information about the travel destination. In this way, the input unit can input more appropriate data by filtering the input data based on the user's current living situation.

[0098] The input unit can estimate the user's emotion and determine the priority of input data based on the estimated user's emotion. The input unit uses, for example, techniques such as facial expression recognition and voice analysis to estimate the user's emotion. For example, the input unit can analyze the user's facial expression data and voice data to estimate the emotion. The input unit can also determine the priority of input data based on the estimated user's emotion. For example, if the user is feeling stressed, the input unit can preferentially input relaxing data. Furthermore, if the user is excited, the input unit can also preferentially input stimulating data. Furthermore, if the user is tired, the input unit can preferentially input relaxing music or video. In this way, the input unit can preferentially input more appropriate data by determining the priority of input data based on the user's emotion.

[0099] During input, the input unit can prioritize inputting highly relevant data in consideration of the user's geographical location information. The input unit, for example, uses GPS data or a location information service to acquire the user's geographical location information. For example, when the user is in a specific area, the input unit can prioritize inputting information related to that area. Furthermore, when the user is traveling, the input unit can prioritize inputting information about the travel destination. Furthermore, when the user is participating in a specific event, the input unit can prioritize inputting information related to the event. In this way, the input unit can input more appropriate data by prioritized inputting highly relevant data in consideration of the user's geographical location information.

[0100] The input unit can analyze the user's social media activity and input related data at the time of input. The input unit, for example, collects data such as the content of posts, the number of likes, and comments in order to analyze the user's social media activity. For example, the input unit can input related information based on content shared by the user on social media. The input unit can also analyze the content posted by accounts the user follows and input related information. Furthermore, the input unit can input related information based on the activities of groups and communities in which the user participates. In this way, the input unit can input related data by analyzing the user's social media activity.

[0101] The calculation unit can estimate the user's emotion and adjust the calculation method based on the estimated user's emotion. The calculation unit uses, for example, techniques such as facial expression recognition and voice analysis to estimate the user's emotion. For example, the calculation unit can analyze the user's facial expression data and voice data to estimate the emotion. The calculation unit can also adjust the calculation method based on the estimated user's emotion. For example, if the user is feeling stressed, the calculation unit can perform calculation in a way that allows the user to relax. Furthermore, if the user is excited, the calculation unit can also perform calculation proactively. Furthermore, if the user is tired, the calculation unit can perform calculation efficiently. As a result, the calculation unit can perform calculation in a more appropriate way by adjusting the calculation method based on the user's emotion.

[0102] During calculation, the calculation unit can select the optimal calculation algorithm by referring to the user's past data. The calculation unit, for example, collects log data and history data to refer to the user's past data. For example, the calculation unit can select the optimal calculation algorithm based on data that the user frequently used in the past. The calculation unit can also analyze the user's past search history and select an algorithm that prioritizes calculation of related information. Furthermore, the calculation unit can select an algorithm that calculates a purchasing pattern by referring to the user's past purchase history. In this way, the calculation unit can select the optimal calculation algorithm by referring to the user's past data.

[0103] The calculation unit can adjust the priority of calculations based on the user's current living situation during calculations. The calculation unit, for example, collects information such as time of day, location, and user status to obtain the user's current living situation. For example, if the user is busy, the calculation unit can prioritize calculation of important data. Also, if the user is relaxed, the calculation unit can prioritize calculation of relaxing data. Furthermore, if the user is traveling, the calculation unit can prioritize calculation of travel destination information. In this way, the calculation unit can prioritize calculation of more appropriate data by adjusting the priority of calculations based on the user's current living situation.

[0104] The calculation unit can estimate the user's emotion and adjust the display method of the calculation result based on the estimated user's emotion. The calculation unit uses, for example, techniques such as facial expression recognition and voice analysis to estimate the user's emotion. For example, the calculation unit can analyze the user's facial expression data or voice data to estimate the emotion. The calculation unit can also adjust the display method of the calculation result based on the estimated user's emotion. For example, if the user is feeling stressed, the calculation unit can display the calculation result in a way that allows the user to relax. If the user is excited, the calculation unit can also display the calculation result in a proactive manner. Furthermore, if the user is tired, the calculation unit can also display the calculation result efficiently. In this way, the calculation unit can display the calculation result in a more appropriate manner by adjusting the display method of the calculation result based on the user's emotion.

[0105] During calculation, the calculation unit can generate optimal calculation results by taking into account the user's geographical location information. The calculation unit, for example, uses GPS data or location information services to acquire the user's geographical location information. For example, if the user is in a specific area, the calculation unit can prioritize calculation of information related to that area. Furthermore, if the user is traveling, the calculation unit can prioritize calculation of information about the travel destination. Furthermore, if the user is participating in a specific event, the calculation unit can prioritize calculation of information related to the event. In this way, the calculation unit can generate more appropriate calculation results by generating optimal calculation results by taking into account the user's geographical location information.

[0106] During the calculation, the calculation unit can analyze the user's social media activity and generate related calculation results. For example, the calculation unit collects data such as the content of posts, the number of likes, and comments to analyze the user's social media activity. For example, the calculation unit can calculate related information based on the content shared by the user on social media. The calculation unit can also analyze the content posted by accounts the user follows and calculate related information. Furthermore, the calculation unit can calculate related information based on the activities of groups and communities in which the user participates. In this way, the calculation unit can generate related calculation results by analyzing the user's social media activity.

[0107] The output unit can estimate the user's emotion and adjust the output method based on the estimated user's emotion. The output unit uses, for example, techniques such as facial expression recognition and voice analysis to estimate the user's emotion. For example, the output unit can analyze the user's facial expression data and voice data to estimate the emotion. The output unit can also adjust the output method based on the estimated user's emotion. For example, the output unit can output in a relaxing manner when the user is feeling stressed. The output unit can also output in an aggressive manner when the user is excited. Furthermore, the output unit can output efficiently when the user is tired. In this way, the output unit can output in a more appropriate manner by adjusting the output method based on the user's emotion.

[0108] At the time of output, the output unit can select the optimal output method by referring to the user's past output history. The output unit, for example, collects log data and history data to refer to the user's past output history. For example, the output unit can select the optimal output method based on output methods that the user has frequently used in the past. The output unit can also analyze the user's past output history and select a method to prioritize output of related information. Furthermore, the output unit can also select the optimal output method by referring to the user's past output history. In this way, the output unit can select the optimal output method by referring to the user's past output history.

[0109] The output unit can customize output data based on the user's current living situation at the time of output. The output unit, for example, collects information such as time of day, location, and user status to obtain the user's current living situation. For example, if the user is busy, the output unit can prioritize outputting important data. Also, if the user is relaxed, the output unit can prioritize outputting data that will help the user relax. Furthermore, if the user is traveling, the output unit can prioritize outputting information about the travel destination. In this way, the output unit can output more appropriate data by customizing the output data based on the user's current living situation.

[0110] The output unit can estimate the user's emotion and determine the priority of output data based on the estimated user's emotion. The output unit uses, for example, techniques such as facial expression recognition and voice analysis to estimate the user's emotion. For example, the output unit can analyze the user's facial expression data and voice data to estimate the emotion. The output unit can also determine the priority of output data based on the estimated user's emotion. For example, the output unit can prioritize relaxing data when the user is stressed. Furthermore, the output unit can prioritize stimulating data when the user is excited. Furthermore, the output unit can prioritize relaxing music and video when the user is tired. In this way, the output unit can prioritize outputting more appropriate data by determining the priority of output data based on the user's emotion.

[0111] The output unit can select the optimal output method at the time of output, taking into consideration the user's geographical location information. The output unit, for example, uses GPS data or location information services to acquire the user's geographical location information. For example, if the user is in a specific area, the output unit can prioritize outputting information related to that area. Furthermore, if the user is traveling, the output unit can prioritize outputting information about the travel destination. Furthermore, if the user is participating in a specific event, the output unit can prioritize outputting information related to the event. In this way, the output unit can select a more appropriate output method by selecting the optimal output method taking into consideration the user's geographical location information.

[0112] At the time of output, the output unit can analyze the user's social media activity and output related data. For example, the output unit collects data such as the content of posts, the number of likes, and comments to analyze the user's social media activity. For example, the output unit can output related information based on the content the user shared on social media. The output unit can also analyze the content of posts from accounts the user follows and output related information. Furthermore, the output unit can output related information based on the activities of groups and communities in which the user participates. In this way, the output unit can output related data by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the learning unit, generation unit, execution unit, input unit, calculation unit, and output unit described above is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the learning unit learns user data via the control unit 46A of the smart device 14, and the generation unit generates a digital clone via the specific processing unit 290 of the data processing device 12. The execution unit executes a task via the control unit 46A of the smart device 14, and the input unit inputs the current situation via the reception device 38 of the smart device 14. The calculation unit calculates the result via the specific processing unit 290 of the data processing device 12, and the output unit outputs the result via the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned learning unit, generation unit, execution unit, input unit, calculation unit, and output unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the learning unit learns user data through the control unit 46A of the smart glasses 214, and the generation unit generates a digital clone through the specific processing unit 290 of the data processing device 12. The execution unit executes a task through the control unit 46A of the smart glasses 214, and the input unit inputs a current situation through the microphone 238 of the smart glasses 214. The calculation unit calculates a result through the specific processing unit 290 of the data processing device 12, and the output unit outputs the result through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned learning unit, generation unit, execution unit, input unit, calculation unit, and output unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the learning unit learns user data via the control unit 46A of the headset type terminal 314, and the generation unit generates a digital clone via the specific processing unit 290 of the data processing device 12. The execution unit executes a task via the control unit 46A of the headset type terminal 314, and the input unit inputs the current situation via the microphone 238 of the headset type terminal 314. The calculation unit calculates a result via the specific processing unit 290 of the data processing device 12, and the output unit outputs the result via the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the learning unit, generation unit, execution unit, input unit, calculation unit, and output unit described above is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the learning unit learns user data through the control unit 46A of the robot 414, and the generation unit generates a digital clone through the specific processing unit 290 of the data processing device 12. The execution unit executes a task through the control unit 46A of the robot 414, and the input unit inputs the current situation through the microphone 238 of the robot 414. The calculation unit calculates the result through the specific processing unit 290 of the data processing device 12, and the output unit outputs the result through the speaker 240 of the robot 414.

[0113] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0114] The digital cloning system may further include a health management unit that collects the user's health data and adjusts task priorities based on the user's health condition. For example, the health management unit may collect the user's heart rate and sleep data and prioritize tasks that will help the user relax if the user is tired. The health management unit may also collect the user's dietary data and suggest nutritionally balanced meals. The health management unit may also collect the user's exercise data and suggest appropriate exercise plans. In this way, the digital cloning system can support a healthier lifestyle by adjusting task priorities based on the user's health condition.

[0115] The learning unit can select learning data based on the user's hobbies and interests. For example, the learning unit can collect data on movies the user has watched or books the user has read in the past and select related content as learning data. The learning unit can also collect data on events the user has attended or travel destinations and select related information as learning data. Furthermore, the learning unit can collect data on social media accounts the user follows and select related information as learning data. In this way, the learning unit can generate a more personalized digital clone by selecting learning data based on the user's hobbies and interests.

[0116] The generation unit can generate a more natural digital clone by imitating the user's tone of voice and speaking style. For example, the generation unit can collect the user's past voice data and learn the characteristics of the user's tone of voice and speaking style. The generation unit can also estimate the user's emotions and adjust the tone of voice and speaking style according to the emotions. Furthermore, the generation unit can learn the rhythm and pace of the user's speaking style and reproduce natural conversation. In this way, the generation unit can generate a more natural digital clone by imitating the user's tone of voice and speaking style.

[0117] The execution unit can analyze the user's past task execution history and optimize the task execution method. For example, the execution unit can analyze how the user has executed tasks in the past and select the optimal execution method. The execution unit can also estimate the user's emotions when executing a task and adjust the execution method based on the emotions. Furthermore, the execution unit can collect environmental data when the user executes a task and select an execution method according to the environment. In this way, the execution unit can optimize the task execution method by analyzing the user's past task execution history.

[0118] The input unit can analyze a user's voice commands and input data based on the voice input. For example, the input unit can analyze the content of a user's voice instructions and input appropriate data. The input unit can also analyze the user's voice tone and speaking style and select input data based on emotions. Furthermore, the input unit can refer to the user's past voice command history and select the optimal input method. In this way, the input unit can realize more intuitive data input by analyzing voice commands.

[0119] The calculation unit can analyze the user's past decision-making data and learn decision-making patterns. For example, the calculation unit can analyze the choices the user has made in the past and extract decision-making patterns. The calculation unit can also estimate the user's emotions at the time of decision-making and adjust the decision-making patterns based on the emotions. Furthermore, the calculation unit can collect environmental data at the time of the user's decision-making and learn decision-making patterns according to the environment. In this way, the calculation unit can learn decision-making patterns by analyzing the user's past decision-making data.

[0120] The output unit can customize the display method of the output data based on the user's visual preferences. For example, the output unit can display data using the user's preferred colors and fonts. The output unit can also adjust the display method to reduce the user's visual fatigue. Furthermore, the output unit can refer to the user's past display history and select the optimal display method. In this way, the output unit can customize the display method of the output data based on the user's visual preferences, thereby achieving a more comfortable data display.

[0121] The learning unit can collect social network data of the user and select learning data based on the user's friendships. For example, the learning unit can collect data on friends and family with whom the user frequently interacts and select related information as learning data. The learning unit can also collect data on groups and communities in which the user participates and select related information as learning data. Furthermore, the learning unit can analyze the user's activity history on social networks and select related information as learning data. In this way, the learning unit can generate a more personalized digital clone by selecting learning data based on the user's friendships.

[0122] The generation unit can adjust the characteristics of the digital clone based on the user's life events. For example, when the user experiences an important life event such as marriage or childbirth, the generation unit can generate a digital clone with characteristics corresponding to the event. In addition, when the user experiences a life event such as changing jobs or moving, the generation unit can generate a digital clone with characteristics corresponding to the event. Furthermore, when the user begins to develop a new hobby or interest, the generation unit can generate a digital clone with characteristics corresponding to the hobby or interest. In this way, the generation unit can generate a more appropriate clone by adjusting the characteristics of the digital clone based on the user's life events.

[0123] The execution unit can receive real-time feedback from the user and instantly adjust the task execution method. For example, the execution unit can be provided with an interface that allows the user to provide real-time feedback on the progress of the task. The execution unit can also instantly adjust the task execution method based on the user's feedback. Furthermore, the execution unit can refer to the user's feedback history and select the optimal execution method. In this way, the execution unit can instantly adjust the task execution method by receiving real-time feedback from the user.

[0124] The processing flow of the second embodiment will be briefly explained below.

[0125] Step 1: The learning unit learns the user's data. The user's data includes behavioral data, emotional data, and historical data. The learning unit uses machine learning and deep learning algorithms to learn the user's behavioral patterns and thought processes. Step 2: The generation unit generates a digital clone based on the data learned by the learning unit. Digital clones include virtual avatars and AI agents. The generation unit generates the digital clone based on the features and model parameters of the learned data, and can also estimate the user's emotions and adjust the generation method. Step 3: The execution unit executes tasks using the digital clone generated by the generation unit. Tasks include replying to emails, managing schedules, and recommending content. The execution unit can estimate the user's emotions and adjust the way the task is performed based on the estimated emotions. Step 4: The input unit inputs the current situation for which future prediction is desired. The current situation includes the current time, location, and user state. The input unit can estimate the user's emotions and select input data based on the estimated emotions. Step 5: The calculation unit calculates the result based on the situation input by the input unit. The calculation may include numerical calculation, statistical analysis, simulation, etc. The calculation unit may estimate the user's emotion and adjust the calculation method based on the estimated emotion. Step 6: The output unit outputs the results calculated by the calculation unit. The results may include text, graphs, reports, etc. The output unit can estimate the user's emotions and adjust the output method based on the estimated emotions.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0128] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0131] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] 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.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0147] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] 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.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0153] 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.

[0154] 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.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0158] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0160] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0165] 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.

[0166] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0168] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0169] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0170] 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.

[0171] 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.

[0172] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0173] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0174] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0175] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0176] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0177] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0180] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0181] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0182] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0183] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0184] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0185] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0186] 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.

[0187] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0188] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0189] 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.

[0190] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0191] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0192] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0193] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0194] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0195] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0196] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0197] [Explanation of symbols]

[0198] 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 learning unit that learns user data; a generation unit that generates a digital clone based on the data learned by the learning unit; an execution unit that executes a task by the digital clone generated by the generation unit; an input section for inputting the current status; a calculation unit that calculates a result based on the situation input by the input unit; an output unit that outputs the result calculated by the calculation unit; Equipped with A system characterized by:

2. The learning unit Learn user behavior patterns or thought processes The system of claim 1 .

3. The generation unit Generate a digital clone based on learned data The system of claim 1 .

4. The execution unit: Responding to emails, scheduling, and recommending content The system of claim 1 .

5. The input unit Enter the current situation you want to predict. The system of claim 1 .

6. The calculation unit Analyzes the input situation and calculates what results can be derived The system of claim 1 .

7. The output unit Output the calculated result The system of claim 1 .

8. The learning unit Estimate the user's emotions and select training data based on the estimated user emotions. The system of claim 1 .

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