system

Abstract

An object of the system according to the embodiment is to automate management and confirmation work of commercial master parameter / DPS / office data and to perform the work efficiently and accurately.SOLUTION: A system according to an embodiment includes a database creation unit, a periodic confirmation unit, a precheck unit, and an inquiry handling unit. The database creation unit uses the generated AI to learn commercial master parameters, DPS, and station information, and creates a database of stations to which the parameters are input. The periodic check unit periodically checks whether there is any omission in the input parameters of the existing station using the parameters learned by the generation AI. The precheck unit checks in advance whether an erroneous parameter is to be input at the time of a new station or a configuration change. The inquiry handling unit performs ChatBot handling for inquiries related to various parameters.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] With conventional technology, managing and verifying commercial mass parametric, DPS, and station data is often done manually, which is inefficient and prone to errors.

[0005] The system according to the embodiment aims to automate the management and confirmation of commercial mass para, DPS, and station data, and to perform the tasks efficiently and accurately. [Means for solving the problem]

[0006] The system according to the embodiment comprises a database creation unit, a periodic confirmation unit, a pre-check unit, and an inquiry response unit. The database creation unit uses a generation AI to learn commercial mass parameter, DPS, and station data, and creates a database of stations into which the relevant parameters have been input. The periodic confirmation unit uses the parameters learned by the generation AI to periodically check for omissions in the input parameters of existing stations. The pre-check unit checks in advance to ensure that incorrect parameters are not being input when a new station or configuration change is made. The inquiry response unit uses a ChatBot to respond to inquiries related to various parameters. [Effects of the Invention]

[0007] The system according to the embodiment automates the management and confirmation of commercial mass para, DPS, and station data, making it possible to perform the tasks efficiently and accurately. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The system according to an embodiment of the present invention uses a generative AI to learn from commercial mass parameter, DPS, and station data, and is based on this data to support a variety of applications. This allows the system to create a database of stations that have entered the relevant parameters, periodically check for omissions in the entered parameters of existing stations, check in advance for incorrect parameters when entering new stations or changing configurations, and use a ChatBot to respond to various parameter-related inquiries.

[0029] The system according to the embodiment includes a generation AI, a database creation unit, a periodic confirmation unit, a pre-check unit, and an inquiry response unit. The generation AI learns commercial mass parameter, DPS, and station data. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM) to understand the parameters and configuration patterns of each station. The generation AI can also use a multimodal generation AI to learn various aspects of the data. The generation AI can also extract and learn important parts of the data. The database creation unit creates a database of stations that have the relevant parameters based on the data learned by the generation AI. For example, it lists stations that have specific parameters and registers that information in the database. The periodic confirmation unit uses the parameters learned by the generation AI to periodically check whether there are any omissions in the parameters entered for existing stations. For example, it periodically checks the parameters of each station to ensure that all necessary parameters have been entered. The pre-check unit checks in advance whether incorrect parameters are being entered when a new station or a configuration change is being made. For example, when entering new parameters, the generation AI checks whether the parameters are correct and issues a warning if there are any errors. The inquiry response section uses a ChatBot using a generation AI to respond to inquiries related to various parameters. For example, if a user asks about a specific parameter, the generation AI will provide an appropriate answer. This allows the system to efficiently manage and operate commercial mass parameter, DPS, and station data. For example, by creating a database of stations into which the relevant parameters have been input, parameter information for each station can be centrally managed. In addition, regular confirmation of input parameters for existing stations and advance parameter checks when adding new stations or changing configurations can prevent parameter omissions and errors. Furthermore, response to inquiries by the ChatBot allows for quick and efficient handling of parameter-related inquiries.

[0030] In data learning, generative AI performs cross-domain learning, including data from different industries, allowing it to understand a wider variety of patterns. For example, generative AI inputs data from different industries and performs cross-domain learning. For example, medical data and financial data are simultaneously trained. Generative AI can also understand a wider variety of patterns by combining data from different industries and training it. For example, data from the manufacturing and service industries is integrated and trained. Generative AI can also understand the similarities and differences between different industries through cross-domain learning. For example, technical fields and consumer behavior data are trained simultaneously. This allows it to learn data from different industries and understand a wider variety of patterns.

[0031] In data learning, generative AI can strengthen the association between past data and current data by taking into account changes over time. For example, generative AI introduces a learning algorithm that takes changes over time into account to strengthen the association between past data and current data. For example, it is made to learn using time series data. Generative AI can also compare past data with current data to understand the association. For example, it can learn by comparing past trends with current trends. Generative AI can also learn data that takes changes over time into account to understand the evolution of data. For example, it can learn about past technological evolution and the current technological situation. This can strengthen the association between past data and current data.

[0032] In data learning, generative AI can analyze voice data and image data to perform multimodal data analysis. For example, generative AI can learn voice data to enhance its voice recognition technology. For example, it can analyze a user's voice commands and generate an appropriate response. Generative AI can also learn image data to enhance its image recognition technology. For example, it can recognize a specific object from image data and analyze that information. Generative AI can also simultaneously learn voice data and image data to perform multimodal data analysis. For example, it can combine voice and images to understand a user's intent. This makes multimodal data analysis possible by learning voice data and image data.

[0033] In data learning, generative AI can integrate different generative AI models to analyze data from multiple perspectives, achieving more accurate learning. For example, generative AI combines different generative AI models to analyze data from multiple angles. For example, a natural language processing model and an image recognition model are integrated for learning. Generative AI also uses multiple generative AI models to improve the accuracy of data analysis. For example, different algorithms are combined to analyze data. Generative AI also combines generative AI models to analyze data from different perspectives. For example, a speech recognition model and a text analysis model are integrated for learning. In this way, more accurate learning can be achieved by combining different generative AI models.

[0034] The database creation unit can analyze the interrelationships between parameters of each station when registering them in the database and automatically link highly related parameters. The database creation unit, for example, analyzes the interrelationships between parameters of each station when creating the database and automatically links highly related parameters. For example, related parameters are grouped and registered. The database creation unit also builds a system that analyzes the interrelationships between parameters and automatically links highly related parameters. For example, related parameters are linked and registered in the database. The database creation unit also develops an algorithm that analyzes the interrelationships between parameters of each station and automatically links highly related parameters. For example, highly related parameters are automatically linked and registered in the database. This makes it possible to analyze the interrelationships between parameters and automatically link highly related parameters.

[0035] The database creation unit can compare past data with current data when creating the database and automatically extract trends of change. For example, the database creation unit builds a system that compares past data with current data and automatically extracts trends of change when creating the database. For example, it compares past parameters with current parameters to extract trends. The database creation unit also develops an algorithm that compares past data with current data and automatically extracts trends of change. For example, it analyzes changes in data and extracts trends. The database creation unit also adds a function that compares past data with current data and automatically extracts trends of change when creating the database. For example, it analyzes changes in data and extracts trends. This makes it possible to compare past data with current data and automatically extract trends of change.

[0036] The database creation unit can record parameters of different industries in the database, enabling comparative analysis between different industries. For example, the database creation unit registers parameters of different industries in the database and builds a system that enables comparative analysis between different industries. For example, parameters of the manufacturing industry and the service industry are registered in the same database. The database creation unit also registers parameters of different industries in the database and performs comparative analysis between different industries. For example, parameters of the medical industry and the financial industry are compared. The database creation unit also registers parameters of different industries in the database and develops an algorithm for comparative analysis between different industries. For example, parameters of different industries are integrated and analyzed. This allows parameters of different industries to be registered and enables comparative analysis between different industries.

[0037] The database creation unit can install a database on the cloud, enabling real-time data updates and access. The database creation unit, for example, builds a database on the cloud and develops a system that enables real-time data updates and access. For example, the database creation unit builds a database using cloud storage. The database creation unit also builds a database on the cloud and realizes real-time data updates and access. For example, the database creation unit builds a database using cloud services. The database creation unit also builds a database on the cloud and develops an algorithm that enables real-time data updates and access. For example, the database creation unit manages the database on the cloud. This allows the database to be built on the cloud and enables real-time data updates and access.

[0038] The periodic verification unit allows the generation AI to analyze past verification results and improve the accuracy of verification. For example, during periodic verification, the generation AI learns from past verification results and improves the accuracy of verification. For example, the verification process is optimized based on past errors and omissions. The periodic verification unit also allows the generation AI to analyze past verification results and develop algorithms to improve the accuracy of verification. For example, the verification procedure is improved based on past data. The periodic verification unit also learns from past verification results and builds a system in which the generation AI improves the accuracy of verification. For example, the verification process is automated based on past verification history. This allows the generation AI to learn from past verification results and improve the accuracy of verification.

[0039] The periodic confirmation unit can automatically generate reports of the results of periodic confirmations and present them to users in a visually easy-to-understand manner. The periodic confirmation unit, for example, builds a system that automatically generates reports of the results of periodic confirmations and presents them to users in a visually easy-to-understand manner. For example, the confirmation results are displayed in graphs and charts. The periodic confirmation unit also uses a generation AI to automatically generate reports of the results of periodic confirmations and present them to users visually. For example, the confirmation results are displayed in dashboard format. The periodic confirmation unit also develops an algorithm that automatically generates reports of the results of periodic confirmations and presents them to users in a visually easy-to-understand manner. For example, the confirmation results are displayed in infographics. This allows the results of periodic confirmations to be automatically generated in reports and presented to users in a visually easy-to-understand manner.

[0040] The periodic verification department integrates the periodic verification process with other systems, and can perform verification by integrating information from different data sources. For example, the periodic verification department builds a system that links the periodic verification process with other systems and integrates information from different data sources to perform verification. For example, it links with an ERP system or a CRM system. The periodic verification department also integrates information from different data sources to improve the accuracy of periodic verification. For example, it uses data from external databases and APIs. The periodic verification department also develops algorithms that link the periodic verification process with other systems and integrates information from different data sources. For example, it performs verification work by integrating multiple data sources. This makes it possible to perform verification by integrating information from different data sources.

[0041] The periodic checking unit can make it possible to change the frequency of periodic checking as appropriate according to the user's needs. For example, the periodic checking unit builds a system that allows the frequency of periodic checking to be flexibly changed according to the user's needs. For example, it provides an interface that allows the user to set the checking frequency. The periodic checking unit also develops an algorithm that dynamically adjusts the frequency of periodic checking according to the user's needs. For example, it changes the checking frequency to match the user's work schedule. The periodic checking unit also adds a function that allows the frequency of periodic checking to be flexibly changed according to the user's needs. For example, it provides a settings screen that allows the user to customize the checking frequency. This allows the frequency of periodic checking to be flexibly changed according to the user's needs.

[0042] The pre-checking unit allows the generation AI to analyze past errors and improve the accuracy of error prediction. For example, during pre-checking, the pre-checking unit builds a system in which the generation AI learns from past errors and improves the accuracy of error prediction. For example, the pre-checking unit optimizes the check process based on past error data. The pre-checking unit also develops an algorithm in which the generation AI analyzes past errors and improves the accuracy of error prediction. For example, it learns from past error patterns and improves prediction accuracy. The pre-checking unit also builds a system in which the generation AI learns from past error data and improves the accuracy of error prediction. For example, it improves the check process based on past error logs. This makes it possible to learn from past errors and improve the accuracy of error prediction.

[0043] The pre-check unit can automatically generate a report of the results of the pre-check and present it to the user in a visually easy-to-understand manner. For example, the pre-check unit builds a system that automatically generates a report of the results of the pre-check and presents it to the user in a visually easy-to-understand manner. For example, it displays the check results in graphs and charts. The pre-check unit also uses a generation AI to automatically generate a report of the results of the pre-check and present it to the user visually. For example, it displays the check results in dashboard format. The pre-check unit also develops an algorithm that automatically generates a report of the results of the pre-check and presents it to the user in a visually easy-to-understand manner. For example, it displays the check results in infographics. This allows the results of the pre-check to be automatically generated in a report and presented to the user in a visually easy-to-understand manner.

[0044] The Pre-Checking Department integrates the pre-checking process with other systems, enabling it to perform checks by integrating information from different data sources. For example, the Pre-Checking Department builds a system that links the pre-checking process with other systems and performs checks by integrating information from different data sources. For example, it links with an ERP system or CRM system. The Pre-Checking Department also integrates information from different data sources to improve the accuracy of pre-checks. For example, it uses data from external databases and APIs. The Pre-Checking Department also develops algorithms that link the pre-checking process with other systems and integrate information from different data sources. For example, it integrates multiple data sources to perform checks. This enables it to perform checks by integrating information from different data sources.

[0045] The pre-check unit can make it possible to change the frequency of pre-checks as appropriate according to the user's needs. For example, the pre-check unit builds a system that allows the frequency of pre-checks to be flexibly changed according to the user's needs. For example, it provides an interface that allows the user to set the check frequency. The pre-check unit also develops an algorithm that dynamically adjusts the frequency of pre-checks according to the user's needs. For example, it changes the check frequency to match the user's work schedule. The pre-check unit also adds a function that allows the frequency of pre-checks to be flexibly changed according to the user's needs. For example, it provides a settings screen that allows the user to customize the check frequency. This allows the frequency of pre-checks to be flexibly changed according to the user's needs.

[0046] The inquiry response unit enables the ChatBot to analyze past inquiry data and provide more appropriate answers. For example, the inquiry response unit builds a system in which the ChatBot learns past inquiry data and provides more appropriate answers. For example, it improves the accuracy of answers based on past inquiry history. The inquiry response unit also analyzes past inquiry data and the ChatBot provides appropriate answers based on that data. For example, it learns past answer patterns and optimizes the response content. The inquiry response unit also develops an algorithm in which the ChatBot learns past inquiry data and provides more appropriate answers to user questions. For example, it improves the accuracy of answers based on the content of past inquiries. This allows the ChatBot to learn from past inquiry data and provide more appropriate answers.

[0047] The inquiry response unit can automatically create a report of the ChatBot's response content and present it to the user in a visually easy-to-understand manner. For example, the inquiry response unit builds a system that automatically creates a report of the ChatBot's response content and presents it to the user in a visually easy-to-understand manner. For example, the response content is displayed in graphs or charts. The inquiry response unit also uses a generation AI to automatically create a report of the ChatBot's response content and present it to the user visually. For example, the response content is displayed in dashboard format. The inquiry response unit also develops an algorithm that automatically creates a report of the ChatBot's response content and presents it to the user in a visually easy-to-understand manner. For example, the response content is displayed in infographics. This allows the ChatBot's response content to be automatically created in a report and presented to the user in a visually easy-to-understand manner.

[0048] The inquiry response department can integrate the ChatBot with other systems and provide answers by integrating information from different data sources. For example, the inquiry response department can link the ChatBot with other systems and build a system that integrates information from different data sources to provide answers. For example, it can link with an ERP system or a CRM system. The inquiry response department can also integrate information from different data sources so that the ChatBot can provide more appropriate answers. For example, it can use data from external databases or APIs. The inquiry response department can also link the ChatBot with other systems and develop algorithms that integrate information from different data sources. For example, it can integrate multiple data sources to provide answers. This makes it possible to provide answers by integrating information from different data sources.

[0049] The inquiry response unit can make the ChatBot's responses multilingual so that it can also accommodate international users. For example, the inquiry response unit makes the ChatBot's responses multilingual and builds a system that can also accommodate international users. For example, it supports multiple languages ​​such as English, French, and Chinese. The inquiry response unit also develops a multilingual ChatBot to appropriately respond to inquiries from international users. For example, it provides appropriate answers to questions in different languages. The inquiry response unit also develops an algorithm that makes the ChatBot's responses multilingual and can also accommodate international users. For example, it automatically translates responses in different languages. This makes the ChatBot's responses multilingual so that it can also accommodate international users.

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

[0051] In data learning, generative AI can integrate different generative AI models to analyze data from multiple perspectives, achieving more accurate learning. For example, generative AI combines different generative AI models to analyze data from multiple angles. For example, a natural language processing model and an image recognition model are integrated for learning. Generative AI also uses multiple generative AI models to improve the accuracy of data analysis. For example, different algorithms are combined to analyze data. Generative AI also combines generative AI models to analyze data from different perspectives. For example, a speech recognition model and a text analysis model are integrated for learning. In this way, more accurate learning can be achieved by combining different generative AI models.

[0052] In data learning, generative AI performs cross-domain learning, including data from different industries, allowing it to understand a wider variety of patterns. For example, generative AI inputs data from different industries and performs cross-domain learning. For example, medical data and financial data are simultaneously trained. Generative AI can also understand a wider variety of patterns by combining data from different industries and training it. For example, data from the manufacturing and service industries is integrated and trained. Generative AI can also understand the similarities and differences between different industries through cross-domain learning. For example, technical fields and consumer behavior data are trained simultaneously. This allows it to learn data from different industries and understand a wider variety of patterns.

[0053] In data learning, generative AI can strengthen the association between past data and current data by taking into account changes over time. For example, generative AI introduces a learning algorithm that takes changes over time into account to strengthen the association between past data and current data. For example, it is made to learn using time series data. Generative AI can also compare past data with current data to understand the association. For example, it can learn by comparing past trends with current trends. Generative AI can also learn data that takes changes over time into account to understand the evolution of data. For example, it can learn about past technological evolution and the current technological situation. This can strengthen the association between past data and current data.

[0054] In data learning, generative AI can analyze voice data and image data to perform multimodal data analysis. For example, generative AI can learn voice data to enhance its voice recognition technology. For example, it can analyze a user's voice commands and generate an appropriate response. Generative AI can also learn image data to enhance its image recognition technology. For example, it can recognize a specific object from image data and analyze that information. Generative AI can also simultaneously learn voice data and image data to perform multimodal data analysis. For example, it can combine voice and images to understand a user's intent. This makes multimodal data analysis possible by learning voice data and image data.

[0055] The database creation unit can analyze the interrelationships between parameters of each station when registering them in the database and automatically link highly related parameters. The database creation unit, for example, analyzes the interrelationships between parameters of each station when creating the database and automatically links highly related parameters. For example, related parameters are grouped and registered. The database creation unit also builds a system that analyzes the interrelationships between parameters and automatically links highly related parameters. For example, related parameters are linked and registered in the database. The database creation unit also develops an algorithm that analyzes the interrelationships between parameters of each station and automatically links highly related parameters. For example, highly related parameters are automatically linked and registered in the database. This makes it possible to analyze the interrelationships between parameters and automatically link highly related parameters.

[0056] The database creation unit can compare past data with current data when creating the database and automatically extract trends of change. For example, the database creation unit builds a system that compares past data with current data and automatically extracts trends of change when creating the database. For example, it compares past parameters with current parameters to extract trends. The database creation unit also develops an algorithm that compares past data with current data and automatically extracts trends of change. For example, it analyzes changes in data and extracts trends. The database creation unit also adds a function that compares past data with current data and automatically extracts trends of change when creating the database. For example, it analyzes changes in data and extracts trends. This makes it possible to compare past data with current data and automatically extract trends of change.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The generative AI learns from commercial mass parametric, DPS, and station data. For example, the generative AI can use text generation AI (e.g., LLM) to analyze the data and understand the parameters and composition patterns of each station. The generative AI can also use multimodal generative AI to learn from various aspects of the data. The generative AI can also extract and learn from important parts of the data. Step 2: The database creation unit creates a database of stations that have the relevant parameters based on the data learned by the generation AI. For example, it creates a list of stations that have specific parameters and registers that information in the database. Step 3: The periodic checking unit uses the parameters learned by the generation AI to periodically check whether there are any omissions in the input parameters of existing stations. For example, it periodically checks the parameters of each station to ensure that all necessary parameters have been input. Step 4: The pre-checking unit checks in advance whether incorrect parameters are being entered when a new station or configuration change is made. For example, when entering new parameters, the generation AI checks whether the parameters are correct and issues a warning if there are any errors. Step 5: In the inquiry response section, a ChatBot using the generation AI responds to inquiries related to various parameters. For example, if a user asks about a specific parameter, the generation AI provides an appropriate answer to that question.

[0059] (Example 2) The system according to an embodiment of the present invention uses a generative AI to learn from commercial mass parameter, DPS, and station data, and is based on this data to support a variety of applications. This allows the system to create a database of stations that have entered the relevant parameters, periodically check for omissions in the entered parameters of existing stations, check in advance for incorrect parameters when entering new stations or changing configurations, and use a ChatBot to respond to various parameter-related inquiries.

[0060] The system according to the embodiment includes a generation AI, a database creation unit, a periodic confirmation unit, a pre-check unit, and an inquiry response unit. The generation AI learns commercial mass parameter, DPS, and station data. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM) to understand the parameters and configuration patterns of each station. The generation AI can also use a multimodal generation AI to learn various aspects of the data. The generation AI can also extract and learn important parts of the data. The database creation unit creates a database of stations that have the relevant parameters based on the data learned by the generation AI. For example, it lists stations that have specific parameters and registers that information in the database. The periodic confirmation unit uses the parameters learned by the generation AI to periodically check whether there are any omissions in the parameters entered for existing stations. For example, it periodically checks the parameters of each station to ensure that all necessary parameters have been entered. The pre-check unit checks in advance whether incorrect parameters are being entered when a new station or a configuration change is being made. For example, when entering new parameters, the generation AI checks whether the parameters are correct and issues a warning if there are any errors. The inquiry response section uses a ChatBot using a generation AI to respond to inquiries related to various parameters. For example, if a user asks about a specific parameter, the generation AI will provide an appropriate answer. This allows the system to efficiently manage and operate commercial mass parameter, DPS, and station data. For example, by creating a database of stations into which the relevant parameters have been input, parameter information for each station can be centrally managed. In addition, regular confirmation of input parameters for existing stations and advance parameter checks when adding new stations or changing configurations can prevent parameter omissions and errors. Furthermore, response to inquiries by the ChatBot allows for quick and efficient handling of parameter-related inquiries.

[0061] Using an emotion estimation function, the generative AI can take user emotions into consideration when learning data and adjust learning priorities. For example, the generative AI can add an emotion estimation function and analyze user emotions in real time when learning data. For example, it can prioritize learning data in which the user shows positive emotions. The generative AI can also use the emotion estimation function to dynamically change the order of data learning based on the user's emotions. For example, it can prioritize learning data in which the user shows interest. The generative AI can also collect user emotion data and adjust learning priorities based on that data. For example, it can postpone data that makes the user feel stressed. This makes it possible to adjust learning priorities based on the user's emotions.

[0062] In data learning, generative AI performs cross-domain learning, including data from different industries, allowing it to understand a wider variety of patterns. For example, generative AI inputs data from different industries and performs cross-domain learning. For example, medical data and financial data are simultaneously trained. Generative AI can also understand a wider variety of patterns by combining data from different industries and training it. For example, data from the manufacturing and service industries is integrated and trained. Generative AI can also understand the similarities and differences between different industries through cross-domain learning. For example, technical fields and consumer behavior data are trained simultaneously. This allows it to learn data from different industries and understand a wider variety of patterns.

[0063] In data learning, generative AI can strengthen the association between past data and current data by taking into account changes over time. For example, generative AI introduces a learning algorithm that takes changes over time into account to strengthen the association between past data and current data. For example, it is made to learn using time series data. Generative AI can also compare past data with current data to understand the association. For example, it can learn by comparing past trends with current trends. Generative AI can also learn data that takes changes over time into account to understand the evolution of data. For example, it can learn about past technological evolution and the current technological situation. This can strengthen the association between past data and current data.

[0064] In data learning, generative AI can analyze voice data and image data to perform multimodal data analysis. For example, generative AI can learn voice data to enhance its voice recognition technology. For example, it can analyze a user's voice commands and generate an appropriate response. Generative AI can also learn image data to enhance its image recognition technology. For example, it can recognize a specific object from image data and analyze that information. Generative AI can also simultaneously learn voice data and image data to perform multimodal data analysis. For example, it can combine voice and images to understand a user's intent. This makes multimodal data analysis possible by learning voice data and image data.

[0065] In data learning, generative AI can integrate different generative AI models to analyze data from multiple perspectives, achieving more accurate learning. For example, generative AI combines different generative AI models to analyze data from multiple angles. For example, a natural language processing model and an image recognition model are integrated for learning. Generative AI also uses multiple generative AI models to improve the accuracy of data analysis. For example, different algorithms are combined to analyze data. Generative AI also combines generative AI models to analyze data from different perspectives. For example, a speech recognition model and a text analysis model are integrated for learning. In this way, more accurate learning can be achieved by combining different generative AI models.

[0066] The generative AI can use an emotion estimation function to reflect the user's emotions in real time when learning data, improving the quality of learning. For example, the generative AI can add an emotion estimation function and provide real-time feedback on the user's emotions when learning data. For example, the quality of learning can be adjusted based on the user's emotion score. The generative AI can also use the emotion estimation function to improve the quality of data learning based on the user's emotions. For example, it can prioritize learning data that indicates positive emotions. The generative AI can also collect user emotion data and improve the quality of learning based on that data. For example, it can adjust the learning content according to changes in the user's emotions. This can improve the quality of learning by providing real-time feedback on the user's emotions.

[0067] The database creation unit can use the emotion estimation function to consider the user's emotions when creating the database and prioritize the registration of parameters that the user is most interested in. For example, the database creation unit uses the emotion estimation function to analyze the user's emotions when creating the database and prioritize the registration of parameters that are of high interest to the user. For example, parameters that indicate positive emotions are prioritized for registration in the database. The database creation unit also determines the priority of parameters to be registered in the database based on the user's emotion data. For example, parameters that the user is interested in are prioritized for registration. The database creation unit also uses the emotion estimation function to dynamically change the order of database creation based on the user's emotions. For example, parameters that the user is interested in are prioritized for registration. This allows the user's emotions to be considered and parameters that the user is most interested in to be prioritized for registration.

[0068] The database creation unit can analyze the interrelationships between parameters of each station when registering them in the database and automatically link highly related parameters. The database creation unit, for example, analyzes the interrelationships between parameters of each station when creating the database and automatically links highly related parameters. For example, related parameters are grouped and registered. The database creation unit also builds a system that analyzes the interrelationships between parameters and automatically links highly related parameters. For example, related parameters are linked and registered in the database. The database creation unit also develops an algorithm that analyzes the interrelationships between parameters of each station and automatically links highly related parameters. For example, highly related parameters are automatically linked and registered in the database. This makes it possible to analyze the interrelationships between parameters and automatically link highly related parameters.

[0069] The database creation unit can compare past data with current data when creating the database and automatically extract trends of change. For example, the database creation unit builds a system that compares past data with current data and automatically extracts trends of change when creating the database. For example, it compares past parameters with current parameters to extract trends. The database creation unit also develops an algorithm that compares past data with current data and automatically extracts trends of change. For example, it analyzes changes in data and extracts trends. The database creation unit also adds a function that compares past data with current data and automatically extracts trends of change when creating the database. For example, it analyzes changes in data and extracts trends. This makes it possible to compare past data with current data and automatically extract trends of change.

[0070] The database creation unit can record parameters of different industries in the database, enabling comparative analysis between different industries. For example, the database creation unit registers parameters of different industries in the database and builds a system that enables comparative analysis between different industries. For example, parameters of the manufacturing industry and the service industry are registered in the same database. The database creation unit also registers parameters of different industries in the database and performs comparative analysis between different industries. For example, parameters of the medical industry and the financial industry are compared. The database creation unit also registers parameters of different industries in the database and develops an algorithm for comparative analysis between different industries. For example, parameters of different industries are integrated and analyzed. This allows parameters of different industries to be registered and enables comparative analysis between different industries.

[0071] The database creation unit can install a database on the cloud, enabling real-time data updates and access. The database creation unit, for example, builds a database on the cloud and develops a system that enables real-time data updates and access. For example, the database creation unit builds a database using cloud storage. The database creation unit also builds a database on the cloud and realizes real-time data updates and access. For example, the database creation unit builds a database using cloud services. The database creation unit also builds a database on the cloud and develops an algorithm that enables real-time data updates and access. For example, the database creation unit manages the database on the cloud. This allows the database to be built on the cloud and enables real-time data updates and access.

[0072] The database creation unit can use the emotion estimation function to collect users' emotional reactions to parameters recorded in the database and use the collected data to improve the database. For example, the database creation unit uses the emotion estimation function to build a system that collects users' emotional reactions to parameters registered in the database. For example, the database is improved based on the users' emotion scores. The database creation unit also collects users' emotional reaction data and uses the collected data to improve the database. For example, parameters that indicate positive emotions are preferentially displayed. The database creation unit also uses the emotion estimation function to collect users' emotional reactions to parameters registered in the database and improve the database based on the collected data. For example, the database is adjusted according to changes in the user's emotions. In this way, the collected data can be used to improve the database.

[0073] The periodic confirmation unit can use the emotion estimation function to evaluate the user's emotions during periodic confirmation and design a confirmation process that elicits positive emotions. The periodic confirmation unit, for example, uses the emotion estimation function to analyze the user's emotions in real time during periodic confirmation and design a confirmation process that elicits positive emotions. For example, a confirmation procedure that does not cause the user stress is introduced. The periodic confirmation unit also designs a confirmation process that elicits positive emotions based on the user's emotion data. For example, an encouraging message is displayed during the confirmation process. The periodic confirmation unit also uses the emotion estimation function to dynamically adjust the confirmation process based on the user's emotions. For example, the confirmation process proceeds if the user shows positive emotions. This makes it possible to analyze the user's emotions and design a confirmation process that elicits positive emotions.

[0074] The periodic verification unit allows the generation AI to analyze past verification results and improve the accuracy of verification. For example, during periodic verification, the generation AI learns from past verification results and improves the accuracy of verification. For example, the verification process is optimized based on past errors and omissions. The periodic verification unit also allows the generation AI to analyze past verification results and develop algorithms to improve the accuracy of verification. For example, the verification procedure is improved based on past data. The periodic verification unit also learns from past verification results and builds a system in which the generation AI improves the accuracy of verification. For example, the verification process is automated based on past verification history. This allows the generation AI to learn from past verification results and improve the accuracy of verification.

[0075] The periodic confirmation unit can automatically generate reports of the results of periodic confirmations and present them to users in a visually easy-to-understand manner. The periodic confirmation unit, for example, builds a system that automatically generates reports of the results of periodic confirmations and presents them to users in a visually easy-to-understand manner. For example, the confirmation results are displayed in graphs and charts. The periodic confirmation unit also uses a generation AI to automatically generate reports of the results of periodic confirmations and present them to users visually. For example, the confirmation results are displayed in dashboard format. The periodic confirmation unit also develops an algorithm that automatically generates reports of the results of periodic confirmations and presents them to users in a visually easy-to-understand manner. For example, the confirmation results are displayed in infographics. This allows the results of periodic confirmations to be automatically generated in reports and presented to users in a visually easy-to-understand manner.

[0076] The periodic verification department integrates the periodic verification process with other systems, and can perform verification by integrating information from different data sources. For example, the periodic verification department builds a system that links the periodic verification process with other systems and integrates information from different data sources to perform verification. For example, it links with an ERP system or a CRM system. The periodic verification department also integrates information from different data sources to improve the accuracy of periodic verification. For example, it uses data from external databases and APIs. The periodic verification department also develops algorithms that link the periodic verification process with other systems and integrates information from different data sources. For example, it performs verification work by integrating multiple data sources. This makes it possible to perform verification by integrating information from different data sources.

[0077] The periodic checking unit can make it possible to change the frequency of periodic checking as appropriate according to the user's needs. For example, the periodic checking unit builds a system that allows the frequency of periodic checking to be flexibly changed according to the user's needs. For example, it provides an interface that allows the user to set the checking frequency. The periodic checking unit also develops an algorithm that dynamically adjusts the frequency of periodic checking according to the user's needs. For example, it changes the checking frequency to match the user's work schedule. The periodic checking unit also adds a function that allows the frequency of periodic checking to be flexibly changed according to the user's needs. For example, it provides a settings screen that allows the user to customize the checking frequency. This allows the frequency of periodic checking to be flexibly changed according to the user's needs.

[0078] The regular confirmation unit uses the emotion estimation function to collect users' emotional reactions to the results of the regular confirmation, and can use the collected data to improve the confirmation process. The regular confirmation unit, for example, uses the emotion estimation function to build a system that collects users' emotional reactions to the results of the regular confirmation. For example, the confirmation process is improved based on the user's emotion score. The regular confirmation unit also collects users' emotional reaction data to help improve the confirmation process. For example, it preferentially adopts confirmation procedures that indicate positive emotions. The regular confirmation unit also uses the emotion estimation function to collect users' emotional reactions to the results of the regular confirmation, and improves the confirmation process based on the data. For example, it adjusts the confirmation procedure according to changes in the user's emotions. In this way, the collected data can be used to help improve the confirmation process.

[0079] The pre-check unit can use the emotion estimation function to evaluate the user's emotions during the pre-check and design a check process that reduces stress. The pre-check unit, for example, uses the emotion estimation function during the pre-check to analyze the user's emotions in real time and design a check process that reduces stress. For example, it provides an interface that allows the user to relax. The pre-check unit also designs a check process that reduces stress based on the user's emotion data. For example, it plays music that has a relaxing effect during the check work. The pre-check unit also uses the emotion estimation function to dynamically adjust the check process based on the user's emotions. For example, it pauses the check work if the user feels stressed. In this way, it is possible to analyze the user's emotions and design a check process that reduces stress.

[0080] The pre-checking unit allows the generation AI to analyze past errors and improve the accuracy of error prediction. For example, during pre-checking, the pre-checking unit builds a system in which the generation AI learns from past errors and improves the accuracy of error prediction. For example, the pre-checking unit optimizes the check process based on past error data. The pre-checking unit also develops an algorithm in which the generation AI analyzes past errors and improves the accuracy of error prediction. For example, it learns from past error patterns and improves prediction accuracy. The pre-checking unit also builds a system in which the generation AI learns from past error data and improves the accuracy of error prediction. For example, it improves the check process based on past error logs. This makes it possible to learn from past errors and improve the accuracy of error prediction.

[0081] The pre-check unit can automatically generate a report of the results of the pre-check and present it to the user in a visually easy-to-understand manner. For example, the pre-check unit builds a system that automatically generates a report of the results of the pre-check and presents it to the user in a visually easy-to-understand manner. For example, it displays the check results in graphs and charts. The pre-check unit also uses a generation AI to automatically generate a report of the results of the pre-check and present it to the user visually. For example, it displays the check results in dashboard format. The pre-check unit also develops an algorithm that automatically generates a report of the results of the pre-check and presents it to the user in a visually easy-to-understand manner. For example, it displays the check results in infographics. This allows the results of the pre-check to be automatically generated in a report and presented to the user in a visually easy-to-understand manner.

[0082] The Pre-Checking Department integrates the pre-checking process with other systems, enabling it to perform checks by integrating information from different data sources. For example, the Pre-Checking Department builds a system that links the pre-checking process with other systems and performs checks by integrating information from different data sources. For example, it links with an ERP system or CRM system. The Pre-Checking Department also integrates information from different data sources to improve the accuracy of pre-checks. For example, it uses data from external databases and APIs. The Pre-Checking Department also develops algorithms that link the pre-checking process with other systems and integrate information from different data sources. For example, it integrates multiple data sources to perform checks. This enables it to perform checks by integrating information from different data sources.

[0083] The pre-check unit can make it possible to change the frequency of pre-checks as appropriate according to the user's needs. For example, the pre-check unit builds a system that allows the frequency of pre-checks to be flexibly changed according to the user's needs. For example, it provides an interface that allows the user to set the check frequency. The pre-check unit also develops an algorithm that dynamically adjusts the frequency of pre-checks according to the user's needs. For example, it changes the check frequency to match the user's work schedule. The pre-check unit also adds a function that allows the frequency of pre-checks to be flexibly changed according to the user's needs. For example, it provides a settings screen that allows the user to customize the check frequency. This allows the frequency of pre-checks to be flexibly changed according to the user's needs.

[0084] The pre-check unit uses the emotion estimation function to collect users' emotional reactions to the pre-check results, and can use the collected data to improve the check process. For example, the pre-check unit uses the emotion estimation function to build a system that collects users' emotional reactions to the pre-check results. For example, the check process is improved based on the user's emotion score. The pre-check unit also collects users' emotional reaction data to help improve the check process. For example, it preferentially adopts check procedures that indicate positive emotions. The pre-check unit also uses the emotion estimation function to collect users' emotional reactions to the pre-check results, and improves the check process based on the data. For example, it adjusts the check procedures according to changes in the user's emotions. In this way, the collected data can be used to help improve the check process.

[0085] The inquiry response unit adds an emotion estimation function to the ChatBot and provides a response that corresponds to the user's emotions. For example, the inquiry response unit adds an emotion estimation function to the ChatBot, analyzes the user's emotions in real time, and provides a response that corresponds to the emotions. For example, if the user is feeling stressed, it provides a response that helps the user relax. The inquiry response unit also uses the emotion estimation function to dynamically change the ChatBot's response content based on the user's emotions. For example, if the user shows positive emotions, it sends an encouraging message. The inquiry response unit also collects user emotion data, and the ChatBot provides a response that corresponds to the emotions based on that data. For example, it adjusts the response content according to changes in the user's emotions. This makes it possible to provide a response that corresponds to the user's emotions.

[0086] The inquiry response unit enables the ChatBot to analyze past inquiry data and provide more appropriate answers. For example, the inquiry response unit builds a system in which the ChatBot learns past inquiry data and provides more appropriate answers. For example, it improves the accuracy of answers based on past inquiry history. The inquiry response unit also analyzes past inquiry data and the ChatBot provides appropriate answers based on that data. For example, it learns past answer patterns and optimizes the response content. The inquiry response unit also develops an algorithm in which the ChatBot learns past inquiry data and provides more appropriate answers to user questions. For example, it improves the accuracy of answers based on the content of past inquiries. This allows the ChatBot to learn from past inquiry data and provide more appropriate answers.

[0087] The inquiry response unit can automatically create a report of the ChatBot's response content and present it to the user in a visually easy-to-understand manner. For example, the inquiry response unit builds a system that automatically creates a report of the ChatBot's response content and presents it to the user in a visually easy-to-understand manner. For example, the response content is displayed in graphs or charts. The inquiry response unit also uses a generation AI to automatically create a report of the ChatBot's response content and present it to the user visually. For example, the response content is displayed in dashboard format. The inquiry response unit also develops an algorithm that automatically creates a report of the ChatBot's response content and presents it to the user in a visually easy-to-understand manner. For example, the response content is displayed in infographics. This allows the ChatBot's response content to be automatically created in a report and presented to the user in a visually easy-to-understand manner.

[0088] The inquiry response department can integrate the ChatBot with other systems and provide answers by integrating information from different data sources. For example, the inquiry response department can link the ChatBot with other systems and build a system that integrates information from different data sources to provide answers. For example, it can link with an ERP system or a CRM system. The inquiry response department can also integrate information from different data sources so that the ChatBot can provide more appropriate answers. For example, it can use data from external databases or APIs. The inquiry response department can also link the ChatBot with other systems and develop algorithms that integrate information from different data sources. For example, it can integrate multiple data sources to provide answers. This makes it possible to provide answers by integrating information from different data sources.

[0089] The inquiry response unit can make the ChatBot's responses multilingual so that it can also accommodate international users. For example, the inquiry response unit makes the ChatBot's responses multilingual and builds a system that can also accommodate international users. For example, it supports multiple languages ​​such as English, French, and Chinese. The inquiry response unit also develops a multilingual ChatBot to appropriately respond to inquiries from international users. For example, it provides appropriate answers to questions in different languages. The inquiry response unit also develops an algorithm that makes the ChatBot's responses multilingual and can also accommodate international users. For example, it automatically translates responses in different languages. This makes the ChatBot's responses multilingual so that it can also accommodate international users.

[0090] The inquiry response unit can use the emotion estimation function to collect users' emotional reactions to ChatBot responses and use the collected data to improve the response content. For example, the inquiry response unit uses the emotion estimation function to build a system that collects users' emotional reactions to ChatBot responses. For example, the response content is improved based on the user's emotion score. The inquiry response unit also collects users' emotional reaction data and improves the ChatBot's response content. For example, it prioritizes the adoption of response content that shows positive emotions. The inquiry response unit also uses the emotion estimation function to collect users' emotional reactions to ChatBot responses and improves the response content based on the data. For example, it adjusts the response content according to changes in the user's emotions. In this way, the user's emotional reactions can be collected and used to improve the response content.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] In data learning, generative AI can integrate different generative AI models to analyze data from multiple perspectives, achieving more accurate learning. For example, generative AI combines different generative AI models to analyze data from multiple angles. For example, a natural language processing model and an image recognition model are integrated for learning. Generative AI also uses multiple generative AI models to improve the accuracy of data analysis. For example, different algorithms are combined to analyze data. Generative AI also combines generative AI models to analyze data from different perspectives. For example, a speech recognition model and a text analysis model are integrated for learning. In this way, more accurate learning can be achieved by combining different generative AI models.

[0093] Using an emotion estimation function, the generative AI can take user emotions into consideration when learning data and adjust learning priorities. For example, the generative AI can add an emotion estimation function and analyze user emotions in real time when learning data. For example, it can prioritize learning data in which the user shows positive emotions. The generative AI can also use the emotion estimation function to dynamically change the order of data learning based on the user's emotions. For example, it can prioritize learning data in which the user shows interest. The generative AI can also collect user emotion data and adjust learning priorities based on that data. For example, it can postpone data that makes the user feel stressed. This makes it possible to adjust learning priorities based on the user's emotions.

[0094] In data learning, generative AI performs cross-domain learning, including data from different industries, allowing it to understand a wider variety of patterns. For example, generative AI inputs data from different industries and performs cross-domain learning. For example, medical data and financial data are simultaneously trained. Generative AI can also understand a wider variety of patterns by combining data from different industries and training it. For example, data from the manufacturing and service industries is integrated and trained. Generative AI can also understand the similarities and differences between different industries through cross-domain learning. For example, technical fields and consumer behavior data are trained simultaneously. This allows it to learn data from different industries and understand a wider variety of patterns.

[0095] In data learning, generative AI can strengthen the association between past data and current data by taking into account changes over time. For example, generative AI introduces a learning algorithm that takes changes over time into account to strengthen the association between past data and current data. For example, it is made to learn using time series data. Generative AI can also compare past data with current data to understand the association. For example, it can learn by comparing past trends with current trends. Generative AI can also learn data that takes changes over time into account to understand the evolution of data. For example, it can learn about past technological evolution and the current technological situation. This can strengthen the association between past data and current data.

[0096] In data learning, generative AI can analyze voice data and image data to perform multimodal data analysis. For example, generative AI can learn voice data to enhance its voice recognition technology. For example, it can analyze a user's voice commands and generate an appropriate response. Generative AI can also learn image data to enhance its image recognition technology. For example, it can recognize a specific object from image data and analyze that information. Generative AI can also simultaneously learn voice data and image data to perform multimodal data analysis. For example, it can combine voice and images to understand a user's intent. This makes multimodal data analysis possible by learning voice data and image data.

[0097] The generative AI can use an emotion estimation function to reflect the user's emotions in real time when learning data, improving the quality of learning. For example, the generative AI can add an emotion estimation function and provide real-time feedback on the user's emotions when learning data. For example, the quality of learning can be adjusted based on the user's emotion score. The generative AI can also use the emotion estimation function to improve the quality of data learning based on the user's emotions. For example, it can prioritize learning data that indicates positive emotions. The generative AI can also collect user emotion data and improve the quality of learning based on that data. For example, it can adjust the learning content according to changes in the user's emotions. This can improve the quality of learning by providing real-time feedback on the user's emotions.

[0098] The database creation unit can use the emotion estimation function to consider the user's emotions when creating the database and prioritize the registration of parameters that the user is most interested in. For example, the database creation unit uses the emotion estimation function to analyze the user's emotions when creating the database and prioritize the registration of parameters that are of high interest to the user. For example, parameters that indicate positive emotions are prioritized for registration in the database. The database creation unit also determines the priority of parameters to be registered in the database based on the user's emotion data. For example, parameters that the user is interested in are prioritized for registration. The database creation unit also uses the emotion estimation function to dynamically change the order of database creation based on the user's emotions. For example, parameters that the user is interested in are prioritized for registration. This allows the user's emotions to be considered and parameters that the user is most interested in to be prioritized for registration.

[0099] The database creation unit can analyze the interrelationships between parameters of each station when registering them in the database and automatically link highly related parameters. The database creation unit, for example, analyzes the interrelationships between parameters of each station when creating the database and automatically links highly related parameters. For example, related parameters are grouped and registered. The database creation unit also builds a system that analyzes the interrelationships between parameters and automatically links highly related parameters. For example, related parameters are linked and registered in the database. The database creation unit also develops an algorithm that analyzes the interrelationships between parameters of each station and automatically links highly related parameters. For example, highly related parameters are automatically linked and registered in the database. This makes it possible to analyze the interrelationships between parameters and automatically link highly related parameters.

[0100] The database creation unit can compare past data with current data when creating the database and automatically extract trends of change. For example, the database creation unit builds a system that compares past data with current data and automatically extracts trends of change when creating the database. For example, it compares past parameters with current parameters to extract trends. The database creation unit also develops an algorithm that compares past data with current data and automatically extracts trends of change. For example, it analyzes changes in data and extracts trends. The database creation unit also adds a function that compares past data with current data and automatically extracts trends of change when creating the database. For example, it analyzes changes in data and extracts trends. This makes it possible to compare past data with current data and automatically extract trends of change.

[0101] The database creation unit can use the emotion estimation function to collect users' emotional reactions to parameters recorded in the database and use the collected data to improve the database. For example, the database creation unit uses the emotion estimation function to build a system that collects users' emotional reactions to parameters registered in the database. For example, the database is improved based on the users' emotion scores. The database creation unit also collects users' emotional reaction data and uses the collected data to improve the database. For example, parameters that indicate positive emotions are preferentially displayed. The database creation unit also uses the emotion estimation function to collect users' emotional reactions to parameters registered in the database and improve the database based on the collected data. For example, the database is adjusted according to changes in the user's emotions. In this way, the collected data can be used to improve the database.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The generative AI learns from commercial mass parametric, DPS, and station data. For example, the generative AI can use text generation AI (e.g., LLM) to analyze the data and understand the parameters and composition patterns of each station. The generative AI can also use multimodal generative AI to learn from various aspects of the data. The generative AI can also extract and learn from important parts of the data. Step 2: The database creation unit creates a database of stations that have the relevant parameters based on the data learned by the generation AI. For example, it creates a list of stations that have specific parameters and registers that information in the database. Step 3: The periodic checking unit uses the parameters learned by the generation AI to periodically check whether there are any omissions in the input parameters of existing stations. For example, it periodically checks the parameters of each station to ensure that all necessary parameters have been input. Step 4: The pre-checking unit checks in advance whether incorrect parameters are being entered when a new station or configuration change is made. For example, when entering new parameters, the generation AI checks whether the parameters are correct and issues a warning if there are any errors. Step 5: In the inquiry response section, a ChatBot using the generation AI responds to inquiries related to various parameters. For example, if a user asks about a specific parameter, the generation AI provides an appropriate answer to that question.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0110] The 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.

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

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

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

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A database creation department uses a generation AI to learn commercial mass parameters, DPS, and station data, and creates a database of stations with the relevant parameters entered; a periodic checking unit that periodically checks whether there are any omissions in the input parameters of the existing stations using the parameters learned by the generating AI; A pre-checking section checks in advance whether incorrect parameters are being entered when a new station or configuration change is made, and An inquiry response unit that uses ChatBot to respond to inquiries related to various parameters. A system characterized by:

2. The generated AI is Adjust learning priorities based on user sentiment during data learning 2. The system of claim 1.

3. The generated AI is In data learning, cross-domain learning is carried out, including data from different industries, to understand more diverse patterns.

2. The system of claim 1.

4. The generated AI is In data learning, strengthening relevance based on the temporal changes of past and current data 2. The system of claim 1.

5. The generated AI is In data learning, we analyze audio data and image data and perform multimodal data analysis.

2. The system of claim 1.

6. The generated AI is In data learning, the different generative AI models are integrated to analyze data from multiple perspectives, achieving more accurate learning.

2. The system of claim 1.

Images