system

Abstract

The system according to this embodiment aims to optimize energy-saving design using advanced semiconductor materials and to adjust the performance and energy consumption of each device in real time. [Solution] The system according to the embodiment comprises a utilization unit, an optimization unit, and an adjustment unit. The utilization unit utilizes advanced semiconductor materials. The optimization unit optimizes the energy-saving design based on the semiconductor materials utilized by the utilization unit. The adjustment unit adjusts the performance and energy consumption of each device in real time based on the energy-saving design optimized by the optimization unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the optimization of energy-saving design using semiconductor materials has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to optimize the energy-saving design by using advanced semiconductor materials and adjust the performance and energy consumption of each device in real time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a utilization unit, an optimization unit, and an adjustment unit. The utilization unit utilizes advanced semiconductor materials. The optimization unit optimizes the energy-saving design based on the semiconductor materials utilized by the utilization unit. The adjustment unit adjusts the performance and energy consumption of each device in real time based on the energy-saving design optimized by the optimization unit. [Effects of the Invention]

[0007] The system according to this embodiment can optimize energy-saving design using advanced semiconductor materials and adjust the performance and energy consumption of each device in real time. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system for developing innovative semiconductor technology to significantly reduce power consumption while maximizing the computing power of AI. This system can be applied in fields such as smart devices, autonomous driving, data centers, and energy efficiency improvement. The AI ​​agent system utilizes advanced semiconductor materials and optimizes energy-saving design. Next, it implements an automatic adjustment function through integration with AI to adjust the performance and energy consumption of each device in real time. This technology can reduce power consumption by up to 50% and improve AI computing speed by up to 40%. It also has the effect of extending the lifespan of devices. For example, the AI ​​agent system reduces power consumption by utilizing advanced semiconductor materials and optimizing energy-saving design. Furthermore, the automatic adjustment function through integration with AI adjusts the performance and energy consumption of each device in real time. This technology is extremely useful for users who have a demand for high-performance and energy-saving semiconductors, such as manufacturers of smart devices and autonomous vehicles, and data center operators. For example, smart device manufacturers can improve the competitiveness of their products by using this technology. Automobile manufacturers can also improve the energy efficiency of autonomous vehicles. Data center operators can reduce operating costs by providing energy-efficient solutions. The innovation of this technology lies in the fusion of semiconductor technology and AI. It optimizes the trade-off between power consumption and performance, enabling sustainable technological development. This technology aims to accelerate technological progress while minimizing environmental impact through energy-efficient semiconductor technology. As a result, AI agent systems can significantly reduce power consumption and improve AI computing speed.

[0029] The AI ​​agent system according to the embodiment comprises a utilization unit, an optimization unit, and an adjustment unit. The utilization unit utilizes advanced semiconductor materials. Advanced semiconductor materials include, but are not limited to, materials with specific names or properties. The utilization unit utilizes, for example, semiconductor materials with specific names. The utilization unit can also utilize semiconductor materials with specific properties. For example, the utilization unit can utilize semiconductor materials with high thermal conductivity. The optimization unit optimizes the energy-saving design based on the semiconductor materials utilized by the utilization unit. The energy-saving design includes, but is not limited to, energy consumption reduction targets and design methods. The optimization unit sets, for example, energy consumption reduction targets and optimizes the design based on them. The optimization unit can also optimize the energy-saving design using specific design methods. For example, the optimization unit can use energy-efficient circuit design methods. The adjustment unit adjusts the performance and energy consumption of each device in real time based on the energy-saving design optimized by the optimization unit. The method of real-time adjustment includes, but is not limited to, the frequency of adjustment and the techniques used. The adjustment unit can, for example, set the frequency of adjustments and perform adjustments in real time based on that. The adjustment unit can also perform adjustments in real time using specific technologies. For example, the adjustment unit can perform adjustments in real time using AI technology. As a result, the AI ​​agent system according to this embodiment can utilize advanced semiconductor materials, optimize energy-saving designs, and adjust the performance and energy consumption of each device in real time.

[0030] The application utilizes advanced semiconductor materials. These advanced semiconductor materials include, but are not limited to, materials with specific names or properties. For example, the application utilizes semiconductor materials with specific names. Specifically, examples include materials such as silicon carbide (SiC) and gallium nitride (GaN). These materials possess high thermal conductivity and high voltage tolerance, enabling efficient energy management. Furthermore, the application can utilize semiconductor materials with specific properties. For example, the application can utilize semiconductor materials with high thermal conductivity. This suppresses heat generation in the device, enabling stable operation over long periods. Additionally, the application can select low-resistance semiconductor materials to achieve low power loss. This is expected to improve energy efficiency and extend the device's lifespan. The application can appropriately select these advanced semiconductor materials to maximize the device's performance.

[0031] The optimization unit optimizes energy-saving designs based on the semiconductor materials used by the utilization unit. Energy-saving designs include, but are not limited to, energy consumption reduction targets and design methods. For example, the optimization unit sets energy consumption reduction targets and optimizes the design based on them. Specifically, energy consumption can be reduced by optimizing the device's operating mode and switching to high-performance mode only when necessary. The optimization unit can also optimize energy-saving designs using specific design methods. For example, the optimization unit can use energy-efficient circuit design methods. These include low-voltage operation, sleep mode utilization, and dynamic voltage scaling (DVS). Furthermore, the optimization unit can utilize AI technology to formulate optimal energy management strategies according to device usage and environmental conditions. AI learns from past data and predicts future energy consumption patterns to achieve more effective energy-saving designs. This allows the optimization unit to maximize device performance while minimizing energy consumption.

[0032] The adjustment unit adjusts the performance and energy consumption of each device in real time based on the energy-saving design optimized by the optimization unit. Methods of real-time adjustment include, but are not limited to, the frequency of adjustment and the technology used. For example, the adjustment unit can set an adjustment frequency and perform adjustments in real time based on that frequency. Specifically, it optimizes energy consumption by constantly monitoring the operating status of devices and switching operating modes as needed. The adjustment unit can also perform real-time adjustments using specific technologies. For example, the adjustment unit can perform real-time adjustments using AI technology. The AI ​​analyzes sensor data and usage data to calculate optimal adjustment parameters. This allows the adjustment unit to manage device performance and energy consumption in a balanced manner. Furthermore, the adjustment unit can share information among multiple devices via a network to improve overall energy efficiency. For example, by distributing the load among devices, it can prevent excessive load on specific devices and reduce overall energy consumption. This allows the adjustment unit to optimize the performance and energy consumption of each device in real time, improving the efficiency of the entire system.

[0033] The adjustment unit can reduce power consumption by up to 50%. The adjustment unit uses specific algorithms to reduce power consumption, for example. For example, the adjustment unit executes an algorithm to reduce power consumption using AI technology. The adjustment unit can also reduce power consumption using specific hardware technology, for example, it can use low-power circuit designs. The adjustment unit can also reduce power consumption using specific software technology, for example, it can execute an energy-efficient software algorithm. This can significantly reduce power consumption. Power consumption reduction includes, but is not limited to, a baseline power consumption value and reduction method. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input a power consumption reduction algorithm into a generating AI, which can then execute the optimal reduction method.

[0034] The adjustment unit can improve AI calculation speed by up to 40%. For example, the adjustment unit may use specific algorithms to improve AI calculation speed. For instance, the adjustment unit may execute an algorithm that improves AI calculation speed using AI technology. The adjustment unit may also improve AI calculation speed using specific hardware technology. For example, the adjustment unit may use high-speed circuit design. Furthermore, the adjustment unit may improve AI calculation speed using specific software technology. For example, the adjustment unit may execute an efficient software algorithm. This can significantly improve AI calculation speed. Improvements in AI calculation speed include, but are not limited to, setting a baseline calculation speed value and improvement methods. Some or all of the above-described processes in the adjustment unit may be performed using AI, or not using AI. For example, the adjustment unit may input an AI calculation speed improvement algorithm into a generating AI, allowing the generating AI to execute the optimal improvement method.

[0035] The adjustment unit can extend the lifespan of the device. For example, the adjustment unit may use a specific algorithm to extend the lifespan of the device. For example, the adjustment unit may use AI technology to execute an algorithm that extends the lifespan of the device. The adjustment unit may also extend the lifespan of the device using specific hardware technology. For example, the adjustment unit may use a low-load circuit design. The adjustment unit may also extend the lifespan of the device using specific software technology. For example, the adjustment unit may execute an efficient power management algorithm. This can extend the lifespan of the device. Extending the lifespan of the device includes, but is not limited to, target values ​​and methods for extension. Some or all of the above-described processes in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit may input a device lifespan extension algorithm into a generating AI, which can then execute the optimal extension method.

[0036] The optimization unit can implement an automatic adjustment function through integration with AI to optimize energy-saving designs. For example, the optimization unit may use specific AI technologies to optimize energy-saving designs. For example, the optimization unit may execute an algorithm that optimizes energy-saving designs using AI technologies. The optimization unit may also optimize energy-saving designs using specific hardware technologies. For example, the optimization unit may use energy-efficient circuit designs. The optimization unit may also optimize energy-saving designs using specific software technologies. For example, the optimization unit may execute an efficient software algorithm. This allows for the optimization of energy-saving designs by implementing an automatic adjustment function through integration with AI. The automatic adjustment function includes, but is not limited to, the AI ​​technologies and adjustment algorithms used. Some or all of the above-described processes in the optimization unit may be performed using AI, or not using AI. For example, the optimization unit may input an energy-saving design optimization algorithm into a generating AI, which can then execute the optimal optimization method.

[0037] The user unit can utilize advanced semiconductor materials. For example, the user unit can utilize semiconductor materials with specific material names. For example, the user unit can utilize semiconductor materials with specific properties. For example, the user unit can utilize semiconductor materials with high thermal conductivity. By utilizing advanced semiconductor materials, the performance of the system can be improved. Advanced semiconductor materials include, but are not limited to, specific material names and properties. Some or all of the processing described above in the user unit may be performed using AI, for example, or without AI. For example, the user unit can input an algorithm for selecting advanced semiconductor materials into a generating AI, and have the generating AI select the optimal material.

[0038] The utilization unit can select the optimal utilization method based on the material's properties when using semiconductor materials. For example, the utilization unit can select a semiconductor material that is resistant to high-temperature environments and optimize the cooling system. For example, when high-speed processing is required, the utilization unit can select a semiconductor material with low latency. For example, when a long lifespan is required, the utilization unit can select a semiconductor material with high durability. By selecting the optimal utilization method based on the material's properties, the system's performance can be improved. Material properties include, but are not limited to, the type of properties and evaluation criteria. Some or all of the processing described above in the utilization unit may be performed using AI, for example, or without AI. For example, the utilization unit can input material property data into a generating AI and have the generating AI execute the optimal utilization method.

[0039] The utilization unit can adjust the utilization method according to environmental conditions when using semiconductor materials. For example, in a high-humidity environment, the utilization unit can select a semiconductor material with high humidity resistance. For example, in a low-temperature environment, the utilization unit can select a semiconductor material suitable for low-temperature operation. For example, in a high-vibration environment, the utilization unit can select a semiconductor material with high vibration resistance. By adjusting the utilization method according to environmental conditions, the performance of the system can be improved. Environmental conditions include, but are not limited to, temperature, humidity, and pressure. Some or all of the above processing in the utilization unit may be performed using, for example, AI, or without AI. For example, the utilization unit can input environmental condition data into a generating AI, and have the generating AI execute the optimal utilization method.

[0040] The utilization unit can adjust its utilization method when using semiconductor materials, taking into account the supply status of the materials. For example, the utilization unit can prioritize the use of materials with a stable supply. For example, the utilization unit can consider alternative materials for materials with an unstable supply. For example, the utilization unit can flexibly change the utilization plan according to the supply status. This improves the stability of the system by adjusting the utilization method in consideration of the material supply status. The material supply status includes, but is not limited to, the supply quantity and the reliability of the supplier. Some or all of the above processing in the utilization unit may be performed using AI, for example, or without AI. For example, the utilization unit can input material supply status data into a generating AI and have the generating AI execute the optimal utilization method.

[0041] The user unit can select a method of use for semiconductor materials while considering compatibility with other devices. For example, the user unit can select semiconductor materials that are compatible with other devices. For example, if incompatible materials are used, the user unit can use an appropriate conversion adapter. For example, the user unit can prioritize the use of compatible materials. This improves system compatibility by selecting a method of use while considering compatibility with other devices. Compatibility with other devices includes, but is not limited to, connection methods and communication protocols. Some or all of the above processing in the user unit may be performed using AI, for example, or without AI. For example, the user unit can input compatibility data with other devices into a generating AI, and have the generating AI execute the optimal method of use.

[0042] The optimization unit can select an optimization algorithm by referring to past data when optimizing energy-saving designs. For example, the optimization unit can select the optimal algorithm based on past energy-saving design data. For example, the optimization unit can select the optimal algorithm based on past device usage data. For example, the optimization unit can select the optimal algorithm based on past energy consumption data. By selecting an optimization algorithm by referring to past data, a more appropriate energy-saving design can be achieved. Past data includes, but is not limited to, data types and referencing methods. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input past data into a generating AI and have the generating AI select the optimal algorithm.

[0043] The optimization unit can adjust the optimization method according to the device usage when optimizing the energy-saving design. For example, the optimization unit can adjust the energy-saving design according to the frequency of device use. For example, the optimization unit can adjust the energy-saving design according to the duration of device use. For example, the optimization unit can adjust the energy-saving design according to the environment in which the device is used. By adjusting the optimization method according to the device usage, a more appropriate energy-saving design can be achieved. Device usage includes, but is not limited to, frequency of use, duration of use, and environment. Some or all of the above processing in the optimization unit may be performed using, for example, AI, or without AI. For example, the optimization unit can input device usage data into a generating AI and have the generating AI execute the optimal optimization method.

[0044] The optimization unit can select an optimization method that takes into account the device's lifespan when optimizing an energy-saving design. For example, the optimization unit can select a low-load, energy-saving design to extend the device's lifespan. For example, the optimization unit can introduce an appropriate cooling system that takes the device's lifespan into account. For example, the optimization unit can perform appropriate power management to extend the device's lifespan. In this way, the device's lifespan can be extended by selecting an optimization method that takes the device's lifespan into account. The device's lifespan includes, but is not limited to, a target lifespan value and an evaluation method. Some or all of the above-described processes in the optimization unit may be performed using, for example, AI, or without AI. For example, the optimization unit can input device lifespan data into a generating AI and have the generating AI execute the optimal optimization method.

[0045] The optimization unit can optimize energy-saving designs in combination with other energy efficiency technologies. For example, the optimization unit can optimize energy-saving designs in combination with renewable energy technologies. For example, the optimization unit can optimize energy-saving designs in combination with high-efficiency cooling technologies. For example, the optimization unit can optimize energy-saving designs in combination with high-efficiency power management technologies. By optimizing in combination with other energy efficiency technologies, more effective energy-saving designs can be achieved. Other energy efficiency technologies include, but are not limited to, the types of technologies used and the methods of combining them. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input data on other energy efficiency technologies into a generating AI and have the generating AI execute the optimal combination method.

[0046] The adjustment unit can select an adjustment algorithm by referring to real-time data when adjusting device performance and energy consumption. For example, the adjustment unit can select the optimal adjustment algorithm based on real-time device usage data. For example, the adjustment unit can select the optimal adjustment algorithm based on real-time energy consumption data. For example, the adjustment unit can select the optimal adjustment algorithm based on real-time environmental data. This allows for more appropriate adjustment by selecting an adjustment algorithm by referring to real-time data. Real-time data includes, but is not limited to, data type and reference method. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or without AI. For example, the adjustment unit can input real-time data into a generating AI and have the generating AI select the optimal adjustment algorithm.

[0047] The adjustment unit can adjust the adjustment method according to the device usage when adjusting the device's performance and energy consumption. For example, the adjustment unit can adjust the adjustment method according to the frequency of device use. For example, the adjustment unit can adjust the adjustment method according to the duration of device use. For example, the adjustment unit can adjust the adjustment method according to the device's usage environment. By adjusting the adjustment method according to the device usage, more appropriate adjustments become possible. Device usage includes, but is not limited to, frequency of use, duration of use, and usage environment. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or without AI. For example, the adjustment unit can input device usage data into a generating AI and have the generating AI execute the optimal adjustment method.

[0048] The adjustment unit can select an adjustment method that takes the device's lifespan into consideration when adjusting the device's performance and energy consumption. For example, the adjustment unit can select a low-load adjustment method to extend the device's lifespan. For example, the adjustment unit can introduce an appropriate cooling system that takes the device's lifespan into consideration. For example, the adjustment unit can perform appropriate power management to extend the device's lifespan. In this way, the device's lifespan can be extended by selecting an adjustment method that takes the device's lifespan into consideration. The device's lifespan includes, but is not limited to, a target lifespan value and an evaluation method. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input device lifespan data into a generating AI and have the generating AI execute the optimal adjustment method.

[0049] The adjustment unit can perform adjustments in combination with other energy efficiency technologies when adjusting the performance and energy consumption of the device. For example, the adjustment unit can perform adjustments in combination with renewable energy technologies. For example, the adjustment unit can perform adjustments in combination with high-efficiency cooling technologies. For example, the adjustment unit can perform adjustments in combination with high-efficiency power management technologies. This allows for more effective adjustments by performing adjustments in combination with other energy efficiency technologies. Other energy efficiency technologies include, but are not limited to, the types of technologies used and the methods of combining them. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input data on other energy efficiency technologies into a generating AI, which can then execute the optimal adjustment method.

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

[0051] The adjustment unit can select an adjustment algorithm by referring to real-time data when adjusting the performance and energy consumption of a device. For example, it can select the optimal adjustment algorithm based on real-time device usage data. It can also select the optimal adjustment algorithm based on real-time energy consumption data. Furthermore, it can select the optimal adjustment algorithm based on real-time environmental data. By selecting an adjustment algorithm by referring to real-time data, more appropriate adjustments become possible. Real-time data includes, but is not limited to, data type and reference method. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input real-time data into a generating AI and have the generating AI select the optimal adjustment algorithm.

[0052] The optimization unit can select an optimization algorithm by referring to past data when optimizing energy-saving designs. For example, it can select the optimal algorithm based on past energy-saving design data. It can also select the optimal algorithm based on past device usage data. Furthermore, it can select the optimal algorithm based on past energy consumption data. By selecting an optimization algorithm by referring to past data, a more appropriate energy-saving design can be achieved. Past data includes, but is not limited to, data types and referencing methods. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can input past data into a generating AI and have the generating AI select the optimal algorithm.

[0053] The utilization unit can select the optimal utilization method based on the material's properties when using semiconductor materials. For example, it can select a semiconductor material that is resistant to high-temperature environments and optimize the cooling system. Furthermore, if high-speed processing is required, a low-latency semiconductor material can be selected. Additionally, if a long lifespan is required, a highly durable semiconductor material can be selected. This allows for improved system performance by selecting the optimal utilization method based on the material's properties. Material properties include, but are not limited to, the types of properties and evaluation criteria. Some or all of the above-described processing in the utilization unit may be performed using AI, or without AI. For example, the utilization unit can input material property data into a generating AI, which can then execute the optimal utilization method.

[0054] The optimization unit can adjust the optimization method according to the device usage when optimizing the energy-saving design. For example, the energy-saving design can be adjusted according to the frequency of device use. It can also be adjusted according to the duration of device use. Furthermore, it can be adjusted according to the environment in which the device is used. By adjusting the optimization method according to the device usage, a more appropriate energy-saving design can be achieved. Device usage includes, but is not limited to, frequency of use, duration of use, and environment. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input device usage data into a generating AI and have the generating AI execute the optimal optimization method.

[0055] The utilization unit can adjust the utilization method according to environmental conditions when using semiconductor materials. For example, in a high-humidity environment, a semiconductor material with high humidity resistance can be selected. In a low-temperature environment, a semiconductor material suitable for low-temperature operation can be selected. Furthermore, in a high-vibration environment, a semiconductor material with high vibration resistance can be selected. By adjusting the utilization method according to environmental conditions, the performance of the system can be improved. Environmental conditions include, but are not limited to, temperature, humidity, and pressure. Some or all of the above processing in the utilization unit may be performed using AI, or it may be performed without AI. For example, the utilization unit can input environmental condition data into a generating AI, and have the generating AI execute the optimal utilization method.

[0056] The optimization unit can optimize energy-saving designs by combining them with other energy efficiency technologies. For example, it can optimize energy-saving designs by combining them with renewable energy technologies. It can also optimize energy-saving designs by combining them with high-efficiency cooling technologies. Furthermore, it can optimize energy-saving designs by combining them with high-efficiency power management technologies. This allows for more effective energy-saving designs by optimizing them in combination with other energy efficiency technologies. Other energy efficiency technologies include, but are not limited to, the types of technologies used and the methods of combining them. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can input data on other energy efficiency technologies into a generating AI, which can then execute the optimal combination method.

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

[0058] Step 1: The application part utilizes advanced semiconductor materials. Advanced semiconductor materials include, but are not limited to, materials with specific names or properties. For example, the application part can utilize semiconductor materials with high thermal conductivity. Step 2: The optimization unit optimizes the energy-saving design based on the semiconductor materials used by the utilization unit. The energy-saving design includes energy consumption reduction targets and design methods. For example, the optimization unit sets energy consumption reduction targets and optimizes the design based on them. It can also use highly energy-efficient circuit design methods. Step 3: The adjustment unit adjusts the performance and energy consumption of each device in real time based on the energy-saving design optimized by the optimization unit. The adjustment method includes the frequency of adjustment and the technology used. For example, the adjustment unit sets the frequency of adjustment and performs adjustments in real time based on that. It can also perform adjustments in real time using AI technology.

[0059] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system for developing innovative semiconductor technology to significantly reduce power consumption while maximizing the computing power of AI. This system can be applied in fields such as smart devices, autonomous driving, data centers, and energy efficiency improvement. The AI ​​agent system utilizes advanced semiconductor materials and optimizes energy-saving design. Next, it implements an automatic adjustment function through integration with AI to adjust the performance and energy consumption of each device in real time. This technology can reduce power consumption by up to 50% and improve AI computing speed by up to 40%. It also has the effect of extending the lifespan of devices. For example, the AI ​​agent system reduces power consumption by utilizing advanced semiconductor materials and optimizing energy-saving design. Furthermore, the automatic adjustment function through integration with AI adjusts the performance and energy consumption of each device in real time. This technology is extremely useful for users who have a demand for high-performance and energy-saving semiconductors, such as manufacturers of smart devices and autonomous vehicles, and data center operators. For example, smart device manufacturers can improve the competitiveness of their products by using this technology. Automobile manufacturers can also improve the energy efficiency of autonomous vehicles. Data center operators can reduce operating costs by providing energy-efficient solutions. The innovation of this technology lies in the fusion of semiconductor technology and AI. It optimizes the trade-off between power consumption and performance, enabling sustainable technological development. This technology aims to accelerate technological progress while minimizing environmental impact through energy-efficient semiconductor technology. As a result, AI agent systems can significantly reduce power consumption and improve AI computing speed.

[0060] The AI ​​agent system according to the embodiment comprises a utilization unit, an optimization unit, and an adjustment unit. The utilization unit utilizes advanced semiconductor materials. Advanced semiconductor materials include, but are not limited to, materials with specific names or properties. The utilization unit utilizes, for example, semiconductor materials with specific names. The utilization unit can also utilize semiconductor materials with specific properties. For example, the utilization unit can utilize semiconductor materials with high thermal conductivity. The optimization unit optimizes the energy-saving design based on the semiconductor materials utilized by the utilization unit. The energy-saving design includes, but is not limited to, energy consumption reduction targets and design methods. The optimization unit sets, for example, energy consumption reduction targets and optimizes the design based on them. The optimization unit can also optimize the energy-saving design using specific design methods. For example, the optimization unit can use energy-efficient circuit design methods. The adjustment unit adjusts the performance and energy consumption of each device in real time based on the energy-saving design optimized by the optimization unit. The method of real-time adjustment includes, but is not limited to, the frequency of adjustment and the techniques used. The adjustment unit can, for example, set the frequency of adjustments and perform adjustments in real time based on that. The adjustment unit can also perform adjustments in real time using specific technologies. For example, the adjustment unit can perform adjustments in real time using AI technology. As a result, the AI ​​agent system according to this embodiment can utilize advanced semiconductor materials, optimize energy-saving designs, and adjust the performance and energy consumption of each device in real time.

[0061] The application utilizes advanced semiconductor materials. These advanced semiconductor materials include, but are not limited to, materials with specific names or properties. For example, the application utilizes semiconductor materials with specific names. Specifically, examples include materials such as silicon carbide (SiC) and gallium nitride (GaN). These materials possess high thermal conductivity and high voltage tolerance, enabling efficient energy management. Furthermore, the application can utilize semiconductor materials with specific properties. For example, the application can utilize semiconductor materials with high thermal conductivity. This suppresses heat generation in the device, enabling stable operation over long periods. Additionally, the application can select low-resistance semiconductor materials to achieve low power loss. This is expected to improve energy efficiency and extend the device's lifespan. The application can appropriately select these advanced semiconductor materials to maximize the device's performance.

[0062] The optimization unit optimizes energy-saving designs based on the semiconductor materials used by the utilization unit. Energy-saving designs include, but are not limited to, energy consumption reduction targets and design methods. For example, the optimization unit sets energy consumption reduction targets and optimizes the design based on them. Specifically, energy consumption can be reduced by optimizing the device's operating mode and switching to high-performance mode only when necessary. The optimization unit can also optimize energy-saving designs using specific design methods. For example, the optimization unit can use energy-efficient circuit design methods. These include low-voltage operation, sleep mode utilization, and dynamic voltage scaling (DVS). Furthermore, the optimization unit can utilize AI technology to formulate optimal energy management strategies according to device usage and environmental conditions. AI learns from past data and predicts future energy consumption patterns to achieve more effective energy-saving designs. This allows the optimization unit to maximize device performance while minimizing energy consumption.

[0063] The adjustment unit adjusts the performance and energy consumption of each device in real time based on the energy-saving design optimized by the optimization unit. Methods of real-time adjustment include, but are not limited to, the frequency of adjustment and the technology used. For example, the adjustment unit can set an adjustment frequency and perform adjustments in real time based on that frequency. Specifically, it optimizes energy consumption by constantly monitoring the operating status of devices and switching operating modes as needed. The adjustment unit can also perform real-time adjustments using specific technologies. For example, the adjustment unit can perform real-time adjustments using AI technology. The AI ​​analyzes sensor data and usage data to calculate optimal adjustment parameters. This allows the adjustment unit to manage device performance and energy consumption in a balanced manner. Furthermore, the adjustment unit can share information among multiple devices via a network to improve overall energy efficiency. For example, by distributing the load among devices, it can prevent excessive load on specific devices and reduce overall energy consumption. This allows the adjustment unit to optimize the performance and energy consumption of each device in real time, improving the efficiency of the entire system.

[0064] The adjustment unit can reduce power consumption by up to 50%. The adjustment unit uses specific algorithms to reduce power consumption, for example. For example, the adjustment unit executes an algorithm to reduce power consumption using AI technology. The adjustment unit can also reduce power consumption using specific hardware technology, for example, it can use low-power circuit designs. The adjustment unit can also reduce power consumption using specific software technology, for example, it can execute an energy-efficient software algorithm. This can significantly reduce power consumption. Power consumption reduction includes, but is not limited to, a baseline power consumption value and reduction method. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input a power consumption reduction algorithm into a generating AI, which can then execute the optimal reduction method.

[0065] The adjustment unit can improve AI calculation speed by up to 40%. For example, the adjustment unit may use specific algorithms to improve AI calculation speed. For instance, the adjustment unit may execute an algorithm that improves AI calculation speed using AI technology. The adjustment unit may also improve AI calculation speed using specific hardware technology. For example, the adjustment unit may use high-speed circuit design. Furthermore, the adjustment unit may improve AI calculation speed using specific software technology. For example, the adjustment unit may execute an efficient software algorithm. This can significantly improve AI calculation speed. Improvements in AI calculation speed include, but are not limited to, setting a baseline calculation speed value and improvement methods. Some or all of the above-described processes in the adjustment unit may be performed using AI, or not using AI. For example, the adjustment unit may input an AI calculation speed improvement algorithm into a generating AI, allowing the generating AI to execute the optimal improvement method.

[0066] The adjustment unit can extend the lifespan of the device. For example, the adjustment unit may use a specific algorithm to extend the lifespan of the device. For example, the adjustment unit may use AI technology to execute an algorithm that extends the lifespan of the device. The adjustment unit may also extend the lifespan of the device using specific hardware technology. For example, the adjustment unit may use a low-load circuit design. The adjustment unit may also extend the lifespan of the device using specific software technology. For example, the adjustment unit may execute an efficient power management algorithm. This can extend the lifespan of the device. Extending the lifespan of the device includes, but is not limited to, target values ​​and methods for extension. Some or all of the above-described processes in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit may input a device lifespan extension algorithm into a generating AI, which can then execute the optimal extension method.

[0067] The optimization unit can implement an automatic adjustment function through integration with AI to optimize energy-saving designs. For example, the optimization unit may use specific AI technologies to optimize energy-saving designs. For example, the optimization unit may execute an algorithm that optimizes energy-saving designs using AI technologies. The optimization unit may also optimize energy-saving designs using specific hardware technologies. For example, the optimization unit may use energy-efficient circuit designs. The optimization unit may also optimize energy-saving designs using specific software technologies. For example, the optimization unit may execute an efficient software algorithm. This allows for the optimization of energy-saving designs by implementing an automatic adjustment function through integration with AI. The automatic adjustment function includes, but is not limited to, the AI ​​technologies and adjustment algorithms used. Some or all of the above-described processes in the optimization unit may be performed using AI, or not using AI. For example, the optimization unit may input an energy-saving design optimization algorithm into a generating AI, which can then execute the optimal optimization method.

[0068] The user unit can utilize advanced semiconductor materials. For example, the user unit can utilize semiconductor materials with specific material names. For example, the user unit can utilize semiconductor materials with specific properties. For example, the user unit can utilize semiconductor materials with high thermal conductivity. By utilizing advanced semiconductor materials, the performance of the system can be improved. Advanced semiconductor materials include, but are not limited to, specific material names and properties. Some or all of the processing described above in the user unit may be performed using AI, for example, or without AI. For example, the user unit can input an algorithm for selecting advanced semiconductor materials into a generating AI, and have the generating AI select the optimal material.

[0069] The user unit can estimate the user's emotions and adjust the selection of semiconductor materials based on the estimated emotions. For example, if the user is stressed, the user unit can select a highly stable semiconductor material. For example, if the user is relaxed, the user unit can select a performance-oriented semiconductor material. For example, if the user is in a hurry, the user unit can select a semiconductor material that can be used quickly. In this way, by adjusting the selection of semiconductor materials based on the user's emotions, a more appropriate material can be selected. Methods for estimating the user's emotions include, but are not limited to, the sensors and algorithms used. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the user unit may be performed using, for example, AI, or not using AI. For example, the user unit can input user emotion data into a generative AI and have the generative AI execute the optimal material selection method.

[0070] The utilization unit can select the optimal utilization method based on the material's properties when using semiconductor materials. For example, the utilization unit can select a semiconductor material that is resistant to high-temperature environments and optimize the cooling system. For example, when high-speed processing is required, the utilization unit can select a semiconductor material with low latency. For example, when a long lifespan is required, the utilization unit can select a semiconductor material with high durability. By selecting the optimal utilization method based on the material's properties, the system's performance can be improved. Material properties include, but are not limited to, the type of properties and evaluation criteria. Some or all of the processing described above in the utilization unit may be performed using AI, for example, or without AI. For example, the utilization unit can input material property data into a generating AI and have the generating AI execute the optimal utilization method.

[0071] The utilization unit can adjust the utilization method according to environmental conditions when using semiconductor materials. For example, in a high-humidity environment, the utilization unit can select a semiconductor material with high humidity resistance. For example, in a low-temperature environment, the utilization unit can select a semiconductor material suitable for low-temperature operation. For example, in a high-vibration environment, the utilization unit can select a semiconductor material with high vibration resistance. By adjusting the utilization method according to environmental conditions, the performance of the system can be improved. Environmental conditions include, but are not limited to, temperature, humidity, and pressure. Some or all of the above processing in the utilization unit may be performed using, for example, AI, or without AI. For example, the utilization unit can input environmental condition data into a generating AI, and have the generating AI execute the optimal utilization method.

[0072] The user unit can estimate the user's emotions and determine the order in which semiconductor materials are used based on the estimated emotions. For example, if the user is stressed, the user unit may prioritize the use of highly stable semiconductor materials. For example, if the user is relaxed, the user unit may prioritize the use of performance-oriented semiconductor materials. For example, if the user is in a hurry, the user unit may prioritize the use of quickly available semiconductor materials. This allows for the use of materials in a more appropriate order by determining the order of use of semiconductor materials based on the user's emotions. Methods for estimating the user's emotions include, but are not limited to, the sensors and algorithms used. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the user unit may be performed using, for example, AI, or not using AI. For example, the user unit can input user emotion data into a generative AI, which can then execute the optimal usage order.

[0073] The utilization unit can adjust its utilization method when using semiconductor materials, taking into account the supply status of the materials. For example, the utilization unit can prioritize the use of materials with a stable supply. For example, the utilization unit can consider alternative materials for materials with an unstable supply. For example, the utilization unit can flexibly change the utilization plan according to the supply status. This improves the stability of the system by adjusting the utilization method in consideration of the material supply status. The material supply status includes, but is not limited to, the supply quantity and the reliability of the supplier. Some or all of the above processing in the utilization unit may be performed using AI, for example, or without AI. For example, the utilization unit can input material supply status data into a generating AI and have the generating AI execute the optimal utilization method.

[0074] The user unit can select a method of use for semiconductor materials while considering compatibility with other devices. For example, the user unit can select semiconductor materials that are compatible with other devices. For example, if incompatible materials are used, the user unit can use an appropriate conversion adapter. For example, the user unit can prioritize the use of compatible materials. This improves system compatibility by selecting a method of use while considering compatibility with other devices. Compatibility with other devices includes, but is not limited to, connection methods and communication protocols. Some or all of the above processing in the user unit may be performed using AI, for example, or without AI. For example, the user unit can input compatibility data with other devices into a generating AI, and have the generating AI execute the optimal method of use.

[0075] The optimization unit can estimate the user's emotions and adjust the energy-saving design optimization method based on the estimated user emotions. For example, if the user is stressed, the optimization unit can perform an energy-saving design that prioritizes stability. For example, if the user is relaxed, the optimization unit can perform an energy-saving design that prioritizes performance. For example, if the user is in a hurry, the optimization unit can perform optimization quickly. This allows for a more appropriate energy-saving design by adjusting the energy-saving design optimization method based on the user's emotions. Methods for estimating the user's emotions include, but are not limited to, the sensors and algorithms used. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using, for example, AI, or not using AI. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI execute the optimal optimization method.

[0076] The optimization unit can select an optimization algorithm by referring to past data when optimizing energy-saving designs. For example, the optimization unit can select the optimal algorithm based on past energy-saving design data. For example, the optimization unit can select the optimal algorithm based on past device usage data. For example, the optimization unit can select the optimal algorithm based on past energy consumption data. By selecting an optimization algorithm by referring to past data, a more appropriate energy-saving design can be achieved. Past data includes, but is not limited to, data types and referencing methods. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input past data into a generating AI and have the generating AI select the optimal algorithm.

[0077] The optimization unit can adjust the optimization method according to the device usage when optimizing the energy-saving design. For example, the optimization unit can adjust the energy-saving design according to the frequency of device use. For example, the optimization unit can adjust the energy-saving design according to the duration of device use. For example, the optimization unit can adjust the energy-saving design according to the environment in which the device is used. By adjusting the optimization method according to the device usage, a more appropriate energy-saving design can be achieved. Device usage includes, but is not limited to, frequency of use, duration of use, and environment. Some or all of the above processing in the optimization unit may be performed using, for example, AI, or without AI. For example, the optimization unit can input device usage data into a generating AI and have the generating AI execute the optimal optimization method.

[0078] The optimization unit can estimate the user's emotions and determine the priority of energy-saving designs based on the estimated user emotions. For example, if the user is stressed, the optimization unit may prioritize energy-saving designs that prioritize stability. For example, if the user is relaxed, the optimization unit may prioritize energy-saving designs that prioritize performance. For example, if the user is in a hurry, the optimization unit can perform optimization quickly. This allows for energy-saving designs to be implemented in a more appropriate order by determining the priority of energy-saving designs based on the user's emotions. Methods for estimating user emotions include, but are not limited to, the sensors and algorithms used. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using, for example, AI, or not using AI. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI determine the optimal priority.

[0079] The optimization unit can select an optimization method that takes into account the device's lifespan when optimizing an energy-saving design. For example, the optimization unit can select a low-load, energy-saving design to extend the device's lifespan. For example, the optimization unit can introduce an appropriate cooling system that takes the device's lifespan into account. For example, the optimization unit can perform appropriate power management to extend the device's lifespan. In this way, the device's lifespan can be extended by selecting an optimization method that takes the device's lifespan into account. The device's lifespan includes, but is not limited to, a target lifespan value and an evaluation method. Some or all of the above-described processes in the optimization unit may be performed using, for example, AI, or without AI. For example, the optimization unit can input device lifespan data into a generating AI and have the generating AI execute the optimal optimization method.

[0080] The optimization unit can optimize energy-saving designs in combination with other energy efficiency technologies. For example, the optimization unit can optimize energy-saving designs in combination with renewable energy technologies. For example, the optimization unit can optimize energy-saving designs in combination with high-efficiency cooling technologies. For example, the optimization unit can optimize energy-saving designs in combination with high-efficiency power management technologies. By optimizing in combination with other energy efficiency technologies, more effective energy-saving designs can be achieved. Other energy efficiency technologies include, but are not limited to, the types of technologies used and the methods of combining them. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input data on other energy efficiency technologies into a generating AI and have the generating AI execute the optimal combination method.

[0081] The adjustment unit can estimate the user's emotions and adjust the device's performance and energy consumption based on the estimated emotions. For example, if the user is stressed, the adjustment unit can select a stability-focused adjustment method. For example, if the user is relaxed, the adjustment unit can select a performance-focused adjustment method. For example, if the user is in a hurry, the adjustment unit can make quick adjustments. This allows for more appropriate adjustments by adjusting the device's performance and energy consumption based on the user's emotions. Methods for estimating the user's emotions include, but are not limited to, the sensors and algorithms used. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI execute the optimal adjustment method.

[0082] The adjustment unit can select an adjustment algorithm by referring to real-time data when adjusting device performance and energy consumption. For example, the adjustment unit can select the optimal adjustment algorithm based on real-time device usage data. For example, the adjustment unit can select the optimal adjustment algorithm based on real-time energy consumption data. For example, the adjustment unit can select the optimal adjustment algorithm based on real-time environmental data. This allows for more appropriate adjustment by selecting an adjustment algorithm by referring to real-time data. Real-time data includes, but is not limited to, data type and reference method. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or without AI. For example, the adjustment unit can input real-time data into a generating AI and have the generating AI select the optimal adjustment algorithm.

[0083] The adjustment unit can adjust the adjustment method according to the device usage when adjusting the device's performance and energy consumption. For example, the adjustment unit can adjust the adjustment method according to the frequency of device use. For example, the adjustment unit can adjust the adjustment method according to the duration of device use. For example, the adjustment unit can adjust the adjustment method according to the device's usage environment. By adjusting the adjustment method according to the device usage, more appropriate adjustments become possible. Device usage includes, but is not limited to, frequency of use, duration of use, and usage environment. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or without AI. For example, the adjustment unit can input device usage data into a generating AI and have the generating AI execute the optimal adjustment method.

[0084] The adjustment unit can estimate the user's emotions and determine the priority of device performance and energy consumption based on the estimated user emotions. For example, if the user is stressed, the adjustment unit may prioritize stability-oriented adjustments. For example, if the user is relaxed, the adjustment unit may prioritize performance-oriented adjustments. For example, if the user is in a hurry, the adjustment unit can perform adjustments quickly. This allows for adjustments to be made in a more appropriate order by determining the priority of device performance and energy consumption based on the user's emotions. Methods for estimating user emotions include, but are not limited to, the sensors and algorithms used. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input user emotion data into a generative AI, which can then determine the optimal priority.

[0085] The adjustment unit can select an adjustment method that takes the device's lifespan into consideration when adjusting the device's performance and energy consumption. For example, the adjustment unit can select a low-load adjustment method to extend the device's lifespan. For example, the adjustment unit can introduce an appropriate cooling system that takes the device's lifespan into consideration. For example, the adjustment unit can perform appropriate power management to extend the device's lifespan. In this way, the device's lifespan can be extended by selecting an adjustment method that takes the device's lifespan into consideration. The device's lifespan includes, but is not limited to, a target lifespan value and an evaluation method. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input device lifespan data into a generating AI and have the generating AI execute the optimal adjustment method.

[0086] The adjustment unit can perform adjustments in combination with other energy efficiency technologies when adjusting the performance and energy consumption of the device. For example, the adjustment unit can perform adjustments in combination with renewable energy technologies. For example, the adjustment unit can perform adjustments in combination with high-efficiency cooling technologies. For example, the adjustment unit can perform adjustments in combination with high-efficiency power management technologies. This allows for more effective adjustments by performing adjustments in combination with other energy efficiency technologies. Other energy efficiency technologies include, but are not limited to, the types of technologies used and the methods of combining them. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input data on other energy efficiency technologies into a generating AI, which can then execute the optimal adjustment method.

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

[0088] The user unit can estimate the user's emotions and adjust the selection of semiconductor materials based on the estimated emotions. For example, if the user is stressed, a highly stable semiconductor material can be selected. If the user is relaxed, a performance-oriented semiconductor material can be selected. Furthermore, if the user is in a hurry, a quickly available semiconductor material can be selected. In this way, by adjusting the selection of semiconductor materials based on the user's emotions, a more appropriate material can be selected. The method for estimating the user's emotions includes, but is not limited to, the sensors and algorithms used. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the user unit may be performed using AI or not. For example, the user unit can input user emotion data into a generative AI and have the generative AI execute the optimal material selection method.

[0089] The adjustment unit can select an adjustment algorithm by referring to real-time data when adjusting the performance and energy consumption of a device. For example, it can select the optimal adjustment algorithm based on real-time device usage data. It can also select the optimal adjustment algorithm based on real-time energy consumption data. Furthermore, it can select the optimal adjustment algorithm based on real-time environmental data. By selecting an adjustment algorithm by referring to real-time data, more appropriate adjustments become possible. Real-time data includes, but is not limited to, data type and reference method. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input real-time data into a generating AI and have the generating AI select the optimal adjustment algorithm.

[0090] The optimization unit can select an optimization algorithm by referring to past data when optimizing energy-saving designs. For example, it can select the optimal algorithm based on past energy-saving design data. It can also select the optimal algorithm based on past device usage data. Furthermore, it can select the optimal algorithm based on past energy consumption data. By selecting an optimization algorithm by referring to past data, a more appropriate energy-saving design can be achieved. Past data includes, but is not limited to, data types and referencing methods. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can input past data into a generating AI and have the generating AI select the optimal algorithm.

[0091] The utilization unit can select the optimal utilization method based on the material's properties when using semiconductor materials. For example, it can select a semiconductor material that is resistant to high-temperature environments and optimize the cooling system. Furthermore, if high-speed processing is required, a low-latency semiconductor material can be selected. Additionally, if a long lifespan is required, a highly durable semiconductor material can be selected. This allows for improved system performance by selecting the optimal utilization method based on the material's properties. Material properties include, but are not limited to, the types of properties and evaluation criteria. Some or all of the above-described processing in the utilization unit may be performed using AI, or without AI. For example, the utilization unit can input material property data into a generating AI, which can then execute the optimal utilization method.

[0092] The adjustment unit can estimate the user's emotions and adjust the device's performance and energy consumption based on the estimated emotions. For example, if the user is stressed, a stability-focused adjustment method can be selected. If the user is relaxed, a performance-focused adjustment method can be selected. Furthermore, if the user is in a hurry, adjustments can be made quickly. This allows for more appropriate adjustments by adjusting the device's performance and energy consumption based on the user's emotions. The method for estimating the user's emotions includes, but is not limited to, the sensors and algorithms used. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI execute the optimal adjustment method.

[0093] The optimization unit can adjust the optimization method according to the device usage when optimizing the energy-saving design. For example, the energy-saving design can be adjusted according to the frequency of device use. It can also be adjusted according to the duration of device use. Furthermore, it can be adjusted according to the environment in which the device is used. By adjusting the optimization method according to the device usage, a more appropriate energy-saving design can be achieved. Device usage includes, but is not limited to, frequency of use, duration of use, and environment. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input device usage data into a generating AI and have the generating AI execute the optimal optimization method.

[0094] The optimization unit can estimate the user's emotions and determine the priority of energy-saving designs based on the estimated emotions. For example, if the user is stressed, a stability-focused energy-saving design can be prioritized. If the user is relaxed, a performance-focused energy-saving design can be prioritized. Furthermore, if the user is in a hurry, optimization can be performed quickly. This allows for energy-saving designs to be implemented in a more appropriate order by determining the priority of energy-saving designs based on the user's emotions. The method for estimating the user's emotions includes, but is not limited to, the sensors and algorithms used. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI determine the optimal priority.

[0095] The utilization unit can adjust the utilization method according to environmental conditions when using semiconductor materials. For example, in a high-humidity environment, a semiconductor material with high humidity resistance can be selected. In a low-temperature environment, a semiconductor material suitable for low-temperature operation can be selected. Furthermore, in a high-vibration environment, a semiconductor material with high vibration resistance can be selected. By adjusting the utilization method according to environmental conditions, the performance of the system can be improved. Environmental conditions include, but are not limited to, temperature, humidity, and pressure. Some or all of the above processing in the utilization unit may be performed using AI, or it may be performed without AI. For example, the utilization unit can input environmental condition data into a generating AI, and have the generating AI execute the optimal utilization method.

[0096] The optimization unit can optimize energy-saving designs by combining them with other energy efficiency technologies. For example, it can optimize energy-saving designs by combining them with renewable energy technologies. It can also optimize energy-saving designs by combining them with high-efficiency cooling technologies. Furthermore, it can optimize energy-saving designs by combining them with high-efficiency power management technologies. This allows for more effective energy-saving designs by optimizing them in combination with other energy efficiency technologies. Other energy efficiency technologies include, but are not limited to, the types of technologies used and the methods of combining them. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can input data on other energy efficiency technologies into a generating AI, which can then execute the optimal combination method.

[0097] The adjustment unit can estimate the user's emotions and determine the priority of device performance and energy consumption based on the estimated emotions. For example, if the user is stressed, stability-focused adjustments can be prioritized. If the user is relaxed, performance-focused adjustments can be prioritized. Furthermore, if the user is in a hurry, adjustments can be made quickly. This allows for adjustments to be made in a more appropriate order by determining the priority of device performance and energy consumption based on the user's emotions. Methods for estimating user emotions include, but are not limited to, the sensors and algorithms used. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input user emotion data into a generative AI, which can then determine the optimal priority.

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

[0099] Step 1: The application part utilizes advanced semiconductor materials. Advanced semiconductor materials include, but are not limited to, materials with specific names or properties. For example, the application part can utilize semiconductor materials with high thermal conductivity. Step 2: The optimization unit optimizes the energy-saving design based on the semiconductor materials used by the utilization unit. The energy-saving design includes energy consumption reduction targets and design methods. For example, the optimization unit sets energy consumption reduction targets and optimizes the design based on them. It can also use highly energy-efficient circuit design methods. Step 3: The adjustment unit adjusts the performance and energy consumption of each device in real time based on the energy-saving design optimized by the optimization unit. The adjustment method includes the frequency of adjustment and the technology used. For example, the adjustment unit sets the frequency of adjustment and performs adjustments in real time based on that. It can also perform adjustments in real time using AI technology.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

[0103] Each of the multiple elements described above, including the utilization unit, optimization unit, and adjustment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the utilization unit is implemented by the control unit 46A of the smart device 14 and utilizes advanced semiconductor materials. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the energy-saving design. The adjustment unit is implemented by the control unit 46A of the smart device 14 and adjusts the performance and energy consumption of each device in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

[0112] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the utilization unit, optimization unit, and adjustment unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the utilization unit is implemented by the control unit 46A of the smart glasses 214 and utilizes advanced semiconductor materials. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the energy-saving design. The adjustment unit is implemented by the control unit 46A of the smart glasses 214 and adjusts the performance and energy consumption of each device in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

[0128] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

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

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

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the utilization unit, optimization unit, and adjustment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the utilization unit is implemented by the control unit 46A of the headset terminal 314 and utilizes advanced semiconductor materials. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the energy-saving design. The adjustment unit is implemented by the control unit 46A of the headset terminal 314 and adjusts the performance and energy consumption of each device in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0137] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

[0143] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

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

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the utilization unit, optimization unit, and adjustment unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the utilization unit is implemented by the control unit 46A of the robot 414 and utilizes advanced semiconductor materials. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes energy-saving design. The adjustment unit is implemented by the control unit 46A of the robot 414 and adjusts the performance and energy consumption of each device in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

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

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

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

[0165] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

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

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

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

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

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

[0171] (Note 1) The application section utilizes advanced semiconductor materials, An optimization unit that optimizes energy-saving designs based on the semiconductor materials used by the aforementioned utilization unit, The system includes an adjustment unit that adjusts the performance and energy consumption of each device in real time based on the energy-saving design optimized by the aforementioned optimization unit. A system characterized by the following features. (Note 2) The adjustment unit is, Reduces power consumption by up to 50% The system described in Appendix 1, characterized by the features described herein. (Note 3) The adjustment unit is, Improve AI calculation speed by up to 40% The system described in Appendix 1, characterized by the features described herein. (Note 4) The adjustment unit is, Extend the lifespan of the device. The system described in Appendix 1, characterized by the features described herein. (Note 5) The optimization unit, To optimize energy-saving design, we will implement an automatic adjustment function through integration with AI. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned utilization unit is, Utilizing advanced semiconductor materials The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned utilization unit is, The system estimates the user's emotions and adjusts the selection of semiconductor materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned utilization unit is, When using semiconductor materials, the optimal usage method is selected based on the material's properties. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned utilization unit is, When using semiconductor materials, adjust the usage method according to environmental conditions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned utilization unit is, The system estimates the user's emotions and determines the order in which semiconductor materials are used based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned utilization unit is, When using semiconductor materials, adjust the usage method considering the supply situation of the materials. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned utilization unit is, When using semiconductor materials, the method of use should be selected considering compatibility with other devices. The system described in Appendix 1, characterized by the features described herein. (Note 13) The optimization unit, It estimates user emotions and adjusts the energy-saving design optimization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The optimization unit, When optimizing energy-saving designs, the optimization algorithm is selected by referring to past data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The optimization unit, When optimizing energy-saving designs, adjust the optimization method according to the device usage. The system described in Appendix 1, characterized by the features described herein. (Note 16) The optimization unit, It estimates user emotions and determines energy-saving design priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The optimization unit, When optimizing energy-saving designs, the optimization method should be selected while considering the lifespan of the device. The system described in Appendix 1, characterized by the features described herein. (Note 18) The optimization unit, When optimizing energy-saving designs, perform optimization in combination with other energy efficiency technologies. The system described in Appendix 1, characterized by the features described herein. (Note 19) The adjustment unit is, The system estimates the user's emotions and adjusts the device's performance and energy consumption based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The adjustment unit is, When adjusting device performance and energy consumption, real-time data is referenced to select the adjustment algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 21) The adjustment unit is, When adjusting device performance and energy consumption, the adjustment method is adjusted according to the device usage. The system described in Appendix 1, characterized by the features described herein. (Note 22) The adjustment unit is, It estimates the user's emotions and determines the priority of device performance and energy consumption based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The adjustment unit is, When adjusting device performance and energy consumption, the adjustment method should be selected considering the device's lifespan. The system described in Appendix 1, characterized by the features described herein. (Note 24) The adjustment unit is, When adjusting device performance and energy consumption, the adjustments are made in combination with other energy efficiency technologies. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. The application section utilizes advanced semiconductor materials, An optimization unit that optimizes energy-saving designs based on the semiconductor materials used by the aforementioned utilization unit, The system includes an adjustment unit that adjusts the performance and energy consumption of each device in real time based on the energy-saving design optimized by the aforementioned optimization unit. A system characterized by the following features.

2. The adjustment unit is, Reduces power consumption by up to 50% The system according to feature 1.

3. The adjustment unit is, Improve AI calculation speed by up to 40% The system according to feature 1.

4. The adjustment unit is, Extend the lifespan of the device. The system according to feature 1.

5. The optimization unit, To optimize energy-saving design, we will implement an automatic adjustment function through integration with AI. The system according to feature 1.

6. The aforementioned utilization unit is, Utilizing advanced semiconductor materials The system according to feature 1.

7. The aforementioned utilization unit is, The system estimates the user's emotions and adjusts the selection of semiconductor materials based on those estimated emotions. The system according to feature 1.

8. The aforementioned utilization unit is, When using semiconductor materials, the optimal usage method is selected based on the material's properties. The system according to feature 1.

Images