system

Abstract

The system according to this embodiment aims to effectively utilize idle resources in communication services and provide rewards to users. [Solution] The system according to the embodiment comprises a resource utilization unit, a reward provision unit, and a generation AI platform provision unit. The resource utilization unit utilizes resources. The reward provision unit provides rewards based on the amount of resources used by the resource utilization unit. The generation AI platform provision unit provides the resources used by the resource utilization unit as a generation AI platform.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, a method for effectively utilizing idle resources of communication services has not been fully established, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively utilize idle resources of communication services and provide rewards to users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a resource utilization unit, a reward provision unit, and a generation AI platform provision unit. The resource utilization unit utilizes resources. The reward provision unit provides rewards based on the amount of resources used by the resource utilization unit. The generation AI platform provision unit provides the resources used by the resource utilization unit as a generation AI platform. [Effects of the Invention]

[0007] The system according to this embodiment can effectively utilize idle resources in communication services and provide rewards to users. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that effectively utilizes the idle resources of a rental in-home router for communication services as a generation AI platform. This system utilizes the idle resources of a rental in-home router used by a user at home as a generation AI platform. In this case, the user can receive a reward (e.g., points) according to the amount of resources used. This allows the user to effectively utilize the router's idle resources while using the internet at home. First, the system utilizes the idle resources of a rental in-home router used by a user at home as a generation AI platform. In this case, the router's idle resources are used for processing the generation AI. For example, the generation AI can perform data analysis and model training using the router's idle resources. This makes it possible to use and provide the generation AI platform at a low cost. Next, the user can receive a reward according to the amount of resources used. Specifically, the reward is given based on the amount of the router's idle resources provided by the user. For example, a discount on the monthly line usage fee or points may be provided as a reward. This allows the user to enjoy economic benefits by providing the router's idle resources while using the internet at home. Furthermore, by effectively utilizing the idle resources of rental in-home routers across the country, the use and provision of the generation AI platform becomes possible at a low cost. This allows communication service providers to use and provide the generation AI platform at a low cost. In addition, the generation AI platform can be used to address various use cases. For example, a butler-type router could be used to deter the increasingly common illegal part-time jobs. This system allows users to effectively utilize the idle resources of their routers while using the internet at home and receive compensation. Communication service providers can also use and provide the generation AI platform at a low cost. This results in increased efficiency and economic benefits for communication services. As a result, the system can effectively utilize the idle resources of rental in-home routers for communication services as a generation AI platform.

[0029] The system according to the embodiment comprises a resource utilization unit, a reward provision unit, and a generation AI platform provision unit. The resource utilization unit utilizes resources. For example, the resource utilization unit uses the router's available resources for generation AI processing. For example, the resource utilization unit enables the generation AI to perform data analysis or model training using the router's available resources. For example, the resource utilization unit enables the generation AI to perform natural language processing using the router's available resources. For example, the resource utilization unit enables the generation AI to perform image recognition using the router's available resources. The reward provision unit provides rewards based on the amount of resources used by the resource utilization unit. For example, the reward provision unit provides points based on the amount of resources provided by the user. For example, the reward provision unit may also provide a discount on the monthly line usage fee based on the amount of resources provided by the user. For example, the reward provision unit may also provide coupons based on the amount of resources provided by the user. The generation AI platform provision unit provides the resources used by the resource utilization unit as a generation AI platform. The Generative AI Platform Provider Division provides a Generative AI platform by, for example, effectively utilizing the idle resources of rental in-home routers throughout Japan. The Generative AI Platform Provider Division can, for example, perform data analysis using the Generative AI Platform. The Generative AI Platform Provider Division can also, for example, train models using the Generative AI Platform. Furthermore, the Generative AI Platform Provider Division can, for example, perform natural language processing using the Generative AI Platform. This enables the system to effectively utilize resources and provide rewards.

[0030] The resource utilization unit utilizes resources. For example, the resource utilization unit uses the router's idle resources for processing the generative AI. Specifically, it can utilize unused portions of the router's CPU and memory to enable the generative AI to perform data analysis and model training. This allows for the effective use of resources that would normally be wasted. The resource utilization unit can also use the router's idle resources to enable the generative AI to perform natural language processing. Natural language processing includes analyzing large amounts of text data, contextual understanding, and sentence generation. This enables the generative AI to generate appropriate responses to user input. The resource utilization unit can also use the router's idle resources to enable the generative AI to perform image recognition. Image recognition includes object detection and classification within images, and face recognition. This allows the generative AI to extract useful information from image data and apply it to various applications. Furthermore, the resource utilization unit can dynamically allocate resources and distribute the load to efficiently perform these processes. For example, by sharing resources among multiple routers and evenly distributing the processing load, the overall system performance can be improved. Furthermore, the resource utilization unit can monitor resource usage in real time and reallocate or adjust resources as needed. This ensures efficient resource utilization and system stability.

[0031] The rewards department provides rewards based on the amount of resources used by the resource utilization department. Specifically, it provides rewards such as points, discounts, and coupons based on the amount of resources provided by the user. For example, points can be provided based on the amount of resources provided by the user. These points can be exchanged for goods or services later. The rewards department can also provide discounts on monthly line usage fees based on the amount of resources provided by the user. This allows users to save on communication costs by providing their resources. Furthermore, the rewards department can provide coupons based on the amount of resources provided by the user. These coupons can be used at affiliated stores and online shops, providing a tangible benefit to the user. By providing these rewards, the rewards department can increase the incentive for users to actively provide resources. In addition, the rewards department can monitor the user's resource provision status in real time and provide rewards at the appropriate time. This allows users to understand how their resources are being used and receive rewards accordingly. The rewards department can also collect user feedback and use it to improve the reward system or introduce new rewards. This allows the rewards department to increase user satisfaction and promote the overall use of the system.

[0032] The Generative AI Platform Provider Division provides resources used by the Resource Utilization Division as a Generative AI platform. Specifically, it effectively utilizes the vacant resources of rental in-home routers throughout Japan to provide the Generative AI platform. This enables the construction of a distributed Generative AI platform, allowing for efficient large-scale data analysis and model training. For example, the Generative AI Platform Provider Division can use the Generative AI platform to perform data analysis. Data analysis includes preprocessing of massive datasets, feature extraction, and statistical analysis. This allows the Generative AI to extract useful information from the data and apply it to various applications. For example, the Generative AI Platform Provider Division can also use the Generative AI platform to train models. Model training includes applying machine learning algorithms using large amounts of data and optimizing hyperparameters. This allows the Generative AI to build highly accurate predictive and classification models. For example, the Generative AI Platform Provider Division can also use the Generative AI platform to perform natural language processing. Natural language processing includes text data analysis, contextual understanding, and sentence generation. This allows the Generative AI to generate appropriate responses to user input. Furthermore, the Generative AI Platform Provider can dynamically allocate resources and distribute the load to efficiently perform these processes. For example, by sharing resources among multiple routers and evenly distributing the processing load, the overall system performance can be improved. The Generative AI Platform Provider can also monitor resource usage in real time and reallocate or adjust resources as needed. As a result, the Generative AI Platform Provider can provide an efficient and stable Generative AI platform, improving the overall system performance.

[0033] The resource utilization unit can use the router's available resources for processing the generating AI. For example, the resource utilization unit can use the router's available resources to enable the generating AI to perform data analysis. The resource utilization unit can also use the router's available resources to enable the generating AI to train a model. The resource utilization unit can also use the router's available resources to enable the generating AI to perform natural language processing. This makes it possible to effectively utilize resources by using the router's available resources for the generating AI's processing. Some or all of the above processing in the resource utilization unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the resource utilization unit can input the router's available resources into the generating AI, which can then perform data analysis.

[0034] The rewards provision unit can calculate and provide rewards based on the amount of resources provided by the user. For example, the rewards provision unit can provide points based on the amount of resources provided by the user. For example, the rewards provision unit can also provide a discount on the monthly line usage fee based on the amount of resources provided by the user. For example, the rewards provision unit can also provide coupons based on the amount of resources provided by the user. This improves user incentives by providing rewards based on resource usage. Some or all of the above processing in the rewards provision unit may be performed using AI or not. For example, the rewards provision unit can input the amount of resources provided by the user into the AI, and the AI ​​can calculate the rewards.

[0035] The Generative AI Platform Provider Unit can provide a Generative AI platform by effectively utilizing the unused resources of rental in-home routers throughout Japan. For example, the Generative AI Platform Provider Unit can provide a Generative AI platform using the unused resources of rental in-home routers throughout Japan. For example, the Generative AI Platform Provider Unit can perform data analysis using the unused resources of rental in-home routers throughout Japan. For example, the Generative AI Platform Provider Unit can train models using the unused resources of rental in-home routers throughout Japan. For example, the Generative AI Platform Provider Unit can perform natural language processing using the unused resources of rental in-home routers throughout Japan. This makes it possible to provide a Generative AI platform at a low cost by effectively utilizing the unused resources of rental in-home routers throughout Japan. Some or all of the above-mentioned processes in the Generative AI Platform Provider Unit may be performed using a Generative AI, or they may be performed without using a Generative AI. For example, the Generative AI Platform Provider Unit can input the unused resources of rental in-home routers throughout Japan into a Generative AI, and the Generative AI can perform data analysis.

[0036] The Generative AI Platform Provider can handle various use cases by utilizing the Generative AI Platform. For example, the Generative AI Platform Provider can perform data analysis using the Generative AI Platform. For example, the Generative AI Platform Provider can also train models using the Generative AI Platform. For example, the Generative AI Platform Provider can also perform natural language processing using the Generative AI Platform. In this way, by utilizing the Generative AI Platform, it becomes possible to handle various use cases. Some or all of the above-mentioned processes in the Generative AI Platform Provider may be performed using the Generative AI, or they may be performed without using the Generative AI. For example, the Generative AI Platform Provider can input the Generative AI Platform into the Generative AI, and the Generative AI can perform data analysis.

[0037] The Generative AI Platform Provider can support use cases where a butler-type router repels illegal part-time jobs. For example, the Generative AI Platform Provider can use a butler-type router to repel illegal part-time jobs. The Generative AI Platform Provider can also use a butler-type router to enhance security. For example, the Generative AI Platform Provider can use a butler-type router to detect illegal part-time jobs. This improves security by supporting use cases where a butler-type router repels illegal part-time jobs. Some or all of the above processing in the Generative AI Platform Provider may be performed using a Generative AI, or it may be performed without using a Generative AI. For example, the Generative AI Platform Provider can input a butler-type router into a Generative AI, and the Generative AI can detect illegal part-time jobs.

[0038] The resource utilization unit can monitor the user's internet usage in real time and select the optimal resource utilization method when using available router resources. For example, if the user is watching a video, the resource utilization unit can refrain from using resources and start using them when internet usage is low. For example, if the user is playing an online game, the resource utilization unit can refrain from using resources and start using them after the game ends. For example, if the user is downloading a large file, the resource utilization unit can refrain from using resources and start using them after the download is complete. This enables optimal resource utilization by monitoring the user's internet usage in real time. Some or all of the above processing in the resource utilization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the resource utilization unit can input the user's internet usage data into a generation AI, which can then select the optimal resource utilization method.

[0039] The resource utilization unit can analyze the user's past resource provision history and select the optimal resource utilization method when utilizing the router's available resources. For example, the resource utilization unit can analyze the time periods when the user previously provided resources and utilize resources during similar time periods. For example, the resource utilization unit can analyze the internet usage conditions when the user previously provided resources and utilize resources under similar conditions. For example, the resource utilization unit can analyze the reward history when the user previously provided resources and utilize resources during the time period when the reward was highest. In this way, optimal resource utilization becomes possible by analyzing the user's past resource provision history. Some or all of the above processing in the resource utilization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the resource utilization unit can input the user's past resource provision history data into a generation AI, and the generation AI can select the optimal resource utilization method.

[0040] The resource utilization unit, when utilizing available router resources, can prioritize the use of highly relevant resources by considering the user's geographical location information. For example, if the user is in an urban area, the resource utilization unit will prioritize the use of urban resources. If the user is in a suburban area, the resource utilization unit can also prioritize the use of suburban resources. If the user is traveling, the resource utilization unit can also prioritize the use of resources in the travel destination. In this way, by considering the user's geographical location information, highly relevant resources can be prioritized. Some or all of the above processing in the resource utilization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the resource utilization unit can input the user's geographical location information data into a generation AI, and the generation AI can select highly relevant resources.

[0041] The resource utilization unit can analyze the user's social media activity and utilize relevant resources when using available router resources. For example, the resource utilization unit can prioritize the use of resources for places the user has shared on social media. The resource utilization unit can also prioritize the use of resources for places the user follows on social media. The resource utilization unit can also prioritize the use of resources for places the user has checked in to on social media. This allows the system to prioritize the use of relevant resources by analyzing the user's social media activity. Some or all of the above processing in the resource utilization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the resource utilization unit can input the user's social media activity data into a generation AI, which can then select relevant resources.

[0042] The rewards provision unit can select the optimal reward calculation method by referring to the user's past reward history when calculating rewards based on resource usage. For example, the rewards provision unit can analyze the time periods in which the user has received high rewards in the past and provide rewards during similar time periods. For example, the rewards provision unit can also analyze the types of rewards the user has received in the past and provide similar rewards. For example, the rewards provision unit can analyze the frequency of rewards the user has received in the past and provide rewards at the optimal frequency. This makes it possible to calculate optimal rewards by referring to the user's past reward history. Some or all of the above processes in the rewards provision unit may be performed using AI or not. For example, the rewards provision unit can input the user's past reward history data into AI, and the AI ​​can select the optimal reward calculation method.

[0043] The reward provisioning unit can improve the accuracy of reward calculations by monitoring the user's internet usage in real time when calculating rewards based on resource usage. For example, the reward provisioning unit may reduce the reward if the user uses the internet a lot. For example, the reward provisioning unit may also increase the reward if the user uses the internet less. For example, the reward provisioning unit can monitor the user's internet usage in real time and provide the optimal reward. This improves the accuracy of reward calculations by monitoring the user's internet usage in real time. Some or all of the above processes in the reward provisioning unit may be performed using AI or not. For example, the reward provisioning unit can input the user's internet usage data into AI, which can then calculate the optimal reward.

[0044] The rewards provisioning unit can prioritize providing highly relevant rewards by considering the user's geographical location when calculating rewards based on resource usage. For example, if the user is in an urban area, the rewards provisioning unit can provide rewards available in urban areas. For example, if the user is in a suburban area, the rewards provisioning unit can also provide rewards available in suburban areas. For example, if the user is traveling, the rewards provisioning unit can also provide rewards available at their travel destination. This allows for the provision of highly relevant rewards by considering the user's geographical location. Some or all of the above processing in the rewards provisioning unit may be performed using AI or not. For example, the rewards provisioning unit can input the user's geographical location data into an AI, which can then select highly relevant rewards.

[0045] The rewards provisioning unit can analyze a user's social media activity and provide relevant rewards when calculating rewards based on resource usage. For example, the rewards provisioning unit can provide rewards available where the user shared on social media. For example, the rewards provisioning unit can also provide rewards available where the user follows on social media. For example, the rewards provisioning unit can provide rewards available where the user checked in on social media. This allows the system to provide relevant rewards by analyzing the user's social media activity. Some or all of the above processing in the rewards provisioning unit may be performed using AI or not. For example, the rewards provisioning unit can input the user's social media activity data into an AI, which can then select relevant rewards.

[0046] The Generative AI Platform Provider Unit can effectively utilize the available resources of rental in-home routers across the country by monitoring users' internet usage in real time and selecting the optimal resource provision method. For example, if a user is using the internet heavily, the Generative AI Platform Provider Unit can reduce resource provision and begin providing resources when internet usage is low. For example, if a user is playing an online game, the Generative AI Platform Provider Unit can reduce resource provision and begin providing resources after the game ends. For example, if a user is downloading a large file, the Generative AI Platform Provider Unit can reduce resource provision and begin providing resources after the download is complete. This enables optimal resource provision by monitoring users' internet usage in real time. Some or all of the above processing in the Generative AI Platform Provider Unit may be performed using a Generative AI, or it may be performed without using a Generative AI. For example, the Generative AI Platform Provider Unit can input user internet usage data into a Generative AI, which can then select the optimal resource provision method.

[0047] The Generative AI Platform Provider Unit can analyze a user's past resource provision history and select the optimal resource provision method when effectively utilizing the vacant resources of rental in-home routers throughout the country. For example, the Generative AI Platform Provider Unit can analyze the time periods when a user has previously provided resources and provide resources during similar time periods. For example, the Generative AI Platform Provider Unit can analyze the internet usage conditions when a user has previously provided resources and provide resources under similar conditions. For example, the Generative AI Platform Provider Unit can analyze the reward history when a user has previously provided resources and provide resources during the time period when the reward was highest. In this way, optimal resource provision becomes possible by analyzing a user's past resource provision history. Some or all of the above processing in the Generative AI Platform Provider Unit may be performed using a Generative AI, or it may be performed without using a Generative AI. For example, the Generative AI Platform Provider Unit can input the user's past resource provision history data into a Generative AI, which can then select the optimal resource provision method.

[0048] The Generative AI Platform Provider can prioritize providing highly relevant resources by considering the user's geographical location when effectively utilizing the available resources of rental in-home routers throughout the country. For example, if the user is in an urban area, the Generative AI Platform Provider can prioritize providing resources in urban areas. For example, if the user is in a suburban area, the Generative AI Platform Provider can also prioritize providing resources in suburban areas. For example, if the user is traveling, the Generative AI Platform Provider can also prioritize providing resources in the travel destination. In this way, highly relevant resources can be provided by considering the user's geographical location. Some or all of the above processing in the Generative AI Platform Provider may be performed using a Generative AI, or it may be performed without using a Generative AI. For example, the Generative AI Platform Provider can input the user's geographical location data into the Generative AI, and the Generative AI can select highly relevant resources.

[0049] The Generative AI Platform Provider can analyze users' social media activity and provide relevant resources when effectively utilizing the vacant resources of rental in-home routers throughout Japan. For example, the Generative AI Platform Provider can prioritize providing resources related to locations shared by users on social media. It can also prioritize providing resources related to locations followed by users on social media. It can also prioritize providing resources related to locations checked in by users on social media. In this way, relevant resources can be provided by analyzing users' social media activity. Some or all of the above processing in the Generative AI Platform Provider may be performed using a Generative AI, or it may be performed without using a Generative AI. For example, the Generative AI Platform Provider can input user social media activity data into a Generative AI, which can then select relevant resources.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The resource utilization unit can monitor the user's internet usage in real time and select the optimal resource utilization method when using available router resources. For example, if the user is watching a video, the resource utilization unit can refrain from using resources and start using them when internet usage is low. For example, if the user is playing an online game, the resource utilization unit can refrain from using resources and start using them after the game ends. For example, if the user is downloading a large file, the resource utilization unit can refrain from using resources and start using them after the download is complete. This enables optimal resource utilization by monitoring the user's internet usage in real time. Some or all of the above processing in the resource utilization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the resource utilization unit can input the user's internet usage data into a generation AI, which can then select the optimal resource utilization method.

[0052] The resource utilization unit can analyze the user's past resource provision history and select the optimal resource utilization method when utilizing the router's available resources. For example, the resource utilization unit can analyze the time periods when the user previously provided resources and utilize resources during similar time periods. For example, the resource utilization unit can analyze the internet usage conditions when the user previously provided resources and utilize resources under similar conditions. For example, the resource utilization unit can analyze the reward history when the user previously provided resources and utilize resources during the time period when the reward was highest. In this way, optimal resource utilization becomes possible by analyzing the user's past resource provision history. Some or all of the above processing in the resource utilization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the resource utilization unit can input the user's past resource provision history data into a generation AI, and the generation AI can select the optimal resource utilization method.

[0053] The resource utilization unit, when utilizing available router resources, can prioritize the use of highly relevant resources by considering the user's geographical location information. For example, if the user is in an urban area, the resource utilization unit will prioritize the use of urban resources. If the user is in a suburban area, the resource utilization unit can also prioritize the use of suburban resources. If the user is traveling, the resource utilization unit can also prioritize the use of resources in the travel destination. In this way, by considering the user's geographical location information, highly relevant resources can be prioritized. Some or all of the above processing in the resource utilization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the resource utilization unit can input the user's geographical location information data into a generation AI, and the generation AI can select highly relevant resources.

[0054] The resource utilization unit can analyze the user's social media activity and utilize relevant resources when using available router resources. For example, the resource utilization unit can prioritize the use of resources for places the user has shared on social media. The resource utilization unit can also prioritize the use of resources for places the user follows on social media. The resource utilization unit can also prioritize the use of resources for places the user has checked in to on social media. This allows the system to prioritize the use of relevant resources by analyzing the user's social media activity. Some or all of the above processing in the resource utilization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the resource utilization unit can input the user's social media activity data into a generation AI, which can then select relevant resources.

[0055] The rewards provision unit can select the optimal reward calculation method by referring to the user's past reward history when calculating rewards based on resource usage. For example, the rewards provision unit can analyze the time periods in which the user has received high rewards in the past and provide rewards during similar time periods. For example, the rewards provision unit can also analyze the types of rewards the user has received in the past and provide similar rewards. For example, the rewards provision unit can analyze the frequency of rewards the user has received in the past and provide rewards at the optimal frequency. This makes it possible to calculate optimal rewards by referring to the user's past reward history. Some or all of the above processes in the rewards provision unit may be performed using AI or not. For example, the rewards provision unit can input the user's past reward history data into AI, and the AI ​​can select the optimal reward calculation method.

[0056] The reward provisioning unit can improve the accuracy of reward calculations by monitoring the user's internet usage in real time when calculating rewards based on resource usage. For example, the reward provisioning unit may reduce the reward if the user uses the internet a lot. For example, the reward provisioning unit may also increase the reward if the user uses the internet less. For example, the reward provisioning unit can monitor the user's internet usage in real time and provide the optimal reward. This improves the accuracy of reward calculations by monitoring the user's internet usage in real time. Some or all of the above processes in the reward provisioning unit may be performed using AI or not. For example, the reward provisioning unit can input the user's internet usage data into AI, which can then calculate the optimal reward.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The resource utilization unit utilizes resources. For example, it can use the router's available resources for generating AI processing, enabling data analysis, model training, natural language processing, and image recognition. Step 2: The reward provision unit provides rewards based on the amount of resources used by the resource utilization unit. For example, points, discounts on monthly line usage fees, or coupons can be provided based on the amount of resources provided by the user. Step 3: The Generative AI Platform Provisioning Unit provides the resources used by the Resource Utilization Unit as a Generative AI Platform. For example, it can effectively utilize the unused resources of rental in-home routers throughout the country to provide a Generative AI Platform that can be used for data analysis, model training, and natural language processing.

[0059] (Example of form 2) The system according to an embodiment of the present invention is a system that effectively utilizes the idle resources of a rental in-home router for communication services as a generation AI platform. This system utilizes the idle resources of a rental in-home router used by a user at home as a generation AI platform. In this case, the user can receive a reward (e.g., points) according to the amount of resources used. This allows the user to effectively utilize the router's idle resources while using the internet at home. First, the system utilizes the idle resources of a rental in-home router used by a user at home as a generation AI platform. In this case, the router's idle resources are used for processing the generation AI. For example, the generation AI can perform data analysis and model training using the router's idle resources. This makes it possible to use and provide the generation AI platform at a low cost. Next, the user can receive a reward according to the amount of resources used. Specifically, the reward is given based on the amount of the router's idle resources provided by the user. For example, a discount on the monthly line usage fee or points may be provided as a reward. This allows the user to enjoy economic benefits by providing the router's idle resources while using the internet at home. Furthermore, by effectively utilizing the idle resources of rental in-home routers across the country, the use and provision of the generation AI platform becomes possible at a low cost. This allows communication service providers to use and provide the generation AI platform at a low cost. In addition, the generation AI platform can be used to address various use cases. For example, a butler-type router could be used to deter the increasingly common illegal part-time jobs. This system allows users to effectively utilize the idle resources of their routers while using the internet at home and receive compensation. Communication service providers can also use and provide the generation AI platform at a low cost. This results in increased efficiency and economic benefits for communication services. As a result, the system can effectively utilize the idle resources of rental in-home routers for communication services as a generation AI platform.

[0060] The system according to the embodiment comprises a resource utilization unit, a reward provision unit, and a generation AI platform provision unit. The resource utilization unit utilizes resources. For example, the resource utilization unit uses the router's available resources for generation AI processing. For example, the resource utilization unit enables the generation AI to perform data analysis or model training using the router's available resources. For example, the resource utilization unit enables the generation AI to perform natural language processing using the router's available resources. For example, the resource utilization unit enables the generation AI to perform image recognition using the router's available resources. The reward provision unit provides rewards based on the amount of resources used by the resource utilization unit. For example, the reward provision unit provides points based on the amount of resources provided by the user. For example, the reward provision unit may also provide a discount on the monthly line usage fee based on the amount of resources provided by the user. For example, the reward provision unit may also provide coupons based on the amount of resources provided by the user. The generation AI platform provision unit provides the resources used by the resource utilization unit as a generation AI platform. The Generative AI Platform Provider Division provides a Generative AI platform by, for example, effectively utilizing the idle resources of rental in-home routers throughout Japan. The Generative AI Platform Provider Division can, for example, perform data analysis using the Generative AI Platform. The Generative AI Platform Provider Division can also, for example, train models using the Generative AI Platform. Furthermore, the Generative AI Platform Provider Division can, for example, perform natural language processing using the Generative AI Platform. This enables the system to effectively utilize resources and provide rewards.

[0061] The resource utilization unit utilizes resources. For example, the resource utilization unit uses the router's idle resources for processing the generative AI. Specifically, it can utilize unused portions of the router's CPU and memory to enable the generative AI to perform data analysis and model training. This allows for the effective use of resources that would normally be wasted. The resource utilization unit can also use the router's idle resources to enable the generative AI to perform natural language processing. Natural language processing includes analyzing large amounts of text data, contextual understanding, and sentence generation. This enables the generative AI to generate appropriate responses to user input. The resource utilization unit can also use the router's idle resources to enable the generative AI to perform image recognition. Image recognition includes object detection and classification within images, and face recognition. This allows the generative AI to extract useful information from image data and apply it to various applications. Furthermore, the resource utilization unit can dynamically allocate resources and distribute the load to efficiently perform these processes. For example, by sharing resources among multiple routers and evenly distributing the processing load, the overall system performance can be improved. Furthermore, the resource utilization unit can monitor resource usage in real time and reallocate or adjust resources as needed. This ensures efficient resource utilization and system stability.

[0062] The rewards department provides rewards based on the amount of resources used by the resource utilization department. Specifically, it provides rewards such as points, discounts, and coupons based on the amount of resources provided by the user. For example, points can be provided based on the amount of resources provided by the user. These points can be exchanged for goods or services later. The rewards department can also provide discounts on monthly line usage fees based on the amount of resources provided by the user. This allows users to save on communication costs by providing their resources. Furthermore, the rewards department can provide coupons based on the amount of resources provided by the user. These coupons can be used at affiliated stores and online shops, providing a tangible benefit to the user. By providing these rewards, the rewards department can increase the incentive for users to actively provide resources. In addition, the rewards department can monitor the user's resource provision status in real time and provide rewards at the appropriate time. This allows users to understand how their resources are being used and receive rewards accordingly. The rewards department can also collect user feedback and use it to improve the reward system or introduce new rewards. This allows the rewards department to increase user satisfaction and promote the overall use of the system.

[0063] The Generative AI Platform Provider Division provides resources used by the Resource Utilization Division as a Generative AI platform. Specifically, it effectively utilizes the vacant resources of rental in-home routers throughout Japan to provide the Generative AI platform. This enables the construction of a distributed Generative AI platform, allowing for efficient large-scale data analysis and model training. For example, the Generative AI Platform Provider Division can use the Generative AI platform to perform data analysis. Data analysis includes preprocessing of massive datasets, feature extraction, and statistical analysis. This allows the Generative AI to extract useful information from the data and apply it to various applications. For example, the Generative AI Platform Provider Division can also use the Generative AI platform to train models. Model training includes applying machine learning algorithms using large amounts of data and optimizing hyperparameters. This allows the Generative AI to build highly accurate predictive and classification models. For example, the Generative AI Platform Provider Division can also use the Generative AI platform to perform natural language processing. Natural language processing includes text data analysis, contextual understanding, and sentence generation. This allows the Generative AI to generate appropriate responses to user input. Furthermore, the Generative AI Platform Provider can dynamically allocate resources and distribute the load to efficiently perform these processes. For example, by sharing resources among multiple routers and evenly distributing the processing load, the overall system performance can be improved. The Generative AI Platform Provider can also monitor resource usage in real time and reallocate or adjust resources as needed. As a result, the Generative AI Platform Provider can provide an efficient and stable Generative AI platform, improving the overall system performance.

[0064] The resource utilization unit can use the router's available resources for processing the generating AI. For example, the resource utilization unit can use the router's available resources to enable the generating AI to perform data analysis. The resource utilization unit can also use the router's available resources to enable the generating AI to train a model. The resource utilization unit can also use the router's available resources to enable the generating AI to perform natural language processing. This makes it possible to effectively utilize resources by using the router's available resources for the generating AI's processing. Some or all of the above processing in the resource utilization unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the resource utilization unit can input the router's available resources into the generating AI, which can then perform data analysis.

[0065] The rewards provision unit can calculate and provide rewards based on the amount of resources provided by the user. For example, the rewards provision unit can provide points based on the amount of resources provided by the user. For example, the rewards provision unit can also provide a discount on the monthly line usage fee based on the amount of resources provided by the user. For example, the rewards provision unit can also provide coupons based on the amount of resources provided by the user. This improves user incentives by providing rewards based on resource usage. Some or all of the above processing in the rewards provision unit may be performed using AI or not. For example, the rewards provision unit can input the amount of resources provided by the user into the AI, and the AI ​​can calculate the rewards.

[0066] The Generative AI Platform Provider Unit can provide a Generative AI platform by effectively utilizing the unused resources of rental in-home routers throughout Japan. For example, the Generative AI Platform Provider Unit can provide a Generative AI platform using the unused resources of rental in-home routers throughout Japan. For example, the Generative AI Platform Provider Unit can perform data analysis using the unused resources of rental in-home routers throughout Japan. For example, the Generative AI Platform Provider Unit can train models using the unused resources of rental in-home routers throughout Japan. For example, the Generative AI Platform Provider Unit can perform natural language processing using the unused resources of rental in-home routers throughout Japan. This makes it possible to provide a Generative AI platform at a low cost by effectively utilizing the unused resources of rental in-home routers throughout Japan. Some or all of the above-mentioned processes in the Generative AI Platform Provider Unit may be performed using a Generative AI, or they may be performed without using a Generative AI. For example, the Generative AI Platform Provider Unit can input the unused resources of rental in-home routers throughout Japan into a Generative AI, and the Generative AI can perform data analysis.

[0067] The Generative AI Platform Provider can handle various use cases by utilizing the Generative AI Platform. For example, the Generative AI Platform Provider can perform data analysis using the Generative AI Platform. For example, the Generative AI Platform Provider can also train models using the Generative AI Platform. For example, the Generative AI Platform Provider can also perform natural language processing using the Generative AI Platform. In this way, by utilizing the Generative AI Platform, it becomes possible to handle various use cases. Some or all of the above-mentioned processes in the Generative AI Platform Provider may be performed using the Generative AI, or they may be performed without using the Generative AI. For example, the Generative AI Platform Provider can input the Generative AI Platform into the Generative AI, and the Generative AI can perform data analysis.

[0068] The Generative AI Platform Provider can support use cases where a butler-type router repels illegal part-time jobs. For example, the Generative AI Platform Provider can use a butler-type router to repel illegal part-time jobs. The Generative AI Platform Provider can also use a butler-type router to enhance security. For example, the Generative AI Platform Provider can use a butler-type router to detect illegal part-time jobs. This improves security by supporting use cases where a butler-type router repels illegal part-time jobs. Some or all of the above processing in the Generative AI Platform Provider may be performed using a Generative AI, or it may be performed without using a Generative AI. For example, the Generative AI Platform Provider can input a butler-type router into a Generative AI, and the Generative AI can detect illegal part-time jobs.

[0069] The resource utilization unit can estimate the user's emotions and adjust the timing of resource utilization based on the estimated emotions. For example, if the user is stressed, the resource utilization unit may refrain from using resources and begin using them when the user is relaxed. For example, if the user is concentrating, the resource utilization unit may refrain from using resources and begin using them during a break. For example, if the user is in a hurry, the resource utilization unit may pause resource utilization and resume it when the user has calmed down. This improves user comfort by adjusting the timing of resource utilization based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the resource utilization unit may be performed using the generative AI or not. For example, the resource utilization unit can input user emotion data into the generative AI, which can estimate the emotions.

[0070] The resource utilization unit can monitor the user's internet usage in real time and select the optimal resource utilization method when using available router resources. For example, if the user is watching a video, the resource utilization unit can refrain from using resources and start using them when internet usage is low. For example, if the user is playing an online game, the resource utilization unit can refrain from using resources and start using them after the game ends. For example, if the user is downloading a large file, the resource utilization unit can refrain from using resources and start using them after the download is complete. This enables optimal resource utilization by monitoring the user's internet usage in real time. Some or all of the above processing in the resource utilization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the resource utilization unit can input the user's internet usage data into a generation AI, which can then select the optimal resource utilization method.

[0071] The resource utilization unit can analyze the user's past resource provision history and select the optimal resource utilization method when utilizing the router's available resources. For example, the resource utilization unit can analyze the time periods when the user previously provided resources and utilize resources during similar time periods. For example, the resource utilization unit can analyze the internet usage conditions when the user previously provided resources and utilize resources under similar conditions. For example, the resource utilization unit can analyze the reward history when the user previously provided resources and utilize resources during the time period when the reward was highest. In this way, optimal resource utilization becomes possible by analyzing the user's past resource provision history. Some or all of the above processing in the resource utilization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the resource utilization unit can input the user's past resource provision history data into a generation AI, and the generation AI can select the optimal resource utilization method.

[0072] The resource utilization unit can estimate the user's emotions and determine resource utilization priorities based on the estimated emotions. For example, if the user is relaxed, the resource utilization unit may set a high priority for resource utilization. For example, if the user is stressed, the resource utilization unit may set a low priority for resource utilization. For example, if the user is focused, the resource utilization unit may set a medium priority for resource utilization. This improves user comfort by determining resource utilization priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the resource utilization unit may be performed using the generative AI or not. For example, the resource utilization unit can input user emotion data into the generative AI, which can then estimate the emotions.

[0073] The resource utilization unit, when utilizing available router resources, can prioritize the use of highly relevant resources by considering the user's geographical location information. For example, if the user is in an urban area, the resource utilization unit will prioritize the use of urban resources. If the user is in a suburban area, the resource utilization unit can also prioritize the use of suburban resources. If the user is traveling, the resource utilization unit can also prioritize the use of resources in the travel destination. In this way, by considering the user's geographical location information, highly relevant resources can be prioritized. Some or all of the above processing in the resource utilization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the resource utilization unit can input the user's geographical location information data into a generation AI, and the generation AI can select highly relevant resources.

[0074] The resource utilization unit can analyze the user's social media activity and utilize relevant resources when using available router resources. For example, the resource utilization unit can prioritize the use of resources for places the user has shared on social media. The resource utilization unit can also prioritize the use of resources for places the user follows on social media. The resource utilization unit can also prioritize the use of resources for places the user has checked in to on social media. This allows the system to prioritize the use of relevant resources by analyzing the user's social media activity. Some or all of the above processing in the resource utilization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the resource utilization unit can input the user's social media activity data into a generation AI, which can then select relevant resources.

[0075] The rewards distribution unit can estimate the user's emotions and adjust the method of reward distribution based on the estimated emotions. For example, if the user is relaxed, the rewards distribution unit may provide points immediately. For example, if the user is stressed, the rewards distribution unit may provide points in a lump sum at a later date. For example, if the user is in a hurry, the rewards distribution unit may provide points immediately and keep the notification brief. This improves user satisfaction by adjusting the method of reward distribution based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the rewards distribution unit may be performed using AI or not. For example, the rewards distribution unit can input user emotion data into a generative AI, which can then estimate the emotions.

[0076] The rewards provision unit can select the optimal reward calculation method by referring to the user's past reward history when calculating rewards based on resource usage. For example, the rewards provision unit can analyze the time periods in which the user has received high rewards in the past and provide rewards during similar time periods. For example, the rewards provision unit can also analyze the types of rewards the user has received in the past and provide similar rewards. For example, the rewards provision unit can analyze the frequency of rewards the user has received in the past and provide rewards at the optimal frequency. This makes it possible to calculate optimal rewards by referring to the user's past reward history. Some or all of the above processes in the rewards provision unit may be performed using AI or not. For example, the rewards provision unit can input the user's past reward history data into AI, and the AI ​​can select the optimal reward calculation method.

[0077] The reward provisioning unit can improve the accuracy of reward calculations by monitoring the user's internet usage in real time when calculating rewards based on resource usage. For example, the reward provisioning unit may reduce the reward if the user uses the internet a lot. For example, the reward provisioning unit may also increase the reward if the user uses the internet less. For example, the reward provisioning unit can monitor the user's internet usage in real time and provide the optimal reward. This improves the accuracy of reward calculations by monitoring the user's internet usage in real time. Some or all of the above processes in the reward provisioning unit may be performed using AI or not. For example, the reward provisioning unit can input the user's internet usage data into AI, which can then calculate the optimal reward.

[0078] The reward system can estimate the user's emotions and determine the priority of rewards based on those emotions. For example, if the user is relaxed, the reward system may set a high priority for rewards. For example, if the user is stressed, the reward system may set a low priority for rewards. For example, if the user is focused, the reward system may set a medium priority for rewards. This improves user satisfaction by determining reward priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reward system may be performed using AI or not. For example, the reward system can input user emotion data into a generative AI, which can then estimate the emotions.

[0079] The rewards provisioning unit can prioritize providing highly relevant rewards by considering the user's geographical location when calculating rewards based on resource usage. For example, if the user is in an urban area, the rewards provisioning unit can provide rewards available in urban areas. For example, if the user is in a suburban area, the rewards provisioning unit can also provide rewards available in suburban areas. For example, if the user is traveling, the rewards provisioning unit can also provide rewards available at their travel destination. This allows for the provision of highly relevant rewards by considering the user's geographical location. Some or all of the above processing in the rewards provisioning unit may be performed using AI or not. For example, the rewards provisioning unit can input the user's geographical location data into an AI, which can then select highly relevant rewards.

[0080] The rewards provisioning unit can analyze a user's social media activity and provide relevant rewards when calculating rewards based on resource usage. For example, the rewards provisioning unit can provide rewards available where the user shared on social media. For example, the rewards provisioning unit can also provide rewards available where the user follows on social media. For example, the rewards provisioning unit can provide rewards available where the user checked in on social media. This allows the system to provide relevant rewards by analyzing the user's social media activity. Some or all of the above processing in the rewards provisioning unit may be performed using AI or not. For example, the rewards provisioning unit can input the user's social media activity data into an AI, which can then select relevant rewards.

[0081] The Generative AI Platform Provider can estimate the user's emotions and adjust the method of providing the Generative AI Platform based on the estimated user emotions. For example, if the user is relaxed, the Generative AI Platform Provider can provide the Generative AI Platform immediately. For example, if the user is stressed, the Generative AI Platform Provider can provide the Generative AI Platform at a later date. For example, if the user is in a hurry, the Generative AI Platform Provider can provide the Generative AI Platform quickly. By adjusting the method of providing the Generative AI Platform based on the user's emotions, user satisfaction is improved. Emotion estimation is achieved using an emotion estimation function with an emotion engine or Generative AI. The Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Generative AI Platform Provider may be performed using the Generative AI or not. For example, the Generative AI Platform Provider can input user emotion data into the Generative AI, and the Generative AI can estimate the emotions.

[0082] The Generative AI Platform Provider Unit can effectively utilize the available resources of rental in-home routers across the country by monitoring users' internet usage in real time and selecting the optimal resource provision method. For example, if a user is using the internet heavily, the Generative AI Platform Provider Unit can reduce resource provision and begin providing resources when internet usage is low. For example, if a user is playing an online game, the Generative AI Platform Provider Unit can reduce resource provision and begin providing resources after the game ends. For example, if a user is downloading a large file, the Generative AI Platform Provider Unit can reduce resource provision and begin providing resources after the download is complete. This enables optimal resource provision by monitoring users' internet usage in real time. Some or all of the above processing in the Generative AI Platform Provider Unit may be performed using a Generative AI, or it may be performed without using a Generative AI. For example, the Generative AI Platform Provider Unit can input user internet usage data into a Generative AI, which can then select the optimal resource provision method.

[0083] The Generative AI Platform Provider Unit can analyze a user's past resource provision history and select the optimal resource provision method when effectively utilizing the vacant resources of rental in-home routers throughout the country. For example, the Generative AI Platform Provider Unit can analyze the time periods when a user has previously provided resources and provide resources during similar time periods. For example, the Generative AI Platform Provider Unit can analyze the internet usage conditions when a user has previously provided resources and provide resources under similar conditions. For example, the Generative AI Platform Provider Unit can analyze the reward history when a user has previously provided resources and provide resources during the time period when the reward was highest. In this way, optimal resource provision becomes possible by analyzing a user's past resource provision history. Some or all of the above processing in the Generative AI Platform Provider Unit may be performed using a Generative AI, or it may be performed without using a Generative AI. For example, the Generative AI Platform Provider Unit can input the user's past resource provision history data into a Generative AI, which can then select the optimal resource provision method.

[0084] The Generative AI Platform Provider can estimate the user's emotions and determine the priority of providing the Generative AI Platform based on the estimated user emotions. For example, if the user is relaxed, the Generative AI Platform Provider can set a high priority for providing the Generative AI Platform. For example, if the user is stressed, the Generative AI Platform Provider can also set a low priority for providing the Generative AI Platform. For example, if the user is focused, the Generative AI Platform Provider can also set a medium priority for providing the Generative AI Platform. This improves user satisfaction by determining the priority of providing the Generative AI Platform based on the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or Generative AI. The Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Generative AI Platform Provider may be performed using the Generative AI or not. For example, the Generative AI Platform Provider can input user emotion data into the Generative AI, and the Generative AI can estimate the emotions.

[0085] The Generative AI Platform Provider can prioritize providing highly relevant resources by considering the user's geographical location when effectively utilizing the available resources of rental in-home routers throughout the country. For example, if the user is in an urban area, the Generative AI Platform Provider can prioritize providing resources in urban areas. For example, if the user is in a suburban area, the Generative AI Platform Provider can also prioritize providing resources in suburban areas. For example, if the user is traveling, the Generative AI Platform Provider can also prioritize providing resources in the travel destination. In this way, highly relevant resources can be provided by considering the user's geographical location. Some or all of the above processing in the Generative AI Platform Provider may be performed using a Generative AI, or it may be performed without using a Generative AI. For example, the Generative AI Platform Provider can input the user's geographical location data into the Generative AI, and the Generative AI can select highly relevant resources.

[0086] The Generative AI Platform Provider can analyze users' social media activity and provide relevant resources when effectively utilizing the vacant resources of rental in-home routers throughout Japan. For example, the Generative AI Platform Provider can prioritize providing resources related to locations shared by users on social media. It can also prioritize providing resources related to locations followed by users on social media. It can also prioritize providing resources related to locations checked in by users on social media. In this way, relevant resources can be provided by analyzing users' social media activity. Some or all of the above processing in the Generative AI Platform Provider may be performed using a Generative AI, or it may be performed without using a Generative AI. For example, the Generative AI Platform Provider can input user social media activity data into a Generative AI, which can then select relevant resources.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The resource utilization unit can estimate the user's emotions and adjust the timing of resource utilization based on the estimated emotions. For example, if the user is stressed, the resource utilization unit may refrain from using resources and begin using them when the user is relaxed. For example, if the user is concentrating, the resource utilization unit may refrain from using resources and begin using them during a break. For example, if the user is in a hurry, the resource utilization unit may pause resource utilization and resume it when the user has calmed down. This improves user comfort by adjusting the timing of resource utilization based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the resource utilization unit may be performed using the generative AI or not. For example, the resource utilization unit can input user emotion data into the generative AI, which can estimate the emotions.

[0089] The resource utilization unit can monitor the user's internet usage in real time and select the optimal resource utilization method when using available router resources. For example, if the user is watching a video, the resource utilization unit can refrain from using resources and start using them when internet usage is low. For example, if the user is playing an online game, the resource utilization unit can refrain from using resources and start using them after the game ends. For example, if the user is downloading a large file, the resource utilization unit can refrain from using resources and start using them after the download is complete. This enables optimal resource utilization by monitoring the user's internet usage in real time. Some or all of the above processing in the resource utilization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the resource utilization unit can input the user's internet usage data into a generation AI, which can then select the optimal resource utilization method.

[0090] The resource utilization unit can analyze the user's past resource provision history and select the optimal resource utilization method when utilizing the router's available resources. For example, the resource utilization unit can analyze the time periods when the user previously provided resources and utilize resources during similar time periods. For example, the resource utilization unit can analyze the internet usage conditions when the user previously provided resources and utilize resources under similar conditions. For example, the resource utilization unit can analyze the reward history when the user previously provided resources and utilize resources during the time period when the reward was highest. In this way, optimal resource utilization becomes possible by analyzing the user's past resource provision history. Some or all of the above processing in the resource utilization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the resource utilization unit can input the user's past resource provision history data into a generation AI, and the generation AI can select the optimal resource utilization method.

[0091] The resource utilization unit can estimate the user's emotions and determine resource utilization priorities based on the estimated emotions. For example, if the user is relaxed, the resource utilization unit may set a high priority for resource utilization. For example, if the user is stressed, the resource utilization unit may set a low priority for resource utilization. For example, if the user is focused, the resource utilization unit may set a medium priority for resource utilization. This improves user comfort by determining resource utilization priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the resource utilization unit may be performed using the generative AI or not. For example, the resource utilization unit can input user emotion data into the generative AI, which can then estimate the emotions.

[0092] The resource utilization unit, when utilizing available router resources, can prioritize the use of highly relevant resources by considering the user's geographical location information. For example, if the user is in an urban area, the resource utilization unit will prioritize the use of urban resources. If the user is in a suburban area, the resource utilization unit can also prioritize the use of suburban resources. If the user is traveling, the resource utilization unit can also prioritize the use of resources in the travel destination. In this way, by considering the user's geographical location information, highly relevant resources can be prioritized. Some or all of the above processing in the resource utilization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the resource utilization unit can input the user's geographical location information data into a generation AI, and the generation AI can select highly relevant resources.

[0093] The resource utilization unit can analyze the user's social media activity and utilize relevant resources when using available router resources. For example, the resource utilization unit can prioritize the use of resources for places the user has shared on social media. The resource utilization unit can also prioritize the use of resources for places the user follows on social media. The resource utilization unit can also prioritize the use of resources for places the user has checked in to on social media. This allows the system to prioritize the use of relevant resources by analyzing the user's social media activity. Some or all of the above processing in the resource utilization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the resource utilization unit can input the user's social media activity data into a generation AI, which can then select relevant resources.

[0094] The rewards distribution unit can estimate the user's emotions and adjust the method of reward distribution based on the estimated emotions. For example, if the user is relaxed, the rewards distribution unit may provide points immediately. For example, if the user is stressed, the rewards distribution unit may provide points in a lump sum at a later date. For example, if the user is in a hurry, the rewards distribution unit may provide points immediately and keep the notification brief. This improves user satisfaction by adjusting the method of reward distribution based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the rewards distribution unit may be performed using AI or not. For example, the rewards distribution unit can input user emotion data into a generative AI, which can then estimate the emotions.

[0095] The rewards provision unit can select the optimal reward calculation method by referring to the user's past reward history when calculating rewards based on resource usage. For example, the rewards provision unit can analyze the time periods in which the user has received high rewards in the past and provide rewards during similar time periods. For example, the rewards provision unit can also analyze the types of rewards the user has received in the past and provide similar rewards. For example, the rewards provision unit can analyze the frequency of rewards the user has received in the past and provide rewards at the optimal frequency. This makes it possible to calculate optimal rewards by referring to the user's past reward history. Some or all of the above processes in the rewards provision unit may be performed using AI or not. For example, the rewards provision unit can input the user's past reward history data into AI, and the AI ​​can select the optimal reward calculation method.

[0096] The reward provisioning unit can improve the accuracy of reward calculations by monitoring the user's internet usage in real time when calculating rewards based on resource usage. For example, the reward provisioning unit may reduce the reward if the user uses the internet a lot. For example, the reward provisioning unit may also increase the reward if the user uses the internet less. For example, the reward provisioning unit can monitor the user's internet usage in real time and provide the optimal reward. This improves the accuracy of reward calculations by monitoring the user's internet usage in real time. Some or all of the above processes in the reward provisioning unit may be performed using AI or not. For example, the reward provisioning unit can input the user's internet usage data into AI, which can then calculate the optimal reward.

[0097] The reward system can estimate the user's emotions and determine the priority of rewards based on those emotions. For example, if the user is relaxed, the reward system may set a high priority for rewards. For example, if the user is stressed, the reward system may set a low priority for rewards. For example, if the user is focused, the reward system may set a medium priority for rewards. This improves user satisfaction by determining reward priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reward system may be performed using AI or not. For example, the reward system can input user emotion data into a generative AI, which can then estimate the emotions.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The resource utilization unit utilizes resources. For example, it can use the router's available resources for generating AI processing, enabling data analysis, model training, natural language processing, and image recognition. Step 2: The reward provision unit provides rewards based on the amount of resources used by the resource utilization unit. For example, points, discounts on monthly line usage fees, or coupons can be provided based on the amount of resources provided by the user. Step 3: The Generative AI Platform Provisioning Unit provides the resources used by the Resource Utilization Unit as a Generative AI Platform. For example, it can effectively utilize the unused resources of rental in-home routers throughout the country to provide a Generative AI Platform that can be used for data analysis, model training, and natural language processing.

[0100] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0101] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0103] Each of the multiple elements described above, including the resource utilization unit, reward provision unit, and generation AI platform provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the resource utilization unit is implemented by the control unit 46A of the smart device 14 and uses the router's idle resources for processing the generation AI. The reward provision unit is implemented by the specific processing unit 290 of the data processing device 12 and provides rewards based on resource usage. The generation AI platform provision unit is implemented by the specific processing unit 290 of the data processing device 12 and provides a generation AI platform by effectively utilizing the idle resources of rental in-home routers throughout the country. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the resource utilization unit, reward provision unit, and generation AI platform provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the resource utilization unit is implemented by the control unit 46A of the smart glasses 214 and uses the router's idle resources for processing the generation AI. The reward provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides rewards based on resource usage. The generation AI platform provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides a generation AI platform by effectively utilizing the idle resources of rental in-home routers throughout the country. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the resource utilization unit, reward provision unit, and generation AI platform provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the resource utilization unit is implemented by the control unit 46A of the headset terminal 314 and uses the router's idle resources for processing the generation AI. The reward provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides rewards based on resource usage. The generation AI platform provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a generation AI platform by effectively utilizing the idle resources of rental in-home routers throughout the country. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] As shown in Figure 7, the 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.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the resource utilization unit, reward provision unit, and generation AI platform provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the resource utilization unit is implemented by the control unit 46A of the robot 414 and uses the router's idle resources for processing the generation AI. The reward provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides rewards based on resource usage. The generation AI platform provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a generation AI platform by effectively utilizing the idle resources of rental in-home routers throughout the country. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0153] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0162] 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.

[0163] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0165] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) Resource utilization unit that utilizes resources, A reward provision unit provides a reward based on the amount of resources used by the resource utilization unit, The system includes a generation AI platform providing unit that provides the resources used by the resource utilization unit as a generation AI platform. A system characterized by the following features. (Note 2) The resource utilization unit is, Use the router's available resources to generate AI processing. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned compensation provision unit, Compensation is calculated and provided based on the amount of resources used by the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned AI generation platform providing unit We will make effective use of the unused resources of rental in-home routers across the country to provide a generation AI platform. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned AI generation platform providing unit We can use the generated AI platform to support a variety of use cases. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned AI generation platform providing unit A butler-type router addresses use cases where it helps deter illegal part-time jobs. The system described in Appendix 1, characterized by the features described herein. (Note 7) The resource utilization unit is, It estimates the user's emotions and adjusts the timing of resource usage based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The resource utilization unit is, When utilizing the router's available resources, the system monitors the user's internet usage in real time and selects the optimal resource utilization method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The resource utilization unit is, When utilizing available router resources, the system analyzes the user's past resource provision history to select the optimal resource utilization method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The resource utilization unit is, It estimates user sentiment and prioritizes resource usage based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The resource utilization unit is, When utilizing available router resources, the system prioritizes the use of relevant resources by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The resource utilization unit is, When utilizing available router resources, the system analyzes the user's social media activity and uses relevant resources. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned compensation provision unit, The system estimates the user's emotions and adjusts the reward system based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned compensation provision unit, When calculating rewards based on resource usage, the system selects the optimal reward calculation method by referring to the user's past reward history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned compensation provision unit, When calculating rewards based on resource usage, real-time monitoring of users' internet usage improves the accuracy of reward calculations. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned compensation provision unit, The system estimates the user's emotions and prioritizes rewards based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned compensation provision unit, When calculating rewards based on resource usage, the system prioritizes providing more relevant rewards by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned compensation provision unit, When calculating rewards based on resource usage, we analyze users' social media activity and provide relevant rewards. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned AI generation platform providing unit We estimate the user's emotions and adjust how the generative AI platform is delivered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned AI generation platform providing unit To effectively utilize the available resources of rental in-home routers across the country, we monitor users' internet usage in real time and select the optimal resource provision method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned AI generation platform providing unit When effectively utilizing the unused resources of rental in-home routers across the country, the system analyzes users' past resource provision history to select the optimal resource provision method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned AI generation platform providing unit It estimates user emotions and determines the priority of providing the generative AI platform based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned AI generation platform providing unit When effectively utilizing the available resources of rental in-home routers across the country, the system prioritizes providing highly relevant resources by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned AI generation platform providing unit When effectively utilizing the unused resources of rental in-home routers across the country, we analyze users' social media activity and provide relevant resources. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. Resource utilization unit that utilizes resources, A reward provision unit provides a reward based on the amount of resources used by the resource utilization unit, The system includes a generation AI platform providing unit that provides the resources used by the resource utilization unit as a generation AI platform. A system characterized by the following features.

2. The resource utilization unit is, Use the router's available resources to generate AI processing. The system according to feature 1.

3. The aforementioned compensation provision unit, Compensation is calculated and provided based on the amount of resources used by the user. The system according to feature 1.

4. The aforementioned AI generation platform providing unit, We will make effective use of the unused resources of rental in-home routers across the country to provide a generation AI platform. The system according to feature 1.

5. The aforementioned AI generation platform providing unit, We use a generative AI platform to support a variety of use cases. The system according to feature 1.

6. The aforementioned AI generation platform providing unit, A butler-type router addresses use cases where it helps deter illegal part-time jobs. The system according to feature 1.

7. The resource utilization unit is, It estimates the user's emotions and adjusts the timing of resource usage based on those estimated emotions. The system according to feature 1.

8. The resource utilization unit is, When utilizing the router's available resources, the system monitors the user's internet usage in real time and selects the optimal resource utilization method. The system according to feature 1.

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