system

Abstract

The system according to this embodiment aims to generate highly reliable training data and support business operations. [Solution] The system according to the embodiment comprises a learning data generation unit, a business support unit, and a provision unit. The learning data generation unit generates learning data. The business support unit provides business support based on the learning data generated by the learning data generation unit. The provision unit provides the results of the business supported by the business support unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, reliable learning data necessary for business has not been sufficiently generated, and there is room for improvement.

[0005] The system according to the embodiment aims to generate reliable learning data and support business.

Means for Solving the Problems

[0006] The system according to the embodiment includes a learning data generation unit, a business support unit, and a providing unit. The learning data generation unit generates learning data. The business support unit supports business based on the learning data generated by the learning data generation unit. The providing unit provides the result of the business supported by the business support unit.

Effects of the Invention

[0007] The system according to this embodiment can generate highly reliable training data and support business operations. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The data utilization system according to an embodiment of the present invention is a system for organizations such as companies, local governments, schools, and hospitals to efficiently utilize data necessary for their operations. This data utilization system aims to improve operational efficiency and accuracy by generating training data based on highly reliable company-owned data, rather than ambiguous information from the web. This system consists of the following steps using a generating AI. First, the training data is updated to be reliable. Basic data includes university-level knowledge, AI literacy, laws, general knowledge, and multilingual support. Furthermore, the training data is created by adding the company's industry data and company-owned data. This allows companies to operate their own dedicated generating AI. This system brings about the following effects. First, improved customer support can be expected. It becomes possible to respond to customer inquiries quickly and accurately, improving the quality of customer support. An increase in orders and sales is also expected. By providing accurate information and advice through dialogue with customers, purchasing intent can be increased. Furthermore, cost reduction can be achieved. It is possible to efficiently process tasks such as automated responses and FAQ responses while reducing human resources and time. Finally, advanced data analysis and extraction of customer insights become possible. By processing large amounts of customer interaction data and gaining insights into customer trends and needs, data-driven decision-making becomes possible. This system improves the efficiency and accuracy of a company's operations and contributes to increased revenue. For example, a data utilization system generates training data based on data held by a company and provides support for operations. For instance, in customer support, it can provide quick and accurate responses to customer inquiries. In the order and sales process, it can make suggestions tailored to customer needs and increase purchasing intent. Furthermore, it can automate and streamline business processes to reduce costs. For example, by introducing FAQ support and automated response systems, human resources can be reduced and operations can be made more efficient. In this way, a data utilization system can efficiently support a company's operations and improve the accuracy of those operations.

[0029] The data utilization system according to the embodiment comprises a learning data generation unit, a business support unit, and a provision unit. The learning data generation unit generates learning data. The learning data generation unit generates highly reliable learning data, for example, using a generation AI. The learning data generation unit can generate learning data based on basic data such as university-level knowledge, AI literacy, laws, general knowledge, and multilingual support. For example, the learning data generation unit can generate learning data including specialized knowledge based on university-level knowledge. The learning data generation unit can also generate learning data on basic AI concepts and algorithms based on AI literacy data. Furthermore, the learning data generation unit can generate learning data on specific legal fields and regulations based on legal data. It can also generate learning data on social common sense and cultural knowledge based on general knowledge data. It can also generate learning data that supports multiple languages ​​based on multilingual support data. The business support unit supports business operations based on the generated learning data. The business support unit can, for example, support customer support. Based on the generated learning data, the business support unit can provide quick and accurate responses to customer inquiries. For example, the Business Support Department can analyze customer inquiries and provide appropriate answers. It can also support order taking and sales. Based on the generated learning data, the Business Support Department can make suggestions tailored to customer needs and increase purchasing intent. For example, it can analyze customer purchase history and suggest relevant products and services. Furthermore, it can support cost reduction. Based on the generated learning data, the Business Support Department can automate and streamline business processes. For example, by implementing FAQ support and automated response systems, the Business Support Department can reduce human resources and improve operational efficiency. The Service Provider Department provides the results of the operations supported by the Business Support Department. For example, the Service Provider Department can provide the results through reports and dashboards. Based on the data generated by the Business Support Department, the Service Provider Department can provide results in a visually easy-to-understand format.For example, the data provisioning unit can display the progress and results of tasks as graphs and charts. Furthermore, the unit can provide real-time results through a notification function. For instance, the unit can support quick responses by notifying users of important tasks and alerts. As a result, the data utilization system according to this embodiment can improve the efficiency and accuracy of tasks by supporting and providing results based on highly reliable training data.

[0030] The learning data generation unit generates learning data. For example, the learning data generation unit generates highly reliable learning data using a generative AI. The generative AI utilizes natural language processing and machine learning techniques to extract useful information from vast datasets and format it as learning data. Specifically, the generative AI can generate learning data based on basic data such as university-level knowledge, AI literacy, laws, general knowledge, and multilingual support. For example, when generating learning data that includes specialized knowledge based on university-level knowledge, the generative AI collects data from reliable sources such as academic papers, textbooks, and specialized books, analyzes it, and generates learning data. Also, when generating learning data on basic AI concepts and algorithms based on AI literacy data, the generative AI creates comprehensive learning data using a dataset that includes the history of AI, basic algorithms, and the latest research results. Furthermore, when generating learning data on specific legal fields or regulations based on legal data, the generative AI collects data from legal databases, case law collections, and legal commentaries, analyzes it, and generates learning data that systematically organizes legal knowledge. When generating training data on social common sense and cultural knowledge based on general knowledge data, the generating AI refers to news articles, encyclopedias, cultural materials, etc., to create training data that covers a wide range of knowledge. When generating training data for multiple languages ​​based on multilingual support data, the generating AI performs accurate and natural translations while considering the grammar, vocabulary, and cultural background of each language to generate multilingual training data. As a result, the training data generation unit can efficiently generate highly reliable training data across diverse fields, improving the overall performance of the system.

[0031] The Business Support Department provides support for business operations based on the generated training data. For example, the Business Support Department can support customer support. Specifically, based on the generated training data, the Business Support Department can provide quick and accurate responses to customer inquiries. For example, to analyze customer inquiries and provide appropriate answers, the Business Support Department uses natural language processing technology to understand the inquiries and extract the best answers from relevant training data. The Business Support Department can also support order taking and sales. Based on the generated training data, the Business Support Department can make suggestions tailored to customer needs and increase purchasing intent. For example, to analyze customer purchase history and propose relevant products and services, the Business Support Department uses machine learning algorithms to analyze customer preferences and purchasing patterns and make optimal suggestions. Furthermore, the Business Support Department can also support cost reduction. Based on the generated training data, the Business Support Department can automate and streamline business processes. For example, by introducing FAQ support and automated response systems, the Business Support Department can reduce human resources and improve operational efficiency by utilizing AI chatbots and automation tools to automate repetitive tasks. This allows the Business Support Department to assist with customer service and streamline business processes, thereby improving the company's operational efficiency and customer satisfaction.

[0032] The Service Provider provides the results of operations supported by the Business Support Department. For example, the Service Provider can provide results through reports and dashboards. Specifically, it can provide results in a visually easy-to-understand format based on data generated by the Business Support Department. For instance, it can use data visualization tools to transform complex data into an intuitively understandable format to display operational progress and results as graphs and charts. The Service Provider can also provide real-time operational results through notification functions. For example, it can use push notifications and email notifications to immediately communicate information to stakeholders to support quick responses by notifying them of important operational events and alerts. Furthermore, the Service Provider provides customizable reporting capabilities, allowing users to freely select the information they need to generate reports. This enables the Service Provider to effectively deliver operational results and support stakeholders in making quick and appropriate decisions. By collaborating with the Business Support Department and consistently providing accurate information based on the latest data, the Service Provider can enhance the overall reliability and usefulness of the system.

[0033] The learning data generation unit can generate learning data based on basic data such as university-level knowledge, AI literacy, laws, general knowledge, and multilingual support. For example, the learning data generation unit can generate learning data including specialized knowledge based on university-level knowledge. For example, the learning data generation unit can collect data on specific academic fields and generate learning data based on that data. Furthermore, the learning data generation unit can generate learning data on basic AI concepts and algorithms based on AI literacy data. For example, the learning data generation unit can collect data on basic AI concepts and algorithms and generate learning data based on that data. In addition, the learning data generation unit can generate learning data on specific legal fields and regulations based on legal data. For example, the learning data generation unit can collect data on specific legal fields and regulations and generate learning data based on that data. It can also generate learning data on social common sense and cultural knowledge based on general knowledge data. For example, the learning data generation unit can collect data on social common sense and cultural knowledge and generate learning data based on that data. It can also generate learning data compatible with multiple languages ​​based on multilingual support data. For example, the learning data generation unit can collect data corresponding to multiple languages ​​and generate learning data based on it. This improves the accuracy of business support by generating learning data based on highly reliable basic data. Some or all of the above-described processes in the learning data generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the learning data generation unit can input basic data such as university-level knowledge, AI literacy, laws, general knowledge, and multilingual support into a generation AI and have the generation AI perform the generation of learning data.

[0034] The learning data generation unit can generate learning data by adding company industry data and internally owned data. For example, the learning data generation unit can generate learning data including industry-specific statistical data and industry reports based on company industry data. For example, the learning data generation unit can collect statistical data related to a specific industry and generate learning data based on it. The learning data generation unit can also generate learning data including customer data, sales data, and business history based on company internally owned data. For example, the learning data generation unit can collect company customer data and generate learning data based on it. Furthermore, the learning data generation unit can collect company sales data and generate learning data based on it. It can also generate learning data including business history and performance data based on data related to business history. For example, the learning data generation unit can collect company business history and generate learning data based on it. This allows for the generation of company-specific learning data by adding company industry data and internally owned data. Some or all of the above processing in the learning data generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the learning data generation unit can input industry data and proprietary data of a company into the generating AI, and have the generating AI perform the generation of learning data.

[0035] The Business Support Department can assist customer support based on the generated training data. For example, the Business Support Department can provide quick and accurate responses to customer inquiries. Based on the generated training data, the Business Support Department can analyze customer inquiries and provide appropriate answers. For example, the Business Support Department can analyze customer inquiries using natural language processing technology and generate the optimal answer. The Business Support Department can also provide answers to similar inquiries based on the customer's past inquiry history. For example, the Business Support Department can search the database for the customer's past inquiry history and provide answers to similar inquiries. Furthermore, the Business Support Department can provide answers tailored to individual needs based on customer attribute information. For example, the Business Support Department can analyze customer attribute information and generate answers tailored to individual needs. As a result, the quality of customer support is improved by assisting customer support based on the generated training data. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or without AI. For example, the Business Support Department can input the generated training data into AI and have the AI ​​perform customer support.

[0036] The Business Support Department can support order taking and sales based on the generated learning data. For example, the Business Support Department can make suggestions tailored to customer needs and increase their willingness to purchase. Based on the generated learning data, the Business Support Department can analyze customer purchase history and suggest relevant products and services. For example, the Business Support Department can search a database for customer purchase history and suggest relevant products and services. The Business Support Department can also make suggestions tailored to individual needs based on customer attribute information. For example, the Business Support Department can analyze customer attribute information and generate suggestions tailored to individual needs. Furthermore, the Business Support Department can improve the content of its suggestions based on customer feedback. For example, the Business Support Department can collect customer feedback and improve the content of its suggestions based on it. As a result, by supporting order taking and sales based on the generated learning data, an increase in sales can be expected. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or not using AI. For example, the Business Support Department can input the generated learning data into AI and have the AI ​​perform order taking and sales support.

[0037] The Business Support Department can assist in cost reduction based on the generated learning data. For example, the Business Support Department can automate and streamline business processes. Based on the generated learning data, the Business Support Department can automate and streamline business processes. For example, by introducing FAQ response and automated response systems, the Business Support Department can reduce human resources and improve operational efficiency. The Business Support Department can also assist in improving business processes. Based on the generated learning data, the Business Support Department can identify areas for improvement in business processes and propose improvement measures. For example, the Business Support Department can analyze business process data, identify bottlenecks, and propose improvement measures. Furthermore, the Business Support Department can also propose measures for cost reduction. Based on the generated learning data, the Business Support Department can propose measures for cost reduction. For example, the Business Support Department can propose measures to reduce expenses. In this way, by supporting cost reduction based on the generated learning data, operational efficiency can be improved. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or without AI. For example, the business support department can input the generated learning data into the AI ​​and have the AI ​​perform cost reduction support.

[0038] The Business Support Department can perform data analysis based on the generated training data and extract customer insights. For example, the Business Support Department can process large amounts of customer conversation data to gain insights into customer trends and needs. Based on the generated training data, the Business Support Department can analyze customer behavior patterns and purchasing trends. For example, the Business Support Department can search for customer behavior patterns in a database and analyze customer trends. The Business Support Department can also gain insights into customer needs based on customer feedback. For example, the Business Support Department can collect customer feedback and use it to gain insights into customer needs. Furthermore, the Business Support Department can make data-driven decisions based on customer insights. Based on the generated training data, the Business Support Department can extract customer insights and make decisions based on them. For example, the Business Support Department can formulate marketing strategies based on customer insights. In this way, by performing data analysis based on the generated training data, customer trends and needs can be grasped. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or not using AI. For example, the business support department can input the generated training data into the AI ​​and have the AI ​​perform data analysis.

[0039] The learning data generation unit can analyze a company's past business data and select the optimal dataset. For example, the learning data generation unit can analyze a company's past sales data and select a dataset that contributes to increased sales. The learning data generation unit can use generative AI to analyze a company's past business data and select the optimal dataset. For example, the learning data generation unit can collect a company's past sales data and select a dataset that contributes to increased sales based on that data. The learning data generation unit can also analyze a company's past customer feedback and select a dataset that contributes to increased customer satisfaction. For example, the learning data generation unit can collect a company's past customer feedback and select a dataset that contributes to increased customer satisfaction based on that data. Furthermore, the learning data generation unit can analyze a company's past project data and select a dataset that contributes to increased project success rates. For example, the learning data generation unit can collect a company's past project data and select a dataset that contributes to increased project success rates based on that data. In this way, the optimal dataset can be selected by analyzing a company's past business data. Some or all of the above processing in the learning data generation unit may be performed using, for example, generative AI, or without using generative AI. For example, the learning data generation unit can input a company's past business data into the generating AI and have the generating AI select the optimal dataset.

[0040] The learning data generation unit can filter data based on the company's current business situation and goals. For example, the learning data generation unit can filter data related to sales improvement based on the company's current sales target. The learning data generation unit can use a generating AI to filter data based on the company's current business situation and goals. For example, the learning data generation unit can filter data related to sales improvement based on the company's current sales target. The learning data generation unit can also filter data related to improving customer satisfaction based on the company's current customer satisfaction target. For example, the learning data generation unit can filter data related to improving customer satisfaction based on the company's current customer satisfaction target. Furthermore, the learning data generation unit can also filter data related to improving project success rates based on the company's current project success rate target. For example, the learning data generation unit can filter data related to improving project success rates based on the company's current project success rate target. This allows for the generation of highly relevant data by filtering data based on the company's current business situation and goals. Some or all of the above processing in the learning data generation unit may be performed using a generating AI, for example, or without using a generating AI. For example, the learning data generation unit can input the company's current business situation and goals into the generating AI, and have the generating AI perform data filtering.

[0041] The learning data generation unit can prioritize the generation of highly relevant data, taking into account the geographical location information of companies. For example, the learning data generation unit can prioritize the generation of region-specific market data based on the location of companies. The learning data generation unit can prioritize the generation of highly relevant data, taking into account the geographical location information of companies, using generation AI. For example, the learning data generation unit can collect region-specific market data based on the location of companies and generate learning data based on it. The learning data generation unit can also prioritize the generation of region-specific customer data based on the location of companies. For example, the learning data generation unit can collect region-specific customer data based on the location of companies and generate learning data based on it. Furthermore, the learning data generation unit can also prioritize the generation of region-specific competitor data based on the location of companies. For example, the learning data generation unit can collect region-specific competitor data based on the location of companies and generate learning data based on it. This allows for the priority generation of region-specific data by taking into account the geographical location information of companies. Some or all of the above-described processes in the learning data generation unit may be performed using, for example, generation AI, or without generation AI. For example, the learning data generation unit can input the geographical location information of companies into the generation AI, and have the generation AI perform the generation of highly relevant data.

[0042] The learning data generation unit can analyze a company's social media activities and generate relevant data. For example, the learning data generation unit can analyze a company's social media posts and generate relevant data based on customer responses. The learning data generation unit can use generative AI to analyze a company's social media activities and generate relevant data. For example, the learning data generation unit can collect a company's social media posts, analyze customer responses based on them, and generate relevant data. The learning data generation unit can also analyze the trends of a company's social media followers and generate relevant data based on the interests of those followers. For example, the learning data generation unit can collect the trends of a company's social media followers, analyze the interests of those followers based on them, and generate relevant data. Furthermore, the learning data generation unit can analyze the results of a company's social media campaigns and generate relevant data based on success factors. For example, the learning data generation unit can collect the results of a company's social media campaigns, analyze success factors based on them, and generate relevant data. This allows for the generation of highly relevant data by analyzing a company's social media activities. Some or all of the above-described processes in the learning data generation unit may be performed using, for example, generative AI, or without using generative AI. For example, the learning data generation unit can input a company's social media activities into the generating AI and have the generating AI perform the generation of data related to that AI.

[0043] The Business Support Department can analyze a company's past business history and select the optimal support method. For example, the Business Support Department can analyze a company's past project history and propose the methodologies of successful projects as support methods. The Business Support Department can use AI to analyze a company's past business history and select the optimal support method. For example, the Business Support Department can collect a company's past project history and, based on that, propose the methodologies of successful projects as support methods. The Business Support Department can also analyze a company's past customer service history and propose the methods that resulted in high customer satisfaction as support methods. For example, the Business Support Department can collect a company's past customer service history and, based on that, propose the methods that resulted in high customer satisfaction as support methods. Furthermore, the Business Support Department can analyze a company's past sales history and propose the methods that contributed to increased sales as support methods. For example, the Business Support Department can collect a company's past sales history and, based on that, propose the methods that contributed to increased sales as support methods. In this way, by analyzing a company's past business history, the optimal support method can be selected. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or without AI. For example, the business support department can input a company's past business history into AI and have the AI ​​select the most suitable support method.

[0044] The Business Support Department can customize support methods based on a company's current business situation. For example, the Business Support Department can customize support methods that contribute to increasing sales based on a company's current sales situation. The Business Support Department can use AI to customize support methods based on a company's current business situation. For example, the Business Support Department can collect data on a company's current sales situation and use that data to customize support methods that contribute to increasing sales. The Business Support Department can also customize support methods that contribute to improving customer satisfaction based on a company's current customer satisfaction situation. For example, the Business Support Department can collect data on a company's current customer satisfaction situation and use that data to customize support methods that contribute to improving customer satisfaction. Furthermore, the Business Support Department can customize support methods that contribute to improving project success rates based on a company's current project progress. For example, the Business Support Department can collect data on a company's current project progress and use that data to customize support methods that contribute to improving project success rates. By customizing support methods based on a company's current business situation, the effectiveness of business support is improved. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or without AI. For example, the business support department can input the company's current business situation into the AI ​​and have the AI ​​customize the means of support.

[0045] The Business Support Department can select the optimal support method by considering the geographical location of a company. For example, the Business Support Department can select a support method that utilizes region-specific market data based on the company's location. The Business Support Department can use AI to select the optimal support method by considering the geographical location of a company. For example, the Business Support Department can collect region-specific market data based on the company's location and select a support method based on that data. The Business Support Department can also select a support method that utilizes region-specific customer data based on the company's location. For example, the Business Support Department can collect region-specific customer data based on the company's location and select a support method based on that data. Furthermore, the Business Support Department can also select a support method that utilizes region-specific competitor data based on the company's location. For example, the Business Support Department can collect region-specific competitor data based on the company's location and select a support method based on that data. In this way, by considering the geographical location of a company, a region-specific support method can be selected. Some or all of the above processing in the Business Support Department may be performed using AI, for example, or without AI. For example, the Business Support Department can input the company's geographical location information into AI and have AI select the optimal support method.

[0046] The Business Support Department can analyze a company's social media activities and propose support measures. For example, the Business Support Department can analyze a company's social media posts and propose support measures based on customer responses. The Business Support Department can use AI to analyze a company's social media activities and propose support measures. For example, the Business Support Department can collect a company's social media posts, analyze customer responses based on them, and propose support measures. The Business Support Department can also analyze the trends of a company's social media followers and propose support measures based on the interests of the followers. For example, the Business Support Department can collect the trends of a company's social media followers, analyze the interests of the followers based on them, and propose support measures. Furthermore, the Business Support Department can analyze the results of a company's social media campaigns and propose support measures based on the success factors. For example, the Business Support Department can collect the results of a company's social media campaigns, analyze the success factors based on them, and propose support measures. In this way, by analyzing a company's social media activities, it is possible to propose highly relevant support measures. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or without AI. For example, the business support department can input a company's social media activities into AI and have the AI ​​suggest and execute support measures.

[0047] The service department can analyze a company's past service history to select the optimal service method. For example, the service department can analyze a company's past service history and select successful service methods. The service department can use AI to analyze a company's past service history and select the optimal service method. For example, the service department can collect a company's past service history and use it to select successful service methods. The service department can also analyze a company's past customer feedback and select service methods that resulted in high customer satisfaction. For example, the service department can collect a company's past customer feedback and use it to select service methods that resulted in high customer satisfaction. Furthermore, the service department can analyze a company's past sales history and select service methods that contributed to increased sales. For example, the service department can collect a company's past sales history and use it to select service methods that contributed to increased sales. In this way, the optimal service method can be selected by analyzing a company's past service history. Some or all of the above processes in the service department may be performed using AI, for example, or without AI. For example, the service department can input a company's past service history into AI and have AI select the optimal service method.

[0048] The service provider can customize the means of service delivery based on the company's current business situation. For example, the service provider can customize the means of service delivery that contribute to increased sales based on the company's current sales situation. The service provider can use AI to customize the means of service delivery based on the company's current business situation. For example, the service provider can collect the company's current sales situation and use that to customize the means of service delivery that contribute to increased sales. The service provider can also customize the means of service delivery that contribute to increased customer satisfaction based on the company's current customer satisfaction situation. For example, the service provider can collect the company's current customer satisfaction situation and use that to customize the means of service delivery that contribute to increased customer satisfaction. Furthermore, the service provider can also customize the means of service delivery that contribute to increased project success rates based on the company's current project progress. For example, the service provider can collect the company's current project progress and use that to customize the means of service delivery that contribute to increased project success rates. By customizing the means of service delivery based on the company's current business situation, the effectiveness of business support is improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the company's current operational status into the AI ​​and have the AI ​​customize the means of service delivery.

[0049] The service provider can select the optimal service delivery method by considering the geographical location information of the company. For example, the service provider can select a service delivery method that utilizes region-specific market data based on the company's location. The service provider can use AI to select the optimal service delivery method by considering the geographical location information of the company. For example, the service provider can collect region-specific market data based on the company's location and select a service delivery method based on that data. The service provider can also select a service delivery method that utilizes region-specific customer data based on the company's location. For example, the service provider can collect region-specific customer data based on the company's location and select a service delivery method based on that data. Furthermore, the service provider can also select a service delivery method that utilizes region-specific competitor data based on the company's location. For example, the service provider can collect region-specific competitor data based on the company's location and select a service delivery method based on that data. In this way, by considering the geographical location information of the company, a region-specific service delivery method can be selected. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the geographical location information of the company into AI and have AI select the optimal service delivery method.

[0050] The service provider can analyze a company's social media activities and propose means of delivery. For example, the service provider can analyze a company's social media posts and propose means of delivery based on customer responses. The service provider can use AI to analyze a company's social media activities and propose means of delivery. For example, the service provider can collect a company's social media posts, analyze customer responses based on them, and propose means of delivery. The service provider can also analyze the trends of a company's social media followers and propose means of delivery based on the interests of the followers. For example, the service provider can collect the trends of a company's social media followers, analyze the interests of the followers based on them, and propose means of delivery. Furthermore, the service provider can analyze the results of a company's social media campaigns and propose means of delivery based on the success factors. For example, the service provider can collect the results of a company's social media campaigns, analyze the success factors based on them, and propose means of delivery. In this way, by analyzing a company's social media activities, it is possible to propose highly relevant means of delivery. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input a company's social media activities into AI and have the AI ​​execute the proposal of means of delivery.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The data utilization system can analyze a company's past business data and select the optimal dataset. For example, it can analyze a company's past sales data and select a dataset that contributes to increased sales. It can also analyze a company's past customer feedback and select a dataset that contributes to improved customer satisfaction. Furthermore, it can analyze a company's past project data and select a dataset that contributes to improved project success rates. In this way, the optimal dataset can be selected by analyzing a company's past business data. Some or all of the above processing in the learning data generation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the learning data generation unit can input a company's past business data into a generating AI and have the generating AI select the optimal dataset.

[0053] The data utilization system can filter data based on a company's current business situation and goals. For example, it can filter data related to increasing sales based on a company's current sales target. It can also filter data related to improving customer satisfaction based on a company's current customer satisfaction target. Furthermore, it can filter data related to improving project success rates based on a company's current project success rate target. In this way, by filtering data based on a company's current business situation and goals, highly relevant data can be generated. Some or all of the above processing in the learning data generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the learning data generation unit can input the company's current business situation and goals into the generation AI and have the generation AI perform data filtering.

[0054] The data utilization system can prioritize the generation of highly relevant data by considering the geographical location information of a company. For example, it can prioritize the generation of region-specific market data based on the company's location. It can also prioritize the generation of region-specific customer data based on the company's location. Furthermore, it can prioritize the generation of region-specific competitor data based on the company's location. In this way, by considering the geographical location information of a company, region-specific data can be prioritized. Some or all of the above processing in the learning data generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the learning data generation unit can input the geographical location information of a company into a generation AI and cause the generation AI to perform the generation of highly relevant data.

[0055] The data utilization system can analyze a company's social media activities and generate relevant data. For example, it can analyze a company's social media posts and generate relevant data based on customer responses. It can also analyze the trends of a company's social media followers and generate relevant data based on their interests. Furthermore, it can analyze the results of a company's social media campaigns and generate relevant data based on success factors. In this way, by analyzing a company's social media activities, highly relevant data can be generated. Some or all of the above processing in the learning data generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the learning data generation unit can input a company's social media activities into a generation AI and have the generation AI generate relevant data.

[0056] The data utilization system can analyze a company's past business history and select the most suitable support method. For example, it can analyze a company's past project history and propose the methodologies used in successful projects as support methods. It can also analyze a company's past customer service history and propose methods that resulted in high customer satisfaction. Furthermore, it can analyze a company's past sales history and propose methods that contributed to increased sales as support methods. In this way, the most suitable support method can be selected by analyzing a company's past business history. Some or all of the above processes in the business support department may be performed using AI, for example, or not. For example, the business support department can input a company's past business history into AI and have the AI ​​select the most suitable support method.

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

[0058] Step 1: The learning data generation unit generates learning data. For example, it generates highly reliable learning data using a generation AI. The learning data generation unit can generate learning data based on basic data such as university-level knowledge, AI literacy, laws, general knowledge, and multilingual support. Step 2: The Business Support Department provides support for operations based on the generated learning data. For example, it supports customer support, providing quick and accurate responses to customer inquiries. It also supports order taking and sales, making proposals tailored to customer needs and increasing purchasing intent. Furthermore, it can support cost reduction and automate and streamline business processes. Step 3: The service provider delivers the results of the tasks supported by the business support department. For example, they provide the results of the tasks in a visually easy-to-understand format through reports and dashboards. They can also provide the results of the tasks in real time through notification functions.

[0059] (Example of form 2) The data utilization system according to an embodiment of the present invention is a system for organizations such as companies, local governments, schools, and hospitals to efficiently utilize data necessary for their operations. This data utilization system aims to improve operational efficiency and accuracy by generating training data based on highly reliable company-owned data, rather than ambiguous information from the web. This system consists of the following steps using a generating AI. First, the training data is updated to be reliable. Basic data includes university-level knowledge, AI literacy, laws, general knowledge, and multilingual support. Furthermore, the training data is created by adding the company's industry data and company-owned data. This allows companies to operate their own dedicated generating AI. This system brings about the following effects. First, improved customer support can be expected. It becomes possible to respond to customer inquiries quickly and accurately, improving the quality of customer support. An increase in orders and sales is also expected. By providing accurate information and advice through dialogue with customers, purchasing intent can be increased. Furthermore, cost reduction can be achieved. It is possible to efficiently process tasks such as automated responses and FAQ responses while reducing human resources and time. Finally, advanced data analysis and extraction of customer insights become possible. By processing large amounts of customer interaction data and gaining insights into customer trends and needs, data-driven decision-making becomes possible. This system improves the efficiency and accuracy of a company's operations and contributes to increased revenue. For example, a data utilization system generates training data based on data held by a company and provides support for operations. For instance, in customer support, it can provide quick and accurate responses to customer inquiries. In the order and sales process, it can make suggestions tailored to customer needs and increase purchasing intent. Furthermore, it can automate and streamline business processes to reduce costs. For example, by introducing FAQ support and automated response systems, human resources can be reduced and operations can be made more efficient. In this way, a data utilization system can efficiently support a company's operations and improve the accuracy of those operations.

[0060] The data utilization system according to the embodiment comprises a learning data generation unit, a business support unit, and a provision unit. The learning data generation unit generates learning data. The learning data generation unit generates highly reliable learning data, for example, using a generation AI. The learning data generation unit can generate learning data based on basic data such as university-level knowledge, AI literacy, laws, general knowledge, and multilingual support. For example, the learning data generation unit can generate learning data including specialized knowledge based on university-level knowledge. The learning data generation unit can also generate learning data on basic AI concepts and algorithms based on AI literacy data. Furthermore, the learning data generation unit can generate learning data on specific legal fields and regulations based on legal data. It can also generate learning data on social common sense and cultural knowledge based on general knowledge data. It can also generate learning data that supports multiple languages ​​based on multilingual support data. The business support unit supports business operations based on the generated learning data. The business support unit can, for example, support customer support. Based on the generated learning data, the business support unit can provide quick and accurate responses to customer inquiries. For example, the Business Support Department can analyze customer inquiries and provide appropriate answers. It can also support order taking and sales. Based on the generated learning data, the Business Support Department can make suggestions tailored to customer needs and increase purchasing intent. For example, it can analyze customer purchase history and suggest relevant products and services. Furthermore, it can support cost reduction. Based on the generated learning data, the Business Support Department can automate and streamline business processes. For example, by implementing FAQ support and automated response systems, the Business Support Department can reduce human resources and improve operational efficiency. The Service Provider Department provides the results of the operations supported by the Business Support Department. For example, the Service Provider Department can provide the results through reports and dashboards. Based on the data generated by the Business Support Department, the Service Provider Department can provide results in a visually easy-to-understand format.For example, the data provisioning unit can display the progress and results of tasks as graphs and charts. Furthermore, the unit can provide real-time results through a notification function. For instance, the unit can support quick responses by notifying users of important tasks and alerts. As a result, the data utilization system according to this embodiment can improve the efficiency and accuracy of tasks by supporting and providing results based on highly reliable training data.

[0061] The learning data generation unit generates learning data. For example, the learning data generation unit generates highly reliable learning data using a generative AI. The generative AI utilizes natural language processing and machine learning techniques to extract useful information from vast datasets and format it as learning data. Specifically, the generative AI can generate learning data based on basic data such as university-level knowledge, AI literacy, laws, general knowledge, and multilingual support. For example, when generating learning data that includes specialized knowledge based on university-level knowledge, the generative AI collects data from reliable sources such as academic papers, textbooks, and specialized books, analyzes it, and generates learning data. Also, when generating learning data on basic AI concepts and algorithms based on AI literacy data, the generative AI creates comprehensive learning data using a dataset that includes the history of AI, basic algorithms, and the latest research results. Furthermore, when generating learning data on specific legal fields or regulations based on legal data, the generative AI collects data from legal databases, case law collections, and legal commentaries, analyzes it, and generates learning data that systematically organizes legal knowledge. When generating training data on social common sense and cultural knowledge based on general knowledge data, the generating AI refers to news articles, encyclopedias, cultural materials, etc., to create training data that covers a wide range of knowledge. When generating training data for multiple languages ​​based on multilingual support data, the generating AI performs accurate and natural translations while considering the grammar, vocabulary, and cultural background of each language to generate multilingual training data. As a result, the training data generation unit can efficiently generate highly reliable training data across diverse fields, improving the overall performance of the system.

[0062] The Business Support Department provides support for business operations based on the generated training data. For example, the Business Support Department can support customer support. Specifically, based on the generated training data, the Business Support Department can provide quick and accurate responses to customer inquiries. For example, to analyze customer inquiries and provide appropriate answers, the Business Support Department uses natural language processing technology to understand the inquiries and extract the best answers from relevant training data. The Business Support Department can also support order taking and sales. Based on the generated training data, the Business Support Department can make suggestions tailored to customer needs and increase purchasing intent. For example, to analyze customer purchase history and propose relevant products and services, the Business Support Department uses machine learning algorithms to analyze customer preferences and purchasing patterns and make optimal suggestions. Furthermore, the Business Support Department can also support cost reduction. Based on the generated training data, the Business Support Department can automate and streamline business processes. For example, by introducing FAQ support and automated response systems, the Business Support Department can reduce human resources and improve operational efficiency by utilizing AI chatbots and automation tools to automate repetitive tasks. This allows the Business Support Department to assist with customer service and streamline business processes, thereby improving the company's operational efficiency and customer satisfaction.

[0063] The Service Provider provides the results of operations supported by the Business Support Department. For example, the Service Provider can provide results through reports and dashboards. Specifically, it can provide results in a visually easy-to-understand format based on data generated by the Business Support Department. For instance, it can use data visualization tools to transform complex data into an intuitively understandable format to display operational progress and results as graphs and charts. The Service Provider can also provide real-time operational results through notification functions. For example, it can use push notifications and email notifications to immediately communicate information to stakeholders to support quick responses by notifying them of important operational events and alerts. Furthermore, the Service Provider provides customizable reporting capabilities, allowing users to freely select the information they need to generate reports. This enables the Service Provider to effectively deliver operational results and support stakeholders in making quick and appropriate decisions. By collaborating with the Business Support Department and consistently providing accurate information based on the latest data, the Service Provider can enhance the overall reliability and usefulness of the system.

[0064] The learning data generation unit can generate learning data based on basic data such as university-level knowledge, AI literacy, laws, general knowledge, and multilingual support. For example, the learning data generation unit can generate learning data including specialized knowledge based on university-level knowledge. For example, the learning data generation unit can collect data on specific academic fields and generate learning data based on that data. Furthermore, the learning data generation unit can generate learning data on basic AI concepts and algorithms based on AI literacy data. For example, the learning data generation unit can collect data on basic AI concepts and algorithms and generate learning data based on that data. In addition, the learning data generation unit can generate learning data on specific legal fields and regulations based on legal data. For example, the learning data generation unit can collect data on specific legal fields and regulations and generate learning data based on that data. It can also generate learning data on social common sense and cultural knowledge based on general knowledge data. For example, the learning data generation unit can collect data on social common sense and cultural knowledge and generate learning data based on that data. It can also generate learning data compatible with multiple languages ​​based on multilingual support data. For example, the learning data generation unit can collect data corresponding to multiple languages ​​and generate learning data based on it. This improves the accuracy of business support by generating learning data based on highly reliable basic data. Some or all of the above-described processes in the learning data generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the learning data generation unit can input basic data such as university-level knowledge, AI literacy, laws, general knowledge, and multilingual support into a generation AI and have the generation AI perform the generation of learning data.

[0065] The learning data generation unit can generate learning data by adding company industry data and internally owned data. For example, the learning data generation unit can generate learning data including industry-specific statistical data and industry reports based on company industry data. For example, the learning data generation unit can collect statistical data related to a specific industry and generate learning data based on it. The learning data generation unit can also generate learning data including customer data, sales data, and business history based on company internally owned data. For example, the learning data generation unit can collect company customer data and generate learning data based on it. Furthermore, the learning data generation unit can collect company sales data and generate learning data based on it. It can also generate learning data including business history and performance data based on data related to business history. For example, the learning data generation unit can collect company business history and generate learning data based on it. This allows for the generation of company-specific learning data by adding company industry data and internally owned data. Some or all of the above processing in the learning data generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the learning data generation unit can input industry data and proprietary data of a company into the generating AI, and have the generating AI perform the generation of learning data.

[0066] The Business Support Department can assist customer support based on the generated training data. For example, the Business Support Department can provide quick and accurate responses to customer inquiries. Based on the generated training data, the Business Support Department can analyze customer inquiries and provide appropriate answers. For example, the Business Support Department can analyze customer inquiries using natural language processing technology and generate the optimal answer. The Business Support Department can also provide answers to similar inquiries based on the customer's past inquiry history. For example, the Business Support Department can search the database for the customer's past inquiry history and provide answers to similar inquiries. Furthermore, the Business Support Department can provide answers tailored to individual needs based on customer attribute information. For example, the Business Support Department can analyze customer attribute information and generate answers tailored to individual needs. As a result, the quality of customer support is improved by assisting customer support based on the generated training data. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or without AI. For example, the Business Support Department can input the generated training data into AI and have the AI ​​perform customer support.

[0067] The Business Support Department can support order taking and sales based on the generated learning data. For example, the Business Support Department can make suggestions tailored to customer needs and increase their willingness to purchase. Based on the generated learning data, the Business Support Department can analyze customer purchase history and suggest relevant products and services. For example, the Business Support Department can search a database for customer purchase history and suggest relevant products and services. The Business Support Department can also make suggestions tailored to individual needs based on customer attribute information. For example, the Business Support Department can analyze customer attribute information and generate suggestions tailored to individual needs. Furthermore, the Business Support Department can improve the content of its suggestions based on customer feedback. For example, the Business Support Department can collect customer feedback and improve the content of its suggestions based on it. As a result, by supporting order taking and sales based on the generated learning data, an increase in sales can be expected. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or not using AI. For example, the Business Support Department can input the generated learning data into AI and have the AI ​​perform order taking and sales support.

[0068] The Business Support Department can assist in cost reduction based on the generated learning data. For example, the Business Support Department can automate and streamline business processes. Based on the generated learning data, the Business Support Department can automate and streamline business processes. For example, by introducing FAQ response and automated response systems, the Business Support Department can reduce human resources and improve operational efficiency. The Business Support Department can also assist in improving business processes. Based on the generated learning data, the Business Support Department can identify areas for improvement in business processes and propose improvement measures. For example, the Business Support Department can analyze business process data, identify bottlenecks, and propose improvement measures. Furthermore, the Business Support Department can also propose measures for cost reduction. Based on the generated learning data, the Business Support Department can propose measures for cost reduction. For example, the Business Support Department can propose measures to reduce expenses. In this way, by supporting cost reduction based on the generated learning data, operational efficiency can be improved. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or without AI. For example, the business support department can input the generated learning data into the AI ​​and have the AI ​​perform cost reduction support.

[0069] The Business Support Department can perform data analysis based on the generated training data and extract customer insights. For example, the Business Support Department can process large amounts of customer conversation data to gain insights into customer trends and needs. Based on the generated training data, the Business Support Department can analyze customer behavior patterns and purchasing trends. For example, the Business Support Department can search for customer behavior patterns in a database and analyze customer trends. The Business Support Department can also gain insights into customer needs based on customer feedback. For example, the Business Support Department can collect customer feedback and use it to gain insights into customer needs. Furthermore, the Business Support Department can make data-driven decisions based on customer insights. Based on the generated training data, the Business Support Department can extract customer insights and make decisions based on them. For example, the Business Support Department can formulate marketing strategies based on customer insights. In this way, by performing data analysis based on the generated training data, customer trends and needs can be grasped. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or not using AI. For example, the business support department can input the generated training data into the AI ​​and have the AI ​​perform data analysis.

[0070] The learning data generation unit can estimate the user's emotions and adjust the timing of learning data generation based on the estimated emotions. For example, if the user is stressed, the generation AI will delay the generation of learning data and wait until the user is relaxed. The learning data generation unit can use the generation AI to estimate the user's emotions and adjust the timing of learning data generation. For example, the learning data generation unit can analyze the user's facial expressions and voice data to estimate the user's emotions. Furthermore, if the user is focused, the generation AI will rapidly generate learning data, taking advantage of the user's concentration. For example, the learning data generation unit can analyze the user's biometric data to estimate the user's level of concentration. In addition, if the user is tired, the generation AI will temporarily suspend the generation of learning data, allowing the user time to rest. For example, the learning data generation unit can analyze the user's heart rate and skin electrical activity to estimate the user's fatigue level. This allows the user's burden to be reduced by adjusting the timing of learning data generation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the training data generation unit may be performed using AI, or not using AI. For example, the training data generation unit can input user emotion data into the AI ​​and have the AI ​​adjust the timing of training data generation.

[0071] The learning data generation unit can analyze a company's past business data and select the optimal dataset. For example, the learning data generation unit can analyze a company's past sales data and select a dataset that contributes to increased sales. The learning data generation unit can use generative AI to analyze a company's past business data and select the optimal dataset. For example, the learning data generation unit can collect a company's past sales data and select a dataset that contributes to increased sales based on that data. The learning data generation unit can also analyze a company's past customer feedback and select a dataset that contributes to increased customer satisfaction. For example, the learning data generation unit can collect a company's past customer feedback and select a dataset that contributes to increased customer satisfaction based on that data. Furthermore, the learning data generation unit can analyze a company's past project data and select a dataset that contributes to increased project success rates. For example, the learning data generation unit can collect a company's past project data and select a dataset that contributes to increased project success rates based on that data. In this way, the optimal dataset can be selected by analyzing a company's past business data. Some or all of the above processing in the learning data generation unit may be performed using, for example, generative AI, or without using generative AI. For example, the learning data generation unit can input a company's past business data into the generating AI and have the generating AI select the optimal dataset.

[0072] The learning data generation unit can filter data based on the company's current business situation and goals. For example, the learning data generation unit can filter data related to sales improvement based on the company's current sales target. The learning data generation unit can use a generating AI to filter data based on the company's current business situation and goals. For example, the learning data generation unit can filter data related to sales improvement based on the company's current sales target. The learning data generation unit can also filter data related to improving customer satisfaction based on the company's current customer satisfaction target. For example, the learning data generation unit can filter data related to improving customer satisfaction based on the company's current customer satisfaction target. Furthermore, the learning data generation unit can also filter data related to improving project success rates based on the company's current project success rate target. For example, the learning data generation unit can filter data related to improving project success rates based on the company's current project success rate target. This allows for the generation of highly relevant data by filtering data based on the company's current business situation and goals. Some or all of the above processing in the learning data generation unit may be performed using a generating AI, for example, or without using a generating AI. For example, the learning data generation unit can input the company's current business situation and goals into the generating AI, and have the generating AI perform data filtering.

[0073] The learning data generation unit can estimate the user's emotions and determine the priority of the learning data to be generated based on the estimated user emotions. For example, if the user is stressed, the generating AI will prioritize generating data that has a relaxing effect. The learning data generation unit can use the generating AI to estimate the user's emotions and determine the priority of the learning data to be generated. For example, the learning data generation unit can analyze the user's facial expressions and voice data to estimate the user's emotions. Furthermore, if the user is concentrating, the generating AI can prioritize generating data that contributes to improving work efficiency. For example, the learning data generation unit can analyze the user's biometric data to estimate the user's level of concentration. In addition, if the user is tired, the generating AI can prioritize generating data that encourages rest. For example, the learning data generation unit can analyze the user's heart rate and skin electrical activity to estimate the user's level of fatigue. In this way, by determining the priority of learning data according to the user's emotions, data that meets the user's needs can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generating AI. The generative AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the training data generation unit may be performed using AI, or not using AI. For example, the training data generation unit may input user sentiment data into the AI ​​and have the AI ​​determine the priority of the training data.

[0074] The learning data generation unit can prioritize the generation of highly relevant data, taking into account the geographical location information of companies. For example, the learning data generation unit can prioritize the generation of region-specific market data based on the location of companies. The learning data generation unit can prioritize the generation of highly relevant data, taking into account the geographical location information of companies, using generation AI. For example, the learning data generation unit can collect region-specific market data based on the location of companies and generate learning data based on it. The learning data generation unit can also prioritize the generation of region-specific customer data based on the location of companies. For example, the learning data generation unit can collect region-specific customer data based on the location of companies and generate learning data based on it. Furthermore, the learning data generation unit can also prioritize the generation of region-specific competitor data based on the location of companies. For example, the learning data generation unit can collect region-specific competitor data based on the location of companies and generate learning data based on it. This allows for the priority generation of region-specific data by taking into account the geographical location information of companies. Some or all of the above-described processes in the learning data generation unit may be performed using, for example, generation AI, or without generation AI. For example, the learning data generation unit can input the geographical location information of companies into the generation AI, and have the generation AI perform the generation of highly relevant data.

[0075] The learning data generation unit can analyze a company's social media activities and generate relevant data. For example, the learning data generation unit can analyze a company's social media posts and generate relevant data based on customer responses. The learning data generation unit can use generative AI to analyze a company's social media activities and generate relevant data. For example, the learning data generation unit can collect a company's social media posts, analyze customer responses based on them, and generate relevant data. The learning data generation unit can also analyze the trends of a company's social media followers and generate relevant data based on the interests of those followers. For example, the learning data generation unit can collect the trends of a company's social media followers, analyze the interests of those followers based on them, and generate relevant data. Furthermore, the learning data generation unit can analyze the results of a company's social media campaigns and generate relevant data based on success factors. For example, the learning data generation unit can collect the results of a company's social media campaigns, analyze success factors based on them, and generate relevant data. This allows for the generation of highly relevant data by analyzing a company's social media activities. Some or all of the above-described processes in the learning data generation unit may be performed using, for example, generative AI, or without using generative AI. For example, the learning data generation unit can input a company's social media activities into the generating AI and have the generating AI perform the generation of data related to that AI.

[0076] The Business Support Department can estimate the user's emotions and adjust the methods of business support based on those estimated emotions. For example, if the user is feeling stressed, the Business Support Department can use AI to reduce the workload of business support and suggest relaxing tasks. The Business Support Department can use AI to estimate the user's emotions and adjust the methods of business support. For example, the Business Support Department can analyze the user's facial expressions and voice data to estimate the user's emotions. Furthermore, if the user is concentrating, the Business Support Department can use AI to increase the number of business support tasks and suggest ways to work more efficiently. For example, the Business Support Department can analyze the user's biometric data to estimate the user's level of concentration. In addition, if the user is tired, the Business Support Department can use AI to reduce the number of business support tasks and allow time for rest. For example, the Business Support Department can analyze the user's heart rate and skin electrical activity to estimate the user's fatigue level. This allows the Business Support Department to reduce the burden on the user by adjusting the methods of business support according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. The generation AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the business support department may be performed using AI, for example, or not using AI. For example, the business support department may input user sentiment data into the AI ​​and have the AI ​​adjust the methods of business support.

[0077] The Business Support Department can analyze a company's past business history and select the optimal support method. For example, the Business Support Department can analyze a company's past project history and propose the methodologies of successful projects as support methods. The Business Support Department can use AI to analyze a company's past business history and select the optimal support method. For example, the Business Support Department can collect a company's past project history and, based on that, propose the methodologies of successful projects as support methods. The Business Support Department can also analyze a company's past customer service history and propose the methods that resulted in high customer satisfaction as support methods. For example, the Business Support Department can collect a company's past customer service history and, based on that, propose the methods that resulted in high customer satisfaction as support methods. Furthermore, the Business Support Department can analyze a company's past sales history and propose the methods that contributed to increased sales as support methods. For example, the Business Support Department can collect a company's past sales history and, based on that, propose the methods that contributed to increased sales as support methods. In this way, by analyzing a company's past business history, the optimal support method can be selected. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or without AI. For example, the business support department can input a company's past business history into AI and have the AI ​​select the most suitable support method.

[0078] The Business Support Department can customize support methods based on a company's current business situation. For example, the Business Support Department can customize support methods that contribute to increasing sales based on a company's current sales situation. The Business Support Department can use AI to customize support methods based on a company's current business situation. For example, the Business Support Department can collect data on a company's current sales situation and use that data to customize support methods that contribute to increasing sales. The Business Support Department can also customize support methods that contribute to improving customer satisfaction based on a company's current customer satisfaction situation. For example, the Business Support Department can collect data on a company's current customer satisfaction situation and use that data to customize support methods that contribute to improving customer satisfaction. Furthermore, the Business Support Department can customize support methods that contribute to improving project success rates based on a company's current project progress. For example, the Business Support Department can collect data on a company's current project progress and use that data to customize support methods that contribute to improving project success rates. By customizing support methods based on a company's current business situation, the effectiveness of business support is improved. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or without AI. For example, the business support department can input the company's current business situation into the AI ​​and have the AI ​​customize the means of support.

[0079] The Business Support Department can estimate a user's emotions and determine the priority of business support based on those emotions. For example, if a user is stressed, the Business Support Department can prioritize tasks that promote relaxation using AI. The Business Support Department can use AI to estimate a user's emotions and determine the priority of business support. For example, the Business Support Department can analyze a user's facial expressions and voice data to estimate their emotions. Furthermore, if a user is focused, the Business Support Department can prioritize tasks that contribute to improving work efficiency using AI. For example, the Business Support Department can analyze a user's biometric data to estimate their level of concentration. In addition, if a user is tired, the Business Support Department can prioritize tasks that encourage rest using AI. For example, the Business Support Department can analyze a user's heart rate and skin electrical activity to estimate their fatigue level. This allows for support tailored to the user's needs by prioritizing business support according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the business support department may be performed using AI, or not using AI. For example, the business support department can input user sentiment data into the AI ​​and have the AI ​​determine the priorities of business support.

[0080] The Business Support Department can select the optimal support method by considering the geographical location of a company. For example, the Business Support Department can select a support method that utilizes region-specific market data based on the company's location. The Business Support Department can use AI to select the optimal support method by considering the geographical location of a company. For example, the Business Support Department can collect region-specific market data based on the company's location and select a support method based on that data. The Business Support Department can also select a support method that utilizes region-specific customer data based on the company's location. For example, the Business Support Department can collect region-specific customer data based on the company's location and select a support method based on that data. Furthermore, the Business Support Department can also select a support method that utilizes region-specific competitor data based on the company's location. For example, the Business Support Department can collect region-specific competitor data based on the company's location and select a support method based on that data. In this way, by considering the geographical location of a company, a region-specific support method can be selected. Some or all of the above processing in the Business Support Department may be performed using AI, for example, or without AI. For example, the Business Support Department can input the company's geographical location information into AI and have AI select the optimal support method.

[0081] The Business Support Department can analyze a company's social media activities and propose support measures. For example, the Business Support Department can analyze a company's social media posts and propose support measures based on customer responses. The Business Support Department can use AI to analyze a company's social media activities and propose support measures. For example, the Business Support Department can collect a company's social media posts, analyze customer responses based on them, and propose support measures. The Business Support Department can also analyze the trends of a company's social media followers and propose support measures based on the interests of the followers. For example, the Business Support Department can collect the trends of a company's social media followers, analyze the interests of the followers based on them, and propose support measures. Furthermore, the Business Support Department can analyze the results of a company's social media campaigns and propose support measures based on the success factors. For example, the Business Support Department can collect the results of a company's social media campaigns, analyze the success factors based on them, and propose support measures. In this way, by analyzing a company's social media activities, it is possible to propose highly relevant support measures. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or without AI. For example, the business support department can input a company's social media activities into AI and have the AI ​​suggest and execute support measures.

[0082] The information provider can estimate the user's emotions and adjust the way the information is presented based on those emotions. For example, if the user is nervous, the provider can provide a simple and highly visual presentation. The provider can use AI to estimate the user's emotions and adjust the way the information is presented. For example, the provider can analyze the user's facial expressions and voice data to estimate the user's emotions. Furthermore, if the user is relaxed, the provider can provide a presentation that includes detailed information. For example, the provider can analyze the user's biometric data to estimate the user's level of relaxation. In addition, if the user is in a hurry, the provider can provide a concise presentation. For example, the provider can analyze the user's heart rate and skin electrical activity to estimate the user's level of urgency. By adjusting the presentation of information according to the user's emotions, it becomes possible to provide information that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generating AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the providing unit may be performed using AI, or not using AI. For example, the providing unit may input user emotion data into the AI ​​and have the AI ​​adjust how the information is presented.

[0083] The service department can analyze a company's past service history to select the optimal service method. For example, the service department can analyze a company's past service history and select successful service methods. The service department can use AI to analyze a company's past service history and select the optimal service method. For example, the service department can collect a company's past service history and use it to select successful service methods. The service department can also analyze a company's past customer feedback and select service methods that resulted in high customer satisfaction. For example, the service department can collect a company's past customer feedback and use it to select service methods that resulted in high customer satisfaction. Furthermore, the service department can analyze a company's past sales history and select service methods that contributed to increased sales. For example, the service department can collect a company's past sales history and use it to select service methods that contributed to increased sales. In this way, the optimal service method can be selected by analyzing a company's past service history. Some or all of the above processes in the service department may be performed using AI, for example, or without AI. For example, the service department can input a company's past service history into AI and have AI select the optimal service method.

[0084] The service provider can customize the means of service delivery based on the company's current business situation. For example, the service provider can customize the means of service delivery that contribute to increased sales based on the company's current sales situation. The service provider can use AI to customize the means of service delivery based on the company's current business situation. For example, the service provider can collect the company's current sales situation and use that to customize the means of service delivery that contribute to increased sales. The service provider can also customize the means of service delivery that contribute to increased customer satisfaction based on the company's current customer satisfaction situation. For example, the service provider can collect the company's current customer satisfaction situation and use that to customize the means of service delivery that contribute to increased customer satisfaction. Furthermore, the service provider can also customize the means of service delivery that contribute to increased project success rates based on the company's current project progress. For example, the service provider can collect the company's current project progress and use that to customize the means of service delivery that contribute to increased project success rates. By customizing the means of service delivery based on the company's current business situation, the effectiveness of business support is improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the company's current operational status into the AI ​​and have the AI ​​customize the means of service delivery.

[0085] The information provider can estimate the user's emotions and prioritize the information to be provided based on those emotions. For example, if the user is stressed, the information provider will prioritize providing information that promotes relaxation. The information provider can use AI to estimate the user's emotions and prioritize the information to be provided. For example, the information provider can analyze the user's facial expressions and voice data to estimate the user's emotions. Furthermore, if the user is concentrating, the information provider can prioritize providing information that contributes to improving work efficiency. For example, the information provider can analyze the user's biometric data to estimate the user's level of concentration. In addition, if the user is tired, the information provider can prioritize providing information that encourages rest. For example, the information provider can analyze the user's heart rate and skin electrical activity to estimate the user's level of fatigue. This allows for the provision of information that meets the user's needs by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can input user emotion data into AI and have AI determine the priority of information.

[0086] The service provider can select the optimal service delivery method by considering the geographical location information of the company. For example, the service provider can select a service delivery method that utilizes region-specific market data based on the company's location. The service provider can use AI to select the optimal service delivery method by considering the geographical location information of the company. For example, the service provider can collect region-specific market data based on the company's location and select a service delivery method based on that data. The service provider can also select a service delivery method that utilizes region-specific customer data based on the company's location. For example, the service provider can collect region-specific customer data based on the company's location and select a service delivery method based on that data. Furthermore, the service provider can also select a service delivery method that utilizes region-specific competitor data based on the company's location. For example, the service provider can collect region-specific competitor data based on the company's location and select a service delivery method based on that data. In this way, by considering the geographical location information of the company, a region-specific service delivery method can be selected. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the geographical location information of the company into AI and have AI select the optimal service delivery method.

[0087] The service provider can analyze a company's social media activities and propose means of delivery. For example, the service provider can analyze a company's social media posts and propose means of delivery based on customer responses. The service provider can use AI to analyze a company's social media activities and propose means of delivery. For example, the service provider can collect a company's social media posts, analyze customer responses based on them, and propose means of delivery. The service provider can also analyze the trends of a company's social media followers and propose means of delivery based on the interests of the followers. For example, the service provider can collect the trends of a company's social media followers, analyze the interests of the followers based on them, and propose means of delivery. Furthermore, the service provider can analyze the results of a company's social media campaigns and propose means of delivery based on the success factors. For example, the service provider can collect the results of a company's social media campaigns, analyze the success factors based on them, and propose means of delivery. In this way, by analyzing a company's social media activities, it is possible to propose highly relevant means of delivery. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input a company's social media activities into AI and have the AI ​​execute the proposal of means of delivery.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The data utilization system can estimate the user's emotions and adjust the way data is provided based on the estimated emotions. For example, if the user is stressed, the system can display data in a simple and easy-to-understand manner. If the user is relaxed, it can provide detailed data. Furthermore, if the user is in a hurry, it can provide concise data. This enables data provision tailored to the user's emotions, reducing the user's burden. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data provision unit may be performed using AI, or not using AI. For example, the data provision unit can input user emotion data into AI and have AI adjust how the information is presented.

[0090] The data utilization system can analyze a company's past business data and select the optimal dataset. For example, it can analyze a company's past sales data and select a dataset that contributes to increased sales. It can also analyze a company's past customer feedback and select a dataset that contributes to improved customer satisfaction. Furthermore, it can analyze a company's past project data and select a dataset that contributes to improved project success rates. In this way, the optimal dataset can be selected by analyzing a company's past business data. Some or all of the above processing in the learning data generation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the learning data generation unit can input a company's past business data into a generating AI and have the generating AI select the optimal dataset.

[0091] The data utilization system can estimate the user's emotions and adjust the timing of training data generation based on the estimated emotions. For example, if the user is stressed, the generating AI can delay the generation of training data and wait until the user relaxes. If the user is focused, the generating AI can quickly generate training data, taking advantage of the user's concentration. Furthermore, if the user is tired, the generating AI can temporarily suspend the generation of training data, allowing the user time to rest. In this way, the burden on the user can be reduced by adjusting the timing of training data generation according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the training data generation unit may be performed using, for example, AI, or not using AI. For example, the training data generation unit can input the user's emotion data into the AI ​​and have the AI ​​adjust the timing of training data generation.

[0092] The data utilization system can filter data based on a company's current business situation and goals. For example, it can filter data related to increasing sales based on a company's current sales target. It can also filter data related to improving customer satisfaction based on a company's current customer satisfaction target. Furthermore, it can filter data related to improving project success rates based on a company's current project success rate target. In this way, by filtering data based on a company's current business situation and goals, highly relevant data can be generated. Some or all of the above processing in the learning data generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the learning data generation unit can input the company's current business situation and goals into the generation AI and have the generation AI perform data filtering.

[0093] The data utilization system can estimate the user's emotions and determine the priority of training data to be generated based on the estimated emotions. For example, if the user is stressed, the generating AI can prioritize generating data that has a relaxing effect. Also, if the user is focused, the generating AI can prioritize generating data that contributes to improving work efficiency. Furthermore, if the user is tired, the generating AI can prioritize generating data that encourages rest. In this way, by determining the priority of training data according to the user's emotions, data that meets the user's needs can be generated. Emotion estimation is achieved using, for example, an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the training data generation unit may be performed using, for example, AI, or not using AI. For example, the training data generation unit can input the user's emotion data into the AI ​​and have the AI ​​determine the priority of the training data.

[0094] The data utilization system can prioritize the generation of highly relevant data by considering the geographical location information of a company. For example, it can prioritize the generation of region-specific market data based on the company's location. It can also prioritize the generation of region-specific customer data based on the company's location. Furthermore, it can prioritize the generation of region-specific competitor data based on the company's location. In this way, by considering the geographical location information of a company, region-specific data can be prioritized. Some or all of the above processing in the learning data generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the learning data generation unit can input the geographical location information of a company into a generation AI and cause the generation AI to perform the generation of highly relevant data.

[0095] The data utilization system can analyze a company's social media activities and generate relevant data. For example, it can analyze a company's social media posts and generate relevant data based on customer responses. It can also analyze the trends of a company's social media followers and generate relevant data based on their interests. Furthermore, it can analyze the results of a company's social media campaigns and generate relevant data based on success factors. In this way, by analyzing a company's social media activities, highly relevant data can be generated. Some or all of the above processing in the learning data generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the learning data generation unit can input a company's social media activities into a generation AI and have the generation AI generate relevant data.

[0096] The data utilization system can estimate the user's emotions and adjust the method of work support based on the estimated emotions. For example, if the user is stressed, the AI ​​can reduce the workload of work support and suggest relaxing tasks. If the user is focused, the AI ​​can increase the work support tasks and suggest ways to work more efficiently. Furthermore, if the user is tired, the AI ​​can reduce the work support tasks and allow time for rest. In this way, the burden on the user can be reduced by adjusting the method of work support according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the work support department may be performed using, for example, AI, or not using AI. For example, the work support department can input user emotion data into the AI ​​and have the AI ​​perform adjustments to the method of work support.

[0097] The data utilization system can analyze a company's past business history and select the most suitable support method. For example, it can analyze a company's past project history and propose the methodologies used in successful projects as support methods. It can also analyze a company's past customer service history and propose methods that resulted in high customer satisfaction. Furthermore, it can analyze a company's past sales history and propose methods that contributed to increased sales as support methods. In this way, the most suitable support method can be selected by analyzing a company's past business history. Some or all of the above processes in the business support department may be performed using AI, for example, or not. For example, the business support department can input a company's past business history into AI and have the AI ​​select the most suitable support method.

[0098] The data utilization system can estimate the user's emotions and determine the priority of work support based on those emotions. For example, if the user is stressed, the AI ​​can prioritize tasks that have a relaxing effect. If the user is focused, the AI ​​can prioritize tasks that contribute to improving work efficiency. Furthermore, if the user is tired, the AI ​​can prioritize tasks that encourage rest. By determining the priority of work support according to the user's emotions, it becomes possible to provide support that meets the user's needs. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the work support department may be performed using, for example, AI, or not using AI. For example, the work support department can input user emotion data into the AI ​​and have the AI ​​determine the priority of work support.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The learning data generation unit generates learning data. For example, it generates highly reliable learning data using a generation AI. The learning data generation unit can generate learning data based on basic data such as university-level knowledge, AI literacy, laws, general knowledge, and multilingual support. Step 2: The Business Support Department provides support for operations based on the generated learning data. For example, it supports customer support, providing quick and accurate responses to customer inquiries. It also supports order taking and sales, making proposals tailored to customer needs and increasing purchasing intent. Furthermore, it can support cost reduction and automate and streamline business processes. Step 3: The service provider delivers the results of the tasks supported by the business support department. For example, they provide the results of the tasks in a visually easy-to-understand format through reports and dashboards. They can also provide the results of the tasks in real time through notification functions.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] Each of the multiple elements described above, including the learning data generation unit, business support unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the learning data generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates highly reliable learning data. The business support unit is implemented by the control unit 46A of the smart device 14 and supports business operations based on the generated learning data. The provision unit is implemented by the output device 40 of the smart device 14 and provides the results of the business operations. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the learning data generation unit, the business support unit, and the provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning data generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates highly reliable learning data. The business support unit is implemented, for example, by the control unit 46A of the smart glasses 214 and supports business operations based on the generated learning data. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214 and provides the results of the business operations. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the learning data generation unit, the business support unit, and the provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the learning data generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates highly reliable learning data. The business support unit is implemented by the control unit 46A of the headset terminal 314 and supports business operations based on the generated learning data. The provision unit is implemented by the display 343 of the headset terminal 314 and provides the results of the business operations. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the learning data generation unit, the business support unit, and the provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the learning data generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates highly reliable learning data. The business support unit is implemented by the control unit 46A of the robot 414 and supports business operations based on the generated learning data. The provision unit is implemented by the speaker 240 of the robot 414 and provides the results of the business operations. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0154] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) A training data generation unit that generates training data, A business support unit that supports operations based on the learning data generated by the aforementioned learning data generation unit, The system comprises a provisioning unit that provides the results of operations supported by the aforementioned business support unit. A system characterized by the following features. (Note 2) The aforementioned learning data generation unit, Training data is generated based on basic data such as university-level knowledge, AI literacy, laws, general knowledge, and multilingual support. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning data generation unit, We generate training data by adding industry data and our own proprietary data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned Business Support Department, Support customer support based on the generated training data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned Business Support Department, Support order taking and sales based on the generated training data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned Business Support Department, Supporting cost reduction based on the generated training data The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned Business Support Department, Based on the generated training data, data analysis is performed to extract customer insights. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning data generation unit, It estimates the user's emotions and adjusts the timing of training data generation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning data generation unit, Analyze a company's past business data and select the optimal dataset. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning data generation unit, Filter data based on the company's current operational status and goals. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning data generation unit, It estimates the user's emotions and determines the priority of the training data to be generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning data generation unit, Prioritize generating highly relevant data by considering the geographical location information of companies. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning data generation unit, Analyze a company's social media activities and generate relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned Business Support Department, It estimates the user's emotions and adjusts the method of providing business support based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned Business Support Department, We analyze a company's past business history to select the most suitable support method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned Business Support Department, Customize the means of support based on the company's current operational situation. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned Business Support Department, It estimates the user's emotions and determines the priority of business support based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned Business Support Department, We select the most suitable support method by considering the geographical location of the company. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned Business Support Department, We analyze companies' social media activities and propose ways to support them. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, We analyze a company's past delivery history to select the optimal delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, Customize the delivery method based on the company's current operational status. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, We will select the optimal delivery method considering the company's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We analyze companies' social media activities and propose delivery methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A training data generation unit that generates training data, A business support unit that supports operations based on the learning data generated by the aforementioned learning data generation unit, The system comprises a provisioning unit that provides the results of operations supported by the aforementioned business support unit. A system characterized by the following features.

2. The aforementioned learning data generation unit, Training data is generated based on basic data such as university-level knowledge, AI literacy, laws, general knowledge, and multilingual support. The system according to feature 1.

3. The aforementioned learning data generation unit, We generate training data by adding industry data and our own proprietary data. The system according to feature 1.

4. The aforementioned Business Support Department, Support customer support based on the generated training data. The system according to feature 1.

5. The aforementioned Business Support Department, Support order taking and sales based on the generated training data. The system according to feature 1.

6. The aforementioned Business Support Department, Supporting cost reduction based on the generated training data The system according to feature 1.

7. The aforementioned Business Support Department, Based on the generated training data, data analysis is performed to extract customer insights. The system according to feature 1.

8. The aforementioned learning data generation unit, It estimates the user's emotions and adjusts the timing of training data generation based on the estimated user emotions. The system according to feature 1.

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