System

Abstract

A system according to an embodiment is directed to efficiently processing computational tasks of a production AI using a smartphone.SOLUTION: A system according to an embodiment includes a calculation task division unit, a task distribution unit, and a reward management unit. The calculation task division unit divides a calculation task of the generation AI. The task distributing unit distributes the calculation task divided by the calculation task dividing unit to the smartphone. The reward manager manages a reward in response to completion of the computational task performed by the smartphone.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 technology has limited means for efficiently processing the enormous amount of calculations required for generative AI, leaving room for improvement.

[0005] The system according to the embodiment aims to efficiently process the computational tasks of generative AI using a smartphone. [Means for solving the problem]

[0006] The system according to the embodiment includes a computational task division unit, a task distribution unit, and a reward management unit. The computational task division unit divides the computational tasks of the generation AI. The task distribution unit distributes the computational tasks divided by the computational task division unit to smartphones. The reward management unit manages rewards according to the completion of the computational tasks executed by the smartphones. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently process the computational tasks of the generative AI using a smartphone. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 distributed computing system according to an embodiment of the present invention is a system that distributes the computational load of a generative AI or AGI using smartphones around the world. This system performs calculations during the idle time of smartphones, and rewards are distributed to the owners of the smartphones used for the calculations. This allows the distributed computing system to efficiently distribute the enormous computational load of a generative AI or AGI. Furthermore, since rewards are distributed to smartphone owners, it can increase users' motivation to participate.

[0029] A distributed computing system according to an embodiment includes a computational task division unit, a task distribution unit, and a reward management unit. The computational task division unit divides a computational task of a generation AI. For example, when the generation AI performs data analysis for image recognition, the computational task division unit divides the analysis task into smaller parts. When the generation AI performs data analysis for text generation, the computational task division unit can also divide the analysis task into smaller parts. When the generation AI performs data analysis for speech recognition, the computational task division unit can also divide the analysis task into smaller parts. The task distribution unit distributes the computational tasks divided by the computational task division unit to smartphones. For example, the task distribution unit assigns optimal tasks taking into account the smartphone's performance and remaining battery life. The task distribution unit can also dynamically adjust tasks according to regional power consumption conditions using the smartphone's geographical location information. The task distribution unit can also use an emotion estimation function to analyze a user's emotional state and prioritize tasks assigned to users with positive emotions. The reward management unit manages rewards according to the completion of computational tasks executed by the smartphone. For example, the reward management unit may award points or virtual currency in response to the completion of a calculation task. The reward management unit may also vary the reward in response to the difficulty and time required for the calculation task. The reward management unit may also distribute rewards in real time in response to the completion of a calculation task. This allows the distributed computing system according to the embodiment to distribute the calculation load of the generation AI and effectively utilize idle time on a smartphone. For example, a smartphone owner may perform a calculation task during idle time, such as during the night, and receive a reward. The smartphone owner may also receive a reward in real time in response to the completion of a calculation task.

[0030] The task distribution unit evaluates smartphone performance and remaining battery power in real time and can optimally allocate computational tasks. For example, the generation AI of the task distribution unit dynamically allocates computational tasks based on real-time monitoring of each smartphone's CPU performance, memory capacity, and remaining battery power. For example, it assigns complex tasks to high-performance smartphones and simple tasks to low-performance smartphones. Furthermore, when a smartphone's remaining battery power falls below a certain level, the generation AI automatically suspends the task to prevent battery drain. For example, if the battery power falls below 20%, the task will be suspended and the system will wait until charging is complete. The task distribution unit also evaluates the usage status of each smartphone in real time and allocates computational tasks to times when the user is not using the smartphone. For example, it detects when the user leaves the smartphone unattended and performs calculations during that time. This allows for efficient computation by optimally allocating tasks based on the smartphone's performance and remaining battery power.

[0031] The task distribution unit can use the smartphone's geographical location information to dynamically adjust computational tasks according to the power consumption situation in each region. For example, the task distribution unit acquires the geographical location information of each smartphone and monitors the power consumption situation in each region in real time. For example, it prioritizes allocating computational tasks to regions with low power consumption. The task distribution unit also dynamically adjusts the allocation of computational tasks according to the power supply situation in each region. For example, it allocates more tasks to regions with stable power supply and fewer tasks to regions with unstable power supply. The task distribution unit also executes computational tasks based on the geographical location information, avoiding times when power consumption is at its peak. For example, it concentrates computations during times of low power consumption at night. In this way, dynamic adjustment of computational tasks according to the power consumption situation in each region contributes to efficient power consumption.

[0032] The computational task division unit can assign computational tasks not only to smartphones but also to smart home appliances and onboard computers. The computational task division unit, for example, includes smart home appliances and onboard computers in distributed computing to diversify computational resources. For example, a smart refrigerator or smart TV performs computational tasks. The computational task division unit also evaluates the performance of smart home appliances and onboard computers and assigns optimal computational tasks based on that evaluation. For example, it assigns complex tasks to high-performance onboard computers. The computational task division unit also monitors the usage status of smart home appliances and onboard computers in real time and executes computational tasks when they are not in use. For example, it performs computations when the car is parked. This diversifies computational resources and improves the efficiency of distributed computing.

[0033] The computational task division unit can apply the computational tasks of the generative AI to tasks specialized in the fields of education and medicine. For example, the computational task division unit applies the computational tasks of the generative AI to the field of education to analyze learning data and generate educational content. For example, it analyzes students' learning progress in real time and provides personalized learning plans. In the medical field, the computational task division unit also uses the computational tasks of the generative AI to analyze medical data and provide diagnostic support. For example, it analyzes patient diagnostic data and proposes optimal treatment plans. In addition, the computational task division unit applies the computational tasks of the generative AI to environmental protection and disaster prevention in order to increase social value. For example, it analyzes environmental data and contributes to the Sustainable Development Goals (SDGs). In this way, applying the computational tasks to tasks specialized in specific fields increases social value.

[0034] To maximize the hardware performance of smartphones, dedicated distributed computing apps can be developed to provide an optimized computing environment. For example, a dedicated distributed computing app can be developed to maximize the hardware performance of smartphones. For example, the CPU and GPU performance can be optimized to execute computing tasks. Furthermore, the dedicated app can efficiently manage smartphone resources and optimize the execution of computing tasks. For example, memory usage and battery consumption can be minimized. Furthermore, the dedicated app can automatically set optimal settings. For example, it can dynamically adjust the priority of computing tasks. This maximizes the hardware performance of the smartphone and enables efficient computing.

[0035] By utilizing the AI ​​chip in a smartphone, it is possible to introduce dedicated algorithms to efficiently process the computational tasks of generative AI. For example, by utilizing the AI ​​chip in a smartphone, a dedicated algorithm can be developed to efficiently process the computational tasks of generative AI. For example, a neural network optimization algorithm can be introduced. In addition, an AI chip can be used to build a system that processes the computational tasks of generative AI in real time. For example, image recognition and voice analysis tasks can be performed at high speed. In addition, an algorithm that performs parallel processing of computational tasks can be introduced by utilizing the AI ​​chip. For example, computational efficiency can be improved by executing multiple tasks simultaneously. In this way, the AI ​​chip can be used to efficiently process the computational tasks of generative AI.

[0036] The increasing performance of smartphones can be used to perform real-time data analysis and simulations, expanding the range of applications of generative AI. The increasing performance of smartphones can be used, for example, to build a system that performs real-time data analysis. For example, sensor data can be analyzed in real time, expanding the range of applications of generative AI. High-performance smartphones can also be used to run complex simulations in real time. For example, weather data simulations and traffic flow analysis can be performed. The increasing performance of smartphones can also be used to develop new algorithms to expand the range of applications of generative AI. For example, image analysis and voice recognition can be performed in real time. This allows real-time data analysis and simulations to be performed, expanding the range of applications of generative AI.

[0037] By taking advantage of the increasing performance of smartphones, computational tasks can also be applied to augmented reality (AR) and virtual reality (VR) applications. By taking advantage of the increasing performance of smartphones, for example, the computational tasks of augmented reality (AR) applications can be executed using distributed computing. For example, real-time rendering of AR games can be performed. Furthermore, the computational tasks of virtual reality (VR) applications can be processed using distributed computing to improve the user experience. For example, real-time generation of VR content can be performed. Furthermore, by taking advantage of the increasing performance of smartphones, a system can be built that distributes the computational load of AR and VR applications. For example, multiple smartphones can cooperate to generate a VR environment. This can be applied to augmented reality (AR) and virtual reality (VR) applications, expanding the scope of computational resource utilization.

[0038] It is possible to develop an algorithm that learns smartphone usage patterns, predicts optimal idle times, and schedules computing tasks. By learning smartphone usage patterns, an algorithm can be developed that predicts optimal idle times, for example. For example, by analyzing a user's past usage data and identifying times at night when usage is less frequent. A system can also be built that automatically adjusts the schedule of computing tasks based on usage patterns. For example, computing tasks can be performed when the user is sleeping. An algorithm can also be introduced that monitors smartphone usage patterns in real time and dynamically predicts idle times. For example, it can detect times when the user leaves their smartphone unattended and perform calculations during those times. This allows the system to learn smartphone usage patterns, predict optimal idle times, and schedule computing tasks, thereby achieving efficient computing.

[0039] To minimize battery consumption on smartphones, technology can be introduced that enables calculations in low-power mode. To minimize battery consumption on smartphones, for example, technology is developed that enables calculations in low-power mode. For example, unnecessary apps and functions are paused while a calculation task is being executed. To achieve calculations in low-power mode, a system is built that dynamically adjusts the priority of calculation tasks. For example, calculation tasks are paused when the battery level is low. To reduce battery consumption on smartphones, technology is introduced that automatically adjusts screen brightness and communication functions while a calculation task is being executed. For example, screen brightness is set to minimum and communication is minimized. This allows calculations to be executed while minimizing battery consumption, thereby extending the operating time of the smartphone.

[0040] By learning smartphone usage patterns, we can execute computational tasks not only during the night but also during other idle times, such as during meetings or watching movies. We will build a system that learns smartphone usage patterns and maximizes computational resources by utilizing not only during the night but also other idle times, such as during meetings or watching movies. For example, we will monitor smartphone usage in real time and perform computations during times when the smartphone is not in use. We will also develop an algorithm that identifies idle times other than during the night based on usage patterns. For example, we will detect times when the user leaves their smartphone unattended and perform computations during those times. We will also build a system that predicts idle times, such as during meetings or watching movies, based on user schedule data and schedules computational tasks. For example, we will analyze data from a calendar app to identify idle times. This will maximize computational resources by utilizing idle times other than during the night.

[0041] Idle time on a smartphone can be used to simultaneously perform not only generative AI computational tasks but also data backups and system updates. A system can be built that utilizes idle time on a smartphone to simultaneously perform other tasks, such as data backups and system updates, in addition to generative AI computational tasks. For example, data backups and computational tasks can be performed simultaneously at night. Furthermore, to maximize idle time, an algorithm can be developed to run multiple tasks in parallel. For example, computational tasks and system updates can be performed simultaneously, improving efficiency. Furthermore, a system can be built that efficiently manages smartphone resources and runs multiple tasks during idle time. For example, data backups and computational tasks can be performed while minimizing battery consumption. This maximizes idle time and improves efficiency by running multiple tasks simultaneously.

[0042] A system that distributes rewards in real time according to the completion of a calculation task can be constructed, thereby increasing user motivation. A system that distributes rewards in real time according to the completion of a calculation task is constructed. For example, points or virtual currency are automatically awarded each time a calculation task is completed. Furthermore, a system that distributes rewards in real time is developed to increase user motivation. For example, the progress of a calculation task is displayed in real time, and rewards are immediately awarded. Furthermore, by distributing rewards in real time according to the completion of a calculation task, users' motivation to participate is increased. For example, a system is constructed in which rewards are reflected immediately each time a calculation task is completed. In this way, by distributing rewards in real time, user motivation is increased.

[0043] The types of rewards can be diversified, and not only points and virtual currency but also discount coupons and special offers can be provided. A system is constructed that diversifies the types of rewards, for example, by providing not only points and virtual currency but also discount coupons and special offers. For example, a discount coupon for an online store can be awarded in response to the completion of a calculation task. A system is also developed that customizes the types of rewards according to the user's preferences. For example, the reward selected by the user is given priority. A system is also constructed that diversifies the types of rewards to increase users' motivation to participate. For example, special offers and exclusive content can be provided in response to the completion of a calculation task. In this way, the diversification of the types of rewards increases users' motivation to participate.

[0044] The reward system can be linked with other applications and services, allowing users to use rewards in a variety of ways. A system can be built by linking the reward system with other applications and services, allowing users to use rewards in a variety of ways. For example, linking with an online store or subscription service. Furthermore, a system can be developed that expands the scope of reward usage by linking with other applications and services. For example, using rewards to purchase movie tickets or music streaming services. Furthermore, the reward system can be linked with various applications and services to improve user convenience. For example, using rewards to purchase transportation tickets. In this way, user convenience can be improved by linking the reward system with other applications and services.

[0045] The distribution of rewards can be linked to a user's social contribution and environmental protection activities to increase social value. We will build a system that links the distribution of rewards to other factors, such as a user's social contribution and environmental protection activities. For example, a user can receive additional rewards by participating in environmental protection activities. We will also develop an algorithm that links the distribution of rewards to social contribution activities to increase social value. For example, participating in volunteer activities increases rewards. We will also build a system that links the distribution of rewards to a user's social behavior. For example, a user can receive rewards by contributing to recycling activities or energy conservation. In this way, social value can be increased by linking the distribution of rewards to social contribution and environmental protection activities.

[0046] Data security can be ensured by implementing end-to-end encryption in the distribution of computational tasks and the return of results. For example, a system is constructed that implements end-to-end encryption in the distribution of computational tasks and the return of results. For example, computational tasks and results are encrypted to prevent unauthorized access by third parties. Also, an algorithm is developed that ensures data security by implementing end-to-end encryption. For example, encryption keys are dynamically generated to enhance security. Also, a system is constructed that protects user privacy by implementing end-to-end encryption in the distribution of computational tasks and the return of results. For example, encryption is performed to prevent personal information from being leaked. In this way, data security is ensured by implementing end-to-end encryption.

[0047] By utilizing the security features of smartphones, it is possible to protect against unauthorized access and malware while a computing task is being executed. By utilizing the security features of smartphones, for example, a system is constructed that protects against unauthorized access and malware while a computing task is being executed. For example, security software is used to monitor in real time. Furthermore, an algorithm is developed that strengthens the security features of smartphones to protect against unauthorized access and malware while a computing task is being executed. For example, a firewall and virus scan are implemented. Furthermore, a system is constructed that ensures data safety by utilizing the security features of smartphones to protect against unauthorized access and malware while a computing task is being executed. For example, a security policy is set and the execution of a computing task is controlled. In this way, the security features of smartphones are utilized to protect against unauthorized access and malware while a computing task is being executed.

[0048] Ensuring security and privacy can be applied to other distributed computing systems and cloud services to improve the overall security level. For example, a system is constructed that applies ensuring security and privacy to other distributed computing systems and cloud services. For example, end-to-end encryption is applied to cloud services. Also, algorithms are developed that strengthen ensuring security and privacy in other distributed computing systems and cloud services. For example, data encryption and access control are strengthened. Also, a system is constructed that improves the overall security level by applying ensuring security and privacy to other distributed computing systems and cloud services. For example, security policies are unified and overall security is strengthened. In this way, the overall security level is improved by applying ensuring security and privacy to other distributed computing systems and cloud services.

[0049] In order to protect user privacy, the content of a computational task can be anonymized to prevent the leakage of personal information. In order to protect user privacy, for example, a system for anonymizing the content of a computational task is constructed. For example, only data that does not contain personal information is distributed as a computational task. In addition, an algorithm is developed to prevent the leakage of personal information by anonymizing the content of a computational task. For example, personal information is protected using data anonymization technology. In addition, in order to protect user privacy, a system is constructed to anonymize the content of a computational task to prevent the leakage of personal information. For example, data is anonymized before the distribution of a computational task. In this way, the content of the computational task is anonymized to protect user privacy.

[0050] In order to protect user privacy, the content of a computational task can be anonymized to prevent the leakage of personal information. In order to protect user privacy, for example, a system for anonymizing the content of a computational task is constructed. For example, only data that does not contain personal information is distributed as a computational task. In addition, an algorithm is developed to prevent the leakage of personal information by anonymizing the content of a computational task. For example, personal information is protected using data anonymization technology. In addition, in order to protect user privacy, a system is constructed to anonymize the content of a computational task to prevent the leakage of personal information. For example, data is anonymized before the distribution of a computational task. In this way, the content of the computational task is anonymized to protect user privacy.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The distributed computing system can further include an energy management unit to maximize energy efficiency. For example, the energy management unit collects energy consumption data from each smartphone and prioritizes allocating computing tasks to smartphones with high energy efficiency. The energy management unit can also monitor the energy supply situation in each region and concentrate computing tasks in regions with stable energy supplies. For example, it can allocate more tasks to urban areas with stable power supplies. The energy management unit can also execute computing tasks to avoid peak energy consumption times. For example, it can concentrate computations during nighttime hours when power consumption is low. This maximizes energy efficiency and realizes sustainable distributed computing.

[0053] The distributed computing system may further include a fitness support unit that collects the user's exercise data and maximizes the effectiveness of the exercise. For example, the fitness support unit may analyze the user's exercise data and assign calculation tasks to times when the exercise is most effective. The fitness support unit may also provide advice to enhance the effectiveness of the exercise based on the user's exercise data. For example, it may send a notification encouraging the user to stretch after exercise. The fitness support unit may also analyze the user's exercise data and provide a personalized exercise plan. For example, it may suggest an exercise menu based on the user's physical strength. This maximizes the effectiveness of the user's exercise and supports efficient fitness.

[0054] The distributed computing system may further include an environmental management unit that collects environmental data from users and supports their environmental protection activities. For example, the environmental management unit may analyze the user's environmental data and provide information useful for environmental protection activities. The environmental management unit may also provide advice to promote environmental protection activities based on the user's environmental data. For example, the environmental management unit may introduce methods for reducing energy consumption. The environmental management unit may also analyze the user's environmental data and provide incentives for participating in environmental protection activities. For example, the environmental management unit may provide additional rewards for participating in environmental protection activities. This may support the user's environmental protection activities and contribute to the realization of a sustainable society.

[0055] The distributed computing system may further include a hobby support unit that provides personalized content based on the user's hobbies and interests. For example, the hobby support unit collects data on the user's hobbies and interests and provides related content based on the collected data. The hobby support unit may also dynamically adjust the personalized content based on the user's hobbies and interests. For example, if the user is interested in music, the hobby support unit may introduce new music. The hobby support unit may also suggest related events and activities based on the user's hobbies and interests. For example, the hobby support unit may provide information about music festivals. This allows the provision of personalized content based on the user's hobbies and interests, thereby improving user satisfaction.

[0056] The distributed computing system may further include a privacy management unit for protecting user privacy. For example, the privacy management unit may anonymize user data to prevent personal information from being leaked. The privacy management unit may also provide settings for protecting user privacy and enable users to control how their data is used. For example, the privacy management unit may provide opt-in and opt-out settings for data collection and use. The privacy management unit may also implement security measures for safely storing user data and protecting it from unauthorized access. For example, the privacy management unit may implement data encryption and strengthen access control. This protects user privacy and allows users to use the distributed computing system with peace of mind.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The computational task division unit divides the computational tasks of the generative AI. For example, if the generative AI performs data analysis for image recognition, text generation, and speech recognition, it divides the analysis tasks into smaller parts. Step 2: The task distribution unit distributes the computational tasks divided by the computational task division unit to smartphones. For example, it can allocate optimal tasks taking into account the smartphone's performance and remaining battery life, dynamically adjust tasks according to the power consumption situation in each region using geographical location information, or prioritize tasks to users with positive emotions using emotion estimation functions. Step 3: The reward management unit manages rewards according to the completion of the calculation task executed by the smartphone. For example, points or virtual currency can be awarded according to the completion of the calculation task, rewards can vary according to the difficulty or time required for the calculation task, and rewards can be distributed in real time according to the completion of the calculation task.

[0059] (Example 2) A distributed computing system according to an embodiment of the present invention is a system that distributes the computational load of a generative AI or AGI using smartphones around the world. This system performs calculations during the idle time of smartphones, and rewards are distributed to the owners of the smartphones used for the calculations. This allows the distributed computing system to efficiently distribute the enormous computational load of a generative AI or AGI. Furthermore, since rewards are distributed to smartphone owners, it can increase users' motivation to participate.

[0060] A distributed computing system according to an embodiment includes a computational task division unit, a task distribution unit, and a reward management unit. The computational task division unit divides a computational task of a generation AI. For example, when the generation AI performs data analysis for image recognition, the computational task division unit divides the analysis task into smaller parts. When the generation AI performs data analysis for text generation, the computational task division unit can also divide the analysis task into smaller parts. When the generation AI performs data analysis for speech recognition, the computational task division unit can also divide the analysis task into smaller parts. The task distribution unit distributes the computational tasks divided by the computational task division unit to smartphones. For example, the task distribution unit assigns optimal tasks taking into account the smartphone's performance and remaining battery life. The task distribution unit can also dynamically adjust tasks according to regional power consumption conditions using the smartphone's geographical location information. The task distribution unit can also use an emotion estimation function to analyze a user's emotional state and prioritize tasks assigned to users with positive emotions. The reward management unit manages rewards according to the completion of computational tasks executed by the smartphone. For example, the reward management unit may award points or virtual currency in response to the completion of a calculation task. The reward management unit may also vary the reward in response to the difficulty and time required for the calculation task. The reward management unit may also distribute rewards in real time in response to the completion of a calculation task. This allows the distributed computing system according to the embodiment to distribute the calculation load of the generation AI and effectively utilize idle time on a smartphone. For example, a smartphone owner may perform a calculation task during idle time, such as during the night, and receive a reward. The smartphone owner may also receive a reward in real time in response to the completion of a calculation task.

[0061] The task distribution unit evaluates smartphone performance and remaining battery power in real time and can optimally allocate computational tasks. For example, the generation AI of the task distribution unit dynamically allocates computational tasks based on real-time monitoring of each smartphone's CPU performance, memory capacity, and remaining battery power. For example, it assigns complex tasks to high-performance smartphones and simple tasks to low-performance smartphones. Furthermore, when a smartphone's remaining battery power falls below a certain level, the generation AI automatically suspends the task to prevent battery drain. For example, if the battery power falls below 20%, the task will be suspended and the system will wait until charging is complete. The task distribution unit also evaluates the usage status of each smartphone in real time and allocates computational tasks to times when the user is not using the smartphone. For example, it detects when the user leaves the smartphone unattended and performs calculations during that time. This allows for efficient computation by optimally allocating tasks based on the smartphone's performance and remaining battery power.

[0062] The task distribution unit can use the smartphone's geographical location information to dynamically adjust computational tasks according to the power consumption situation in each region. For example, the task distribution unit acquires the geographical location information of each smartphone and monitors the power consumption situation in each region in real time. For example, it prioritizes allocating computational tasks to regions with low power consumption. The task distribution unit also dynamically adjusts the allocation of computational tasks according to the power supply situation in each region. For example, it allocates more tasks to regions with stable power supply and fewer tasks to regions with unstable power supply. The task distribution unit also executes computational tasks based on the geographical location information, avoiding times when power consumption is at its peak. For example, it concentrates computations during times of low power consumption at night. In this way, dynamic adjustment of computational tasks according to the power consumption situation in each region contributes to efficient power consumption.

[0063] The task distribution unit can use the emotion estimation function to analyze the emotional state of a user and preferentially allocate computational tasks to users who have positive emotions. For example, the task distribution unit uses the emotion estimation function to analyze the emotional state of a user in real time and preferentially allocate computational tasks to users who have positive emotions. For example, tasks are distributed during times when the user is relaxed. The task distribution unit also provides an incentive to increase rewards to users who have strong positive emotions based on the user's emotion data. For example, additional rewards are given to users with high emotion scores. The task distribution unit also uses the emotion estimation function to avoid allocating computational tasks when the user is feeling stressed. For example, tasks are not distributed during times when the user is tired. In this way, preferential allocation of tasks to users who have positive emotions increases users' motivation to participate.

[0064] The computational task division unit can assign computational tasks not only to smartphones but also to smart home appliances and onboard computers. The computational task division unit, for example, includes smart home appliances and onboard computers in distributed computing to diversify computational resources. For example, a smart refrigerator or smart TV performs computational tasks. The computational task division unit also evaluates the performance of smart home appliances and onboard computers and assigns optimal computational tasks based on that evaluation. For example, it assigns complex tasks to high-performance onboard computers. The computational task division unit also monitors the usage status of smart home appliances and onboard computers in real time and executes computational tasks when they are not in use. For example, it performs computations when the car is parked. This diversifies computational resources and improves the efficiency of distributed computing.

[0065] The computational task division unit can apply the computational tasks of the generative AI to tasks specialized in the fields of education and medicine. For example, the computational task division unit applies the computational tasks of the generative AI to the field of education to analyze learning data and generate educational content. For example, it analyzes students' learning progress in real time and provides personalized learning plans. In the medical field, the computational task division unit also uses the computational tasks of the generative AI to analyze medical data and provide diagnostic support. For example, it analyzes patient diagnostic data and proposes optimal treatment plans. In addition, the computational task division unit applies the computational tasks of the generative AI to environmental protection and disaster prevention in order to increase social value. For example, it analyzes environmental data and contributes to the Sustainable Development Goals (SDGs). In this way, applying the computational tasks to tasks specialized in specific fields increases social value.

[0066] The emotion estimation function can monitor the emotional response of a user when accepting a computational task in real time and distribute computational tasks at the optimal timing. The emotion estimation function, for example, monitors the emotional response of a user when accepting a computational task in real time. For example, it distributes tasks during times when the user is relaxed. The emotion estimation function also builds a system that distributes computational tasks at the optimal timing based on the user's emotional data. For example, it distributes tasks during times when the user is feeling positive. The emotion estimation function also avoids distributing computational tasks if the user is feeling stressed. For example, it does not distribute tasks during times when the user is tired. In this way, the burden on the user is reduced by monitoring the user's emotional response and distributing tasks at the optimal timing.

[0067] To maximize the hardware performance of smartphones, dedicated distributed computing apps can be developed to provide an optimized computing environment. For example, a dedicated distributed computing app can be developed to maximize the hardware performance of smartphones. For example, the CPU and GPU performance can be optimized to execute computing tasks. Furthermore, the dedicated app can efficiently manage smartphone resources and optimize the execution of computing tasks. For example, memory usage and battery consumption can be minimized. Furthermore, the dedicated app can automatically set optimal settings. For example, it can dynamically adjust the priority of computing tasks. This maximizes the hardware performance of the smartphone and enables efficient computing.

[0068] By utilizing the AI ​​chip in a smartphone, it is possible to introduce dedicated algorithms to efficiently process the computational tasks of generative AI. For example, by utilizing the AI ​​chip in a smartphone, a dedicated algorithm can be developed to efficiently process the computational tasks of generative AI. For example, a neural network optimization algorithm can be introduced. In addition, an AI chip can be used to build a system that processes the computational tasks of generative AI in real time. For example, image recognition and voice analysis tasks can be performed at high speed. In addition, an algorithm that performs parallel processing of computational tasks can be introduced by utilizing the AI ​​chip. For example, computational efficiency can be improved by executing multiple tasks simultaneously. In this way, the AI ​​chip can be used to efficiently process the computational tasks of generative AI.

[0069] The emotion estimation function can dynamically adjust smartphone performance according to the user's emotional state and optimize the efficiency of computational tasks. The emotion estimation function dynamically adjusts smartphone performance according to the user's emotional state, for example, maximizing performance during times when the user is relaxed. The emotion estimation function also builds a system that optimizes smartphone resource allocation based on the user's emotional data. For example, it increases CPU and memory usage when the emotion score is high. The emotion estimation function also reduces smartphone performance when the user is feeling stressed. For example, it pauses computational tasks to reduce battery consumption. This adjusts smartphone performance according to the user's emotional state and optimizes computational efficiency.

[0070] The increasing performance of smartphones can be used to perform real-time data analysis and simulations, expanding the range of applications of generative AI. The increasing performance of smartphones can be used, for example, to build a system that performs real-time data analysis. For example, sensor data can be analyzed in real time, expanding the range of applications of generative AI. High-performance smartphones can also be used to run complex simulations in real time. For example, weather data simulations and traffic flow analysis can be performed. The increasing performance of smartphones can also be used to develop new algorithms to expand the range of applications of generative AI. For example, image analysis and voice recognition can be performed in real time. This allows real-time data analysis and simulations to be performed, expanding the range of applications of generative AI.

[0071] By taking advantage of the increasing performance of smartphones, computational tasks can also be applied to augmented reality (AR) and virtual reality (VR) applications. By taking advantage of the increasing performance of smartphones, for example, the computational tasks of augmented reality (AR) applications can be executed using distributed computing. For example, real-time rendering of AR games can be performed. Furthermore, the computational tasks of virtual reality (VR) applications can be processed using distributed computing to improve the user experience. For example, real-time generation of VR content can be performed. Furthermore, by taking advantage of the increasing performance of smartphones, a system can be built that distributes the computational load of AR and VR applications. For example, multiple smartphones can cooperate to generate a VR environment. This can be applied to augmented reality (AR) and virtual reality (VR) applications, expanding the scope of computational resource utilization.

[0072] The emotion estimation function can provide personalized computational tasks based on the user's emotions, improving the user experience. The emotion estimation function, for example, builds a system that provides personalized computational tasks based on the user's emotions. For example, it provides content that has a relaxing effect when the user is relaxing. The emotion estimation function also generates personalized computational tasks based on the user's emotion data, improving the user experience. For example, it provides entertainment content when the emotion score is high. The emotion estimation function also dynamically adjusts computational tasks according to the user's emotional state, providing an optimal user experience. For example, it provides a task that has a relaxing effect when the user is feeling stressed. In this way, the user experience is improved by providing personalized computational tasks based on the user's emotions.

[0073] It is possible to develop an algorithm that learns smartphone usage patterns, predicts optimal idle times, and schedules computing tasks. By learning smartphone usage patterns, an algorithm can be developed that predicts optimal idle times, for example. For example, by analyzing a user's past usage data and identifying times at night when usage is less frequent. A system can also be built that automatically adjusts the schedule of computing tasks based on usage patterns. For example, computing tasks can be performed when the user is sleeping. An algorithm can also be introduced that monitors smartphone usage patterns in real time and dynamically predicts idle times. For example, it can detect times when the user leaves their smartphone unattended and perform calculations during those times. This allows the system to learn smartphone usage patterns, predict optimal idle times, and schedule computing tasks, thereby achieving efficient computing.

[0074] To minimize battery consumption on smartphones, technology can be introduced that enables calculations in low-power mode. To minimize battery consumption on smartphones, for example, technology is developed that enables calculations in low-power mode. For example, unnecessary apps and functions are paused while a calculation task is being executed. To achieve calculations in low-power mode, a system is built that dynamically adjusts the priority of calculation tasks. For example, calculation tasks are paused when the battery level is low. To reduce battery consumption on smartphones, technology is introduced that automatically adjusts screen brightness and communication functions while a calculation task is being executed. For example, screen brightness is set to minimum and communication is minimized. This allows calculations to be executed while minimizing battery consumption, thereby extending the operating time of the smartphone.

[0075] The emotion estimation function can analyze a user's sleep patterns and execute calculation tasks at the optimal timing. For example, the emotion estimation function analyzes a user's sleep patterns in real time and builds a system that executes calculation tasks at the optimal timing. For example, calculations can be started during the time period when the user is in deep sleep. The emotion estimation function also develops an algorithm that dynamically adjusts the schedule of calculation tasks based on the user's sleep data. For example, calculation tasks can be scheduled before the user goes to bed and completed before waking up. The emotion estimation function also analyzes a user's sleep patterns and optimizes the execution timing of calculation tasks. For example, calculations can be executed during times when the user is relaxed. This reduces the burden on the user by analyzing the user's sleep patterns and executing calculation tasks at the optimal timing.

[0076] By learning smartphone usage patterns, we can execute computational tasks not only during the night but also during other idle times, such as during meetings or watching movies. We will build a system that learns smartphone usage patterns and maximizes computational resources by utilizing not only during the night but also other idle times, such as during meetings or watching movies. For example, we will monitor smartphone usage in real time and perform computations during times when the smartphone is not in use. We will also develop an algorithm that identifies idle times other than during the night based on usage patterns. For example, we will detect times when the user leaves their smartphone unattended and perform computations during those times. We will also build a system that predicts idle times, such as during meetings or watching movies, based on user schedule data and schedules computational tasks. For example, we will analyze data from a calendar app to identify idle times. This will maximize computational resources by utilizing idle times other than during the night.

[0077] Idle time on a smartphone can be used to simultaneously perform not only generative AI computational tasks but also data backups and system updates. A system can be built that utilizes idle time on a smartphone to simultaneously perform other tasks, such as data backups and system updates, in addition to generative AI computational tasks. For example, data backups and computational tasks can be performed simultaneously at night. Furthermore, to maximize idle time, an algorithm can be developed to run multiple tasks in parallel. For example, computational tasks and system updates can be performed simultaneously, improving efficiency. Furthermore, a system can be built that efficiently manages smartphone resources and runs multiple tasks during idle time. For example, data backups and computational tasks can be performed while minimizing battery consumption. This maximizes idle time and improves efficiency by running multiple tasks simultaneously.

[0078] The emotion estimation function can identify time periods when a user is relaxed and execute calculation tasks during those time periods. The emotion estimation function, for example, builds a system that identifies time periods when a user is relaxed in real time and executes calculation tasks during those time periods. For example, calculations are started during the time periods when the user is relaxed. The emotion estimation function also develops an algorithm that predicts time periods when a user is relaxed based on the user's emotion data. For example, past emotion data is analyzed to identify time periods when the user is relaxed. The emotion estimation function also improves the user experience by executing calculation tasks during time periods when the user is relaxed. For example, performing calculations during time periods when the user is relaxed minimizes battery consumption. This improves the user experience by executing calculation tasks during time periods when the user is relaxed.

[0079] A system that distributes rewards in real time according to the completion of a calculation task can be constructed, thereby increasing user motivation. A system that distributes rewards in real time according to the completion of a calculation task is constructed. For example, points or virtual currency are automatically awarded each time a calculation task is completed. Furthermore, a system that distributes rewards in real time is developed to increase user motivation. For example, the progress of a calculation task is displayed in real time, and rewards are immediately awarded. Furthermore, by distributing rewards in real time according to the completion of a calculation task, users' motivation to participate is increased. For example, a system is constructed in which rewards are reflected immediately each time a calculation task is completed. In this way, by distributing rewards in real time, user motivation is increased.

[0080] The types of rewards can be diversified, and not only points and virtual currency but also discount coupons and special offers can be provided. A system is constructed that diversifies the types of rewards, for example, by providing not only points and virtual currency but also discount coupons and special offers. For example, a discount coupon for an online store can be awarded in response to the completion of a calculation task. A system is also developed that customizes the types of rewards according to the user's preferences. For example, the reward selected by the user is given priority. A system is also constructed that diversifies the types of rewards to increase users' motivation to participate. For example, special offers and exclusive content can be provided in response to the completion of a calculation task. In this way, the diversification of the types of rewards increases users' motivation to participate.

[0081] The emotion estimation function can dynamically adjust the type and amount of rewards according to the user's emotional state. The emotion estimation function, for example, builds a system that dynamically adjusts the type and amount of rewards according to the user's emotional state. For example, if the user has positive emotions, the reward is increased. The emotion estimation function also develops an algorithm that dynamically adjusts the type and amount of rewards based on the user's emotional data. For example, if the emotional score is high, a special service is provided. The emotion estimation function also builds a system that customizes rewards according to the user's emotional state. For example, if the user is relaxed, a reward with a relaxing effect is provided. In this way, the type and amount of rewards are adjusted according to the user's emotional state, thereby improving user satisfaction.

[0082] The reward system can be linked with other applications and services, allowing users to use rewards in a variety of ways. A system can be built by linking the reward system with other applications and services, allowing users to use rewards in a variety of ways. For example, linking with an online store or subscription service. Furthermore, a system can be developed that expands the scope of reward usage by linking with other applications and services. For example, using rewards to purchase movie tickets or music streaming services. Furthermore, the reward system can be linked with various applications and services to improve user convenience. For example, using rewards to purchase transportation tickets. In this way, user convenience can be improved by linking the reward system with other applications and services.

[0083] The distribution of rewards can be linked to a user's social contribution and environmental protection activities to increase social value. We will build a system that links the distribution of rewards to other factors, such as a user's social contribution and environmental protection activities. For example, a user can receive additional rewards by participating in environmental protection activities. We will also develop an algorithm that links the distribution of rewards to social contribution activities to increase social value. For example, participating in volunteer activities increases rewards. We will also build a system that links the distribution of rewards to a user's social behavior. For example, a user can receive rewards by contributing to recycling activities or energy conservation. In this way, social value can be increased by linking the distribution of rewards to social contribution and environmental protection activities.

[0084] The emotion estimation function can identify the reward that the user will enjoy most and provide that reward preferentially. The emotion estimation function, for example, builds a system that identifies the reward that the user will enjoy most. For example, it analyzes the user's emotional data and provides rewards that receive a lot of positive responses. The emotion estimation function also develops an algorithm that dynamically adjusts the most enjoyable reward based on the user's emotional data. For example, it provides rewards with high emotional scores preferentially. The emotion estimation function also builds a system that identifies the reward that the user will enjoy most and provides those rewards preferentially. For example, it provides rewards that have a relaxing effect during times when the user is relaxing. In this way, the reward that the user will enjoy most can be identified and provided preferentially, thereby improving user satisfaction.

[0085] Data security can be ensured by implementing end-to-end encryption in the distribution of computational tasks and the return of results. For example, a system is constructed that implements end-to-end encryption in the distribution of computational tasks and the return of results. For example, computational tasks and results are encrypted to prevent unauthorized access by third parties. Also, an algorithm is developed that ensures data security by implementing end-to-end encryption. For example, encryption keys are dynamically generated to enhance security. Also, a system is constructed that protects user privacy by implementing end-to-end encryption in the distribution of computational tasks and the return of results. For example, encryption is performed to prevent personal information from being leaked. In this way, data security is ensured by implementing end-to-end encryption.

[0086] By utilizing the security features of smartphones, it is possible to protect against unauthorized access and malware while a computing task is being executed. By utilizing the security features of smartphones, for example, a system is constructed that protects against unauthorized access and malware while a computing task is being executed. For example, security software is used to monitor in real time. Furthermore, an algorithm is developed that strengthens the security features of smartphones to protect against unauthorized access and malware while a computing task is being executed. For example, a firewall and virus scan are implemented. Furthermore, a system is constructed that ensures data safety by utilizing the security features of smartphones to protect against unauthorized access and malware while a computing task is being executed. For example, a security policy is set and the execution of a computing task is controlled. In this way, the security features of smartphones are utilized to protect against unauthorized access and malware while a computing task is being executed.

[0087] The emotion estimation function can detect user anxieties and concerns in real time and strengthen security measures. The emotion estimation function, for example, detects user anxieties and concerns in real time and builds a system to strengthen security measures. For example, if a user feels anxious, additional security measures are implemented. The emotion estimation function also develops an algorithm to detect anxiety and concerns based on user emotion data. For example, if the emotion score is low, a security warning is displayed. The emotion estimation function also builds a system to detect user anxieties and concerns in real time and strengthen security measures, thereby improving the user's sense of security. For example, if a user feels anxious, security settings are automatically strengthened. In this way, the user's anxieties and concerns can be detected in real time and security measures strengthened, thereby improving the user's sense of security.

[0088] Ensuring security and privacy can be applied to other distributed computing systems and cloud services to improve the overall security level. For example, a system is constructed that applies ensuring security and privacy to other distributed computing systems and cloud services. For example, end-to-end encryption is applied to cloud services. Also, algorithms are developed that strengthen ensuring security and privacy in other distributed computing systems and cloud services. For example, data encryption and access control are strengthened. Also, a system is constructed that improves the overall security level by applying ensuring security and privacy to other distributed computing systems and cloud services. For example, security policies are unified and overall security is strengthened. In this way, the overall security level is improved by applying ensuring security and privacy to other distributed computing systems and cloud services.

[0089] In order to protect user privacy, the content of a computational task can be anonymized to prevent the leakage of personal information. In order to protect user privacy, for example, a system for anonymizing the content of a computational task is constructed. For example, only data that does not contain personal information is distributed as a computational task. In addition, an algorithm is developed to prevent the leakage of personal information by anonymizing the content of a computational task. For example, personal information is protected using data anonymization technology. In addition, in order to protect user privacy, a system is constructed to anonymize the content of a computational task to prevent the leakage of personal information. For example, data is anonymized before the distribution of a computational task. In this way, the content of the computational task is anonymized to protect user privacy.

[0090] In order to protect user privacy, the content of a computational task can be anonymized to prevent the leakage of personal information. In order to protect user privacy, for example, a system for anonymizing the content of a computational task is constructed. For example, only data that does not contain personal information is distributed as a computational task. In addition, an algorithm is developed to prevent the leakage of personal information by anonymizing the content of a computational task. For example, personal information is protected using data anonymization technology. In addition, in order to protect user privacy, a system is constructed to anonymize the content of a computational task to prevent the leakage of personal information. For example, data is anonymized before the distribution of a computational task. In this way, the content of the computational task is anonymized to protect user privacy.

[0091] The emotion estimation function can evaluate a user's sense of security regarding security and optimize security measures based on the results. The emotion estimation function, for example, builds a system that evaluates a user's sense of security regarding security in real time. For example, it analyzes the user's emotional data and calculates a sense of security score. The emotion estimation function also develops an algorithm that optimizes security measures based on the user's emotional data. For example, it implements additional security measures when the sense of security score is low. The emotion estimation function also builds a system that evaluates a user's sense of security regarding security and optimizes security measures based on the results. For example, it maintains security settings when the user feels secure, and strengthens security settings when the user feels anxious. In this way, the user's sense of security is improved by evaluating the user's sense of security regarding security and optimizing security measures based on the results.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The distributed computing system may further include a health management unit that collects user health data and assigns computing tasks based on the user's health condition. For example, the health management unit may monitor the user's heart rate and sleep data and assign computing tasks if the user's health condition is good. The health management unit may also suspend computing tasks if the user's health condition is deteriorating based on the user's health data. For example, if the user is feeling stressed, the health management unit may stop computing tasks and provide time for the user to relax. The health management unit may also analyze the user's health data and provide advice useful for improving the user's health condition. For example, the health management unit may send a notification encouraging the user to exercise if the user is not exercising enough. In this way, the user's health can be supported by allocating computing tasks based on the user's health condition.

[0094] The distributed computing system can further include an energy management unit to maximize energy efficiency. For example, the energy management unit collects energy consumption data from each smartphone and prioritizes allocating computing tasks to smartphones with high energy efficiency. The energy management unit can also monitor the energy supply situation in each region and concentrate computing tasks in regions with stable energy supplies. For example, it can allocate more tasks to urban areas with stable power supplies. The energy management unit can also execute computing tasks to avoid peak energy consumption times. For example, it can concentrate computations during nighttime hours when power consumption is low. This maximizes energy efficiency and realizes sustainable distributed computing.

[0095] The distributed computing system may further include an education support unit that collects user learning data and maximizes learning effectiveness. For example, the education support unit may analyze the user's learning progress data and assign calculation tasks to times when learning effectiveness is high. The education support unit may also provide advice to improve learning effectiveness based on the user's learning data. For example, it may send notifications encouraging students to study during times when they are most likely to concentrate. The education support unit may also analyze the user's learning data and provide an individualized learning plan. For example, it may provide learning content specialized for weak areas. This maximizes the user's learning effectiveness and supports efficient learning.

[0096] The distributed computing system may further include an entertainment unit that provides entertainment content according to the user's emotional state based on the user's emotional data. For example, the entertainment unit may provide relaxing music or videos when the user is relaxing. The entertainment unit may also dynamically adjust entertainment content according to the user's emotional state based on the user's emotional data. For example, if the user is feeling stressed, the entertainment unit may provide content that has a relaxing effect. The entertainment unit may also analyze the user's emotional data and provide advice that is useful for improving the user's emotional state. For example, the entertainment unit may introduce breathing techniques to help the user relax. In this way, entertainment content according to the user's emotional state may be provided, supporting the user's relaxation.

[0097] The distributed computing system may further include a fitness support unit that collects the user's exercise data and maximizes the effectiveness of the exercise. For example, the fitness support unit may analyze the user's exercise data and assign calculation tasks to times when the exercise is most effective. The fitness support unit may also provide advice to enhance the effectiveness of the exercise based on the user's exercise data. For example, it may send a notification encouraging the user to stretch after exercise. The fitness support unit may also analyze the user's exercise data and provide a personalized exercise plan. For example, it may suggest an exercise menu based on the user's physical strength. This maximizes the effectiveness of the user's exercise and supports efficient fitness.

[0098] The distributed computing system may further include an environmental management unit that collects environmental data from users and supports their environmental protection activities. For example, the environmental management unit may analyze the user's environmental data and provide information useful for environmental protection activities. The environmental management unit may also provide advice to promote environmental protection activities based on the user's environmental data. For example, the environmental management unit may introduce methods for reducing energy consumption. The environmental management unit may also analyze the user's environmental data and provide incentives for participating in environmental protection activities. For example, the environmental management unit may provide additional rewards for participating in environmental protection activities. This may support the user's environmental protection activities and contribute to the realization of a sustainable society.

[0099] The distributed computing system may further include a health advice unit that provides health advice according to the user's emotional state based on the user's emotional data. For example, the health advice unit provides advice on how to relax when the user is feeling stressed. The health advice unit may also dynamically adjust the health advice according to the user's emotional state based on the user's emotional data. For example, if the user is relaxed, the health advice unit provides advice on how to maintain the relaxation effect. The health advice unit may also analyze the user's emotional data and provide advice that is useful for improving the user's emotional state. For example, the health advice unit may introduce exercise methods to reduce stress. In this way, health advice according to the user's emotional state can be provided to support the user's health.

[0100] The distributed computing system may further include a hobby support unit that provides personalized content based on the user's hobbies and interests. For example, the hobby support unit collects data on the user's hobbies and interests and provides related content based on the collected data. The hobby support unit may also dynamically adjust the personalized content based on the user's hobbies and interests. For example, if the user is interested in music, the hobby support unit may introduce new music. The hobby support unit may also suggest related events and activities based on the user's hobbies and interests. For example, the hobby support unit may provide information about music festivals. This allows the provision of personalized content based on the user's hobbies and interests, thereby improving user satisfaction.

[0101] The distributed computing system may further include a reward adjustment unit that provides a reward according to the emotional state of the user based on the emotional data of the user. For example, the reward adjustment unit provides an additional reward when the user has positive emotions. The reward adjustment unit may also dynamically adjust the reward according to the emotional state based on the emotional data of the user. For example, when the user is relaxed, it may provide a reward that has a relaxing effect. The reward adjustment unit may also analyze the emotional data of the user and provide a reward that helps improve the emotional state. For example, it may provide a relaxation service to reduce stress. This makes it possible to provide a reward according to the emotional state of the user and improve user satisfaction.

[0102] The distributed computing system may further include a privacy management unit for protecting user privacy. For example, the privacy management unit may anonymize user data to prevent personal information from being leaked. The privacy management unit may also provide settings for protecting user privacy and enable users to control how their data is used. For example, the privacy management unit may provide opt-in and opt-out settings for data collection and use. The privacy management unit may also implement security measures for safely storing user data and protecting it from unauthorized access. For example, the privacy management unit may implement data encryption and strengthen access control. This protects user privacy and allows users to use the distributed computing system with peace of mind.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The computational task division unit divides the computational tasks of the generative AI. For example, if the generative AI performs data analysis for image recognition, text generation, and speech recognition, it divides the analysis tasks into smaller parts. Step 2: The task distribution unit distributes the computational tasks divided by the computational task division unit to smartphones. For example, it can allocate optimal tasks taking into account the smartphone's performance and remaining battery life, dynamically adjust tasks according to the power consumption situation in each region using geographical location information, or prioritize tasks to users with positive emotions using emotion estimation functions. Step 3: The reward management unit manages rewards according to the completion of the calculation task executed by the smartphone. For example, points or virtual currency can be awarded according to the completion of the calculation task, rewards can vary according to the difficulty or time required for the calculation task, and rewards can be distributed in real time according to the completion of the calculation task.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] 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.

[0120] 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.

[0121] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0134] 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.

[0135] 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.

[0136] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0149] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0150] 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.

[0151] 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.

[0152] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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. [Explanation of symbols]

[0172] 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 computational task division unit that divides the computational tasks of the generation AI; a task distribution unit that distributes the computational tasks divided by the computational task division unit to smartphones; a reward management unit that manages rewards according to completion of the computational tasks performed by the smartphone. A system characterized by:

2. The task distribution unit Evaluating the smartphone's performance and remaining battery life in real time and optimally allocating the computing tasks 2. The system of claim 1.

3. The computation task division unit Allocating the computing tasks not only to the smartphone but also to smart home appliances and in-vehicle computers 2. The system of claim 1.

4. To maximize the hardware performance of the smartphone, a dedicated distributed computing application will be developed to provide an optimized computing environment.

2. The system of claim 1.

5. A system is constructed to distribute the reward in real time according to the completion of the computational task, thereby increasing the motivation of the user.

2. The system of claim 1.

6. The task distribution unit Analyze the emotional state of the user and preferentially assign the computational tasks to users with positive emotions.

2. The system of claim 1.

Images