system

Abstract

An object of a system according to an embodiment is to construct a generative AI cluster by effectively utilizing an old GPU or an idle GPU.SOLUTION: A system according to an embodiment includes a collection unit, an evaluation unit, and a cluster construction unit. The collection unit collects an old GPU and an idle GPU. The evaluation unit evaluates the performance and state of the old GPU and idle GPU collected by the collection unit. The cluster construction unit constructs a generation AI cluster using the old GPU or the idle GPU evaluated by the evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not fully utilize outdated or idle GPUs, which can lead to increased waste and wasted resources.

[0005] The system according to the embodiment aims to build a generative AI cluster by effectively utilizing old GPUs and idle GPUs. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an evaluation unit, and a cluster construction unit. The collection unit collects old GPUs and idle GPUs. The evaluation unit evaluates the performance and status of the old GPUs and idle GPUs collected by the collection unit. The cluster construction unit constructs a generation AI cluster using the old GPUs and idle GPUs evaluated by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively utilize old GPUs and idle GPUs to build a generative AI cluster. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The generative AI infrastructure system according to an embodiment of the present invention is a system that collects and evaluates old and unused GPUs and builds generative AI clusters. This allows the generative AI infrastructure system to effectively utilize old and unused GPUs and realize low-cost infrastructure.

[0029] A generative AI infrastructure system according to an embodiment includes a collection unit, an evaluation unit, and a cluster construction unit. The collection unit collects old and idle GPUs. For example, a company may donate an old GPU that it plans to discard, or an individual may sell a GPU that they no longer use. The collection unit can also collect GPUs from a wide range of locations through an online platform. For example, the collection unit may operate a website that accepts donations from companies and individuals and list the GPUs to be collected. The evaluation unit evaluates the performance and status of the old and idle GPUs collected by the collection unit. For example, the evaluation unit may conduct benchmark tests to measure the performance of each GPU. The evaluation unit may also monitor the temperature and power consumption of the GPU to evaluate its operating status. For example, the evaluation unit may evaluate performance based on the GPU's operating frequency and memory capacity and detect overheating using a temperature sensor. The cluster construction unit constructs a generative AI cluster using the old and idle GPUs evaluated by the evaluation unit. For example, the cluster construction unit may construct a system that performs computational processing by linking multiple GPUs. The cluster construction unit can also construct a cluster used for training and inference of the generative AI. For example, the cluster construction unit installs the collected GPUs on a server and performs distributed processing to construct a high-performance generative AI infrastructure. As a result, the generative AI infrastructure system according to the embodiment can effectively utilize old and unused GPUs and realize an inexpensive infrastructure. For example, the collection unit collects old and unused GPUs from a wide range of locations, the evaluation unit evaluates the performance and status of the collected GPUs, and the cluster construction unit constructs a generative AI cluster using the evaluated GPUs.

[0030] The collection unit can use the generation AI to automatically evaluate the performance and status of GPUs and develop an optimal collection plan. For example, to automatically evaluate the GPUs to be collected, the collection unit uses the generation AI to analyze the performance and status of each GPU. For example, the evaluation is based on the GPU's operating frequency, memory capacity, and past usage history. The collection unit also utilizes the generation AI to monitor the status of the GPUs to be collected in real time and develop an optimal collection plan. For example, it prioritizes collection of GPUs with a high risk of failure. The collection unit also automatically evaluates the performance of the GPUs to be collected, and the generation AI proposes an optimal collection route. For example, it plans a route for efficiently collecting high-performance GPUs. This enables efficient collection by automatically evaluating the performance and status of the GPUs to be collected and developing an optimal collection plan.

[0031] The evaluation unit can analyze GPU history data and evaluate reusability based on usage and failure history. The evaluation unit, for example, analyzes collected GPU history data and evaluates reusability based on past usage. For example, it analyzes usage time and load status to determine whether or not reuse is possible. The evaluation unit also builds a system to evaluate the reusability of collected GPUs based on failure history. For example, it excludes GPUs that have frequently experienced failures in the past. The evaluation unit also analyzes collected GPU history data and develops an algorithm for evaluating reusability. For example, it performs an evaluation taking into account frequency of use and maintenance history. In this way, efficient reuse is possible by analyzing collected GPU history data and evaluating reusability.

[0032] The collection unit can simultaneously collect computer parts such as CPUs and memory in addition to GPUs, thereby enabling resource reuse. The collection unit, for example, builds a system that simultaneously collects other computer parts such as CPUs and memory in addition to GPUs. For example, it takes over entire computers scheduled for disposal. The collection unit also expands the scope of collection and develops algorithms for reusing computer parts other than GPUs. For example, it evaluates the performance of CPUs and memory to determine whether they can be reused. The collection unit also collects computer parts other than GPUs in order to achieve comprehensive resource reuse and builds a system that selects reusable parts. For example, it checks their operation and identifies reusable parts. This makes it possible to not limit the collection target to GPUs, but to simultaneously collect other computer parts, enabling comprehensive resource reuse.

[0033] The Collection Department can provide the collected GPUs to educational institutions and research institutions to use them in research and education on generative AI. For example, the Collection Department can provide the collected GPUs to educational institutions and build a program to use them in research and education on generative AI. For example, the Collection Department can donate GPUs to university laboratories. The Collection Department can also provide the collected GPUs to research institutions to use them for training and inference on generative AI. For example, the Collection Department can provide GPUs necessary for research projects. The Collection Department can also collaborate with educational institutions and research institutions to promote joint research using the collected GPUs. For example, the GPUs can be used to develop new algorithms for generative AI. In this way, the collected GPUs can be provided to educational institutions and research institutions to be used in research and education on generative AI.

[0034] The cluster construction unit uses generative AI to optimize the load distribution of GPUs within the cluster, enabling efficient computational processing. For example, the cluster construction unit uses generative AI to develop an algorithm that optimizes the load distribution of each GPU within the cluster. For example, it distributes the load evenly based on the performance and status of each GPU. The cluster construction unit also monitors the load of GPUs within the cluster in real time, and constructs a system in which generative AI optimally distributes the load. For example, it detects overloaded GPUs and adjusts the load. The cluster construction unit also uses generative AI to develop a load distribution strategy to improve the efficiency of computational processing within the cluster. For example, it optimally allocates computational tasks to each GPU. This allows generative AI to optimize the load distribution of each GPU within the cluster, enabling efficient computational processing and improving cluster performance.

[0035] The cluster construction unit can monitor the temperature and power consumption of GPUs in real time during the cluster construction process, thereby maintaining an optimal operating environment. For example, the cluster construction unit builds a system that monitors the temperature and power consumption of each GPU in real time during the cluster construction process. For example, it uses sensors to monitor the status of each GPU. The cluster construction unit also develops an algorithm to maintain an optimal operating environment based on the temperature and power consumption of each GPU. For example, it performs control to cool down an overheated GPU. The cluster construction unit also uses generative AI to build a system that optimizes the temperature and power consumption of each GPU in the cluster. For example, it reduces the load on GPUs that consume high amounts of power. This improves the stability and efficiency of the cluster by monitoring the temperature and power consumption of each GPU in real time during the cluster construction process and maintaining an optimal operating environment.

[0036] The cluster construction unit can utilize clusters using used GPUs for different purposes and provide multipurpose infrastructure. For example, the cluster construction unit builds a system that utilizes clusters using used GPUs for blockchain mining. For example, it optimizes mining algorithms to perform calculations efficiently. The cluster construction unit also builds clusters that utilize used GPUs for scientific computing. For example, it provides a computing environment specialized for simulation and data analysis. The cluster construction unit also develops systems that provide clusters using used GPUs as multipurpose infrastructure. For example, it can support different purposes such as generative AI, mining, and scientific computing. This allows clusters using used GPUs to be utilized for different purposes and provides multipurpose infrastructure, enabling effective use of resources.

[0037] The cluster construction unit can provide a portion of the cluster as a remotely accessible cloud service, making it available to a wide range of users. The cluster construction unit, for example, constructs a system that provides a portion of a cluster using used GPUs as a remotely accessible cloud service. For example, it allows users to access the cluster via the Internet. The cluster construction unit also develops an interface for providing the cloud service and makes it available to a wide range of users. For example, it allows cluster resources to be used through an API. The cluster construction unit also strengthens security measures for providing the remotely accessible cloud service. For example, it implements access control and data encryption. This allows a portion of the cluster to be provided as a remotely accessible cloud service, making it available to a wide range of users, thereby improving user convenience.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The collection unit can simultaneously collect computer parts such as CPUs and memory in addition to GPUs, thereby enabling resource reuse. For example, a system can be built to simultaneously collect other computer parts such as CPUs and memory in addition to GPUs. For example, the collection unit can take over entire computers scheduled for disposal. The collection unit can also expand the scope of collection and develop algorithms to reuse computer parts other than GPUs. For example, the performance of CPUs and memory can be evaluated to determine whether they can be reused. The collection unit can also collect computer parts other than GPUs in order to achieve comprehensive resource reuse, and build a system to select reusable parts. For example, the collection unit can check their operation and identify reusable parts. This makes it possible to not limit the collection to GPUs, but to simultaneously collect other computer parts, enabling comprehensive resource reuse.

[0040] The Collection Department can provide the collected GPUs to educational institutions and research institutions to use them in research and education on generative AI. For example, the Department can provide the collected GPUs to educational institutions and build a program to use them in research and education on generative AI. For example, the Department can donate GPUs to university laboratories. The Collection Department can also provide the collected GPUs to research institutions to use them for training and inference on generative AI. For example, the Department can provide GPU resources necessary for research projects. The Collection Department can also collaborate with educational institutions and research institutions to promote joint research using the collected GPUs. For example, the GPUs can be used to develop new algorithms for generative AI. In this way, the collected GPUs can be provided to educational institutions and research institutions to be used in research and education on generative AI.

[0041] The cluster construction unit can use generative AI to optimize the load distribution of GPUs within a cluster and achieve efficient computational processing. For example, generative AI can be used to develop an algorithm that optimizes the load distribution of each GPU within a cluster. For example, the load can be evenly distributed based on the performance and status of each GPU. The cluster construction unit also monitors the load of GPUs within a cluster in real time, and constructs a system in which generative AI optimally distributes the load. For example, it detects overloaded GPUs and adjusts the load. The cluster construction unit also uses generative AI to devise a load distribution strategy to improve the efficiency of computational processing within the cluster. For example, it optimally allocates computational tasks to each GPU. This allows generative AI to optimize the load distribution of each GPU within a cluster and achieve efficient computational processing, thereby improving cluster performance.

[0042] The cluster construction unit can monitor the temperature and power consumption of GPUs in real time during the cluster construction process, thereby maintaining an optimal operating environment. For example, during the cluster construction process, a system is built that monitors the temperature and power consumption of each GPU in real time. For example, sensors are used to monitor the status of each GPU. The cluster construction unit also develops algorithms to maintain an optimal operating environment based on the temperature and power consumption of each GPU. For example, it performs control to cool down overheated GPUs. The cluster construction unit also uses generative AI to build a system that optimizes the temperature and power consumption of each GPU in the cluster. For example, it reduces the load on GPUs with high power consumption. This allows the temperature and power consumption of each GPU to be monitored in real time during the cluster construction process, thereby maintaining an optimal operating environment and improving the stability and efficiency of the cluster.

[0043] The cluster construction department can utilize clusters using used GPUs for different purposes and provide multipurpose infrastructure. For example, it can build a system that utilizes clusters using used GPUs for blockchain mining. For example, it can optimize mining algorithms to perform calculations efficiently. The cluster construction department can also build clusters that utilize used GPUs for scientific computing. For example, it can provide a computing environment specialized for simulation and data analysis. The cluster construction department can also develop systems that provide clusters using used GPUs as multipurpose infrastructure. For example, it can support different uses such as generative AI, mining, and scientific computing. This allows clusters using used GPUs to be utilized for different purposes and provide multipurpose infrastructure, enabling effective use of resources.

[0044] The cluster construction unit can provide a portion of the cluster as a remotely accessible cloud service, making it available to a wide range of users. For example, a system can be constructed that provides a portion of a cluster using used GPUs as a remotely accessible cloud service. For example, users can access the cluster via the Internet. The cluster construction unit can also develop an interface for providing the cloud service and make it available to a wide range of users. For example, the cluster resources can be made available through an API. The cluster construction unit can also strengthen security measures for providing the remotely accessible cloud service. For example, it can implement access control and data encryption. This allows a portion of the cluster to be provided as a remotely accessible cloud service, making it available to a wide range of users, thereby improving user convenience.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The collection unit collects old or unused GPUs. For example, companies may donate old GPUs that are scheduled to be discarded, or individuals may sell GPUs that they no longer use. The collection unit can also collect GPUs widely through an online platform. For example, the collection unit may operate a website that accepts donations from companies and individuals and lists the GPUs to be collected. Step 2: The evaluation unit evaluates the performance and status of the old and unused GPUs collected by the collection unit. For example, the evaluation unit performs benchmark tests to measure the performance of each GPU. The evaluation unit can also monitor the temperature and power consumption of the GPU to evaluate its operating status. For example, the evaluation unit evaluates performance based on the GPU's operating frequency and memory capacity, and uses a temperature sensor to detect overheating. Step 3: The cluster construction unit builds a generative AI cluster using the old and unused GPUs evaluated by the evaluation unit. For example, the cluster construction unit builds a system that performs computational processing by linking multiple GPUs. The cluster construction unit can also build clusters that are used for training and inference of the generative AI. For example, the cluster construction unit installs the collected GPUs on a server and performs distributed processing to build a high-performance generative AI platform.

[0047] (Example 2) The generative AI infrastructure system according to an embodiment of the present invention is a system that collects and evaluates old and unused GPUs and builds generative AI clusters. This allows the generative AI infrastructure system to effectively utilize old and unused GPUs and realize low-cost infrastructure.

[0048] A generative AI infrastructure system according to an embodiment includes a collection unit, an evaluation unit, and a cluster construction unit. The collection unit collects old and idle GPUs. For example, a company may donate an old GPU that it plans to discard, or an individual may sell a GPU that they no longer use. The collection unit can also collect GPUs from a wide range of locations through an online platform. For example, the collection unit may operate a website that accepts donations from companies and individuals and list the GPUs to be collected. The evaluation unit evaluates the performance and status of the old and idle GPUs collected by the collection unit. For example, the evaluation unit may conduct benchmark tests to measure the performance of each GPU. The evaluation unit may also monitor the temperature and power consumption of the GPU to evaluate its operating status. For example, the evaluation unit may evaluate performance based on the GPU's operating frequency and memory capacity and detect overheating using a temperature sensor. The cluster construction unit constructs a generative AI cluster using the old and idle GPUs evaluated by the evaluation unit. For example, the cluster construction unit may construct a system that performs computational processing by linking multiple GPUs. The cluster construction unit can also construct a cluster used for training and inference of the generative AI. For example, the cluster construction unit installs the collected GPUs on a server and performs distributed processing to construct a high-performance generative AI infrastructure. As a result, the generative AI infrastructure system according to the embodiment can effectively utilize old and unused GPUs and realize an inexpensive infrastructure. For example, the collection unit collects old and unused GPUs from a wide range of locations, the evaluation unit evaluates the performance and status of the collected GPUs, and the cluster construction unit constructs a generative AI cluster using the evaluated GPUs.

[0049] The collection unit can use the generation AI to automatically evaluate the performance and status of GPUs and develop an optimal collection plan. For example, to automatically evaluate the GPUs to be collected, the collection unit uses the generation AI to analyze the performance and status of each GPU. For example, the evaluation is based on the GPU's operating frequency, memory capacity, and past usage history. The collection unit also utilizes the generation AI to monitor the status of the GPUs to be collected in real time and develop an optimal collection plan. For example, it prioritizes collection of GPUs with a high risk of failure. The collection unit also automatically evaluates the performance of the GPUs to be collected, and the generation AI proposes an optimal collection route. For example, it plans a route for efficiently collecting high-performance GPUs. This enables efficient collection by automatically evaluating the performance and status of the GPUs to be collected and developing an optimal collection plan.

[0050] The evaluation unit can analyze GPU history data and evaluate reusability based on usage and failure history. The evaluation unit, for example, analyzes collected GPU history data and evaluates reusability based on past usage. For example, it analyzes usage time and load status to determine whether or not reuse is possible. The evaluation unit also builds a system to evaluate the reusability of collected GPUs based on failure history. For example, it excludes GPUs that have frequently experienced failures in the past. The evaluation unit also analyzes collected GPU history data and develops an algorithm for evaluating reusability. For example, it performs an evaluation taking into account frequency of use and maintenance history. In this way, efficient reuse is possible by analyzing collected GPU history data and evaluating reusability.

[0051] The collection unit can use the emotion estimation function to monitor the satisfaction of GPU providers in real time and provide incentives according to the provider's emotions. The collection unit, for example, uses the emotion estimation function to build a system that monitors the satisfaction of GPU providers in real time. For example, it calculates an emotion score by analyzing the provider's facial expressions and voice. The collection unit also develops an algorithm for providing incentives according to the provider's emotions. For example, it provides additional rewards to providers with strong positive emotions. The collection unit also builds a system that evaluates the provider's satisfaction and adjusts incentives based on the emotion estimation data. For example, it increases or decreases rewards according to the provider's emotion score. In this way, the satisfaction of GPU providers can be monitored in real time and incentives according to their emotions can be provided, thereby improving provider satisfaction.

[0052] The collection unit can simultaneously collect computer parts such as CPUs and memory in addition to GPUs, thereby enabling resource reuse. The collection unit, for example, builds a system that simultaneously collects other computer parts such as CPUs and memory in addition to GPUs. For example, it takes over entire computers scheduled for disposal. The collection unit also expands the scope of collection and develops algorithms for reusing computer parts other than GPUs. For example, it evaluates the performance of CPUs and memory to determine whether they can be reused. The collection unit also collects computer parts other than GPUs in order to achieve comprehensive resource reuse and builds a system that selects reusable parts. For example, it checks their operation and identifies reusable parts. This makes it possible to not limit the collection target to GPUs, but to simultaneously collect other computer parts, enabling comprehensive resource reuse.

[0053] The Collection Department can provide the collected GPUs to educational institutions and research institutions to use them in research and education on generative AI. For example, the Collection Department can provide the collected GPUs to educational institutions and build a program to use them in research and education on generative AI. For example, the Collection Department can donate GPUs to university laboratories. The Collection Department can also provide the collected GPUs to research institutions to use them for training and inference on generative AI. For example, the Collection Department can provide GPUs necessary for research projects. The Collection Department can also collaborate with educational institutions and research institutions to promote joint research using the collected GPUs. For example, the GPUs can be used to develop new algorithms for generative AI. In this way, the collected GPUs can be provided to educational institutions and research institutions to be used in research and education on generative AI.

[0054] The collection unit can use the emotion estimation function to optimize the advertisements and promotions of the collection campaign and develop a marketing strategy based on the emotions of the provider. The collection unit, for example, uses the emotion estimation function to build a system that optimizes the advertisements and promotions of the collection campaign. For example, the advertisement content is adjusted based on the emotional reactions of the provider. The collection unit also develops an algorithm for developing a marketing strategy based on the emotions of the provider. For example, advertisements that elicit positive emotions are created. The collection unit also builds a system that evaluates the effectiveness of the collection campaign and optimizes the marketing strategy based on the emotion estimation data. For example, advertisements with high emotion scores are preferentially displayed. In this way, the emotion estimation function is used to optimize the advertisements and promotions of the collection campaign and develop a marketing strategy based on the emotions of the provider, thereby improving collection efficiency.

[0055] The cluster construction unit uses generative AI to optimize the load distribution of GPUs within the cluster, enabling efficient computational processing. For example, the cluster construction unit uses generative AI to develop an algorithm that optimizes the load distribution of each GPU within the cluster. For example, it distributes the load evenly based on the performance and status of each GPU. The cluster construction unit also monitors the load of GPUs within the cluster in real time, and constructs a system in which generative AI optimally distributes the load. For example, it detects overloaded GPUs and adjusts the load. The cluster construction unit also uses generative AI to develop a load distribution strategy to improve the efficiency of computational processing within the cluster. For example, it optimally allocates computational tasks to each GPU. This allows generative AI to optimize the load distribution of each GPU within the cluster, enabling efficient computational processing and improving cluster performance.

[0056] The cluster construction unit can monitor the temperature and power consumption of GPUs in real time during the cluster construction process, thereby maintaining an optimal operating environment. For example, the cluster construction unit builds a system that monitors the temperature and power consumption of each GPU in real time during the cluster construction process. For example, it uses sensors to monitor the status of each GPU. The cluster construction unit also develops an algorithm to maintain an optimal operating environment based on the temperature and power consumption of each GPU. For example, it performs control to cool down an overheated GPU. The cluster construction unit also uses generative AI to build a system that optimizes the temperature and power consumption of each GPU in the cluster. For example, it reduces the load on GPUs that consume high amounts of power. This improves the stability and efficiency of the cluster by monitoring the temperature and power consumption of each GPU in real time during the cluster construction process and maintaining an optimal operating environment.

[0057] The cluster construction unit can use the emotion estimation function to evaluate the satisfaction of users in a cluster in real time and provide support according to the user's emotions. The cluster construction unit, for example, uses the emotion estimation function to construct a system that evaluates the satisfaction of users in a cluster in real time. For example, it calculates an emotion score by analyzing the user's facial expressions and voice. The cluster construction unit also develops an algorithm for providing support according to the user's emotions. For example, it provides additional support to users with strong negative emotions. The cluster construction unit also constructs a system that evaluates user satisfaction and adjusts support based on the emotion estimation data. For example, it changes the support content according to the emotion score. In this way, the emotion estimation function is used to evaluate the satisfaction of users in a cluster in real time and provides support according to the user's emotions, thereby improving user satisfaction.

[0058] The cluster construction unit can utilize clusters using used GPUs for different purposes and provide multipurpose infrastructure. For example, the cluster construction unit builds a system that utilizes clusters using used GPUs for blockchain mining. For example, it optimizes mining algorithms to perform calculations efficiently. The cluster construction unit also builds clusters that utilize used GPUs for scientific computing. For example, it provides a computing environment specialized for simulation and data analysis. The cluster construction unit also develops systems that provide clusters using used GPUs as multipurpose infrastructure. For example, it can support different purposes such as generative AI, mining, and scientific computing. This allows clusters using used GPUs to be utilized for different purposes and provides multipurpose infrastructure, enabling effective use of resources.

[0059] The cluster construction unit can provide a portion of the cluster as a remotely accessible cloud service, making it available to a wide range of users. The cluster construction unit, for example, constructs a system that provides a portion of a cluster using used GPUs as a remotely accessible cloud service. For example, it allows users to access the cluster via the Internet. The cluster construction unit also develops an interface for providing the cloud service and makes it available to a wide range of users. For example, it allows cluster resources to be used through an API. The cluster construction unit also strengthens security measures for providing the remotely accessible cloud service. For example, it implements access control and data encryption. This allows a portion of the cluster to be provided as a remotely accessible cloud service, making it available to a wide range of users, thereby improving user convenience.

[0060] The cluster construction unit can use the emotion estimation function to collect feedback from users of the cluster and continuously improve the performance and functions of the cluster. The cluster construction unit, for example, uses the emotion estimation function to construct a system that collects feedback from users of the cluster in real time. For example, it calculates an emotion score by analyzing the user's facial expressions and voice. The cluster construction unit also develops an algorithm for continuously improving the performance and functions of the cluster based on the user's emotional response data. For example, it improves functions that have a high level of negative emotions. The cluster construction unit also analyzes feedback from users of the cluster based on the emotion estimation data and constructs a system that improves performance and functions. For example, it identifies areas for improvement based on the emotion score. In this way, the emotion estimation function is used to collect feedback from users of the cluster and continuously improve the performance and functions of the cluster, thereby improving user satisfaction.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The collection unit uses the emotion estimation function to monitor the satisfaction of GPU providers in real time and provide incentives according to the provider's emotions. For example, it calculates an emotion score by analyzing the provider's facial expressions and voice. The collection unit also develops an algorithm to provide incentives according to the provider's emotions. For example, it provides additional rewards to providers with strong positive emotions. The collection unit also builds a system that evaluates provider satisfaction and adjusts incentives based on the emotion estimation data. For example, it increases or decreases rewards according to the provider's emotion score. In this way, the satisfaction of GPU providers can be monitored in real time and incentives according to their emotions can be provided, thereby improving provider satisfaction.

[0063] The collection unit can simultaneously collect computer parts such as CPUs and memory in addition to GPUs, thereby enabling resource reuse. For example, a system can be built to simultaneously collect other computer parts such as CPUs and memory in addition to GPUs. For example, the collection unit can take over entire computers scheduled for disposal. The collection unit can also expand the scope of collection and develop algorithms to reuse computer parts other than GPUs. For example, the performance of CPUs and memory can be evaluated to determine whether they can be reused. The collection unit can also collect computer parts other than GPUs in order to achieve comprehensive resource reuse, and build a system to select reusable parts. For example, the collection unit can check their operation and identify reusable parts. This makes it possible to not limit the collection to GPUs, but to simultaneously collect other computer parts, enabling comprehensive resource reuse.

[0064] The Collection Department can provide the collected GPUs to educational institutions and research institutions to use them in research and education on generative AI. For example, the Department can provide the collected GPUs to educational institutions and build a program to use them in research and education on generative AI. For example, the Department can donate GPUs to university laboratories. The Collection Department can also provide the collected GPUs to research institutions to use them for training and inference on generative AI. For example, the Department can provide GPU resources necessary for research projects. The Collection Department can also collaborate with educational institutions and research institutions to promote joint research using the collected GPUs. For example, the GPUs can be used to develop new algorithms for generative AI. In this way, the collected GPUs can be provided to educational institutions and research institutions to be used in research and education on generative AI.

[0065] The collection unit can use the emotion estimation function to optimize the advertisements and promotions of the collection campaign and develop a marketing strategy based on the emotions of the provider. For example, the emotion estimation function is used to build a system that optimizes the advertisements and promotions of the collection campaign. For example, the advertisement content is adjusted based on the emotional reactions of the provider. The collection unit also develops an algorithm for developing a marketing strategy based on the emotions of the provider. For example, advertisements that elicit positive emotions are created. The collection unit also builds a system that evaluates the effectiveness of the collection campaign and optimizes the marketing strategy based on the emotion estimation data. For example, advertisements with high emotion scores are preferentially displayed. In this way, the emotion estimation function is used to optimize the advertisements and promotions of the collection campaign and develop a marketing strategy based on the emotions of the provider, thereby improving collection efficiency.

[0066] The cluster construction unit can use generative AI to optimize the load distribution of GPUs within a cluster and achieve efficient computational processing. For example, generative AI can be used to develop an algorithm that optimizes the load distribution of each GPU within a cluster. For example, the load can be evenly distributed based on the performance and status of each GPU. The cluster construction unit also monitors the load of GPUs within a cluster in real time, and constructs a system in which generative AI optimally distributes the load. For example, it detects overloaded GPUs and adjusts the load. The cluster construction unit also uses generative AI to devise a load distribution strategy to improve the efficiency of computational processing within the cluster. For example, it optimally allocates computational tasks to each GPU. This allows generative AI to optimize the load distribution of each GPU within a cluster and achieve efficient computational processing, thereby improving cluster performance.

[0067] The cluster construction unit can monitor the temperature and power consumption of GPUs in real time during the cluster construction process, thereby maintaining an optimal operating environment. For example, during the cluster construction process, a system is built that monitors the temperature and power consumption of each GPU in real time. For example, sensors are used to monitor the status of each GPU. The cluster construction unit also develops algorithms to maintain an optimal operating environment based on the temperature and power consumption of each GPU. For example, it performs control to cool down overheated GPUs. The cluster construction unit also uses generative AI to build a system that optimizes the temperature and power consumption of each GPU in the cluster. For example, it reduces the load on GPUs with high power consumption. This allows the temperature and power consumption of each GPU to be monitored in real time during the cluster construction process, thereby maintaining an optimal operating environment and improving the stability and efficiency of the cluster.

[0068] The cluster construction unit can use the emotion estimation function to evaluate the satisfaction of users in a cluster in real time and provide support according to the user's emotions. For example, the emotion estimation function is used to build a system that evaluates the satisfaction of users in a cluster in real time. For example, an emotion score is calculated by analyzing the user's facial expressions and voice. The cluster construction unit also develops an algorithm for providing support according to the user's emotions. For example, additional support is provided to users with strong negative emotions. The cluster construction unit also builds a system that evaluates user satisfaction and adjusts support based on the emotion estimation data. For example, the support content is changed according to the emotion score. In this way, the emotion estimation function is used to evaluate the satisfaction of users in a cluster in real time and support is provided according to the user's emotions, thereby improving user satisfaction.

[0069] The cluster construction department can utilize clusters using used GPUs for different purposes and provide multipurpose infrastructure. For example, it can build a system that utilizes clusters using used GPUs for blockchain mining. For example, it can optimize mining algorithms to perform calculations efficiently. The cluster construction department can also build clusters that utilize used GPUs for scientific computing. For example, it can provide a computing environment specialized for simulation and data analysis. The cluster construction department can also develop systems that provide clusters using used GPUs as multipurpose infrastructure. For example, it can support different uses such as generative AI, mining, and scientific computing. This allows clusters using used GPUs to be utilized for different purposes and provide multipurpose infrastructure, enabling effective use of resources.

[0070] The cluster construction unit can provide a portion of the cluster as a remotely accessible cloud service, making it available to a wide range of users. For example, a system can be constructed that provides a portion of a cluster using used GPUs as a remotely accessible cloud service. For example, users can access the cluster via the Internet. The cluster construction unit can also develop an interface for providing the cloud service and make it available to a wide range of users. For example, the cluster resources can be made available through an API. The cluster construction unit can also strengthen security measures for providing the remotely accessible cloud service. For example, it can implement access control and data encryption. This allows a portion of the cluster to be provided as a remotely accessible cloud service, making it available to a wide range of users, thereby improving user convenience.

[0071] The cluster construction unit can use the emotion estimation function to collect feedback from users of the cluster and continuously improve the performance and functions of the cluster. For example, the emotion estimation function is used to build a system that collects feedback from users of the cluster in real time. For example, an emotion score is calculated by analyzing the user's facial expressions and voice. The cluster construction unit also develops an algorithm for continuously improving the performance and functions of the cluster based on the user's emotional response data. For example, it improves functions that have a high level of negative emotions. The cluster construction unit also analyzes feedback from users of the cluster based on the emotion estimation data and builds a system that improves performance and functions. For example, it identifies areas for improvement based on the emotion score. In this way, the emotion estimation function is used to collect feedback from users of the cluster and continuously improve the performance and functions of the cluster, thereby improving user satisfaction.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The collection unit collects old or unused GPUs. For example, companies may donate old GPUs that are scheduled to be discarded, or individuals may sell GPUs that they no longer use. The collection unit can also collect GPUs widely through an online platform. For example, the collection unit may operate a website that accepts donations from companies and individuals and lists the GPUs to be collected. Step 2: The evaluation unit evaluates the performance and status of the old and unused GPUs collected by the collection unit. For example, the evaluation unit performs benchmark tests to measure the performance of each GPU. The evaluation unit can also monitor the temperature and power consumption of the GPU to evaluate its operating status. For example, the evaluation unit evaluates performance based on the GPU's operating frequency and memory capacity, and uses a temperature sensor to detect overheating. Step 3: The cluster construction unit builds a generative AI cluster using the old and unused GPUs evaluated by the evaluation unit. For example, the cluster construction unit builds a system that performs computational processing by linking multiple GPUs. The cluster construction unit can also build clusters that are used for training and inference of the generative AI. For example, the cluster construction unit installs the collected GPUs on a server and performs distributed processing to build a high-performance generative AI platform.

[0074] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0075] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0076] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0080] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0081] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0082] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0083] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0084] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0085] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0086] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0087] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0088] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0089] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0090] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0094] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0095] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0096] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0099] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0100] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0102] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0103] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0115] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0124] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0125] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0126] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0127] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0128] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0129] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0130] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0131] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0132] 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.

[0133] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0134] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0135] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0136] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0137] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0138] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0139] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0140] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0141] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects old and unused GPUs, an evaluation unit that evaluates the performance and status of the old GPUs and the idle GPUs collected by the collection unit; a cluster construction unit that constructs a generation AI cluster using the old GPU or the idle GPU evaluated by the evaluation unit. A system characterized by:

2. The collecting unit The generation AI is used to automatically evaluate the performance and status of the GPU and create an optimal collection plan.

2. The system of claim 1.

3. The evaluation unit Analyze the historical data of the GPU and evaluate the possibility of reuse based on usage and failure history.

2. The system of claim 1.

4. The collecting unit The satisfaction of the GPU providers is monitored in real time, and incentives are provided according to the provider's sentiment.

2. The system of claim 1.

5. The collecting unit In addition to the GPU, computer parts such as the CPU and memory are also collected at the same time to reuse resources.

2. The system of claim 1.

6. The collecting unit The collected GPUs will be provided to educational institutions and research institutions for use in research and education on generative AI.

2. The system of claim 1.

7. The collecting unit Optimize advertising and promotions for collection campaigns and develop marketing strategies based on donor sentiment 2. The system of claim 1.

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