System

Abstract

An object of the system according to the embodiment is to efficiently manage a large amount of device information and parameters and provide the device information and the parameters to a user.SOLUTION: A system includes an instruction reception unit, an analysis unit, a management unit, and an output unit. The instruction reception unit receives a natural language instruction from a user. The analysis unit analyzes the instruction received by the instruction reception unit. The management unit performs data management based on the instruction analyzed by the analysis unit. The output unit provides the user with the data managed by the management unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenges of making it difficult to efficiently manage vast amounts of equipment information and parameters, and of the high cost of developing dedicated systems.

[0005] The system according to the embodiment aims to efficiently manage a huge amount of device information and parameters and provide them to users. [Means for solving the problem]

[0006] The system according to the embodiment includes an instruction receiving unit, an analysis unit, a management unit, and an output unit. The instruction receiving unit receives natural language instructions from a user. The analysis unit analyzes the instructions received by the instruction receiving unit. The management unit manages data based on the instructions analyzed by the analysis unit. The output unit provides the data managed by the management unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage a huge amount of device information and parameters and provide them to the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The interactive data management system according to an embodiment of the present invention is a system for efficiently managing a huge number of devices and their many parameters. This system is operated through dialogue with a user using AI. This enables the interactive data management system to efficiently manage a huge number of devices and their parameters, reducing costs and simplifying operation.

[0029] An interactive data management system according to an embodiment includes an instruction receiving unit, an analysis unit, a management unit, and an output unit. The instruction receiving unit receives natural language instructions from a user. For example, it can receive voice instructions or text instructions. The instruction receiving unit can also receive user instructions in real time. The analysis unit analyzes the instructions received by the instruction receiving unit. For example, it can perform morphological analysis to understand the content of the instructions. The analysis unit can also perform grammatical analysis and semantic analysis to understand the intent of the instructions. The management unit manages data based on the instructions analyzed by the analysis unit. For example, it can assign a new IP address to a specific router. The management unit can also check the usage status of existing IP addresses and select and assign an appropriate IP address. The output unit provides the data managed by the management unit to a user. For example, it can provide the results by displaying the results on a screen, outputting voice, sending an email, or other methods. The output unit can also collect user feedback in real time and reflect it in improving the interface. As a result, the interactive data management system according to an embodiment can efficiently manage a huge number of devices and their parameters, thereby reducing costs and simplifying operation.

[0030] The management unit can assign a new IP address to a specific router. For example, the generation AI checks the usage status of existing IP addresses and selects and assigns an appropriate IP address. The management unit can also assign IP addresses using dynamic or static allocation methods. This allows a new IP address to be assigned to a specific router.

[0031] The management unit can generate a list of routers located in prefecture X. For example, the management unit generates a list of routers located in prefecture X. For example, the generation AI collects information on all routers located in prefecture X and outputs it as a list. The management unit can also search for devices based on specific parameters and generate a list. This makes it possible to generate a list of routers located in prefecture X.

[0032] The management unit can check the usage status of existing IP addresses and select and assign appropriate IP addresses. For example, the management unit checks the usage status of existing IP addresses and selects and assigns appropriate IP addresses. For example, the generation AI checks a list of IP addresses in use, and selects and assigns unused IP addresses. The management unit can also monitor IP address usage in real time and make optimal assignments. This makes it possible to check the usage status of existing IP addresses and select and assign appropriate IP addresses.

[0033] The management unit can automatically change or update the interface information. The management unit automatically changes or updates the interface information, for example. For example, the generation AI automatically changes or updates network settings or user interface settings. The management unit can also automatically record the change history of the interface information to assist with troubleshooting. This allows the interface information to be automatically changed or updated.

[0034] The management unit can search for devices based on specific parameters. For example, the generation AI searches for devices based on parameters such as IP address, MAC address, or device name. The management unit also provides the search results to the user, allowing the user to quickly obtain the required information. This makes it possible to search for devices based on specific parameters.

[0035] The management unit can integrate and centrally manage systems using generative AI. The management unit, for example, integrates and centrally manages systems using generative AI. For example, generative AI integrates data from a network management system and a device management system and centrally manages them. The management unit can also automatically integrate data between different systems, thereby reducing costs. This allows the management unit to integrate and centrally manage systems using generative AI.

[0036] The instruction receiving unit can receive natural language instructions from the user as voice input. The instruction receiving unit, for example, receives natural language instructions from the user as voice input. For example, the generation AI analyzes the voice instructions using voice recognition technology and performs data management. The instruction receiving unit can also display the voice input as text at the same time. This allows natural language instructions from the user to be received as voice input.

[0037] The instruction receiving unit can process simultaneous instructions from multiple users. The instruction receiving unit processes simultaneous instructions from multiple users. For example, the generation AI analyzes instructions from multiple users and responds appropriately. The instruction receiving unit can also set the number of instructions to be received simultaneously and their priorities. This allows simultaneous instructions from multiple users to be processed.

[0038] The analysis unit can learn the user's operation history and predict and suggest the next operation. The analysis unit, for example, learns the user's operation history and predicts and suggests the next operation. For example, a generation AI analyzes the user's past operation history and predicts and suggests the next operation to be performed. The analysis unit can also learn frequently performed operations and automatically suggest them the next time the user performs the operation. This makes it possible to learn the user's operation history and predict and suggest the next operation.

[0039] The analysis unit can analyze the user's operation patterns and automatically generate optimal operation procedures. The analysis unit, for example, analyzes the user's operation patterns and automatically generates optimal operation procedures. For example, a generation AI analyzes the user's operation patterns and proposes efficient procedures. The analysis unit can also learn the series of operations performed by the user and automatically generate optimal operation procedures. This makes it possible to analyze the user's operation patterns and automatically generate optimal operation procedures.

[0040] The management unit can integrate and centrally manage systems using generative AI. The management unit, for example, integrates and centrally manages systems using generative AI. For example, generative AI integrates data from a network management system and a device management system and centrally manages them. The management unit can also automatically integrate data between different systems, thereby reducing costs. This allows the management unit to integrate and centrally manage systems using generative AI.

[0041] The instruction receiving unit can receive natural language instructions from the user as voice input. The instruction receiving unit, for example, receives natural language instructions from the user as voice input. For example, the generation AI analyzes the voice instructions using voice recognition technology and performs data management. The instruction receiving unit can also display the voice input as text at the same time. This allows natural language instructions from the user to be received as voice input.

[0042] The instruction receiving unit can process simultaneous instructions from multiple users. The instruction receiving unit processes simultaneous instructions from multiple users. For example, the generation AI analyzes instructions from multiple users and responds appropriately. The instruction receiving unit can also set the number of instructions to be received simultaneously and their priorities. This allows simultaneous instructions from multiple users to be processed.

[0043] The analysis unit can detect ambiguity in natural language instructions and ask the user for confirmation. For example, if the generation AI's instruction to "reassign the IP address of router A" is ambiguous, the analysis unit will ask the user for more specific instructions. The analysis unit can also analyze ambiguous words and contexts and ask the user for confirmation. This allows the analysis unit to detect ambiguity in natural language instructions and ask the user for confirmation.

[0044] The analysis unit can automatically determine the priority of natural language instructions and prioritize processing of important tasks. The analysis unit can, for example, automatically determine the priority of natural language instructions and prioritize processing of important tasks. For example, the generation AI analyzes the urgency and importance of instructions and sets the priority. The analysis unit can also prioritize processing based on the importance and urgency of tasks. This allows the priority of natural language instructions to be automatically determined and important tasks to be prioritized.

[0045] The analysis unit can automatically translate instructions in different languages ​​to achieve multilingual support. The analysis unit can, for example, automatically translate instructions in different languages ​​to achieve multilingual support. For example, the generation AI can translate instructions in English or Chinese into Japanese. The analysis unit can also set the translation algorithm to be used and the types of languages ​​to be supported. This allows instructions in different languages ​​to be automatically translated to achieve multilingual support.

[0046] The analysis unit can save the instruction history and reuse past instructions. The analysis unit, for example, saves the instruction history and reuses past instructions. For example, the generation AI saves the user's past instructions in a database and reuses them. The analysis unit can also search the instruction history and quickly obtain necessary information. This allows the instruction history to be saved and past instructions to be reused.

[0047] The management unit monitors the usage status of IP addresses in real time and can make optimal allocations. The management unit, for example, monitors the usage status of IP addresses in real time and can make optimal allocations. For example, the generation AI automatically assigns unused IP addresses. The management unit can also monitor the usage status of IP addresses in real time using a monitoring tool. This allows the usage status of IP addresses to be monitored in real time and can make optimal allocations.

[0048] The management unit can automatically record the change history of the interface information to support troubleshooting. The management unit can, for example, automatically record the change history of the interface information to support troubleshooting. For example, the generation AI identifies the cause of a problem based on the past change history. The management unit can also record the change history using a log file or database. This makes it possible to automatically record the change history of the interface information to support troubleshooting.

[0049] The management unit can support different network protocols and achieve flexible management. The management unit can support different network protocols and achieve flexible management. For example, the generation AI supports both IPv4 and IPv6. The management unit can also support network protocols such as TCP / IP, UDP, and HTTP. This allows it to support different network protocols and achieve flexible management.

[0050] The management unit can automate the allocation of IP addresses to minimize manual operations. For example, the management unit can automate the allocation of IP addresses to minimize manual operations. For example, a generation AI can automatically assign IP addresses in response to user instructions. The management unit can also automate the allocation of IP addresses using scripts or automation tools. This automates the allocation of IP addresses to minimize manual operations.

[0051] The management unit can monitor the status of equipment in real time and issue an alert if an abnormality is detected. For example, the generation AI can detect an abnormality in a router and notify the user. The management unit can also detect abnormalities based on error log analysis and performance degradation. This allows the status of equipment to be monitored in real time and an alert to be issued if an abnormality is detected.

[0052] The management unit can analyze the performance data of the equipment and propose the optimal operation method. For example, the generation AI analyzes the performance of a router and proposes the optimal settings. The management unit can also propose operation methods based on performance data such as CPU usage and memory usage. This makes it possible to analyze the performance data of the equipment and propose the optimal operation method.

[0053] The management unit can display the location information of devices on a map and manage them visually. The management unit, for example, displays the location information of devices on a map and manages them visually. For example, the generation AI maps and displays the location of a router on a map. The management unit can also obtain and display location information using GPS data or a map service. This allows the location information of devices to be displayed on a map and managed visually.

[0054] The management unit can automatically record the maintenance history of equipment and optimize the maintenance plan. The management unit can, for example, automatically record the maintenance history of equipment and optimize the maintenance plan. For example, the generation AI can suggest the next maintenance date based on past maintenance history. The management unit can also record the maintenance history using log files and databases. This makes it possible to automatically record the maintenance history of equipment and optimize the maintenance plan.

[0055] The management unit can automatically integrate data between different systems to achieve centralized management. For example, the generation AI can integrate data from a network management system and a device management system to achieve centralized management. The management unit can also integrate data using methods such as merging databases or using APIs. This allows data between different systems to be automatically integrated to achieve centralized management.

[0056] The management department can analyze the system's operating costs in real time and make proposals for cost reduction. For example, the generation AI can propose reducing wasted resources. The management department can also make proposals based on the breakdown of costs and cost reduction methods. This allows the system's operating costs to be analyzed in real time and proposals for cost reduction.

[0057] The management unit can link with cloud-based systems to provide flexible scalability. For example, the generative AI can link with cloud services such as AWS, Google Cloud, and Azure to automatically expand resources according to demand. The management unit can also set the type of cloud-based system and the linking method. This allows linking with cloud-based systems to provide flexible scalability.

[0058] The management unit can automatically optimize system performance and achieve efficient operation. The management unit can automatically optimize system performance and achieve efficient operation. For example, a generative AI analyzes resource usage and proposes optimal settings. The management unit can also optimize performance using methods such as resource reallocation and cache use. This makes it possible to automatically optimize system performance and achieve efficient operation.

[0059] The output unit can learn the user's operation history and automatically generate an optimal interface. The output unit, for example, learns the user's operation history and automatically generates an optimal interface. For example, a generation AI analyzes the user's operation history and prioritizes displaying frequently used functions. The output unit can also automatically generate an interface using methods such as changing the layout based on the user's operation history. This makes it possible to learn the user's operation history and automatically generate an optimal interface.

[0060] The output unit can collect user feedback in real time and reflect it in improving the interface. For example, the output unit can collect user feedback in real time and reflect it in improving the interface. For example, the generation AI adjusts the interface based on the user's opinions. The output unit can also collect feedback using methods such as real-time questionnaires and user behavior logs. This allows user feedback to be collected in real time and reflected in improving the interface.

[0061] The output unit can also be compatible with mobile devices, making it possible to manage data anywhere. The output unit can also be compatible with mobile devices, making it possible to manage data anywhere. For example, the generation AI can manage data from a smartphone or tablet. The output unit can also be compatible with mobile devices using methods such as iOS and Android, responsive design, etc. This makes it possible to support mobile devices, making it possible to manage data anywhere.

[0062] The output unit can provide a tutorial function that guides the user through operations, making it easy for even beginners to operate. The output unit can provide a tutorial function that guides the user through operations, making it easy for even beginners to operate. For example, a generation AI can display a step-by-step guide. The output unit can also provide the tutorial function using methods such as a video tutorial or an interactive guide. This provides a tutorial function that guides the user through operations, making it easy for even beginners to operate.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The analysis unit can learn the user's operation history and predict and suggest the next operation. For example, the generation AI analyzes the user's past operation history and predicts and suggests the next operation to be performed. The analysis unit can also learn frequently performed operations and automatically suggest them the next time the user performs an operation. This allows the system to learn the user's operation history and predict and suggest the next operation.

[0065] The management unit can support different network protocols, enabling flexible management. For example, the generation AI supports both IPv4 and IPv6. The management unit can also support network protocols such as TCP / IP, UDP, and HTTP. This allows for flexible management, supporting different network protocols.

[0066] The management unit can link with cloud-based systems to provide flexible scalability. For example, the generative AI can link with cloud services such as AWS, Google Cloud, and Azure to automatically expand resources according to demand. The management unit can also set the type of cloud-based system and the linking method. This allows linking with cloud-based systems to provide flexible scalability.

[0067] The management unit can display the location information of devices on a map, allowing for visual management. For example, the generation AI can map and display the location of a router on a map. The management unit can also obtain and display location information using GPS data or map services. This allows for the location information of devices to be displayed on a map, allowing for visual management.

[0068] The management department can analyze the system's operating costs in real time and make proposals for cost reduction. For example, the generative AI can suggest cutting wasted resources. The management department can also make proposals based on cost breakdowns and cost reduction methods. This allows the system's operating costs to be analyzed in real time and proposals for cost reduction to be made.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The instruction receiving unit receives instructions in natural language from the user. For example, it can receive voice instructions or text instructions, and can also receive instructions in real time. Step 2: The analysis unit analyzes the instruction received by the instruction reception unit, for example, by performing morphological analysis, grammatical analysis, and semantic analysis to understand the content and intent of the instruction. Step 3: The management unit manages the data based on the instructions analyzed by the analysis unit. For example, it assigns a new IP address to a specific router, or checks the usage status of existing IP addresses to select and assign an appropriate IP address. Step 4: The output unit provides the data managed by the management unit to the user. For example, the results are displayed on the screen, output as audio, or sent via email. User feedback is collected in real time and reflected in improvements to the interface.

[0071] (Example 2) The interactive data management system according to an embodiment of the present invention is a system for efficiently managing a huge number of devices and their many parameters. This system is operated through dialogue with a user using AI. This enables the interactive data management system to efficiently manage a huge number of devices and their parameters, reducing costs and simplifying operation.

[0072] An interactive data management system according to an embodiment includes an instruction receiving unit, an analysis unit, a management unit, and an output unit. The instruction receiving unit receives natural language instructions from a user. For example, it can receive voice instructions or text instructions. The instruction receiving unit can also receive user instructions in real time. The analysis unit analyzes the instructions received by the instruction receiving unit. For example, it can perform morphological analysis to understand the content of the instructions. The analysis unit can also perform grammatical analysis and semantic analysis to understand the intent of the instructions. The management unit manages data based on the instructions analyzed by the analysis unit. For example, it can assign a new IP address to a specific router. The management unit can also check the usage status of existing IP addresses and select and assign an appropriate IP address. The output unit provides the data managed by the management unit to a user. For example, it can provide the results by displaying the results on a screen, outputting voice, sending an email, or other methods. The output unit can also collect user feedback in real time and reflect it in improving the interface. As a result, the interactive data management system according to an embodiment can efficiently manage a huge number of devices and their parameters, thereby reducing costs and simplifying operation.

[0073] The management unit can assign a new IP address to a specific router. For example, the generation AI checks the usage status of existing IP addresses and selects and assigns an appropriate IP address. The management unit can also assign IP addresses using dynamic or static allocation methods. This allows a new IP address to be assigned to a specific router.

[0074] The management unit can generate a list of routers located in prefecture X. For example, the management unit generates a list of routers located in prefecture X. For example, the generation AI collects information on all routers located in prefecture X and outputs it as a list. The management unit can also search for devices based on specific parameters and generate a list. This makes it possible to generate a list of routers located in prefecture X.

[0075] The management unit can check the usage status of existing IP addresses and select and assign appropriate IP addresses. For example, the management unit checks the usage status of existing IP addresses and selects and assigns appropriate IP addresses. For example, the generation AI checks a list of IP addresses in use, and selects and assigns unused IP addresses. The management unit can also monitor IP address usage in real time and make optimal assignments. This makes it possible to check the usage status of existing IP addresses and select and assign appropriate IP addresses.

[0076] The management unit can automatically change or update the interface information. The management unit automatically changes or updates the interface information, for example. For example, the generation AI automatically changes or updates network settings or user interface settings. The management unit can also automatically record the change history of the interface information to assist with troubleshooting. This allows the interface information to be automatically changed or updated.

[0077] The management unit can search for devices based on specific parameters. For example, the generation AI searches for devices based on parameters such as IP address, MAC address, or device name. The management unit also provides the search results to the user, allowing the user to quickly obtain the required information. This makes it possible to search for devices based on specific parameters.

[0078] The management unit can integrate and centrally manage systems using generative AI. The management unit, for example, integrates and centrally manages systems using generative AI. For example, generative AI integrates data from a network management system and a device management system and centrally manages them. The management unit can also automatically integrate data between different systems, thereby reducing costs. This allows the management unit to integrate and centrally manage systems using generative AI.

[0079] The instruction receiving unit can receive natural language instructions from the user as voice input. The instruction receiving unit, for example, receives natural language instructions from the user as voice input. For example, the generation AI analyzes the voice instructions using voice recognition technology and performs data management. The instruction receiving unit can also display the voice input as text at the same time. This allows natural language instructions from the user to be received as voice input.

[0080] The instruction receiving unit can process simultaneous instructions from multiple users. The instruction receiving unit processes simultaneous instructions from multiple users. For example, the generation AI analyzes instructions from multiple users and responds appropriately. The instruction receiving unit can also set the number of instructions to be received simultaneously and their priorities. This allows simultaneous instructions from multiple users to be processed.

[0081] The analysis unit can learn the user's operation history and predict and suggest the next operation. The analysis unit, for example, learns the user's operation history and predicts and suggests the next operation. For example, a generation AI analyzes the user's past operation history and predicts and suggests the next operation to be performed. The analysis unit can also learn frequently performed operations and automatically suggest them the next time the user performs the operation. This makes it possible to learn the user's operation history and predict and suggest the next operation.

[0082] The analysis unit can analyze the user's operation patterns and automatically generate optimal operation procedures. The analysis unit, for example, analyzes the user's operation patterns and automatically generates optimal operation procedures. For example, a generation AI analyzes the user's operation patterns and proposes efficient procedures. The analysis unit can also learn the series of operations performed by the user and automatically generate optimal operation procedures. This makes it possible to analyze the user's operation patterns and automatically generate optimal operation procedures.

[0083] The analysis unit uses the emotion estimation function to generate dialogue content according to the user's emotional state, thereby reducing stress. The analysis unit, for example, uses the emotion estimation function to generate dialogue content according to the user's emotional state, thereby reducing stress. For example, the generation AI analyzes the user's emotional state and provides dialogue content that relaxes the user. Furthermore, if the user is feeling stressed, the analysis unit can adjust the dialogue content to reduce stress. This allows dialogue content according to the user's emotional state to be generated, thereby reducing stress.

[0084] The management unit can integrate and centrally manage systems using generative AI. The management unit, for example, integrates and centrally manages systems using generative AI. For example, generative AI integrates data from a network management system and a device management system and centrally manages them. The management unit can also automatically integrate data between different systems, thereby reducing costs. This allows the management unit to integrate and centrally manage systems using generative AI.

[0085] The instruction receiving unit can receive natural language instructions from the user as voice input. The instruction receiving unit, for example, receives natural language instructions from the user as voice input. For example, the generation AI analyzes the voice instructions using voice recognition technology and performs data management. The instruction receiving unit can also display the voice input as text at the same time. This allows natural language instructions from the user to be received as voice input.

[0086] The instruction receiving unit can process simultaneous instructions from multiple users. The instruction receiving unit processes simultaneous instructions from multiple users. For example, the generation AI analyzes instructions from multiple users and responds appropriately. The instruction receiving unit can also set the number of instructions to be received simultaneously and their priorities. This allows simultaneous instructions from multiple users to be processed.

[0087] The analysis unit can dynamically change the interface design according to the user's emotional state using the emotion estimation function. The analysis unit, for example, uses the emotion estimation function to dynamically change the interface design based on the user's emotional state. For example, the generation AI analyzes the user's emotional state and changes the design to one that is relaxing. Furthermore, if the user is feeling stressed, the analysis unit can adjust the interface design to reduce stress. This makes it possible to dynamically change the interface design according to the user's emotional state.

[0088] The analysis unit can detect ambiguity in natural language instructions and ask the user for confirmation. For example, if the generation AI's instruction to "reassign the IP address of router A" is ambiguous, the analysis unit will ask the user for more specific instructions. The analysis unit can also analyze ambiguous words and contexts and ask the user for confirmation. This allows the analysis unit to detect ambiguity in natural language instructions and ask the user for confirmation.

[0089] The analysis unit can automatically determine the priority of natural language instructions and prioritize processing of important tasks. The analysis unit can, for example, automatically determine the priority of natural language instructions and prioritize processing of important tasks. For example, the generation AI analyzes the urgency and importance of instructions and sets the priority. The analysis unit can also prioritize processing based on the importance and urgency of tasks. This allows the priority of natural language instructions to be automatically determined and important tasks to be prioritized.

[0090] The analysis unit can use the emotion estimation function to adjust the interpretation of instructions based on the user's emotions. The analysis unit, for example, uses the emotion estimation function to adjust the interpretation of instructions based on the user's emotional state. For example, the generation AI analyzes the user's emotional state, and if the user is feeling stressed, it simplifies and interprets the instructions. The analysis unit can also adjust the interpretation of instructions according to the user's emotions. This makes it possible to adjust the interpretation of instructions based on the user's emotions.

[0091] The analysis unit can automatically translate instructions in different languages ​​to achieve multilingual support. The analysis unit can, for example, automatically translate instructions in different languages ​​to achieve multilingual support. For example, the generation AI can translate instructions in English or Chinese into Japanese. The analysis unit can also set the translation algorithm to be used and the types of languages ​​to be supported. This allows instructions in different languages ​​to be automatically translated to achieve multilingual support.

[0092] The analysis unit can save the instruction history and reuse past instructions. The analysis unit, for example, saves the instruction history and reuses past instructions. For example, the generation AI saves the user's past instructions in a database and reuses them. The analysis unit can also search the instruction history and quickly obtain necessary information. This allows the instruction history to be saved and past instructions to be reused.

[0093] The analysis unit can use the emotion estimation function to provide feedback according to the user's emotions. For example, the analysis unit can use the emotion estimation function to provide feedback based on the user's emotional state. For example, the generation AI can analyze the user's emotional state and provide words of encouragement. Furthermore, if the user is feeling stressed, the analysis unit can provide feedback to help the user relax. This makes it possible to provide feedback according to the user's emotions.

[0094] The management unit monitors the usage status of IP addresses in real time and can make optimal allocations. The management unit, for example, monitors the usage status of IP addresses in real time and can make optimal allocations. For example, the generation AI automatically assigns unused IP addresses. The management unit can also monitor the usage status of IP addresses in real time using a monitoring tool. This allows the usage status of IP addresses to be monitored in real time and can make optimal allocations.

[0095] The management unit can automatically record the change history of the interface information to support troubleshooting. The management unit can, for example, automatically record the change history of the interface information to support troubleshooting. For example, the generation AI identifies the cause of a problem based on the past change history. The management unit can also record the change history using a log file or database. This makes it possible to automatically record the change history of the interface information to support troubleshooting.

[0096] The management unit can use the emotion estimation function to adjust the allocation of IP addresses based on the user's emotions. The management unit, for example, uses the emotion estimation function to adjust the allocation of IP addresses based on the user's emotional state. For example, the generation AI analyzes the user's emotional state, and if the user is feeling stressed, it assigns an IP address with a simple operation. The management unit can also adjust the allocation of IP addresses according to the user's emotions. This makes it possible to adjust the allocation of IP addresses based on the user's emotions.

[0097] The management unit can support different network protocols and achieve flexible management. The management unit can support different network protocols and achieve flexible management. For example, the generation AI supports both IPv4 and IPv6. The management unit can also support network protocols such as TCP / IP, UDP, and HTTP. This allows it to support different network protocols and achieve flexible management.

[0098] The management unit can automate the allocation of IP addresses to minimize manual operations. For example, the management unit can automate the allocation of IP addresses to minimize manual operations. For example, a generation AI can automatically assign IP addresses in response to user instructions. The management unit can also automate the allocation of IP addresses using scripts or automation tools. This automates the allocation of IP addresses to minimize manual operations.

[0099] The management unit can use the emotion estimation function to change the display method of interface information according to the user's emotion. For example, the management unit uses the emotion estimation function to change the display method of interface information based on the user's emotional state. For example, the generation AI analyzes the user's emotional state and changes the display to a simpler one. Furthermore, the management unit can adjust the display method of interface information if the user is feeling stressed. This makes it possible to change the display method of interface information according to the user's emotion.

[0100] The management unit can monitor the status of equipment in real time and issue an alert if an abnormality is detected. For example, the generation AI can detect an abnormality in a router and notify the user. The management unit can also detect abnormalities based on error log analysis and performance degradation. This allows the status of equipment to be monitored in real time and an alert to be issued if an abnormality is detected.

[0101] The management unit can analyze the performance data of the equipment and propose the optimal operation method. For example, the generation AI analyzes the performance of a router and proposes the optimal settings. The management unit can also propose operation methods based on performance data such as CPU usage and memory usage. This makes it possible to analyze the performance data of the equipment and propose the optimal operation method.

[0102] The management unit can use the emotion estimation function to adjust the display order of search results based on the user's emotions. For example, the management unit uses the emotion estimation function to adjust the display order of search results based on the user's emotional state. For example, the generation AI analyzes the user's emotional state and prioritizes displaying important information. The management unit can also adjust the display order of search results based on criteria such as most relevant or most recent. This makes it possible to adjust the display order of search results based on the user's emotions.

[0103] The management unit can display the location information of devices on a map and manage them visually. The management unit, for example, displays the location information of devices on a map and manages them visually. For example, the generation AI maps and displays the location of a router on a map. The management unit can also obtain and display location information using GPS data or a map service. This allows the location information of devices to be displayed on a map and managed visually.

[0104] The management unit can automatically record the maintenance history of equipment and optimize the maintenance plan. The management unit can, for example, automatically record the maintenance history of equipment and optimize the maintenance plan. For example, the generation AI can suggest the next maintenance date based on past maintenance history. The management unit can also record the maintenance history using log files and databases. This makes it possible to automatically record the maintenance history of equipment and optimize the maintenance plan.

[0105] The management unit can use the emotion estimation function to suggest search filters according to the user's emotions. For example, the management unit uses the emotion estimation function to suggest search filters based on the user's emotional state. For example, the generation AI analyzes the user's emotional state and suggests simple filters. The management unit can also suggest search filters based on criteria such as keyword filters and category filters. This makes it possible to suggest search filters according to the user's emotions.

[0106] The management unit can automatically integrate data between different systems to achieve centralized management. For example, the generation AI can integrate data from a network management system and a device management system to achieve centralized management. The management unit can also integrate data using methods such as merging databases or using APIs. This allows data between different systems to be automatically integrated to achieve centralized management.

[0107] The management department can analyze the system's operating costs in real time and make proposals for cost reduction. For example, the generation AI can propose reducing wasted resources. The management department can also make proposals based on the breakdown of costs and cost reduction methods. This allows the system's operating costs to be analyzed in real time and proposals for cost reduction.

[0108] The management unit can use the emotion estimation function to adjust the system operation method based on the user's emotions. The management unit, for example, uses the emotion estimation function to adjust the system operation method based on the user's emotional state. For example, the generation AI analyzes the user's emotional state and simplifies the system operation if the user is feeling stressed. The management unit can also adjust the operation method using methods such as changing scheduling and reallocating resources. This makes it possible to adjust the system operation method based on the user's emotions.

[0109] The management unit can link with cloud-based systems to provide flexible scalability. For example, the generative AI can link with cloud services such as AWS, Google Cloud, and Azure to automatically expand resources according to demand. The management unit can also set the type of cloud-based system and the linking method. This allows linking with cloud-based systems to provide flexible scalability.

[0110] The management unit can automatically optimize system performance and achieve efficient operation. The management unit can automatically optimize system performance and achieve efficient operation. For example, a generative AI analyzes resource usage and proposes optimal settings. The management unit can also optimize performance using methods such as resource reallocation and cache use. This makes it possible to automatically optimize system performance and achieve efficient operation.

[0111] The management unit can use the emotion estimation function to suggest system setting changes according to the user's emotions. For example, the management unit uses the emotion estimation function to suggest system setting changes based on the user's emotional state. For example, the generation AI analyzes the user's emotional state and simplifies the system settings if the user is feeling stressed. The management unit can also suggest setting changes using methods such as changing default settings or proposing custom settings. This makes it possible to suggest system setting changes according to the user's emotions.

[0112] The output unit can learn the user's operation history and automatically generate an optimal interface. The output unit, for example, learns the user's operation history and automatically generates an optimal interface. For example, a generation AI analyzes the user's operation history and prioritizes displaying frequently used functions. The output unit can also automatically generate an interface using methods such as changing the layout based on the user's operation history. This makes it possible to learn the user's operation history and automatically generate an optimal interface.

[0113] The output unit can collect user feedback in real time and reflect it in improving the interface. For example, the output unit can collect user feedback in real time and reflect it in improving the interface. For example, the generation AI adjusts the interface based on the user's opinions. The output unit can also collect feedback using methods such as real-time questionnaires and user behavior logs. This allows user feedback to be collected in real time and reflected in improving the interface.

[0114] The output unit can dynamically change the interface design based on the user's emotions using the emotion estimation function. The output unit, for example, uses the emotion estimation function to dynamically change the interface design based on the user's emotional state. For example, the generation AI analyzes the user's emotional state and changes the design to one that is relaxing. The output unit can also dynamically change the interface design using methods such as changing the color or layout. This makes it possible to dynamically change the interface design based on the user's emotions.

[0115] The output unit can also be compatible with mobile devices, making it possible to manage data anywhere. The output unit can also be compatible with mobile devices, making it possible to manage data anywhere. For example, the generation AI can manage data from a smartphone or tablet. The output unit can also be compatible with mobile devices using methods such as iOS and Android, responsive design, etc. This makes it possible to support mobile devices, making it possible to manage data anywhere.

[0116] The output unit can provide a tutorial function that guides the user through operations, making it easy for even beginners to operate. The output unit can provide a tutorial function that guides the user through operations, making it easy for even beginners to operate. For example, a generation AI can display a step-by-step guide. The output unit can also provide the tutorial function using methods such as a video tutorial or an interactive guide. This provides a tutorial function that guides the user through operations, making it easy for even beginners to operate.

[0117] The output unit can use the emotion estimation function to suggest interface customization according to the user's emotions. For example, the output unit can use the emotion estimation function to suggest interface customization based on the user's emotional state. For example, the generation AI analyzes the user's emotional state and suggests customization that will help them relax. The output unit can also suggest interface customization using methods such as changing colors or layouts. This makes it possible to suggest interface customization according to the user's emotions.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The analysis unit can learn the user's operation history and predict and suggest the next operation. For example, the generation AI analyzes the user's past operation history and predicts and suggests the next operation to be performed. The analysis unit can also learn frequently performed operations and automatically suggest them the next time the user performs an operation. This allows the system to learn the user's operation history and predict and suggest the next operation.

[0120] The management unit can support different network protocols, enabling flexible management. For example, the generation AI supports both IPv4 and IPv6. The management unit can also support network protocols such as TCP / IP, UDP, and HTTP. This allows for flexible management, supporting different network protocols.

[0121] The management unit can link with cloud-based systems to provide flexible scalability. For example, the generative AI can link with cloud services such as AWS, Google Cloud, and Azure to automatically expand resources according to demand. The management unit can also set the type of cloud-based system and the linking method. This allows linking with cloud-based systems to provide flexible scalability.

[0122] The management unit can display the location information of devices on a map, allowing for visual management. For example, the generation AI can map and display the location of a router on a map. The management unit can also obtain and display location information using GPS data or map services. This allows for the location information of devices to be displayed on a map, allowing for visual management.

[0123] The management department can analyze the system's operating costs in real time and make proposals for cost reduction. For example, the generative AI can suggest cutting wasted resources. The management department can also make proposals based on cost breakdowns and cost reduction methods. This allows the system's operating costs to be analyzed in real time and proposals for cost reduction to be made.

[0124] The analysis unit uses the emotion estimation function to generate dialogue content that corresponds to the user's emotional state, thereby reducing stress. For example, the generation AI analyzes the user's emotional state and provides dialogue content that relaxes the user. Furthermore, if the user is feeling stressed, the analysis unit can adjust the dialogue content to reduce stress. This allows dialogue content to be generated that corresponds to the user's emotional state, thereby reducing stress.

[0125] The analysis unit can use the emotion estimation function to dynamically change the interface design based on the user's emotional state. For example, the generation AI analyzes the user's emotional state and changes the design to one that is relaxing. In addition, if the user is feeling stressed, the analysis unit can adjust the interface design to reduce stress. This makes it possible to dynamically change the interface design according to the user's emotional state.

[0126] The analysis unit can use the emotion estimation function to adjust the interpretation of instructions based on the user's emotions. For example, if the generation AI analyzes the user's emotional state and determines that the user is feeling stressed, it will simplify and interpret the instructions. The analysis unit can also adjust the interpretation of instructions according to the user's emotions. This allows the interpretation of instructions to be adjusted based on the user's emotions.

[0127] The management unit can use the emotion estimation function to adjust the display order of search results based on the user's emotions. For example, the generation AI analyzes the user's emotional state and prioritizes the display of important information. The management unit can also adjust the display order of search results based on criteria such as most relevant or most recent. This makes it possible to adjust the display order of search results based on the user's emotions.

[0128] The management unit can use the emotion estimation function to change the way interface information is displayed according to the user's emotions. For example, the generation AI analyzes the user's emotional state and changes the display to a simpler one. In addition, the management unit can adjust the way interface information is displayed if the user is feeling stressed. This makes it possible to change the way interface information is displayed according to the user's emotions.

[0129] The processing flow of the second embodiment will be briefly explained below.

[0130] Step 1: The instruction receiving unit receives instructions in natural language from the user. For example, it can receive voice instructions or text instructions, and can also receive instructions in real time. Step 2: The analysis unit analyzes the instruction received by the instruction reception unit, for example, by performing morphological analysis, grammatical analysis, and semantic analysis to understand the content and intent of the instruction. Step 3: The management unit manages the data based on the instructions analyzed by the analysis unit. For example, it assigns a new IP address to a specific router, or checks the usage status of existing IP addresses to select and assign an appropriate IP address. Step 4: The output unit provides the data managed by the management unit to the user. For example, the results are displayed on the screen, output as audio, or sent via email. User feedback is collected in real time and reflected in improvements to the interface.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0135] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0159] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0165] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0168] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0170] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0171] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0172] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0173] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0174] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0175] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0176] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0177] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0178] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0179] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0180] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0181] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0182] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0183] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0184] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0185] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0186] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0187] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0188] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0189] 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.

[0190] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0191] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0192] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0193] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0194] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0195] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0196] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0197] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an instruction receiving unit that receives a natural language instruction from a user; an analysis unit that analyzes the instruction received by the instruction receiving unit; a management unit that manages data based on the instructions analyzed by the analysis unit; an output unit that provides the data managed by the management unit to a user; A system characterized by:

2. The instruction receiving unit It can accept natural language instructions from the user as voice input.

2. The system of claim 1.

3. The analysis unit It can learn the user's operation history and predict and suggest the next operation.

2. The system of claim 1.

4. The management unit IP address usage can be monitored in real time to ensure optimal allocation.

2. The system of claim 1.

5. The output unit The interface design can be dynamically changed based on the user's emotions.

2. The system of claim 1.

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