system

Abstract

The system according to this embodiment aims to reduce power consumption while enhancing the computing power of AI. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a generation unit, and a provision unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit to enhance the computing power of the AI. The generation unit generates an optimal circuit layout to reduce power consumption based on the data analyzed by the analysis unit. The provision unit provides the circuit layout generated by the generation unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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, sufficient efforts have not been made to enhance the computing power of AI while reducing power consumption, and there is room for improvement.

[0005] The system according to the embodiment aims to enhance the computing power of AI while reducing power consumption.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and a provision unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit to enhance the computing power of the AI. The generation unit generates an optimal circuit layout to reduce power consumption based on the data analyzed by the analysis unit. The provision unit provides the circuit layout generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can reduce power consumption while enhancing the computing power of the AI. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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) The system utilizing next-generation semiconductor technology according to the embodiment of the present invention is a system that aims for innovative improvements in areas such as smart devices, autonomous driving technology, advanced data centers, and improved energy efficiency. This system is particularly focused on enhancing the computing power of AI and reducing power consumption. First, the computing power of AI is enhanced using next-generation semiconductor technology. This enables more advanced processing in smart devices, autonomous driving technology, and advanced data centers. For example, in smart devices, faster data processing becomes possible, improving the user experience. In autonomous driving technology, real-time data analysis becomes possible, improving safety. In advanced data centers, large amounts of data can be processed efficiently, reducing operating costs. Next, energy efficiency is improved. By using next-generation semiconductor technology, power consumption is reduced and energy efficiency is improved. This makes it possible to maintain high performance while suppressing energy consumption in smart devices, autonomous driving technology, and advanced data centers. For example, in smart devices, battery life is extended, allowing users to use them for longer periods. In autonomous driving technology, energy consumption of vehicles is reduced, mitigating the environmental impact. In advanced data centers, cooling costs are reduced, lowering operating costs. Furthermore, semiconductor design using generative AI enables optimal circuit layout and energy-efficient design. Generative AI analyzes vast amounts of data and proposes the optimal circuit layout. This improves semiconductor performance and energy efficiency. For example, semiconductors designed using generative AI consume less power and perform higher than those designed using conventional methods. In this way, leveraging next-generation semiconductor technology can bring about innovative improvements in areas such as smart devices, autonomous driving technology, advanced data centers, and energy efficiency. In particular, by enhancing the computing power of AI and reducing power consumption, high performance and energy efficiency can be achieved in these areas. As a result, systems utilizing next-generation semiconductor technology can enhance the computing power of AI and reduce power consumption.

[0029] The system utilizing next-generation semiconductor technology according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and a provision unit. The data collection unit collects data. The data collection unit collects data from, for example, smart devices, autonomous driving technology, and advanced data centers. The data collection unit can collect specific data such as sensor data, log data, and user data. The analysis unit analyzes the data collected by the data collection unit to enhance the computing power of the AI. The analysis unit analyzes the data by applying, for example, statistical analysis or machine learning algorithms. The analysis unit uses specific methods to enhance the computing power of the AI, such as improving processing speed and accuracy. The generation unit generates an optimal circuit layout to reduce power consumption based on the data analyzed by the analysis unit. The generation unit generates the optimal circuit layout using a generation AI. The generation unit generates the circuit layout using, for example, a generation model or a neural network. The provision unit provides the circuit layout generated by the generation unit. The provision unit provides the generated circuit layout to improve energy efficiency, for example. The provision unit provides the generated circuit layout to the user to achieve improved energy efficiency. As a result, the system utilizing next-generation semiconductor technology according to this embodiment can enhance the computing power of AI and reduce power consumption through data collection, analysis, generation, and provision.

[0030] The data collection unit collects data from various sources, such as smart devices, autonomous driving technology, and advanced data centers. Specifically, it collects user operation logs, sensor data, and location information from smart devices, including smartphones, wearable devices, and smart home devices. From autonomous driving technology, it collects vehicle driving data, sensor data, and camera images, enabling a detailed understanding of the vehicle's movements and surrounding environment. From advanced data centers, it collects data such as server logs, network traffic data, and storage usage, allowing for real-time monitoring of the data center's operational status and performance. The data collection unit centrally manages the data collected from these diverse data sources, enabling efficient use by the analysis and generation units. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. For example, when a specific event occurs, the data collection frequency can be increased to obtain more detailed data. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit to enhance the AI's computing power. Specifically, it analyzes the data by applying statistical analysis and machine learning algorithms. Statistical analysis reveals the data's distribution and correlations, and understands its characteristics. Machine learning algorithms train models using the collected data to perform predictions and classifications. For example, an anomaly detection model can be trained using collected sensor data to detect abnormal behavior or conditions early. The analysis unit employs specific methods to enhance the AI's computing power, such as improving processing speed and accuracy. For example, it can improve computing speed using hardware acceleration. This includes using dedicated hardware such as GPUs and TPUs. It can also efficiently process large datasets using distributed processing technology. Furthermore, the analysis unit performs data preprocessing and feature engineering to improve model accuracy. As a result, the analysis unit can analyze the collected data quickly and accurately, enhancing the AI's computing power.

[0032] The generation unit generates the optimal circuit layout to reduce power consumption based on the data analyzed by the analysis unit. Specifically, it uses generative AI to generate the optimal circuit layout. The generative AI generates circuit layouts using generative models and neural networks. For example, a generative model learns from past circuit layout data and finds patterns to generate new circuit layouts. A neural network is a model with multiple layers that learns complex relationships from input data and generates the optimal circuit layout. The generation unit combines these technologies to generate a circuit layout that minimizes power consumption. Furthermore, the generation unit evaluates the generated circuit layout and selects the optimal layout. The evaluation uses simulations and experiments to confirm the performance of the generated circuit layout. As a result, the generation unit can provide a high-performance circuit layout while reducing power consumption.

[0033] The provider unit provides the circuit layouts generated by the generation unit. Specifically, it provides the generated circuit layouts to improve energy efficiency. The provider unit provides the generated circuit layouts to the user, thereby realizing improved energy efficiency. For example, the provider unit provides the generated circuit layouts as design drawings, allowing users to use them in actual circuit design. The provider unit also integrates the generated circuit layouts into simulation tools and design tools, making them easily usable by users. Furthermore, the provider unit provides information on the performance and energy efficiency of the generated circuit layouts, helping users make optimal choices. In this way, the provider unit can provide users with high-performance and energy-efficient circuit layouts, promoting the widespread adoption and use of next-generation semiconductor technology.

[0034] The data collection unit can collect data from smart devices, autonomous driving technologies, and advanced data centers. For example, the data collection unit can collect sensor data from smart devices. For example, the data collection unit can also collect log data from autonomous driving technologies. For example, the data collection unit can also collect user data from advanced data centers. This enables extensive data analysis by collecting data from various devices and technologies. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input sensor data collected from smart devices into an AI and have the AI ​​perform the data collection.

[0035] The analysis unit can analyze the collected data and enhance the AI's computing power. For example, the analysis unit can perform statistical analysis on the collected data. The analysis unit can also apply machine learning algorithms to the collected data for analysis. The analysis unit can also use the collected data to improve the AI's processing speed. This enhances the AI's computing power through data analysis. Some or all of the above-described processes in the analysis unit may be performed using the AI, or not. For example, the analysis unit can input the collected data into the AI ​​and have the AI ​​perform the data analysis.

[0036] The generation unit can generate an optimal circuit layout to reduce power consumption using a generation AI. The generation unit can generate an optimal circuit layout using a generation AI, for example. The generation unit can also generate a circuit layout using a generation model, for example. The generation unit can also generate a circuit layout using a neural network, for example. In this way, by using a generation AI, an optimal circuit layout to reduce power consumption can be generated. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data into a generation AI and have the generation AI perform the generation of an optimal circuit layout.

[0037] The providing unit can provide a generated circuit layout and improve energy efficiency. The providing unit, for example, provides the generated circuit layout to the user. The providing unit can also provide the generated circuit layout for the purpose of improving energy efficiency. The providing unit can also reduce energy consumption using the generated circuit layout. In this way, by providing the generated circuit layout, energy efficiency can be improved. Some or all of the above processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the generated circuit layout into AI and have AI perform the provision for improving energy efficiency.

[0038] The data collection unit can select the optimal data collection method considering the operating status of devices when collecting data from smart devices, autonomous driving technology, and advanced data centers. For example, the data collection unit can minimize data collection when a smart device is in low-power mode. For example, the data collection unit can temporarily stop data collection when autonomous driving technology is under high load. For example, the data collection unit can shift data collection to off-peak times when an advanced data center is experiencing peak load. This enables efficient data collection by selecting the optimal data collection method according to the operating status of the devices. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input device operating status data into AI and have the AI ​​select the optimal data collection method.

[0039] The data collection unit can filter data based on the device's operating environment during data collection. For example, if a smart device is outdoors, the data collection unit can filter out environmental noise. For example, if an autonomous driving system is operating in an urban area, the data collection unit can prioritize the collection of traffic data. For example, if a sophisticated data center is in a high-temperature environment, the data collection unit can filter and collect temperature data. This improves the accuracy of the collected data by filtering based on the device's operating environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input device operating environment data into AI and leave the filtering to the AI.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the device's geographical location information during data collection. For example, if a smart device is located in a specific area, the data collection unit will prioritize the collection of data related to that area. For example, if autonomous driving technology is traveling on a specific road, the data collection unit can also prioritize the collection of data related to that road. For example, if an advanced data center is located in a specific city, the data collection unit can also prioritize the collection of data related to that city. This enables efficient data collection by prioritizing the collection of highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the device's geographical location information into the AI ​​and have the AI ​​perform the collection of highly relevant data.

[0041] The data collection unit can analyze the device's social media activity during data collection and collect relevant data. For example, if a smart device user is active on a particular social media platform, the data collection unit can collect data related to that activity. For example, if a user of autonomous driving technology is sharing traffic information on social media, the data collection unit can also collect that information. For example, if an operator of an advanced data center is sharing technical information on social media, the data collection unit can also collect that information. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into AI and have the AI ​​perform the collection of relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis according to the importance of the data, efficient data analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an image analysis algorithm to image data. For example, the analysis unit can also apply a natural language processing algorithm to text data. For example, the analysis unit can also apply a statistical analysis algorithm to numerical data. By applying an appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​execute the application of an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also postpone the analysis of older data. For example, the analysis unit may prioritize the analysis of data collected during a specific period. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI ​​and have the AI ​​determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, the analysis unit may postpone the analysis of data with low relevance. For example, the analysis unit may moderately analyze data with moderate relevance. By adjusting the order of analysis based on the relevance of the data, efficient data analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​perform the adjustment of the order of analysis.

[0046] The generation unit can improve the accuracy of generation by considering the interrelationships of the data during generation. For example, the generation unit can analyze the correlations between data and generate the optimal circuit layout. The generation unit can also generate an efficient circuit layout by considering the dependencies between data. The generation unit can also generate a highly accurate circuit layout by considering the interactions between data. In this way, a highly accurate circuit layout can be generated by considering the interrelationships of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the interrelationships of the data into the generation AI and have the generation AI perform the improvement of generation accuracy.

[0047] The generation unit can perform generation while considering the attribute information of the data submitter. For example, if the submitter is an expert, the generation unit can generate a detailed circuit layout. For example, if the submitter is a beginner, the generation unit can also generate a simple circuit layout. For example, if the submitter is an expert in a particular field, the generation unit can also generate a circuit layout specialized for that field. In this way, by considering the submitter's attribute information, it is possible to generate a circuit layout that is suitable for the submitter. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the submitter's attribute information into the generation AI and have the generation AI perform the circuit layout generation.

[0048] The generation unit can perform generation while considering the geographical distribution of the data. For example, if the data is concentrated in a particular region, the generation unit can generate a circuit layout specific to that region. For example, if the data is widely distributed, the generation unit can also generate a circuit layout that covers the entire area. For example, if the data is concentrated in a particular city, the generation unit can also generate a circuit layout specific to that city. In this way, by considering the geographical distribution of the data, it is possible to generate a circuit layout specific to a particular region. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the geographical distribution of the data into a generation AI and have the generation AI perform the generation of the circuit layout.

[0049] The generation unit can improve the accuracy of generation by referring to relevant literature during generation. For example, the generation unit generates the optimal circuit layout based on relevant literature. The generation unit can also generate an efficient circuit layout by utilizing the insights from relevant literature. The generation unit can also generate a highly accurate circuit layout by referring to data from relevant literature. Thus, a highly accurate circuit layout can be generated by referring to relevant literature. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data from relevant literature into a generation AI and have the generation AI perform the circuit layout generation.

[0050] The service provider can select the optimal service delivery method by referring to the user's past operation history at the time of delivery. For example, the service provider may prioritize providing display methods previously used by the user. For example, the service provider may also suggest the optimal display method based on the user's past operation history. For example, the service provider may analyze the user's past operation history and provide an efficient display method. This allows the service provider to select the optimal service delivery method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input user operation history data into AI and have the AI ​​select the optimal service delivery method.

[0051] The service provider can select the optimal service delivery method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the service provider can also provide a concise and highly visible display method. This allows the service provider to select the optimal service delivery method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI and have the AI ​​select the optimal service delivery method.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] Systems utilizing next-generation semiconductor technology can also incorporate a predictive unit. This unit can forecast future trends based on collected data. For example, it can analyze smart device usage patterns and predict battery depletion. In autonomous driving technology, it can predict changes in traffic conditions and suggest optimal routes. Advanced data centers can predict data growth trends and secure necessary resources in advance. Thus, using a predictive unit can improve system efficiency and user experience.

[0054] Systems utilizing next-generation semiconductor technology can also incorporate a feedback unit. This feedback unit can collect user feedback and use it to improve the system. For example, it can collect feedback on the user experience of smart devices and incorporate it into UI / UX improvements. In autonomous driving technology, it can collect driver feedback to improve safety. In advanced data centers, it can collect feedback from operators to improve operational efficiency. Thus, using a feedback unit can improve system quality and user satisfaction.

[0055] Systems utilizing next-generation semiconductor technology can also incorporate customization options. These customization options allow for system settings to be customized according to user needs. For example, smart device settings can be customized to user preferences. In autonomous driving technology, system behavior can be customized to match the driver's driving style. In advanced data centers, resource allocation can be customized according to operator requirements. Thus, using customization options can improve user satisfaction and system flexibility.

[0056] Systems utilizing next-generation semiconductor technology can also incorporate a security unit. This security unit provides functions to ensure the security of collected data. For example, it can encrypt data on smart devices to protect against unauthorized access. In autonomous driving technology, it can secure communication data and protect vehicles from hacking. Advanced data centers can provide data backup and recovery functions to prevent data loss. Thus, using a security unit can improve the safety and reliability of the system.

[0057] Systems utilizing next-generation semiconductor technology can also incorporate an energy management unit. This unit can monitor and optimize the overall energy consumption of the system. For example, it can monitor the battery consumption of smart devices in real time, promoting efficient energy use. In autonomous driving technology, it can optimize vehicle energy consumption and improve fuel efficiency. In advanced data centers, it can monitor the energy consumption of cooling systems, enabling efficient operation. Thus, using an energy management unit can improve the energy efficiency and sustainability of the system.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The collection unit collects data. The collection unit collects data from, for example, smart devices, autonomous driving technology, and advanced data centers. The collection unit can collect specific data such as sensor data, log data, and user data. Step 2: The analysis unit analyzes the data collected by the data collection unit to enhance the AI's computing power. The analysis unit analyzes the data by applying statistical analysis or machine learning algorithms, for example. The analysis unit uses specific methods to enhance the AI's computing power, such as improving processing speed and accuracy. Step 3: The generation unit generates the optimal circuit layout to reduce power consumption based on the data analyzed by the analysis unit. The generation unit generates the optimal circuit layout using generative AI. The generation unit generates the circuit layout using, for example, generative models or neural networks. Step 4: The supply unit provides the circuit layout generated by the generation unit. The supply unit provides the generated circuit layout, for example, to improve energy efficiency. The supply unit provides the generated circuit layout to the user, thereby achieving improved energy efficiency.

[0060] (Example of form 2) The system utilizing next-generation semiconductor technology according to the embodiment of the present invention is a system that aims for innovative improvements in areas such as smart devices, autonomous driving technology, advanced data centers, and improved energy efficiency. This system is particularly focused on enhancing the computing power of AI and reducing power consumption. First, the computing power of AI is enhanced using next-generation semiconductor technology. This enables more advanced processing in smart devices, autonomous driving technology, and advanced data centers. For example, in smart devices, faster data processing becomes possible, improving the user experience. In autonomous driving technology, real-time data analysis becomes possible, improving safety. In advanced data centers, large amounts of data can be processed efficiently, reducing operating costs. Next, energy efficiency is improved. By using next-generation semiconductor technology, power consumption is reduced and energy efficiency is improved. This makes it possible to maintain high performance while suppressing energy consumption in smart devices, autonomous driving technology, and advanced data centers. For example, in smart devices, battery life is extended, allowing users to use them for longer periods. In autonomous driving technology, energy consumption of vehicles is reduced, mitigating the environmental impact. In advanced data centers, cooling costs are reduced, lowering operating costs. Furthermore, semiconductor design using generative AI enables optimal circuit layout and energy-efficient design. Generative AI analyzes vast amounts of data and proposes the optimal circuit layout. This improves semiconductor performance and energy efficiency. For example, semiconductors designed using generative AI consume less power and perform higher than those designed using conventional methods. In this way, leveraging next-generation semiconductor technology can bring about innovative improvements in areas such as smart devices, autonomous driving technology, advanced data centers, and energy efficiency. In particular, by enhancing the computing power of AI and reducing power consumption, high performance and energy efficiency can be achieved in these areas. As a result, systems utilizing next-generation semiconductor technology can enhance the computing power of AI and reduce power consumption.

[0061] The system utilizing next-generation semiconductor technology according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and a provision unit. The data collection unit collects data. The data collection unit collects data from, for example, smart devices, autonomous driving technology, and advanced data centers. The data collection unit can collect specific data such as sensor data, log data, and user data. The analysis unit analyzes the data collected by the data collection unit to enhance the computing power of the AI. The analysis unit analyzes the data by applying, for example, statistical analysis or machine learning algorithms. The analysis unit uses specific methods to enhance the computing power of the AI, such as improving processing speed and accuracy. The generation unit generates an optimal circuit layout to reduce power consumption based on the data analyzed by the analysis unit. The generation unit generates the optimal circuit layout using a generation AI. The generation unit generates the circuit layout using, for example, a generation model or a neural network. The provision unit provides the circuit layout generated by the generation unit. The provision unit provides the generated circuit layout to improve energy efficiency, for example. The provision unit provides the generated circuit layout to the user to achieve improved energy efficiency. As a result, the system utilizing next-generation semiconductor technology according to this embodiment can enhance the computing power of AI and reduce power consumption through data collection, analysis, generation, and provision.

[0062] The data collection unit collects data from various sources, such as smart devices, autonomous driving technology, and advanced data centers. Specifically, it collects user operation logs, sensor data, and location information from smart devices, including smartphones, wearable devices, and smart home devices. From autonomous driving technology, it collects vehicle driving data, sensor data, and camera images, enabling a detailed understanding of the vehicle's movements and surrounding environment. From advanced data centers, it collects data such as server logs, network traffic data, and storage usage, allowing for real-time monitoring of the data center's operational status and performance. The data collection unit centrally manages the data collected from these diverse data sources, enabling efficient use by the analysis and generation units. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. For example, when a specific event occurs, the data collection frequency can be increased to obtain more detailed data. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0063] The analysis unit analyzes the data collected by the collection unit to enhance the AI's computing power. Specifically, it analyzes the data by applying statistical analysis and machine learning algorithms. Statistical analysis reveals the data's distribution and correlations, and understands its characteristics. Machine learning algorithms train models using the collected data to perform predictions and classifications. For example, an anomaly detection model can be trained using collected sensor data to detect abnormal behavior or conditions early. The analysis unit employs specific methods to enhance the AI's computing power, such as improving processing speed and accuracy. For example, it can improve computing speed using hardware acceleration. This includes using dedicated hardware such as GPUs and TPUs. It can also efficiently process large datasets using distributed processing technology. Furthermore, the analysis unit performs data preprocessing and feature engineering to improve model accuracy. As a result, the analysis unit can analyze the collected data quickly and accurately, enhancing the AI's computing power.

[0064] The generation unit generates the optimal circuit layout to reduce power consumption based on the data analyzed by the analysis unit. Specifically, it uses generative AI to generate the optimal circuit layout. The generative AI generates circuit layouts using generative models and neural networks. For example, a generative model learns from past circuit layout data and finds patterns to generate new circuit layouts. A neural network is a model with multiple layers that learns complex relationships from input data and generates the optimal circuit layout. The generation unit combines these technologies to generate a circuit layout that minimizes power consumption. Furthermore, the generation unit evaluates the generated circuit layout and selects the optimal layout. The evaluation uses simulations and experiments to confirm the performance of the generated circuit layout. As a result, the generation unit can provide a high-performance circuit layout while reducing power consumption.

[0065] The provider unit provides the circuit layouts generated by the generation unit. Specifically, it provides the generated circuit layouts to improve energy efficiency. The provider unit provides the generated circuit layouts to the user, thereby realizing improved energy efficiency. For example, the provider unit provides the generated circuit layouts as design drawings, allowing users to use them in actual circuit design. The provider unit also integrates the generated circuit layouts into simulation tools and design tools, making them easily usable by users. Furthermore, the provider unit provides information on the performance and energy efficiency of the generated circuit layouts, helping users make optimal choices. In this way, the provider unit can provide users with high-performance and energy-efficient circuit layouts, promoting the widespread adoption and use of next-generation semiconductor technology.

[0066] The data collection unit can collect data from smart devices, autonomous driving technologies, and advanced data centers. For example, the data collection unit can collect sensor data from smart devices. For example, the data collection unit can also collect log data from autonomous driving technologies. For example, the data collection unit can also collect user data from advanced data centers. This enables extensive data analysis by collecting data from various devices and technologies. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input sensor data collected from smart devices into an AI and have the AI ​​perform the data collection.

[0067] The analysis unit can analyze the collected data and enhance the AI's computing power. For example, the analysis unit can perform statistical analysis on the collected data. The analysis unit can also apply machine learning algorithms to the collected data for analysis. The analysis unit can also use the collected data to improve the AI's processing speed. This enhances the AI's computing power through data analysis. Some or all of the above-described processes in the analysis unit may be performed using the AI, or not. For example, the analysis unit can input the collected data into the AI ​​and have the AI ​​perform the data analysis.

[0068] The generation unit can generate an optimal circuit layout to reduce power consumption using a generation AI. The generation unit can generate an optimal circuit layout using a generation AI, for example. The generation unit can also generate a circuit layout using a generation model, for example. The generation unit can also generate a circuit layout using a neural network, for example. In this way, by using a generation AI, an optimal circuit layout to reduce power consumption can be generated. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data into a generation AI and have the generation AI perform the generation of an optimal circuit layout.

[0069] The providing unit can provide a generated circuit layout and improve energy efficiency. The providing unit, for example, provides the generated circuit layout to the user. The providing unit can also provide the generated circuit layout for the purpose of improving energy efficiency. The providing unit can also reduce energy consumption using the generated circuit layout. In this way, by providing the generated circuit layout, energy efficiency can be improved. Some or all of the above processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the generated circuit layout into AI and have AI perform the provision for improving energy efficiency.

[0070] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can shorten the timing of data collection to collect data quickly. This reduces the burden on the user by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into AI and have the AI ​​adjust the timing of data collection.

[0071] The data collection unit can select the optimal data collection method considering the operating status of devices when collecting data from smart devices, autonomous driving technology, and advanced data centers. For example, the data collection unit can minimize data collection when a smart device is in low-power mode. For example, the data collection unit can temporarily stop data collection when autonomous driving technology is under high load. For example, the data collection unit can shift data collection to off-peak times when an advanced data center is experiencing peak load. This enables efficient data collection by selecting the optimal data collection method according to the operating status of the devices. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input device operating status data into AI and have the AI ​​select the optimal data collection method.

[0072] The data collection unit can filter data based on the device's operating environment during data collection. For example, if a smart device is outdoors, the data collection unit can filter out environmental noise. For example, if an autonomous driving system is operating in an urban area, the data collection unit can prioritize the collection of traffic data. For example, if a sophisticated data center is in a high-temperature environment, the data collection unit can filter and collect temperature data. This improves the accuracy of the collected data by filtering based on the device's operating environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input device operating environment data into AI and leave the filtering to the AI.

[0073] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may postpone the collection of less important data. For example, if the user is relaxed, the data collection unit may prioritize the collection of detailed data. For example, if the user is in a hurry, the data collection unit may prioritize the collection of highly important data. This allows for the priority collection of important data by determining data priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​determine the data priority.

[0074] The data collection unit can prioritize the collection of highly relevant data by considering the device's geographical location information during data collection. For example, if a smart device is located in a specific area, the data collection unit will prioritize the collection of data related to that area. For example, if autonomous driving technology is traveling on a specific road, the data collection unit can also prioritize the collection of data related to that road. For example, if an advanced data center is located in a specific city, the data collection unit can also prioritize the collection of data related to that city. This enables efficient data collection by prioritizing the collection of highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the device's geographical location information into the AI ​​and have the AI ​​perform the collection of highly relevant data.

[0075] The data collection unit can analyze the device's social media activity during data collection and collect relevant data. For example, if a smart device user is active on a particular social media platform, the data collection unit can collect data related to that activity. For example, if a user of autonomous driving technology is sharing traffic information on social media, the data collection unit can also collect that information. For example, if an operator of an advanced data center is sharing technical information on social media, the data collection unit can also collect that information. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into AI and have the AI ​​perform the collection of relevant data.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can also provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can also provide concise analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the presentation of the analysis.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis according to the importance of the data, efficient data analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an image analysis algorithm to image data. For example, the analysis unit can also apply a natural language processing algorithm to text data. For example, the analysis unit can also apply a statistical analysis algorithm to numerical data. By applying an appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​execute the application of an appropriate analysis algorithm.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis result. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into an AI and have the AI ​​adjust the length of the analysis.

[0080] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also postpone the analysis of older data. For example, the analysis unit may prioritize the analysis of data collected during a specific period. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI ​​and have the AI ​​determine the analysis priority.

[0081] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, the analysis unit may postpone the analysis of data with low relevance. For example, the analysis unit may moderately analyze data with moderate relevance. By adjusting the order of analysis based on the relevance of the data, efficient data analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​perform the adjustment of the order of analysis.

[0082] The generation unit can estimate the user's emotions and determine the priority of the circuit layouts to be generated based on the estimated user emotions. For example, if the user is stressed, the generation unit will prioritize generating high-importance circuit layouts. For example, if the user is relaxed, the generation unit may also prioritize generating detailed circuit layouts. For example, if the user is in a hurry, the generation unit may also prioritize generating circuit layouts that can be generated quickly. In this way, by determining the priority of circuit layouts according to the user's emotions, important circuit layouts can be generated preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI determine the priority of circuit layouts.

[0083] The generation unit can improve the accuracy of generation by considering the interrelationships of the data during generation. For example, the generation unit can analyze the correlations between data and generate the optimal circuit layout. The generation unit can also generate an efficient circuit layout by considering the dependencies between data. The generation unit can also generate a highly accurate circuit layout by considering the interactions between data. In this way, a highly accurate circuit layout can be generated by considering the interrelationships of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the interrelationships of the data into the generation AI and have the generation AI perform the improvement of generation accuracy.

[0084] The generation unit can perform generation while considering the attribute information of the data submitter. For example, if the submitter is an expert, the generation unit can generate a detailed circuit layout. For example, if the submitter is a beginner, the generation unit can also generate a simple circuit layout. For example, if the submitter is an expert in a particular field, the generation unit can also generate a circuit layout specialized for that field. In this way, by considering the submitter's attribute information, it is possible to generate a circuit layout that is suitable for the submitter. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the submitter's attribute information into the generation AI and have the generation AI perform the circuit layout generation.

[0085] The generation unit can estimate the user's emotions and adjust the display method of the generated circuit layout based on the estimated user emotions. For example, if the user is tense, the generation unit can provide a simple and highly visible display method. For example, if the user is relaxed, the generation unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the generation unit can also provide a display method that gets straight to the point. By adjusting the display method of the circuit layout according to the user's emotions, a highly visible display is possible for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform the adjustment of the display method.

[0086] The generation unit can perform generation while considering the geographical distribution of the data. For example, if the data is concentrated in a particular region, the generation unit can generate a circuit layout specific to that region. For example, if the data is widely distributed, the generation unit can also generate a circuit layout that covers the entire area. For example, if the data is concentrated in a particular city, the generation unit can also generate a circuit layout specific to that city. In this way, by considering the geographical distribution of the data, it is possible to generate a circuit layout specific to a particular region. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the geographical distribution of the data into a generation AI and have the generation AI perform the generation of the circuit layout.

[0087] The generation unit can improve the accuracy of generation by referring to relevant literature during generation. For example, the generation unit generates the optimal circuit layout based on relevant literature. The generation unit can also generate an efficient circuit layout by utilizing the insights from relevant literature. The generation unit can also generate a highly accurate circuit layout by referring to data from relevant literature. Thus, a highly accurate circuit layout can be generated by referring to relevant literature. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data from relevant literature into a generation AI and have the generation AI perform the circuit layout generation.

[0088] The service provider can estimate the user's emotions and adjust the display method of the circuit layout based on the estimated user emotions. For example, if the user is tense, the service provider can provide a simple and highly visible display method. For example, if the user is relaxed, the service provider can also provide a display method that includes detailed information. For example, if the user is in a hurry, the service provider can also provide a display method that gets straight to the point. By adjusting the display method of the circuit layout according to the user's emotions, a highly visible display is possible for the user. Emotion estimation is achieved using an emotion estimation function, for example, using 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into an AI and have the AI ​​perform the adjustment of the display method.

[0089] The service provider can select the optimal service delivery method by referring to the user's past operation history at the time of delivery. For example, the service provider may prioritize providing display methods previously used by the user. For example, the service provider may also suggest the optimal display method based on the user's past operation history. For example, the service provider may analyze the user's past operation history and provide an efficient display method. This allows the service provider to select the optimal service delivery method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input user operation history data into AI and have the AI ​​select the optimal service delivery method.

[0090] The service provider can estimate the user's emotions and adjust the operating procedures for the circuit configuration based on the estimated user emotions. For example, if the user is tense, the service provider can provide simple and intuitive operating procedures. For example, if the user is relaxed, the service provider can also provide detailed operating procedures. For example, if the user is in a hurry, the service provider can also provide procedures that allow for quick operation. By adjusting the operating procedures according to the user's emotions, the service becomes intuitive and easy to use for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into AI and have the AI ​​perform the adjustment of the operating procedures.

[0091] The service provider can select the optimal service delivery method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the service provider can also provide a concise and highly visible display method. This allows the service provider to select the optimal service delivery method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI and have the AI ​​select the optimal service delivery method.

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

[0093] Systems utilizing next-generation semiconductor technology can also incorporate a predictive unit. This unit can forecast future trends based on collected data. For example, it can analyze smart device usage patterns and predict battery depletion. In autonomous driving technology, it can predict changes in traffic conditions and suggest optimal routes. Advanced data centers can predict data growth trends and secure necessary resources in advance. Thus, using a predictive unit can improve system efficiency and user experience.

[0094] Systems utilizing next-generation semiconductor technology can also incorporate a feedback unit. This feedback unit can collect user feedback and use it to improve the system. For example, it can collect feedback on the user experience of smart devices and incorporate it into UI / UX improvements. In autonomous driving technology, it can collect driver feedback to improve safety. In advanced data centers, it can collect feedback from operators to improve operational efficiency. Thus, using a feedback unit can improve system quality and user satisfaction.

[0095] Systems utilizing next-generation semiconductor technology can also incorporate customization options. These customization options allow for system settings to be customized according to user needs. For example, smart device settings can be customized to user preferences. In autonomous driving technology, system behavior can be customized to match the driver's driving style. In advanced data centers, resource allocation can be customized according to operator requirements. Thus, using customization options can improve user satisfaction and system flexibility.

[0096] Systems utilizing next-generation semiconductor technology can also incorporate a security unit. This security unit provides functions to ensure the security of collected data. For example, it can encrypt data on smart devices to protect against unauthorized access. In autonomous driving technology, it can secure communication data and protect vehicles from hacking. Advanced data centers can provide data backup and recovery functions to prevent data loss. Thus, using a security unit can improve the safety and reliability of the system.

[0097] Systems utilizing next-generation semiconductor technology can also incorporate an energy management unit. This unit can monitor and optimize the overall energy consumption of the system. For example, it can monitor the battery consumption of smart devices in real time, promoting efficient energy use. In autonomous driving technology, it can optimize vehicle energy consumption and improve fuel efficiency. In advanced data centers, it can monitor the energy consumption of cooling systems, enabling efficient operation. Thus, using an energy management unit can improve the energy efficiency and sustainability of the system.

[0098] Systems utilizing next-generation semiconductor technology can further estimate user emotions and adjust system operation based on those emotions. For example, if a smart device detects user stress, it can reduce the frequency of notifications, thereby alleviating the user's burden. In autonomous driving technology, if the driver is stressed, driver assistance functions can be enhanced, improving safety. In advanced data centers, if the operator is fatigued, system alerts can be reduced, easing the operator's burden. By adjusting system operation according to user emotions, user comfort and safety can be improved.

[0099] Systems utilizing next-generation semiconductor technology can further estimate user emotions and adjust data display based on those emotions. For example, if a smart device detects a user's emotions, it can adjust the amount and format of information displayed to provide a user-friendly interface. In autonomous driving technology, if the driver is relaxed, detailed information can be displayed to aid their understanding. In advanced data centers, if the operator is in a hurry, concise information can be displayed to support rapid decision-making. In this way, by adjusting data display according to the user's emotions, user understanding and efficiency can be improved.

[0100] Systems utilizing next-generation semiconductor technology can further estimate user emotions and customize the system interface based on those emotions. For example, if a smart device senses a user's emotions, it can change the interface's color and layout to provide a design that matches the user's mood. In autonomous driving technology, if the driver is stressed, the interface can be simplified to reduce the driver's burden. In advanced data centers, if the operator is fatigued, the interface can be made more intuitive to improve operational efficiency. In this way, customizing the interface according to the user's emotions can improve user comfort and usability.

[0101] Systems utilizing next-generation semiconductor technology can further estimate user emotions and adjust notification methods based on those emotions. For example, if a smart device detects a user's emotions, it can adjust the volume and frequency of notifications to reduce user stress. In autonomous driving technology, if the driver is relaxed, detailed notifications can be provided to aid the driver's understanding. In advanced data centers, if the operator is in a hurry, only important notifications can be displayed to support a quick response. In this way, by adjusting notification methods according to the user's emotions, user comfort and efficiency can be improved.

[0102] Systems utilizing next-generation semiconductor technology can further estimate user emotions and switch the system's operating mode based on those emotions. For example, if a smart device senses a user's emotions, it can switch to eco mode or performance mode to provide operation tailored to the user's needs. In autonomous driving technology, if the driver is stressed, the system can switch to safety mode to enhance driving assistance. In advanced data centers, if the operator is fatigued, the system load can be reduced, easing the operator's burden. By switching the system's operating mode according to the user's emotions, user comfort and safety can be improved.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The collection unit collects data. The collection unit collects data from, for example, smart devices, autonomous driving technology, and advanced data centers. The collection unit can collect specific data such as sensor data, log data, and user data. Step 2: The analysis unit analyzes the data collected by the data collection unit to enhance the AI's computing power. The analysis unit analyzes the data by applying statistical analysis or machine learning algorithms, for example. The analysis unit uses specific methods to enhance the AI's computing power, such as improving processing speed and accuracy. Step 3: The generation unit generates the optimal circuit layout to reduce power consumption based on the data analyzed by the analysis unit. The generation unit generates the optimal circuit layout using generative AI. The generation unit generates the circuit layout using, for example, generative models or neural networks. Step 4: The supply unit provides the circuit layout generated by the generation unit. The supply unit provides the generated circuit layout, for example, to improve energy efficiency. The supply unit provides the generated circuit layout to the user, thereby achieving improved energy efficiency.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0106] 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.

[0107] 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.

[0108] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented as a function to collect sensor data and log data from the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and enhances the computing power of the AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates an optimal circuit layout based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14, which provides the generated circuit layout. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented as a function to collect sensor data and log data from the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and enhances the computing power of the AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates an optimal circuit layout based on the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214, which provides the generated circuit layout. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented as a function to collect sensor data and log data from the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and enhances the computing power of the AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates an optimal circuit layout based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314, which provides the generated circuit layout. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented as a function to collect sensor data and log data from the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and enhances the computing power of the AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates an optimal circuit layout based on the analysis results. The provision unit is implemented by the control unit 46A of the robot 414, which provides the generated circuit layout. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit and enhances the computing power of the AI, A generation unit generates an optimal circuit layout for reducing power consumption based on the data analyzed by the analysis unit, The system comprises a providing unit that provides the circuit arrangement generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collecting data from smart devices, autonomous driving technology, and advanced data centers. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to enhance the AI's computing power. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Using generative AI, we generate the optimal circuit layout to reduce power consumption. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provides a generated circuit layout to improve energy efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting data from smart devices, autonomous driving technologies, and advanced data centers, the optimal data collection method is selected considering the operating status of the devices. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the device's usage environment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the device's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the device's social media activity is analyzed to collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and determines the priority of the circuit layout to be generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, the accuracy of the generation is improved by considering the interrelationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the data is generated while taking into account the attribute information of the data submitter. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts how the circuit layout is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, we refer to relevant literature to improve the accuracy of the data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the display method of the circuit layout based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and adjusts the operating procedures for the circuit layout based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0177] 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. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit to enhance the computing power of the AI, A generation unit generates an optimal circuit layout for reducing power consumption based on the data analyzed by the analysis unit, The system comprises a providing unit that provides the circuit arrangement generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collecting data from smart devices, autonomous driving technology, and advanced data centers. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to enhance the AI's computing power. The system according to feature 1.

4. The generating unit is Using generative AI, we generate the optimal circuit layout to reduce power consumption. The system according to feature 1.

5. The aforementioned supply unit is, Provides a generated circuit layout to improve energy efficiency. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is When collecting data from smart devices, autonomous driving technologies, and advanced data centers, the optimal data collection method is selected considering the operating status of the devices. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filtering is performed based on the device's usage environment. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the device's geographical location. The system according to feature 1.

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