system

Abstract

An object of the system according to the embodiment is to optimize SB-Chat for a specific type of business and meet the needs of a company.SOLUTION: A system includes an optimization part, a data stock part, and a package generation part. The optimization unit optimizes the SB-Chat for each business type. The data stock unit stocks data collected through utilization in a company. The package generation part analyzes the data stocked by the data stock part and generates a package for a specific industry.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that chat systems are not optimized for specific industries and cannot fully meet the needs of companies.

[0005] The system according to the embodiment aims to optimize SB-Chat for specific industries and meet the needs of companies. [Means for solving the problem]

[0006] The system according to the embodiment includes an optimization unit, a data storage unit, and a package generation unit. The optimization unit optimizes SB-Chat for each industry. The data storage unit stores data collected through use by companies. The package generation unit analyzes the data stored by the data storage unit and generates packages for specific industries. [Effects of the Invention]

[0007] The system according to the embodiment can optimize SB-Chat for specific industries and meet the needs of companies. [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 chat optimization system according to an embodiment of the present invention optimizes SB-Chat for each industry and generates packages for specific industries based on data collected through use by companies. This allows the chat optimization system to improve business efficiency and customer satisfaction for companies.

[0029] The chat optimization system according to the embodiment includes an optimization unit, a data storage unit, and a package generation unit. The optimization unit optimizes SB-Chat for each industry. For example, the generation AI adds functions to facilitate communication with patients in the medical industry. The generation AI also enhances production management and inventory management functions in the manufacturing industry. The generation AI analyzes the characteristics and needs of each industry and customizes SB-Chat's functions based on the analysis. The data storage unit stores data collected through corporate use. For example, data related to chat history with customers and business processes is collected. The data storage unit also analyzes the data stored by the generation AI to identify trends and issues for each industry. The package generation unit generates packages for specific industries based on the stored data and the analysis results. For example, the generation AI provides chat functions linked to patient management and reservation systems for the medical industry. The generation AI also provides specialized functions for production line monitoring and quality control for the manufacturing industry. As a result, the chat optimization system according to the embodiment can optimize SB-Chat for each industry and generate packages for specific industries based on data collected through use in companies.

[0030] The optimization unit uses the generation AI to automatically learn specialized terminology and phrases for each industry, thereby improving the accuracy of chat. For example, the optimization unit uses the generation AI to automatically learn specialized terminology and phrases for the medical industry to facilitate communication with patients. For example, it learns medical terminology and phrases related to medical treatment and generates appropriate responses. The optimization unit also uses the generation AI to learn specialized terminology related to production management and inventory management for the manufacturing industry to provide efficient chat responses. For example, it provides accurate information in response to questions about the status of the production line or the status of inventory. The optimization unit also uses the generation AI to learn specialized terminology related to financial products and investments for the financial industry to optimize communication with customers. For example, it generates appropriate responses in response to investment consultations or explanations of financial products. In this way, by learning specialized terminology and phrases for each industry, the accuracy of chat is improved.

[0031] The optimization unit can analyze the business flows of each industry and generate optimal chatbot response scenarios. For example, for the medical industry, the generation AI analyzes the business flows of medical appointments and patient management and generates optimal response scenarios. For example, it provides smooth responses to questions about confirming or changing medical appointments. For the manufacturing industry, the generation AI analyzes the business flows of production management and quality control and generates efficient response scenarios. For example, it provides appropriate responses to questions about production line troubleshooting and quality inspections. For the retail industry, the generation AI analyzes the business flows of inventory management and customer support and generates optimal response scenarios. For example, it provides quick responses to questions about checking stock status and return procedures. This makes it possible to generate optimal response scenarios by analyzing business flows.

[0032] The data stock department can link the collected data with other business systems and perform integrated data analysis. For example, for the medical industry, the generation AI in the data stock department links medical data with an electronic medical record system and performs integrated data analysis. For example, it can comprehensively evaluate a patient's health condition based on medical history and test results. For the manufacturing industry, the generation AI in the data stock department links production data with an ERP system and performs integrated data analysis. For example, it can optimize production efficiency based on production plans and inventory management. For the retail industry, the generation AI in the data stock department links sales data with a CRM system and performs integrated data analysis. For example, it can improve customer satisfaction based on customer purchase history and feedback. This makes it possible to link the collected data with other business systems and perform integrated data analysis.

[0033] The package generation unit can use generation AI to automatically generate customization packages tailored to the needs of each industry. For example, for the medical industry, the generation AI automatically generates customization packages that are linked to patient management and reservation systems. For example, this provides functions for optimizing medical appointments and patient follow-up. In addition, for the manufacturing industry, the generation AI automatically generates customization packages specialized for production line monitoring and quality control. For example, this provides improved production efficiency and automated quality inspections. In addition, for the retail industry, the generation AI automatically generates customization packages specialized for inventory management and customer support. For example, this provides inventory optimization and improved customer satisfaction. This makes it possible to automatically generate customization packages tailored to the needs of each industry.

[0034] The package generation unit can combine packages for different industries to develop a hybrid package for a new industry. For example, the package generation unit combines packages for the medical industry and the manufacturing industry to develop a hybrid package for a new industry. For example, a package that integrates medical device production management and patient management is provided. The package generation unit also combines packages for the retail industry and the financial industry to develop a hybrid package for a new industry. For example, a package that integrates customer support and risk management is provided. The package generation unit also combines packages for the education industry and the service industry to develop a hybrid package for a new industry. For example, a package that integrates schedule management and feedback collection is provided. In this way, by combining packages for different industries, hybrid packages for new industries can be developed.

[0035] In addition to industry-specific packages, the package generation unit can also customize for different company sizes. For example, the package generation unit customizes for different company sizes for the medical industry. For example, it provides functions for managing multiple medical departments for large hospitals, and simple patient management functions for small clinics. The package generation unit also customizes for different company sizes for the manufacturing industry. For example, it provides detailed production line monitoring functions for large factories, and basic production management functions for small factories. The package generation unit also customizes for different company sizes for the retail industry. For example, it provides integrated inventory management and customer support functions for large chain stores, and simple inventory management functions for small stores. This makes it possible to customize for different company sizes in addition to industry-specific packages.

[0036] The data stock department can analyze trends for each industry in real time based on the collected data. For example, for the medical industry, the data stock department's generation AI analyzes patient medical data in real time to identify the latest medical trends. For example, it analyzes trends related to new treatments and diagnostic technologies. For the manufacturing industry, the data stock department's generation AI analyzes production data in real time to identify the latest production trends. For example, it analyzes trends related to improving production efficiency and quality control. For the retail industry, the data stock department's generation AI analyzes sales data in real time to identify the latest consumer trends. For example, it analyzes trends related to popular products and purchasing patterns. This makes it possible to analyze trends for each industry in real time based on the collected data.

[0037] The Data Stock Department can automatically generate solutions to problems for each industry based on the results of data analysis. For example, for the medical industry, the generation AI automatically generates solutions to problems related to improving patient management and medical efficiency based on the results of medical data analysis. For example, it makes suggestions for optimizing medical appointments and patient follow-up. For the manufacturing industry, the generation AI automatically generates solutions to problems related to production efficiency and quality control based on the results of production data analysis. For example, it makes suggestions for optimizing production lines and automating quality inspections. For the retail industry, the generation AI automatically generates solutions to problems related to inventory management and customer service based on the results of sales data analysis. For example, it makes suggestions for optimizing inventory and improving customer satisfaction. This makes it possible to automatically generate solutions to problems for each industry based on the results of data analysis.

[0038] The Data Stock Department can compare and analyze data from different industries to extract common issues and success stories. For example, the Data Stock Department can compare and analyze data from the medical and manufacturing industries to extract common issues and success stories. For example, they can share successful examples of efficient business processes and quality control. The Data Stock Department can also compare and analyze data from the retail and financial industries to extract common issues and success stories. For example, they can share successful examples of customer service and risk management. The Data Stock Department can also compare and analyze data from the education and service industries to extract common issues and success stories. For example, they can share successful examples of schedule management and feedback collection. In this way, by comparing and analyzing data from different industries, common issues and success stories can be extracted.

[0039] The data stock department can extract common needs between different industries based on the collected data and develop a general-purpose optimization package. For example, the data stock department extracts common needs between the medical industry and the manufacturing industry and develops a general-purpose optimization package. For example, it provides common functions related to reservation management and inventory management. The data stock department can also extract common needs between the retail industry and the financial industry and develop a general-purpose optimization package. For example, it provides common functions related to customer support and inquiry management. The data stock department can also extract common needs between the education industry and the service industry and develop a general-purpose optimization package. For example, it provides common functions related to schedule management and feedback collection. This makes it possible to extract common needs between different industries and develop a general-purpose optimization package.

[0040] The data stock unit can perform customization taking into account regional characteristics. For example, the data stock unit performs customization taking into account regional characteristics for the medical industry. For example, it provides the optimal response based on the medical system and consultation hours of each region. The data stock unit also performs customization taking into account regional characteristics for the manufacturing industry. For example, it provides the optimal response based on the labor regulations and production schedules of each region. The data stock unit also performs customization taking into account regional characteristics for the retail industry. For example, it provides the optimal response based on the consumer needs and purchasing patterns of each region. This makes it possible to perform customization taking into account regional characteristics.

[0041] The data stock department can use the generation AI to analyze usage data and identify new needs and issues. For example, for the medical industry, the generation AI analyzes medical data to identify new needs and issues. For example, it identifies areas for improvement in optimizing medical appointments and patient follow-up. The data stock department can also use the generation AI to analyze production data for the manufacturing industry to identify new needs and issues. For example, it can identify areas for improvement in production line efficiency and quality control. The data stock department can also use the generation AI to analyze sales data for the retail industry to identify new needs and issues. For example, it can identify areas for improvement in inventory management and customer service. This makes it possible to analyze usage data and identify new needs and issues.

[0042] The data stock department provides regular updates to keep up with the latest industry trends and technologies. For example, for the medical industry, the data stock department provides regular updates to keep the generative AI up to date with the latest medical trends and technologies. For example, it adds functions to accommodate new diagnostic methods and medical devices. The data stock department also provides regular updates to keep the generative AI up to date with the latest production technologies and trends for the manufacturing industry. For example, it adds functions to accommodate new production management methods and quality control techniques. The data stock department also provides regular updates to keep the generative AI up to date with the latest consumer trends and technologies for the retail industry. For example, it adds functions to accommodate new purchasing patterns and customer service techniques. This allows for regular updates to keep up with the latest industry trends and technologies.

[0043] The Data Stock Department can integrate feedback from different industries and extract common areas for improvement. For example, the Data Stock Department can integrate feedback from the medical and manufacturing industries and extract common areas for improvement. For example, they can share areas for improvement in efficient business processes and quality control. The Data Stock Department can also integrate feedback from the retail and financial industries and extract common areas for improvement. For example, they can share areas for improvement in customer service and risk management. The Data Stock Department can also integrate feedback from the education and service industries and extract common areas for improvement. For example, they can share areas for improvement in schedule management and feedback collection. This makes it possible to integrate feedback from different industries and extract common areas for improvement.

[0044] The data stock unit visualizes the update contents and can explain them to users in an easy-to-understand manner. For example, for the medical industry, the generation AI in the data stock unit visualizes the update contents and explains them to users in an easy-to-understand manner. For example, new medical treatment functions and improvements are shown in graphs and diagrams. The data stock unit also visualizes the update contents for the manufacturing industry, and explains them to users in an easy-to-understand manner. For example, new production management functions and improvements are shown in graphs and diagrams. The data stock unit also visualizes the update contents for the retail industry, and explains them to users in an easy-to-understand manner. For example, new inventory management functions and improvements are shown in graphs and diagrams. This visualizes the update contents and can explain them to users in an easy-to-understand manner.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The optimization unit uses the generation AI to automatically learn specialized terminology and phrases for each industry, improving the accuracy of chat. For example, it can automatically learn specialized terminology and phrases for the medical industry to facilitate communication with patients. For example, it can learn medical terminology and phrases related to medical treatment and generate appropriate responses. The optimization unit also uses the generation AI to learn specialized terminology related to production management and inventory management for the manufacturing industry to provide efficient chat responses. For example, it can provide accurate information in response to questions about the status of the production line or inventory status. The optimization unit also uses the generation AI to learn specialized terminology related to financial products and investments for the financial industry to optimize communication with customers. For example, it can generate appropriate responses in response to investment consultations or explanations of financial products. In this way, chat accuracy is improved by learning specialized terminology and phrases for each industry.

[0047] The optimization unit can analyze the business flows of each industry and generate optimal chatbot response scenarios. For example, for the medical industry, the generation AI analyzes the business flows of medical appointments and patient management and generates the optimal response scenario. For example, it provides a smooth response to questions about confirming or changing medical appointments. In addition, for the manufacturing industry, the generation AI analyzes the business flows of production management and quality control and generates efficient response scenarios. For example, it provides appropriate responses to questions about production line troubleshooting and quality inspections. In addition, for the retail industry, the generation AI analyzes the business flows of inventory management and customer support and generates the optimal response scenario. For example, it provides a quick response to questions about checking stock status and return procedures. In this way, optimal response scenarios can be generated by analyzing business flows.

[0048] The data stock department can link the collected data with other business systems and perform integrated data analysis. For example, for the medical industry, the generation AI links medical data with an electronic medical record system and performs integrated data analysis. For example, it can comprehensively evaluate a patient's health condition based on medical history and test results. For the manufacturing industry, the generation AI also links production data with an ERP system and performs integrated data analysis. For example, it can optimize production efficiency based on production plans and inventory management. For the retail industry, the generation AI also links sales data with a CRM system and performs integrated data analysis. For example, it can improve customer satisfaction based on customer purchase history and feedback. This makes it possible to link the collected data with other business systems and perform integrated data analysis.

[0049] The package generation unit can use generation AI to automatically generate customization packages tailored to the needs of each industry. For example, for the medical industry, generation AI automatically generates a customization package that is linked to patient management and reservation systems. For example, it provides functions for optimizing medical appointments and patient follow-up. In addition, for the manufacturing industry, generation AI automatically generates customization packages specialized for production line monitoring and quality control. For example, it provides improved production efficiency and automated quality inspections. In addition, for the retail industry, generation AI automatically generates customization packages specialized for inventory management and customer support. For example, it provides inventory optimization and improved customer satisfaction. This makes it possible to automatically generate customization packages tailored to the needs of each industry.

[0050] The package generation unit can combine packages for different industries to develop hybrid packages for new industries. For example, by combining packages for the medical industry and the manufacturing industry, a hybrid package for a new industry can be developed. For example, a package that integrates medical device production management and patient management can be provided. The package generation unit can also combine packages for the retail industry and the financial industry to develop a hybrid package for a new industry. For example, a package that integrates customer support and risk management can be provided. The package generation unit can also combine packages for the education industry and the service industry to develop a hybrid package for a new industry. For example, a package that integrates schedule management and feedback collection can be provided. In this way, hybrid packages for new industries can be developed by combining packages for different industries.

[0051] In addition to industry-specific packages, the package generation unit can also customize for different company sizes. For example, customization for different company sizes is performed for the medical industry. For example, it provides functions for managing multiple medical departments for large hospitals, and simple patient management functions for small clinics. The package generation unit also customizes for different company sizes for the manufacturing industry. For example, it provides detailed production line monitoring functions for large factories, and basic production management functions for small factories. The package generation unit also customizes for different company sizes for the retail industry. For example, it provides integrated inventory management and customer support functions for large chain stores, and simple inventory management functions for small stores. This makes it possible to customize for different company sizes in addition to industry-specific packages.

[0052] The data stock department can analyze trends for each industry in real time based on the collected data. For example, for the medical industry, the generation AI analyzes patient medical data in real time to identify the latest medical trends. For example, it analyzes trends related to new treatments and diagnostic technologies. The data stock department also analyzes production data for the manufacturing industry in real time to identify the latest production trends. For example, it analyzes trends related to improving production efficiency and quality control. The data stock department also analyzes sales data for the retail industry in real time to identify the latest consumer trends. For example, it analyzes trends related to popular products and purchasing patterns. This allows trends for each industry to be analyzed in real time based on the collected data.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The optimization unit optimizes SB-Chat for each industry. For example, the generation AI adds features to facilitate communication with patients in the medical industry, and enhances production and inventory management functions for the manufacturing industry. The generation AI analyzes the characteristics and needs of each industry and customizes SB-Chat's functions based on that. Step 2: The Data Stock Department stores data collected through corporate use. For example, data on chat history with customers and business processes is collected. The Data Stock Department also analyzes the data stored by the generative AI to identify trends and issues for each industry. Step 3: The package generation unit generates packages for specific industries based on the stored data and its analysis results. For example, the generation AI could provide a chat function linked to patient management and reservation systems for the medical industry, or specialized functions for production line monitoring and quality control for the manufacturing industry.

[0055] (Example 2) The chat optimization system according to an embodiment of the present invention optimizes SB-Chat for each industry and generates packages for specific industries based on data collected through use by companies. This allows the chat optimization system to improve business efficiency and customer satisfaction for companies.

[0056] The chat optimization system according to the embodiment includes an optimization unit, a data storage unit, and a package generation unit. The optimization unit optimizes SB-Chat for each industry. For example, the generation AI adds functions to facilitate communication with patients in the medical industry. The generation AI also enhances production management and inventory management functions in the manufacturing industry. The generation AI analyzes the characteristics and needs of each industry and customizes SB-Chat's functions based on the analysis. The data storage unit stores data collected through corporate use. For example, data related to chat history with customers and business processes is collected. The data storage unit also analyzes the data stored by the generation AI to identify trends and issues for each industry. The package generation unit generates packages for specific industries based on the stored data and the analysis results. For example, the generation AI provides chat functions linked to patient management and reservation systems for the medical industry. The generation AI also provides specialized functions for production line monitoring and quality control for the manufacturing industry. As a result, the chat optimization system according to the embodiment can optimize SB-Chat for each industry and generate packages for specific industries based on data collected through use in companies.

[0057] The optimization unit uses the generation AI to automatically learn specialized terminology and phrases for each industry, thereby improving the accuracy of chat. For example, the optimization unit uses the generation AI to automatically learn specialized terminology and phrases for the medical industry to facilitate communication with patients. For example, it learns medical terminology and phrases related to medical treatment and generates appropriate responses. The optimization unit also uses the generation AI to learn specialized terminology related to production management and inventory management for the manufacturing industry to provide efficient chat responses. For example, it provides accurate information in response to questions about the status of the production line or the status of inventory. The optimization unit also uses the generation AI to learn specialized terminology related to financial products and investments for the financial industry to optimize communication with customers. For example, it generates appropriate responses in response to investment consultations or explanations of financial products. In this way, by learning specialized terminology and phrases for each industry, the accuracy of chat is improved.

[0058] The optimization unit can analyze the business flows of each industry and generate optimal chatbot response scenarios. For example, for the medical industry, the generation AI analyzes the business flows of medical appointments and patient management and generates optimal response scenarios. For example, it provides smooth responses to questions about confirming or changing medical appointments. For the manufacturing industry, the generation AI analyzes the business flows of production management and quality control and generates efficient response scenarios. For example, it provides appropriate responses to questions about production line troubleshooting and quality inspections. For the retail industry, the generation AI analyzes the business flows of inventory management and customer support and generates optimal response scenarios. For example, it provides quick responses to questions about checking stock status and return procedures. This makes it possible to generate optimal response scenarios by analyzing business flows.

[0059] The optimization unit uses the emotion estimation function to generate responses based on the user's emotions, enabling more personalized communication. For example, in the medical industry, the optimization unit uses a generation AI to estimate a patient's emotions and generate a response that provides a sense of security. For example, a message of encouragement and reassurance is provided to a patient who is anxious about the outcome of a medical examination. In addition, in the manufacturing industry, the optimization unit uses a generation AI to estimate an employee's emotions and generate a response that increases motivation. For example, an employee who is stressed about a problem on the production line is provided with encouragement and a solution. In addition, in the financial industry, the optimization unit uses a generation AI to estimate a customer's emotions and generate a response that increases trust. For example, a customer who is anxious about investing is provided with a message of reassurance regarding risk management and investment strategies. This allows for more personalized communication by generating responses based on the user's emotions.

[0060] The data stock department can link the collected data with other business systems and perform integrated data analysis. For example, for the medical industry, the generation AI in the data stock department links medical data with an electronic medical record system and performs integrated data analysis. For example, it can comprehensively evaluate a patient's health condition based on medical history and test results. For the manufacturing industry, the generation AI in the data stock department links production data with an ERP system and performs integrated data analysis. For example, it can optimize production efficiency based on production plans and inventory management. For the retail industry, the generation AI in the data stock department links sales data with a CRM system and performs integrated data analysis. For example, it can improve customer satisfaction based on customer purchase history and feedback. This makes it possible to link the collected data with other business systems and perform integrated data analysis.

[0061] The package generation unit can use generation AI to automatically generate customization packages tailored to the needs of each industry. For example, for the medical industry, the generation AI automatically generates customization packages that are linked to patient management and reservation systems. For example, this provides functions for optimizing medical appointments and patient follow-up. In addition, for the manufacturing industry, the generation AI automatically generates customization packages specialized for production line monitoring and quality control. For example, this provides improved production efficiency and automated quality inspections. In addition, for the retail industry, the generation AI automatically generates customization packages specialized for inventory management and customer support. For example, this provides inventory optimization and improved customer satisfaction. This makes it possible to automatically generate customization packages tailored to the needs of each industry.

[0062] The package generation unit can use the emotion estimation function to customize packages based on the user's emotions. For example, for the medical industry, the package generation unit customizes a function in which a generation AI estimates a patient's emotions and gives them a sense of security. For example, it provides a response to reduce anxiety about the results of a medical examination. In addition, for the manufacturing industry, the package generation unit customizes a function in which a generation AI estimates an employee's emotions and increases their motivation. For example, it provides a response to reduce stress caused by a problem on a production line. In addition, for the retail industry, the package generation unit customizes a function in which a generation AI estimates a customer's emotions and improves the purchasing experience. For example, it provides suggestions for improving services to increase customer satisfaction. This makes it possible to customize packages based on the user's emotions.

[0063] The package generation unit can combine packages for different industries to develop a hybrid package for a new industry. For example, the package generation unit combines packages for the medical industry and the manufacturing industry to develop a hybrid package for a new industry. For example, a package that integrates medical device production management and patient management is provided. The package generation unit also combines packages for the retail industry and the financial industry to develop a hybrid package for a new industry. For example, a package that integrates customer support and risk management is provided. The package generation unit also combines packages for the education industry and the service industry to develop a hybrid package for a new industry. For example, a package that integrates schedule management and feedback collection is provided. In this way, by combining packages for different industries, hybrid packages for new industries can be developed.

[0064] In addition to industry-specific packages, the package generation unit can also customize for different company sizes. For example, the package generation unit customizes for different company sizes for the medical industry. For example, it provides functions for managing multiple medical departments for large hospitals, and simple patient management functions for small clinics. The package generation unit also customizes for different company sizes for the manufacturing industry. For example, it provides detailed production line monitoring functions for large factories, and basic production management functions for small factories. The package generation unit also customizes for different company sizes for the retail industry. For example, it provides integrated inventory management and customer support functions for large chain stores, and simple inventory management functions for small stores. This makes it possible to customize for different company sizes in addition to industry-specific packages.

[0065] The package generation unit uses the emotion estimation function to monitor package usage and add functions according to the user's emotions. For example, for the medical industry, the package generation unit adds a function in which the generation AI monitors patients' emotions and gives them a sense of security. For example, it provides a response to reduce anxiety about medical results. In addition, for the manufacturing industry, the package generation unit adds a function in which the generation AI monitors employees' emotions and increases their motivation. For example, it provides a response to reduce stress caused by problems on the production line. In addition, for the retail industry, the package generation unit adds a function in which the generation AI monitors customers' emotions and improves the purchasing experience. For example, it provides suggestions for service improvements to increase customer satisfaction. This makes it possible to monitor package usage and add functions according to the user's emotions.

[0066] The data stock department can analyze trends for each industry in real time based on the collected data. For example, for the medical industry, the data stock department's generation AI analyzes patient medical data in real time to identify the latest medical trends. For example, it analyzes trends related to new treatments and diagnostic technologies. For the manufacturing industry, the data stock department's generation AI analyzes production data in real time to identify the latest production trends. For example, it analyzes trends related to improving production efficiency and quality control. For the retail industry, the data stock department's generation AI analyzes sales data in real time to identify the latest consumer trends. For example, it analyzes trends related to popular products and purchasing patterns. This makes it possible to analyze trends for each industry in real time based on the collected data.

[0067] The Data Stock Department can automatically generate solutions to problems for each industry based on the results of data analysis. For example, for the medical industry, the generation AI automatically generates solutions to problems related to improving patient management and medical efficiency based on the results of medical data analysis. For example, it makes suggestions for optimizing medical appointments and patient follow-up. For the manufacturing industry, the generation AI automatically generates solutions to problems related to production efficiency and quality control based on the results of production data analysis. For example, it makes suggestions for optimizing production lines and automating quality inspections. For the retail industry, the generation AI automatically generates solutions to problems related to inventory management and customer service based on the results of sales data analysis. For example, it makes suggestions for optimizing inventory and improving customer satisfaction. This makes it possible to automatically generate solutions to problems for each industry based on the results of data analysis.

[0068] The data stock department can use the emotion estimation function to analyze customer emotion data and provide insights that lead to improved customer satisfaction. For example, for the medical industry, the data stock department's generative AI analyzes patient emotion data and provides insights that lead to an improved medical experience. For example, it makes suggestions to reduce anxiety during medical treatment. For the manufacturing industry, the data stock department's generative AI analyzes employee emotion data and provides insights that lead to an improvement in the work environment. For example, it makes suggestions to reduce stress and increase motivation. For the retail industry, the data stock department's generative AI analyzes customer emotion data and provides insights that lead to an improved purchasing experience. For example, it makes suggestions to improve services to increase customer satisfaction. In this way, by analyzing customer emotion data, it is possible to provide insights that lead to improved customer satisfaction.

[0069] The Data Stock Department can compare and analyze data from different industries to extract common issues and success stories. For example, the Data Stock Department can compare and analyze data from the medical and manufacturing industries to extract common issues and success stories. For example, they can share successful examples of efficient business processes and quality control. The Data Stock Department can also compare and analyze data from the retail and financial industries to extract common issues and success stories. For example, they can share successful examples of customer service and risk management. The Data Stock Department can also compare and analyze data from the education and service industries to extract common issues and success stories. For example, they can share successful examples of schedule management and feedback collection. In this way, by comparing and analyzing data from different industries, common issues and success stories can be extracted.

[0070] The data stock department uses the emotion estimation function to monitor fluctuations in customer emotions for each industry and propose appropriate countermeasures. For example, for the medical industry, the data stock department's generation AI monitors fluctuations in patients' emotions and proposes countermeasures that will improve the medical experience. For example, it makes suggestions to reduce anxiety during medical treatment. For the manufacturing industry, the data stock department's generation AI monitors fluctuations in employees' emotions and proposes countermeasures that will improve the work environment. For example, it makes suggestions to reduce stress and increase motivation. For the retail industry, the data stock department's generation AI monitors fluctuations in customers' emotions and proposes countermeasures that will improve the purchasing experience. For example, it makes suggestions to improve services to increase customer satisfaction. This makes it possible to monitor fluctuations in customer emotions for each industry and propose appropriate countermeasures.

[0071] The data stock department can extract common needs between different industries based on the collected data and develop a general-purpose optimization package. For example, the data stock department extracts common needs between the medical industry and the manufacturing industry and develops a general-purpose optimization package. For example, it provides common functions related to reservation management and inventory management. The data stock department can also extract common needs between the retail industry and the financial industry and develop a general-purpose optimization package. For example, it provides common functions related to customer support and inquiry management. The data stock department can also extract common needs between the education industry and the service industry and develop a general-purpose optimization package. For example, it provides common functions related to schedule management and feedback collection. This makes it possible to extract common needs between different industries and develop a general-purpose optimization package.

[0072] The data stock unit can perform customization taking into account regional characteristics. For example, the data stock unit performs customization taking into account regional characteristics for the medical industry. For example, it provides the optimal response based on the medical system and consultation hours of each region. The data stock unit also performs customization taking into account regional characteristics for the manufacturing industry. For example, it provides the optimal response based on the labor regulations and production schedules of each region. The data stock unit also performs customization taking into account regional characteristics for the retail industry. For example, it provides the optimal response based on the consumer needs and purchasing patterns of each region. This makes it possible to perform customization taking into account regional characteristics.

[0073] The data stock unit uses the emotion estimation function to analyze the emotional tendencies of users in each industry and perform optimization based on that. For example, for the medical industry, the data stock unit's generation AI analyzes the emotional tendencies of patients and provides the optimal response based on that. For example, it generates a response to reduce anxiety about medical results. For the manufacturing industry, the data stock unit's generation AI analyzes the emotional tendencies of employees and provides the optimal response based on that. For example, it generates a response to reduce stress caused by production line problems. For the financial industry, the data stock unit's generation AI analyzes the emotional tendencies of customers and provides the optimal response based on that. For example, it generates a response to reduce anxiety about investments. This makes it possible to analyze the emotional tendencies of users in each industry and perform optimization based on that.

[0074] The data stock department can use the generation AI to analyze usage data and identify new needs and issues. For example, for the medical industry, the generation AI analyzes medical data to identify new needs and issues. For example, it identifies areas for improvement in optimizing medical appointments and patient follow-up. The data stock department can also use the generation AI to analyze production data for the manufacturing industry to identify new needs and issues. For example, it can identify areas for improvement in production line efficiency and quality control. The data stock department can also use the generation AI to analyze sales data for the retail industry to identify new needs and issues. For example, it can identify areas for improvement in inventory management and customer service. This makes it possible to analyze usage data and identify new needs and issues.

[0075] The data stock department provides regular updates to keep up with the latest industry trends and technologies. For example, for the medical industry, the data stock department provides regular updates to keep the generative AI up to date with the latest medical trends and technologies. For example, it adds functions to accommodate new diagnostic methods and medical devices. The data stock department also provides regular updates to keep the generative AI up to date with the latest production technologies and trends for the manufacturing industry. For example, it adds functions to accommodate new production management methods and quality control techniques. The data stock department also provides regular updates to keep the generative AI up to date with the latest consumer trends and technologies for the retail industry. For example, it adds functions to accommodate new purchasing patterns and customer service techniques. This allows for regular updates to keep up with the latest industry trends and technologies.

[0076] The data stock unit can use the emotion estimation function to automatically generate improvement proposals based on the user's emotions. For example, for the medical industry, the data stock unit's generation AI analyzes patient emotion data and automatically generates improvement proposals that will improve the medical experience. For example, it makes proposals to reduce anxiety during medical treatment. For the manufacturing industry, the data stock unit's generation AI analyzes employee emotion data and automatically generates improvement proposals that will improve the work environment. For example, it makes proposals to reduce stress and increase motivation. For the retail industry, the data stock unit's generation AI analyzes customer emotion data and automatically generates improvement proposals that will improve the purchasing experience. For example, it makes proposals to improve services to increase customer satisfaction. This makes it possible to automatically generate improvement proposals based on the user's emotions.

[0077] The Data Stock Department can integrate feedback from different industries and extract common areas for improvement. For example, the Data Stock Department can integrate feedback from the medical and manufacturing industries and extract common areas for improvement. For example, they can share areas for improvement in efficient business processes and quality control. The Data Stock Department can also integrate feedback from the retail and financial industries and extract common areas for improvement. For example, they can share areas for improvement in customer service and risk management. The Data Stock Department can also integrate feedback from the education and service industries and extract common areas for improvement. For example, they can share areas for improvement in schedule management and feedback collection. This makes it possible to integrate feedback from different industries and extract common areas for improvement.

[0078] The data stock unit visualizes the update contents and can explain them to users in an easy-to-understand manner. For example, for the medical industry, the generation AI in the data stock unit visualizes the update contents and explains them to users in an easy-to-understand manner. For example, new medical treatment functions and improvements are shown in graphs and diagrams. The data stock unit also visualizes the update contents for the manufacturing industry, and explains them to users in an easy-to-understand manner. For example, new production management functions and improvements are shown in graphs and diagrams. The data stock unit also visualizes the update contents for the retail industry, and explains them to users in an easy-to-understand manner. For example, new inventory management functions and improvements are shown in graphs and diagrams. This visualizes the update contents and can explain them to users in an easy-to-understand manner.

[0079] The data stock unit uses the emotion estimation function to monitor users' emotional reactions after an update and reflect them in the next update. For example, for the medical industry, the data stock unit uses the generation AI to monitor patients' emotional reactions after an update and reflect them in the next update. For example, it identifies areas for improvement in the medical treatment function and reflects them in the next update. Similarly, for the manufacturing industry, the data stock unit uses the generation AI to monitor employees' emotional reactions after an update and reflects them in the next update. For example, it identifies areas for improvement in the production management function and reflects them in the next update. Similarly, for the retail industry, the data stock unit uses the generation AI to monitor customers' emotional reactions after an update and reflects them in the next update. For example, it identifies areas for improvement in the inventory management function and reflects them in the next update. This makes it possible to monitor users' emotional reactions after an update and reflect them in the next update.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The optimization unit uses the generation AI to automatically learn specialized terminology and phrases for each industry, improving the accuracy of chat. For example, it can automatically learn specialized terminology and phrases for the medical industry to facilitate communication with patients. For example, it can learn medical terminology and phrases related to medical treatment and generate appropriate responses. The optimization unit also uses the generation AI to learn specialized terminology related to production management and inventory management for the manufacturing industry to provide efficient chat responses. For example, it can provide accurate information in response to questions about the status of the production line or inventory status. The optimization unit also uses the generation AI to learn specialized terminology related to financial products and investments for the financial industry to optimize communication with customers. For example, it can generate appropriate responses in response to investment consultations or explanations of financial products. In this way, chat accuracy is improved by learning specialized terminology and phrases for each industry.

[0082] The optimization unit can analyze the business flows of each industry and generate optimal chatbot response scenarios. For example, for the medical industry, the generation AI analyzes the business flows of medical appointments and patient management and generates the optimal response scenario. For example, it provides a smooth response to questions about confirming or changing medical appointments. In addition, for the manufacturing industry, the generation AI analyzes the business flows of production management and quality control and generates efficient response scenarios. For example, it provides appropriate responses to questions about production line troubleshooting and quality inspections. In addition, for the retail industry, the generation AI analyzes the business flows of inventory management and customer support and generates the optimal response scenario. For example, it provides a quick response to questions about checking stock status and return procedures. In this way, optimal response scenarios can be generated by analyzing business flows.

[0083] The optimization unit uses the emotion estimation function to generate responses based on the user's emotions, enabling more personalized communication. For example, in the medical industry, the generation AI estimates the patient's emotions and generates a response that provides a sense of security. For example, a patient who is anxious about the outcome of their medical treatment can receive a message of encouragement and reassurance. In addition, in the manufacturing industry, the generation AI estimates the emotions of employees and generates responses that increase their motivation. For example, an employee who is stressed about a production line problem can receive encouragement and a solution. In addition, in the financial industry, the generation AI estimates the customer's emotions and generates responses that increase trust. For example, a customer who is anxious about investing can receive a message of reassurance regarding risk management and investment strategies. This allows for more personalized communication by generating responses based on the user's emotions.

[0084] The data stock department can link the collected data with other business systems and perform integrated data analysis. For example, for the medical industry, the generation AI links medical data with an electronic medical record system and performs integrated data analysis. For example, it can comprehensively evaluate a patient's health condition based on medical history and test results. For the manufacturing industry, the generation AI also links production data with an ERP system and performs integrated data analysis. For example, it can optimize production efficiency based on production plans and inventory management. For the retail industry, the generation AI also links sales data with a CRM system and performs integrated data analysis. For example, it can improve customer satisfaction based on customer purchase history and feedback. This makes it possible to link the collected data with other business systems and perform integrated data analysis.

[0085] The package generation unit can use generation AI to automatically generate customization packages tailored to the needs of each industry. For example, for the medical industry, generation AI automatically generates a customization package that is linked to patient management and reservation systems. For example, it provides functions for optimizing medical appointments and patient follow-up. In addition, for the manufacturing industry, generation AI automatically generates customization packages specialized for production line monitoring and quality control. For example, it provides improved production efficiency and automated quality inspections. In addition, for the retail industry, generation AI automatically generates customization packages specialized for inventory management and customer support. For example, it provides inventory optimization and improved customer satisfaction. This makes it possible to automatically generate customization packages tailored to the needs of each industry.

[0086] The package generation unit can use the emotion estimation function to customize packages based on the user's emotions. For example, for the medical industry, the generation AI estimates the patient's emotions and customizes a function that provides a sense of security. For example, it provides a response to reduce anxiety about the results of medical treatment. In addition, for the manufacturing industry, the package generation unit estimates the emotions of employees and customizes a function to increase motivation. For example, it provides a response to reduce stress caused by problems on the production line. In addition, for the retail industry, the package generation unit estimates the customer's emotions and customizes a function to improve the purchasing experience. For example, it provides suggestions for improving services to increase customer satisfaction. This makes it possible to customize packages based on the user's emotions.

[0087] The package generation unit can combine packages for different industries to develop hybrid packages for new industries. For example, by combining packages for the medical industry and the manufacturing industry, a hybrid package for a new industry can be developed. For example, a package that integrates medical device production management and patient management can be provided. The package generation unit can also combine packages for the retail industry and the financial industry to develop a hybrid package for a new industry. For example, a package that integrates customer support and risk management can be provided. The package generation unit can also combine packages for the education industry and the service industry to develop a hybrid package for a new industry. For example, a package that integrates schedule management and feedback collection can be provided. In this way, hybrid packages for new industries can be developed by combining packages for different industries.

[0088] In addition to industry-specific packages, the package generation unit can also customize for different company sizes. For example, customization for different company sizes is performed for the medical industry. For example, it provides functions for managing multiple medical departments for large hospitals, and simple patient management functions for small clinics. The package generation unit also customizes for different company sizes for the manufacturing industry. For example, it provides detailed production line monitoring functions for large factories, and basic production management functions for small factories. The package generation unit also customizes for different company sizes for the retail industry. For example, it provides integrated inventory management and customer support functions for large chain stores, and simple inventory management functions for small stores. This makes it possible to customize for different company sizes in addition to industry-specific packages.

[0089] The package generation unit uses the emotion estimation function to monitor package usage and add features based on the user's emotions. For example, for the medical industry, the generation AI monitors patients' emotions and adds a feature to provide a sense of security. For example, it provides a response to reduce anxiety about medical results. In addition, for the manufacturing industry, the package generation unit monitors employees' emotions and adds a feature to increase their motivation. For example, it provides a response to reduce stress caused by production line problems. In addition, for the retail industry, the generation AI monitors customers' emotions and adds a feature to improve the purchasing experience. For example, it provides suggestions for service improvements to increase customer satisfaction. This makes it possible to monitor package usage and add features based on the user's emotions.

[0090] The data stock department can analyze trends for each industry in real time based on the collected data. For example, for the medical industry, the generation AI analyzes patient medical data in real time to identify the latest medical trends. For example, it analyzes trends related to new treatments and diagnostic technologies. The data stock department also analyzes production data for the manufacturing industry in real time to identify the latest production trends. For example, it analyzes trends related to improving production efficiency and quality control. The data stock department also analyzes sales data for the retail industry in real time to identify the latest consumer trends. For example, it analyzes trends related to popular products and purchasing patterns. This allows trends for each industry to be analyzed in real time based on the collected data.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The optimization unit optimizes SB-Chat for each industry. For example, the generation AI adds features to facilitate communication with patients in the medical industry, and enhances production and inventory management functions for the manufacturing industry. The generation AI analyzes the characteristics and needs of each industry and customizes SB-Chat's functions based on that. Step 2: The Data Stock Department stores data collected through corporate use. For example, data on chat history with customers and business processes is collected. The Data Stock Department also analyzes the data stored by the generative AI to identify trends and issues for each industry. Step 3: The package generation unit generates packages for specific industries based on the stored data and its analysis results. For example, the generation AI could provide a chat function linked to patient management and reservation systems for the medical industry, or specialized functions for production line monitoring and quality control for the manufacturing industry.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An optimization department that optimizes SB-Chat for each industry, The Data Stock Department stores data collected through use within the company, a package generation unit that analyzes the data stored by the data storage unit and generates a package for a specific industry. A system characterized by:

2. The optimization unit Using the generative AI, the company will automatically learn industry-specific terminology and phrases to improve chat accuracy.

2. The system of claim 1.

3. The optimization unit Analyze the business flow of each industry and generate optimal chatbot response scenarios 2. The system of claim 1.

4. The optimization unit Generate responses based on the user's emotions to enable more personalized communication 2. The system of claim 1.

5. The data stock unit The collected data will be linked to other business systems to perform integrated data analysis.

2. The system of claim 1.

6. The package generation unit Using the generation AI, customized packages are automatically generated according to the needs of each industry.

2. The system of claim 1.

7. The package generation unit Customize the package based on user sentiment 2. The system of claim 1.

8. The package generation unit Combining packages for different industries to develop new hybrid packages for those industries 2. The system of claim 1.

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