system

Abstract

The system according to this embodiment aims to automatically generate optimal training content for each company and manage and provide it on the cloud. [Solution] The system according to the embodiment comprises a collection unit, a learning unit, a generation unit, a management unit, and a provision unit. The collection unit collects business content and information of companies. The learning unit learns the information collected by the collection unit. The generation unit automatically generates training content based on the information learned by the learning unit. The management unit manages the training content generated by the generation unit on the cloud. The provision unit provides the training content managed by the management unit so that companies can use it.

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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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, there is a problem that it is difficult to efficiently generate and manage optimal training content for each company.

[0005] The system according to the embodiment aims to automatically generate optimal training content for each company and manage and provide it on the cloud.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a learning unit, a generation unit, a management unit, and a provision unit. The collection unit collects information on a company's business operations. The learning unit learns from the information collected by the collection unit. The generation unit automatically generates training content based on the information learned by the learning unit. The management unit manages the training content generated by the generation unit on the cloud. The provision unit provides the training content managed by the management unit so that companies can use it. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate optimal training content for each company and manage and provide it on the cloud. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The cloud system according to an embodiment of the present invention is a system for streamlining new employee training in companies. This cloud system provides a mechanism for automatically generating training content tailored to each company using multimodal AI. This makes it possible for small and medium-sized enterprises (SMEs) that do not have specialized personnel or organizations to conduct effective training. First, the cloud system collects the company's business operations and information and trains the AI. Next, the AI ​​automatically generates training content based on the collected information. For example, it can generate training content related to business operations such as accounting, finance, SCM, and CRM. The generated training content is managed on the cloud and provided on a monthly subscription basis. This cloud system consists of the following steps: First, the cloud system collects the company's business operations and information. Next, the AI ​​is trained on the collected information. Based on the trained information, the AI ​​automatically generates training content tailored to each company. The generated training content is managed on the cloud and can be easily accessed and used by companies. This cloud system allows companies to solve problems related to securing training resources and costs. In addition, the digitalization of training progresses, improving the quality of training. Furthermore, because the AI ​​automatically generates training content, it becomes easier to update training, and training based on the latest information can be provided. For example, in customer support training, AI learns from inquiry FAQ data and generates training content. The generated training content includes role-playing and scoring functions, enabling practical training. This allows new staff to efficiently acquire skills and adapt to their work quickly. This cloud system solves the problem of insufficient training resources for companies and improves the quality of training. Furthermore, the digitalization of training improves corporate productivity and staff retention rates. In addition, because AI automatically generates training content, it becomes easy to update training, and training based on the latest information can be provided. In this way, the cloud system can solve the problems of securing training resources and costs for companies and improve the quality of training.

[0029] The cloud system according to this embodiment comprises a collection unit, a learning unit, a generation unit, a management unit, and a provision unit. The collection unit collects information on a company's business operations. For example, the collection unit can collect sales data, customer information, financial data, etc. The collection unit can collect information using methods such as database extraction and surveys. For example, the collection unit can extract sales data from a company's database and collect customer information. The collection unit can also conduct surveys to collect information on a company's business operations. The learning unit trains an AI with the information collected by the collection unit. For example, the learning unit can train the AI ​​using machine learning algorithms such as deep learning and supervised learning. The learning unit preprocesses the collected information and trains the AI. For example, the learning unit cleans the collected data and inputs it into the AI. The learning unit can also extract data features and train the AI. The generation unit automatically generates training content based on the information trained by the learning unit. For example, the generation unit can generate training content using algorithms such as natural language generation and template-based generation. The generation unit automatically generates company-specific training content based on learned information. For example, the generation unit generates training content related to business operations such as accounting, finance, SCM, and CRM. It can also learn from customer support inquiry FAQ data and generate training content. The management unit manages the training content generated by the generation unit on the cloud. The management unit can manage the training content using cloud services, for example. The management unit stores the generated training content on the cloud and makes it accessible to companies. For example, the management unit can store the training content using cloud storage and make it easily accessible to companies. The management unit can also implement data security measures to safely manage the training content. The delivery unit provides the training content managed by the management unit so that companies can use it. The delivery unit can provide the training content through web portals or mobile apps, for example.The service provider offers training content in a way that companies can easily access and use. For example, the service provider offers training content through a web portal, allowing companies to access it from their browsers. Alternatively, the service provider offers training content through a mobile app, allowing companies to access it from their smartphones and tablets. As a result, the cloud system according to this embodiment can solve problems related to securing training resources and costs for companies, and improve the quality of training.

[0030] The data collection department collects information about a company's operations. For example, it can collect sales data, customer information, and financial data. The department can collect information using methods such as database extraction and surveys. Specifically, it extracts sales data from a company's database and collects customer information. Sales data includes sales figures, transaction history, and sales volume by product; this data is useful for detailed analysis of a company's sales activities. Customer information includes basic customer information (name, address, contact information, etc.), purchase history, customer preferences, and feedback, allowing for an understanding of customer behavior patterns and needs. The data collection department can also conduct surveys to collect information about a company's operations. Surveys provide detailed insights into employees' work content, processes, operational challenges, and areas for improvement. This allows for the collection of specific data to improve the company's operational efficiency and productivity. Furthermore, the data collection department can also collect information from external data sources. For example, it can collect industry market trends, competitor information, and economic indicators to aid in strategic planning and decision-making. This allows the data collection unit to integrate internal and external company data, enabling comprehensive information gathering.

[0031] The learning unit trains the AI ​​with the information collected by the collection unit. The learning unit can train the AI ​​using machine learning algorithms such as deep learning or supervised learning. Specifically, the learning unit preprocesses the collected information before training the AI. Preprocessing includes data cleaning, normalization, and feature extraction. For example, the learning unit cleans the collected data, removing missing and outlier values. It also normalizes the data, unifying data at different scales. Furthermore, the learning unit can extract data features and train the AI. Statistical methods and data mining techniques can be used for feature extraction. For example, sales trends and seasonality can be extracted from sales data, and customer purchasing patterns and preferences can be extracted from customer information. This allows the AI ​​to learn about a company's operations and customer behavior patterns, acquiring knowledge useful for future predictions and decision-making. Additionally, the learning unit can improve the accuracy and performance of the AI ​​by continuously training it with collected information. For example, by retraining the AI ​​each time new data is collected, it can perform predictions and analyses based on the latest information. This allows the learning department to respond quickly to the company's business operations and customer needs, and to provide highly accurate analysis based on the latest information at all times.

[0032] The generation unit automatically generates training content based on information learned by the learning unit. The generation unit can generate training content using algorithms such as natural language generation and template-based generation. Specifically, the generation unit automatically generates company-specific training content based on learned information. For example, the generation unit generates training content related to business operations such as accounting, finance, SCM, and CRM. Accounting and finance training content includes basic accounting principles, how to read financial statements, budget management, and cost reduction methods. SCM (supply chain management) training content includes basic supply chain concepts, inventory management, and logistics optimization. CRM (customer relationship management) training content includes understanding customer needs, improving customer satisfaction, and strengthening customer loyalty. Furthermore, the generation unit can learn from FAQ data in customer support training and generate training content. For example, this could include common inquiries, troubleshooting methods, and basic customer service skills. This allows the generation unit to automatically generate customized training content tailored to a company's business operations and needs, contributing to securing training resources and reducing costs. Furthermore, the generation unit can continuously update the generated training content to keep up with the latest information and trends. For example, it can automatically update training content in conjunction with the introduction of new business processes or technologies, ensuring that employees always acquire the latest knowledge and skills. In this way, the generation unit can improve the quality of corporate training and contribute to employee skill development and improved work efficiency.

[0033] The management department manages the training content generated by the generation department on the cloud. The management department can manage training content using cloud services, for example. Specifically, the management department stores the generated training content on the cloud and makes it accessible to the company. For example, the management department can store training content using cloud storage and make it easily accessible to the company. Cloud storage provides data redundancy and backup functions, ensuring secure storage and rapid access to training content. The management department can also implement data security measures to securely manage training content. For example, access control and encryption technologies can be used to prevent unauthorized access to training content and data leaks. Furthermore, the management department can perform version control of training content, saving past versions and update history. This allows the company to access, compare, and refer to past training content as needed. The management department can also monitor the usage and access history of training content to evaluate the effectiveness of the company's training activities. For example, they can analyze the number of views, usage time, and employee feedback of training content to understand the effectiveness of training and areas for improvement. This allows the management department to efficiently and effectively support the company's training activities and improve the quality of training.

[0034] The Delivery Department provides training content managed by the Management Department, making it available to companies. The Delivery Department can provide training content through, for example, a web portal or mobile app. Specifically, the Delivery Department provides training content in a way that makes it easily accessible and usable by companies. For example, the Delivery Department provides training content through a web portal, allowing companies to access it via a browser. The web portal has a user-friendly interface and is designed for intuitive operation by company employees. The Delivery Department also provides training content through a mobile app, allowing companies to access it from smartphones and tablets. The mobile app has offline functionality, enabling employees to receive training regardless of location or time. Furthermore, the Delivery Department offers training content customization capabilities, allowing for the creation of training programs tailored to the company's needs. For example, training content can be customized according to the company's business operations and employee skill levels to provide an optimal training program. This allows the Delivery Department to solve problems related to securing training resources and costs, and improve the quality of training. Additionally, the Delivery Department can monitor training progress and results in real time and provide feedback to the company. For example, the effectiveness of training can be evaluated by analyzing training completion rates, test scores, and employee feedback. This allows the training provider to continuously improve the company's training activities and contribute to employee skill development and improved work efficiency.

[0035] The data collection unit can collect information about a company's business operations. For example, the data collection unit can collect sales data, customer information, and financial data. The data collection unit can collect information using methods such as database extraction and surveys. For example, the data collection unit can extract sales data from a company's database and collect customer information. The data collection unit can also conduct surveys to collect information about a company's business operations. By collecting information about a company's business operations, it is possible to provide the data necessary for generating training content. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can extract sales data from a company's database and input it into AI to collect information.

[0036] The learning unit can train the AI ​​with collected information. The learning unit can train the information using machine learning algorithms such as deep learning or supervised learning. The learning unit preprocesses the collected information and trains the AI. For example, the learning unit cleans the collected data and inputs it into the AI. The learning unit can also extract features from the data and train the AI. In this way, by training the AI ​​with collected information, it is possible to provide the knowledge necessary to generate training content. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can clean the collected data and input it into the AI ​​for training.

[0037] The generation unit can automatically generate training content based on learned information. The generation unit can generate training content using algorithms such as natural language generation or template-based generation. Based on the learned information, the generation unit automatically generates company-specific training content. For example, the generation unit generates training content related to business operations such as accounting, finance, SCM, and CRM. The generation unit can also learn inquiry FAQ data for customer support training and generate training content from it. This allows for the provision of optimal training content for each company by automatically generating training content based on learned information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can automatically generate training content using AI based on learned information.

[0038] The management department can manage the generated training content on the cloud. For example, the management department can manage the training content using cloud services. The management department stores the generated training content on the cloud and makes it accessible to companies. For example, the management department can store the training content using cloud storage and make it easily accessible to companies. The management department can also implement data security measures and manage the training content securely. This makes it possible for companies to easily access and use the generated training content by managing it on the cloud. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can store the training content using cloud storage and implement data security measures using AI.

[0039] The service provider can provide managed training content for use by companies. The service provider can provide training content, for example, through a web portal or mobile app. The service provider provides training content in a way that makes it easily accessible and usable by companies. For example, the service provider can provide training content through a web portal, allowing companies to access it from a browser. Alternatively, the service provider can provide training content through a mobile app, allowing companies to access it from smartphones and tablets. This allows companies to access managed training content, thereby solving problems related to securing training resources and costs. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can provide training content through a web portal and use AI to make it easily accessible to companies.

[0040] The generation unit can learn from inquiry FAQ data in customer support training and generate training content. For example, the generation unit can learn from past inquiry history and frequently asked questions and their answers to generate practical training content. Based on the inquiry FAQ data, the generation unit generates training content that includes customer support scenarios and role-playing exercises. For example, the generation unit learns from past inquiry history and generates training content that includes answers to frequently asked questions. The generation unit can also generate customer support role-playing scenarios based on the inquiry FAQ data. This makes it possible to provide practical training content by learning from inquiry FAQ data in customer support training. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input inquiry FAQ data into AI and generate training content using AI.

[0041] The generation unit can generate training content that includes role-playing and scoring functions. For example, the generation unit can generate training content that includes customer support role-playing scenarios and scoring criteria. Based on the role-playing scenarios, the generation unit can conduct practical training. For example, the generation unit can generate a customer support role-playing scenario, allowing trainees to simulate handling actual inquiries. The generation unit can also evaluate trainee performance based on scoring criteria. For example, the generation unit can score trainee responses based on the role-playing scenario and provide feedback. This enables practical training by generating training content that includes role-playing and scoring functions. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input a role-playing scenario into AI and generate training content using AI.

[0042] The service provider can provide training content in a way that makes it easily accessible and usable by companies. For example, the service provider can provide training content through a web portal or mobile app. The service provider can provide training content in a way that makes it easily accessible and usable by companies. For example, the service provider can provide training content through a web portal, allowing companies to access it from their browsers. Alternatively, the service provider can provide training content through a mobile app, allowing companies to access it from their smartphones or tablets. This makes it possible to solve problems related to securing training resources and costs for companies by providing training content in a way that makes it easily accessible and usable by companies. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can provide training content through a web portal and use AI to make it easily accessible to companies.

[0043] The data collection unit can analyze a company's past business data and select the optimal information collection method. For example, the data collection unit can analyze a company's past business data and prioritize the collection of information related to specific business operations. The data collection unit can optimize the frequency and timing of information collection based on a company's past business data. The data collection unit can also narrow down the target of information collection based on a company's past business data and perform efficient information collection. In this way, efficient information collection can be performed by analyzing a company's past business data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input a company's past business data into AI and use AI to select the optimal information collection method.

[0044] The data collection unit can filter information based on a company's current projects and areas of interest during the information gathering process. For example, the data collection unit can prioritize the collection of information related to a company's current projects. The data collection unit can narrow down the scope of information collection based on a company's areas of interest. The data collection unit can also adjust the timing of information collection according to the progress of a company's current projects. This allows for the collection of highly relevant information by filtering information based on a company's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input a company's current project data into an AI and use the AI ​​to filter the information.

[0045] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of companies during data collection. For example, the data collection unit can prioritize the collection of information related to the company's location. Based on the company's geographical location, the data collection unit can collect region-specific information. Based on the company's geographical location, the data collection unit can also collect information on nearby competitor companies. This allows for the efficient collection of region-specific information by considering the company's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the company's geographical location data into AI and use AI to prioritize the collection of highly relevant information.

[0046] The data collection unit can analyze a company's social media activities and collect relevant information during the information gathering process. For example, the data collection unit can analyze a company's social media activities and collect information on relevant topics. Based on the company's social media responses, the data collection unit can prioritize the collection of information of high interest. The data collection unit can also understand the activities of competitors through a company's social media activities and collect relevant information. In this way, by analyzing a company's social media activities, highly relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a company's social media data into AI and use AI to collect relevant information.

[0047] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can adjust the parameters of the learning algorithm based on past learning data. The learning unit can extract areas for improvement in the learning algorithm from past learning data and optimize it. The learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. In this way, the accuracy of the learning algorithm can be improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into AI and optimize the learning algorithm using AI.

[0048] The learning unit can apply different learning algorithms to each business category of the company during the learning process. For example, the learning unit can apply a learning algorithm specialized for accounting operations. The learning unit can apply a learning algorithm specialized for customer support operations. The learning unit can also apply a learning algorithm specialized for SCM and CRM operations. This allows for efficient learning by applying different learning algorithms to each business category of the company. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input company business category data into AI and use AI to apply different learning algorithms.

[0049] The learning unit can weight the learning data based on when the information was submitted during the learning process. For example, the learning unit can prioritize learning and weighting the most recent information. The learning unit can also learn and weight older information only as reference. The learning unit can also adjust the importance of the learning data based on the submission date. This allows for efficient learning by weighting the learning data based on when the information was submitted. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the information submission date data into AI and use AI to weight the learning data.

[0050] The learning unit can improve the accuracy of its learning by referring to relevant company literature during the learning process. For example, the learning unit can improve the accuracy of the learning data by referring to relevant company literature. The learning unit can extract areas for improvement in the learning algorithm from the relevant company literature and improve its accuracy. The learning unit can also improve accuracy by supplementing the learning data based on the relevant company literature. In this way, the accuracy of the learning data can be improved by referring to relevant company literature. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input company literature data into AI and use AI to improve the accuracy of its learning.

[0051] The generation unit can adjust the level of detail based on the importance of the training content during generation. For example, the generation unit may include detailed explanations for highly important training content, and concise explanations for less important training content. The generation unit can also adjust the level of detail of the training content according to its importance. This allows for efficient training by adjusting the level of detail based on the importance of the training content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input training content importance data into AI and use AI to adjust the level of detail.

[0052] The generation unit can apply different generation algorithms depending on the category of the training content during generation. For example, the generation unit can apply a generation algorithm specialized for accounting to accounting training content. For customer support training content, it can apply a generation algorithm specialized for customer support. The generation unit can also apply specialized generation algorithms to SCM and CRM training content. This allows for efficient training by applying different generation algorithms depending on the category of the training content. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input training content category data into AI and use AI to apply different generation algorithms.

[0053] The generation unit can determine priorities based on the submission dates of the training content during generation. For example, the generation unit can prioritize the generation of training content with approaching submission deadlines. The generation unit can determine the generation order of training content based on submission dates. The generation unit can also adjust the training content generation schedule according to submission dates. This allows for efficient training by prioritizing training content based on submission dates. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input training content submission date data into AI and use AI to determine priorities.

[0054] The generation unit can adjust the order of training content based on its relevance during generation. For example, the generation unit can prioritize the generation of highly relevant training content. The generation unit can determine the generation order of training content based on its relevance. The generation unit can also adjust the training content generation schedule according to its relevance. This allows for efficient training by adjusting the order of training content based on its relevance. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance data of the training content into AI and use AI to adjust the order.

[0055] The management department can select the optimal management method by referring to past management data during management. For example, the management department can select the optimal management method based on past management data. The management department can extract areas for improvement in management methods from past management data and select the optimal management method. The management department can also improve the accuracy of management methods by referring to past management data. In this way, the optimal management method can be selected by referring to past management data. Some or all of the above processes in the management department may be performed using AI, for example, or without using AI. For example, the management department can input past management data into AI and use AI to select the optimal management method.

[0056] The management department can apply different management methods to each business category of the company during management. For example, the management department can apply a management method specialized for accounting operations. The management department can apply a management method specialized for customer support operations. The management department can also apply a management method specialized for SCM and CRM operations. By applying different management methods to each business category of the company, efficient management can be achieved. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the company's business category data into AI and use AI to apply different management methods.

[0057] The management department can weight management data based on the submission timing of training content during the management process. For example, the management department can prioritize the management of training content with approaching submission deadlines. The management department can weight management data based on submission timing. The management department can also adjust the importance of management data according to submission timing. This allows for efficient management by weighting management data based on the submission timing of training content. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input training content submission timing data into AI and use AI to weight management data.

[0058] The management department can improve the accuracy of its management by referring to relevant company literature during the management process. For example, the management department can improve the accuracy of management data by referring to relevant company literature. The management department can extract areas for improvement in management methods from relevant company literature and improve accuracy. The management department can also supplement management data based on relevant company literature and improve accuracy. In this way, the accuracy of management data can be improved by referring to relevant company literature. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input relevant company literature data into AI and use AI to improve the accuracy of management.

[0059] The service provider can select the optimal service delivery method by referring to the company's past usage history at the time of delivery. For example, the service provider can select the optimal service delivery method based on the company's past usage history. The service provider can extract areas for improvement in the service delivery method from the company's past usage history and optimize it. The service provider can also improve the accuracy of the service delivery method by referring to the company's past usage history. In this way, the optimal service delivery method can be selected by referring to the company's past usage history. Some or all of the above processes 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 past usage history data into AI and use AI to select the optimal service delivery method.

[0060] The service delivery unit can apply different delivery methods to each business category of the company at the time of delivery. For example, the service delivery unit can apply a delivery method specialized for accounting operations. The service delivery unit can apply a delivery method specialized for customer support operations. The service delivery unit can also apply a delivery method specialized for SCM and CRM operations. By applying different delivery methods to each business category of the company, efficient delivery can be achieved. Some or all of the above-described processes in the service delivery unit may be performed using AI, for example, or not using AI. For example, the service delivery unit can input company business category data into AI and use AI to apply different delivery methods.

[0061] The service provider can weight the provided data based on the submission timing of the training content. For example, the service provider can prioritize providing training content with an approaching submission deadline. The service provider can weight the provided data based on the submission timing. The service provider can also adjust the importance of the provided data according to the submission timing. This allows for efficient provision by weighting the provided data based on the submission timing of the training content. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the training content submission timing data into AI and use AI to weight the provided data.

[0062] The provisioning unit can improve the accuracy of its provision by referring to relevant company literature at the time of provision. For example, the provisioning unit can improve the accuracy of the provided data by referring to relevant company literature. The provisioning unit can improve the accuracy by extracting areas for improvement in the provisioning method from relevant company literature. The provisioning unit can also improve the accuracy by supplementing the provided data based on relevant company literature. In this way, the accuracy of the provided data can be improved by referring to relevant company literature. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without using AI. For example, the provisioning unit can input relevant company literature data into AI and use AI to improve the accuracy of the provision.

[0063] The delivery unit can provide training content in a way that makes it easy for companies to access and use it at the time of delivery. For example, the delivery unit can provide an interface that is easy for companies to access. The delivery unit can enhance the search function of the training content to make it easier for companies to use. The delivery unit can also provide a download function for the training content to make it quickly available to companies. This enables efficient delivery by providing training content that is easy for companies to access and use. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input an interface that is easy for companies to access into AI and deliver it using AI.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The learning unit, when training the AI ​​with collected information, can identify bottlenecks in business processes and propose ways to improve them, thereby streamlining a company's business processes. For example, the learning unit analyzes collected data to identify the cause of delays in specific business processes. It can also propose ways to improve business processes and increase their efficiency. Furthermore, the learning unit can evaluate the effects after improvements have been implemented and propose further areas for improvement. This leads to increased efficiency in a company's business processes and is expected to improve productivity.

[0066] The generation unit can automatically generate training content based on learned information, adjusting the difficulty level of the content according to the skill level of the company's employees. For example, training content for beginners will explain basic concepts and procedures in detail. Training content for intermediate users can include practical challenges and application examples. Furthermore, training content for advanced users can deal with specialized knowledge and advanced techniques. This allows for effective skill development by providing training content tailored to the skill level of employees.

[0067] When the management department manages generated training content on the cloud, it can analyze the access history of company employees and prioritize displaying popular and frequently used content. For example, if a particular training content is used by many employees, it will be displayed on the top page. It can also organize frequently used content into categories for easy access by employees. Furthermore, employee feedback can be collected and used to improve the content. This allows employees to quickly access the training content they need, improving the effectiveness of the training.

[0068] The data collection department can analyze a company's social media activities and collect relevant information when gathering information about the company's business operations. For example, it can analyze a company's social media activities and collect information on relevant topics. Based on a company's social media responses, the data collection department can prioritize collecting information of high interest. The data collection department can also understand the activities of competitors through a company's social media activities and collect relevant information. In this way, by analyzing a company's social media activities, it can collect highly relevant information.

[0069] The service provider can select the optimal delivery method when providing managed training content to companies by referring to the past usage history of company employees. For example, the optimal delivery method can be selected based on the past usage history of employees. The service provider can extract areas for improvement in the delivery method from the past usage history of employees and optimize it. Furthermore, it can improve the accuracy of the delivery method by referring to the past usage history of employees. In this way, the optimal delivery method can be selected by referring to the past usage history of employees.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The data collection department collects information about the company's operations. The data collection department can collect, for example, sales data, customer information, and financial data. The data collection department can collect information using methods such as database extraction and surveys. For example, the data collection department can extract sales data from the company's database and collect customer information. The data collection department can also conduct surveys to collect information about the company's operations. Step 2: The learning unit trains the AI ​​with the information collected by the collection unit. The learning unit can train the information using machine learning algorithms such as deep learning or supervised learning. The learning unit preprocesses the collected information and trains the AI. For example, the learning unit cleans the collected data and inputs it into the AI. The learning unit can also extract features from the data and train the AI ​​with them. Step 3: The generation unit automatically generates training content based on the information learned by the learning unit. The generation unit can generate training content using algorithms such as natural language generation or template-based generation. Based on the learned information, the generation unit automatically generates company-specific training content. For example, the generation unit generates training content related to business operations such as accounting, finance, SCM, and CRM. The generation unit can also learn from customer support inquiry FAQ data and generate training content. Step 4: The management department manages the training content generated by the generation department on the cloud. The management department can manage the training content using, for example, a cloud service. The management department stores the generated training content on the cloud and makes it accessible to the company. For example, the management department can store the training content using cloud storage and make it easily accessible to the company. The management department can also implement data security measures to safely manage the training content. Step 5: The delivery department provides the training content managed by the administration department so that companies can use it. The delivery department can provide the training content, for example, through a web portal or a mobile app. The delivery department provides the training content so that companies can easily access and use it. For example, the delivery department can provide the training content through a web portal so that companies can access it from their browsers. Alternatively, the delivery department can provide the training content through a mobile app so that companies can access it from their smartphones or tablets.

[0072] (Example of form 2) The cloud system according to an embodiment of the present invention is a system for streamlining new employee training in companies. This cloud system provides a mechanism for automatically generating training content tailored to each company using multimodal AI. This makes it possible for small and medium-sized enterprises (SMEs) that do not have specialized personnel or organizations to conduct effective training. First, the cloud system collects the company's business operations and information and trains the AI. Next, the AI ​​automatically generates training content based on the collected information. For example, it can generate training content related to business operations such as accounting, finance, SCM, and CRM. The generated training content is managed on the cloud and provided on a monthly subscription basis. This cloud system consists of the following steps: First, the cloud system collects the company's business operations and information. Next, the AI ​​is trained on the collected information. Based on the trained information, the AI ​​automatically generates training content tailored to each company. The generated training content is managed on the cloud and can be easily accessed and used by companies. This cloud system allows companies to solve problems related to securing training resources and costs. In addition, the digitalization of training progresses, improving the quality of training. Furthermore, because the AI ​​automatically generates training content, it becomes easier to update training, and training based on the latest information can be provided. For example, in customer support training, AI learns from inquiry FAQ data and generates training content. The generated training content includes role-playing and scoring functions, enabling practical training. This allows new staff to efficiently acquire skills and adapt to their work quickly. This cloud system solves the problem of insufficient training resources for companies and improves the quality of training. Furthermore, the digitalization of training improves corporate productivity and staff retention rates. In addition, because AI automatically generates training content, it becomes easy to update training, and training based on the latest information can be provided. In this way, the cloud system can solve the problems of securing training resources and costs for companies and improve the quality of training.

[0073] The cloud system according to this embodiment comprises a collection unit, a learning unit, a generation unit, a management unit, and a provision unit. The collection unit collects information on a company's business operations. For example, the collection unit can collect sales data, customer information, financial data, etc. The collection unit can collect information using methods such as database extraction and surveys. For example, the collection unit can extract sales data from a company's database and collect customer information. The collection unit can also conduct surveys to collect information on a company's business operations. The learning unit trains an AI with the information collected by the collection unit. For example, the learning unit can train the AI ​​using machine learning algorithms such as deep learning and supervised learning. The learning unit preprocesses the collected information and trains the AI. For example, the learning unit cleans the collected data and inputs it into the AI. The learning unit can also extract data features and train the AI. The generation unit automatically generates training content based on the information trained by the learning unit. For example, the generation unit can generate training content using algorithms such as natural language generation and template-based generation. The generation unit automatically generates company-specific training content based on learned information. For example, the generation unit generates training content related to business operations such as accounting, finance, SCM, and CRM. It can also learn from customer support inquiry FAQ data and generate training content. The management unit manages the training content generated by the generation unit on the cloud. The management unit can manage the training content using cloud services, for example. The management unit stores the generated training content on the cloud and makes it accessible to companies. For example, the management unit can store the training content using cloud storage and make it easily accessible to companies. The management unit can also implement data security measures to safely manage the training content. The delivery unit provides the training content managed by the management unit so that companies can use it. The delivery unit can provide the training content through web portals or mobile apps, for example.The service provider offers training content in a way that companies can easily access and use. For example, the service provider offers training content through a web portal, allowing companies to access it from their browsers. Alternatively, the service provider offers training content through a mobile app, allowing companies to access it from their smartphones and tablets. As a result, the cloud system according to this embodiment can solve problems related to securing training resources and costs for companies, and improve the quality of training.

[0074] The data collection department collects information about a company's operations. For example, it can collect sales data, customer information, and financial data. The department can collect information using methods such as database extraction and surveys. Specifically, it extracts sales data from a company's database and collects customer information. Sales data includes sales figures, transaction history, and sales volume by product; this data is useful for detailed analysis of a company's sales activities. Customer information includes basic customer information (name, address, contact information, etc.), purchase history, customer preferences, and feedback, allowing for an understanding of customer behavior patterns and needs. The data collection department can also conduct surveys to collect information about a company's operations. Surveys provide detailed insights into employees' work content, processes, operational challenges, and areas for improvement. This allows for the collection of specific data to improve the company's operational efficiency and productivity. Furthermore, the data collection department can also collect information from external data sources. For example, it can collect industry market trends, competitor information, and economic indicators to aid in strategic planning and decision-making. This allows the data collection unit to integrate internal and external company data, enabling comprehensive information gathering.

[0075] The learning unit trains the AI ​​with the information collected by the collection unit. The learning unit can train the AI ​​using machine learning algorithms such as deep learning or supervised learning. Specifically, the learning unit preprocesses the collected information before training the AI. Preprocessing includes data cleaning, normalization, and feature extraction. For example, the learning unit cleans the collected data, removing missing and outlier values. It also normalizes the data, unifying data at different scales. Furthermore, the learning unit can extract data features and train the AI. Statistical methods and data mining techniques can be used for feature extraction. For example, sales trends and seasonality can be extracted from sales data, and customer purchasing patterns and preferences can be extracted from customer information. This allows the AI ​​to learn about a company's operations and customer behavior patterns, acquiring knowledge useful for future predictions and decision-making. Additionally, the learning unit can improve the accuracy and performance of the AI ​​by continuously training it with collected information. For example, by retraining the AI ​​each time new data is collected, it can perform predictions and analyses based on the latest information. This allows the learning department to respond quickly to the company's business operations and customer needs, and to provide highly accurate analysis based on the latest information at all times.

[0076] The generation unit automatically generates training content based on information learned by the learning unit. The generation unit can generate training content using algorithms such as natural language generation and template-based generation. Specifically, the generation unit automatically generates company-specific training content based on learned information. For example, the generation unit generates training content related to business operations such as accounting, finance, SCM, and CRM. Accounting and finance training content includes basic accounting principles, how to read financial statements, budget management, and cost reduction methods. SCM (supply chain management) training content includes basic supply chain concepts, inventory management, and logistics optimization. CRM (customer relationship management) training content includes understanding customer needs, improving customer satisfaction, and strengthening customer loyalty. Furthermore, the generation unit can learn from FAQ data in customer support training and generate training content. For example, this could include common inquiries, troubleshooting methods, and basic customer service skills. This allows the generation unit to automatically generate customized training content tailored to a company's business operations and needs, contributing to securing training resources and reducing costs. Furthermore, the generation unit can continuously update the generated training content to keep up with the latest information and trends. For example, it can automatically update training content in conjunction with the introduction of new business processes or technologies, ensuring that employees always acquire the latest knowledge and skills. In this way, the generation unit can improve the quality of corporate training and contribute to employee skill development and improved work efficiency.

[0077] The management department manages the training content generated by the generation department on the cloud. The management department can manage training content using cloud services, for example. Specifically, the management department stores the generated training content on the cloud and makes it accessible to the company. For example, the management department can store training content using cloud storage and make it easily accessible to the company. Cloud storage provides data redundancy and backup functions, ensuring secure storage and rapid access to training content. The management department can also implement data security measures to securely manage training content. For example, access control and encryption technologies can be used to prevent unauthorized access to training content and data leaks. Furthermore, the management department can perform version control of training content, saving past versions and update history. This allows the company to access, compare, and refer to past training content as needed. The management department can also monitor the usage and access history of training content to evaluate the effectiveness of the company's training activities. For example, they can analyze the number of views, usage time, and employee feedback of training content to understand the effectiveness of training and areas for improvement. This allows the management department to efficiently and effectively support the company's training activities and improve the quality of training.

[0078] The Delivery Department provides training content managed by the Management Department, making it available to companies. The Delivery Department can provide training content through, for example, a web portal or mobile app. Specifically, the Delivery Department provides training content in a way that makes it easily accessible and usable by companies. For example, the Delivery Department provides training content through a web portal, allowing companies to access it via a browser. The web portal has a user-friendly interface and is designed for intuitive operation by company employees. The Delivery Department also provides training content through a mobile app, allowing companies to access it from smartphones and tablets. The mobile app has offline functionality, enabling employees to receive training regardless of location or time. Furthermore, the Delivery Department offers training content customization capabilities, allowing for the creation of training programs tailored to the company's needs. For example, training content can be customized according to the company's business operations and employee skill levels to provide an optimal training program. This allows the Delivery Department to solve problems related to securing training resources and costs, and improve the quality of training. Additionally, the Delivery Department can monitor training progress and results in real time and provide feedback to the company. For example, the effectiveness of training can be evaluated by analyzing training completion rates, test scores, and employee feedback. This allows the training provider to continuously improve the company's training activities and contribute to employee skill development and improved work efficiency.

[0079] The data collection unit can collect information about a company's business operations. For example, the data collection unit can collect sales data, customer information, and financial data. The data collection unit can collect information using methods such as database extraction and surveys. For example, the data collection unit can extract sales data from a company's database and collect customer information. The data collection unit can also conduct surveys to collect information about a company's business operations. By collecting information about a company's business operations, it is possible to provide the data necessary for generating training content. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can extract sales data from a company's database and input it into AI to collect information.

[0080] The learning unit can train the AI ​​with collected information. The learning unit can train the information using machine learning algorithms such as deep learning or supervised learning. The learning unit preprocesses the collected information and trains the AI. For example, the learning unit cleans the collected data and inputs it into the AI. The learning unit can also extract features from the data and train the AI. In this way, by training the AI ​​with collected information, it is possible to provide the knowledge necessary to generate training content. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can clean the collected data and input it into the AI ​​for training.

[0081] The generation unit can automatically generate training content based on learned information. The generation unit can generate training content using algorithms such as natural language generation or template-based generation. Based on the learned information, the generation unit automatically generates company-specific training content. For example, the generation unit generates training content related to business operations such as accounting, finance, SCM, and CRM. The generation unit can also learn inquiry FAQ data for customer support training and generate training content from it. This allows for the provision of optimal training content for each company by automatically generating training content based on learned information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can automatically generate training content using AI based on learned information.

[0082] The management department can manage the generated training content on the cloud. For example, the management department can manage the training content using cloud services. The management department stores the generated training content on the cloud and makes it accessible to companies. For example, the management department can store the training content using cloud storage and make it easily accessible to companies. The management department can also implement data security measures and manage the training content securely. This makes it possible for companies to easily access and use the generated training content by managing it on the cloud. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can store the training content using cloud storage and implement data security measures using AI.

[0083] The service provider can provide managed training content for use by companies. The service provider can provide training content, for example, through a web portal or mobile app. The service provider provides training content in a way that makes it easily accessible and usable by companies. For example, the service provider can provide training content through a web portal, allowing companies to access it from a browser. Alternatively, the service provider can provide training content through a mobile app, allowing companies to access it from smartphones and tablets. This allows companies to access managed training content, thereby solving problems related to securing training resources and costs. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can provide training content through a web portal and use AI to make it easily accessible to companies.

[0084] The generation unit can learn from inquiry FAQ data in customer support training and generate training content. For example, the generation unit can learn from past inquiry history and frequently asked questions and their answers to generate practical training content. Based on the inquiry FAQ data, the generation unit generates training content that includes customer support scenarios and role-playing exercises. For example, the generation unit learns from past inquiry history and generates training content that includes answers to frequently asked questions. The generation unit can also generate customer support role-playing scenarios based on the inquiry FAQ data. This makes it possible to provide practical training content by learning from inquiry FAQ data in customer support training. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input inquiry FAQ data into AI and generate training content using AI.

[0085] The generation unit can generate training content that includes role-playing and scoring functions. For example, the generation unit can generate training content that includes customer support role-playing scenarios and scoring criteria. Based on the role-playing scenarios, the generation unit can conduct practical training. For example, the generation unit can generate a customer support role-playing scenario, allowing trainees to simulate handling actual inquiries. The generation unit can also evaluate trainee performance based on scoring criteria. For example, the generation unit can score trainee responses based on the role-playing scenario and provide feedback. This enables practical training by generating training content that includes role-playing and scoring functions. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input a role-playing scenario into AI and generate training content using AI.

[0086] The service provider can provide training content in a way that makes it easily accessible and usable by companies. For example, the service provider can provide training content through a web portal or mobile app. The service provider can provide training content in a way that makes it easily accessible and usable by companies. For example, the service provider can provide training content through a web portal, allowing companies to access it from their browsers. Alternatively, the service provider can provide training content through a mobile app, allowing companies to access it from their smartphones or tablets. This makes it possible to solve problems related to securing training resources and costs for companies by providing training content in a way that makes it easily accessible and usable by companies. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can provide training content through a web portal and use AI to make it easily accessible to companies.

[0087] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection and collect information when the user is relaxed. If the user is concentrating, the data collection unit can adjust the timing of information collection to avoid interrupting the user's work. If the user is tired, the data collection unit can temporarily stop information collection and resume it after the user has rested. By adjusting the timing of information collection based on the user's emotions, the burden on the user can be reduced and information can be collected efficiently. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the user's facial expression data into an AI, use the AI ​​to estimate emotions, and adjust the timing of information collection.

[0088] The data collection unit can analyze a company's past business data and select the optimal information collection method. For example, the data collection unit can analyze a company's past business data and prioritize the collection of information related to specific business operations. The data collection unit can optimize the frequency and timing of information collection based on a company's past business data. The data collection unit can also narrow down the target of information collection based on a company's past business data and perform efficient information collection. In this way, efficient information collection can be performed by analyzing a company's past business data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input a company's past business data into AI and use AI to select the optimal information collection method.

[0089] The data collection unit can filter information based on a company's current projects and areas of interest during the information gathering process. For example, the data collection unit can prioritize the collection of information related to a company's current projects. The data collection unit can narrow down the scope of information collection based on a company's areas of interest. The data collection unit can also adjust the timing of information collection according to the progress of a company's current projects. This allows for the collection of highly relevant information by filtering information based on a company's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input a company's current project data into an AI and use the AI ​​to filter the information.

[0090] The data collection unit can estimate the user's emotions and prioritize the information to be collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important information and prioritize collecting more important information. If the user is relaxed, the data collection unit can prioritize collecting detailed information. If the user is in a hurry, the data collection unit can also prioritize collecting information that can be collected quickly. This allows for efficient information collection by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user facial expression data into an AI, use the AI ​​to estimate emotions, and determine the priority of information.

[0091] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of companies during data collection. For example, the data collection unit can prioritize the collection of information related to the company's location. Based on the company's geographical location, the data collection unit can collect region-specific information. Based on the company's geographical location, the data collection unit can also collect information on nearby competitor companies. This allows for the efficient collection of region-specific information by considering the company's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the company's geographical location data into AI and use AI to prioritize the collection of highly relevant information.

[0092] The data collection unit can analyze a company's social media activities and collect relevant information during the information gathering process. For example, the data collection unit can analyze a company's social media activities and collect information on relevant topics. Based on the company's social media responses, the data collection unit can prioritize the collection of information of high interest. The data collection unit can also understand the activities of competitors through a company's social media activities and collect relevant information. In this way, by analyzing a company's social media activities, highly relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a company's social media data into AI and use AI to collect relevant information.

[0093] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit can select detailed training data. If the user is in a hurry, the learning unit can select training data that gets straight to the point. If the user is excited, the learning unit can also select visually stimulating training data. This allows for efficient learning by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not using AI. For example, the learning unit can input user facial expression data into an AI, use the AI ​​to estimate emotions, and select training data.

[0094] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can adjust the parameters of the learning algorithm based on past learning data. The learning unit can extract areas for improvement in the learning algorithm from past learning data and optimize it. The learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. In this way, the accuracy of the learning algorithm can be improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into AI and optimize the learning algorithm using AI.

[0095] The learning unit can apply different learning algorithms to each business category of the company during the learning process. For example, the learning unit can apply a learning algorithm specialized for accounting operations. The learning unit can apply a learning algorithm specialized for customer support operations. The learning unit can also apply a learning algorithm specialized for SCM and CRM operations. This allows for efficient learning by applying different learning algorithms to each business category of the company. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input company business category data into AI and use AI to apply different learning algorithms.

[0096] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit can reduce the learning frequency. If the user is relaxed, the learning unit can increase the learning frequency. If the user is in a hurry, the learning unit can also adjust the learning frequency to enable efficient learning. This allows for efficient learning by adjusting the learning frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user facial expression data into AI, use AI to estimate emotions, and adjust the learning frequency.

[0097] The learning unit can weight the learning data based on when the information was submitted during the learning process. For example, the learning unit can prioritize learning and weighting the most recent information. The learning unit can also learn and weight older information only as reference. The learning unit can also adjust the importance of the learning data based on the submission date. This allows for efficient learning by weighting the learning data based on when the information was submitted. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the information submission date data into AI and use AI to weight the learning data.

[0098] The learning unit can improve the accuracy of its learning by referring to relevant company literature during the learning process. For example, the learning unit can improve the accuracy of the learning data by referring to relevant company literature. The learning unit can extract areas for improvement in the learning algorithm from the relevant company literature and improve its accuracy. The learning unit can also improve accuracy by supplementing the learning data based on the relevant company literature. In this way, the accuracy of the learning data can be improved by referring to relevant company literature. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input company literature data into AI and use AI to improve the accuracy of its learning.

[0099] The generation unit can estimate the user's emotions and adjust the presentation of the training content based on the estimated emotions. For example, if the user is relaxed, the generation unit can adopt a visually relaxing presentation. If the user is in a hurry, the generation unit can adopt a concise presentation that gets straight to the point. If the user is excited, the generation unit can also adopt a visually stimulating presentation. By adjusting the presentation of the training content based on the user's emotions, more effective training can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user facial expression data into an AI, use the AI ​​to estimate emotions, and adjust the presentation of the training content.

[0100] The generation unit can adjust the level of detail based on the importance of the training content during generation. For example, the generation unit may include detailed explanations for highly important training content, and concise explanations for less important training content. The generation unit can also adjust the level of detail of the training content according to its importance. This allows for efficient training by adjusting the level of detail based on the importance of the training content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input training content importance data into AI and use AI to adjust the level of detail.

[0101] The generation unit can apply different generation algorithms depending on the category of the training content during generation. For example, the generation unit can apply a generation algorithm specialized for accounting to accounting training content. For customer support training content, it can apply a generation algorithm specialized for customer support. The generation unit can also apply specialized generation algorithms to SCM and CRM training content. This allows for efficient training by applying different generation algorithms depending on the category of the training content. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input training content category data into AI and use AI to apply different generation algorithms.

[0102] The generation unit can estimate the user's emotions and adjust the length of the training content based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise training content. If the user is relaxed, the generation unit can generate longer training content with detailed explanations. If the user is excited, the generation unit can also generate training content with visually stimulating effects. This allows for more effective training by adjusting the length of the training content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user facial expression data into an AI, use the AI ​​to estimate emotions, and adjust the length of the training content.

[0103] The generation unit can determine priorities based on the submission dates of the training content during generation. For example, the generation unit can prioritize the generation of training content with approaching submission deadlines. The generation unit can determine the generation order of training content based on submission dates. The generation unit can also adjust the training content generation schedule according to submission dates. This allows for efficient training by prioritizing training content based on submission dates. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input training content submission date data into AI and use AI to determine priorities.

[0104] The generation unit can adjust the order of training content based on its relevance during generation. For example, the generation unit can prioritize the generation of highly relevant training content. The generation unit can determine the generation order of training content based on its relevance. The generation unit can also adjust the training content generation schedule according to its relevance. This allows for efficient training by adjusting the order of training content based on its relevance. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance data of the training content into AI and use AI to adjust the order.

[0105] The management unit can estimate the user's emotions and adjust the management method of training content based on the estimated user emotions. For example, if the user is stressed, the management unit can provide a simple management interface. If the user is relaxed, the management unit can provide detailed management options. If the user is in a hurry, the management unit can also provide a management method that can be accessed quickly. This allows for efficient management by adjusting the management method of training content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input user facial expression data into AI, use AI to estimate emotions, and adjust the management method.

[0106] The management department can select the optimal management method by referring to past management data during management. For example, the management department can select the optimal management method based on past management data. The management department can extract areas for improvement in management methods from past management data and select the optimal management method. The management department can also improve the accuracy of management methods by referring to past management data. In this way, the optimal management method can be selected by referring to past management data. Some or all of the above processes in the management department may be performed using AI, for example, or without using AI. For example, the management department can input past management data into AI and use AI to select the optimal management method.

[0107] The management department can apply different management methods to each business category of the company during management. For example, the management department can apply a management method specialized for accounting operations. The management department can apply a management method specialized for customer support operations. The management department can also apply a management method specialized for SCM and CRM operations. By applying different management methods to each business category of the company, efficient management can be achieved. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the company's business category data into AI and use AI to apply different management methods.

[0108] The management department can estimate the user's emotions and adjust the frequency of managing training content based on the estimated emotions. For example, if the user is stressed, the management department can reduce the frequency of management. If the user is relaxed, the management department can increase the frequency of management. If the user is in a hurry, the management department can also adjust the frequency of management to ensure efficient management. This allows for efficient management by adjusting the frequency of managing training content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI or not using AI. For example, the management department can input user facial expression data into AI, use AI to estimate emotions, and adjust the frequency of management.

[0109] The management department can weight management data based on the submission timing of training content during the management process. For example, the management department can prioritize the management of training content with approaching submission deadlines. The management department can weight management data based on submission timing. The management department can also adjust the importance of management data according to submission timing. This allows for efficient management by weighting management data based on the submission timing of training content. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input training content submission timing data into AI and use AI to weight management data.

[0110] The management department can improve the accuracy of its management by referring to relevant company literature during the management process. For example, the management department can improve the accuracy of management data by referring to relevant company literature. The management department can extract areas for improvement in management methods from relevant company literature and improve accuracy. The management department can also supplement management data based on relevant company literature and improve accuracy. In this way, the accuracy of management data can be improved by referring to relevant company literature. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input relevant company literature data into AI and use AI to improve the accuracy of management.

[0111] The delivery unit can estimate the user's emotions and adjust the delivery method of training content based on the estimated user emotions. For example, if the user is stressed, the delivery unit can provide a simple delivery interface. If the user is relaxed, the delivery unit can provide detailed delivery options. If the user is in a hurry, the delivery unit can also provide a delivery method that can be accessed quickly. This allows for efficient delivery by adjusting the delivery method of training content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not using AI. For example, the delivery unit can input user facial expression data into AI, use AI to estimate emotions, and adjust the delivery method.

[0112] The service provider can select the optimal service delivery method by referring to the company's past usage history at the time of delivery. For example, the service provider can select the optimal service delivery method based on the company's past usage history. The service provider can extract areas for improvement in the service delivery method from the company's past usage history and optimize it. The service provider can also improve the accuracy of the service delivery method by referring to the company's past usage history. In this way, the optimal service delivery method can be selected by referring to the company's past usage history. Some or all of the above processes 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 past usage history data into AI and use AI to select the optimal service delivery method.

[0113] The service delivery unit can apply different delivery methods to each business category of the company at the time of delivery. For example, the service delivery unit can apply a delivery method specialized for accounting operations. The service delivery unit can apply a delivery method specialized for customer support operations. The service delivery unit can also apply a delivery method specialized for SCM and CRM operations. By applying different delivery methods to each business category of the company, efficient delivery can be achieved. Some or all of the above-described processes in the service delivery unit may be performed using AI, for example, or not using AI. For example, the service delivery unit can input company business category data into AI and use AI to apply different delivery methods.

[0114] The delivery unit can estimate the user's emotions and adjust the frequency of delivery of training content based on the estimated user emotions. For example, if the user is stressed, the delivery unit can reduce the delivery frequency. If the user is relaxed, the delivery unit can increase the delivery frequency. If the user is in a hurry, the delivery unit can also adjust the delivery frequency to provide efficient delivery. This allows for efficient delivery by adjusting the delivery frequency of training content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user facial expression data into AI, use AI to estimate emotions, and adjust the delivery frequency.

[0115] The service provider can weight the provided data based on the submission timing of the training content. For example, the service provider can prioritize providing training content with an approaching submission deadline. The service provider can weight the provided data based on the submission timing. The service provider can also adjust the importance of the provided data according to the submission timing. This allows for efficient provision by weighting the provided data based on the submission timing of the training content. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the training content submission timing data into AI and use AI to weight the provided data.

[0116] The provisioning unit can improve the accuracy of its provision by referring to relevant company literature at the time of provision. For example, the provisioning unit can improve the accuracy of the provided data by referring to relevant company literature. The provisioning unit can improve the accuracy by extracting areas for improvement in the provisioning method from relevant company literature. The provisioning unit can also improve the accuracy by supplementing the provided data based on relevant company literature. In this way, the accuracy of the provided data can be improved by referring to relevant company literature. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without using AI. For example, the provisioning unit can input relevant company literature data into AI and use AI to improve the accuracy of the provision.

[0117] The delivery unit can provide training content in a way that makes it easy for companies to access and use it at the time of delivery. For example, the delivery unit can provide an interface that is easy for companies to access. The delivery unit can enhance the search function of the training content to make it easier for companies to use. The delivery unit can also provide a download function for the training content to make it quickly available to companies. This enables efficient delivery by providing training content that is easy for companies to access and use. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input an interface that is easy for companies to access into AI and deliver it using AI.

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

[0119] The data collection unit can estimate the emotions of employees when gathering information about a company's operations and adjust its data collection methods based on these estimates. For example, if employees are stressed, the unit can reduce the number of questions in a survey and limit them to simpler questions. If employees are relaxed, the unit can conduct interview-style surveys to gather more detailed information. Furthermore, if employees are focused, the unit can adjust the timing of data collection to avoid interrupting their work. This enables data collection that takes employees' emotions into consideration, resulting in more accurate and useful data.

[0120] The learning unit, when training the AI ​​with collected information, can identify bottlenecks in business processes and propose ways to improve them, thereby streamlining a company's business processes. For example, the learning unit analyzes collected data to identify the cause of delays in specific business processes. It can also propose ways to improve business processes and increase their efficiency. Furthermore, the learning unit can evaluate the effects after improvements have been implemented and propose further areas for improvement. This leads to increased efficiency in a company's business processes and is expected to improve productivity.

[0121] The generation unit can automatically generate training content based on learned information, adjusting the difficulty level of the content according to the skill level of the company's employees. For example, training content for beginners will explain basic concepts and procedures in detail. Training content for intermediate users can include practical challenges and application examples. Furthermore, training content for advanced users can deal with specialized knowledge and advanced techniques. This allows for effective skill development by providing training content tailored to the skill level of employees.

[0122] When the management department manages generated training content on the cloud, it can analyze the access history of company employees and prioritize displaying popular and frequently used content. For example, if a particular training content is used by many employees, it will be displayed on the top page. It can also organize frequently used content into categories for easy access by employees. Furthermore, employee feedback can be collected and used to improve the content. This allows employees to quickly access the training content they need, improving the effectiveness of the training.

[0123] The delivery unit can estimate the emotions of company employees when providing managed training content to companies and adjust the delivery method based on those estimates. For example, if employees are stressed, the delivery unit can provide a simple interface to allow quick access to necessary information. If employees are relaxed, it can provide detailed explanations and additional resources. Furthermore, if employees are in a hurry, it can provide concise content that gets straight to the point. This enables a delivery method that is sensitive to employees' emotions and improves the effectiveness of the training.

[0124] The data collection department can analyze a company's social media activities and collect relevant information when gathering information about the company's business operations. For example, it can analyze a company's social media activities and collect information on relevant topics. Based on a company's social media responses, the data collection department can prioritize collecting information of high interest. The data collection department can also understand the activities of competitors through a company's social media activities and collect relevant information. In this way, by analyzing a company's social media activities, it can collect highly relevant information.

[0125] The learning unit can estimate the emotions of company employees when training the AI ​​with collected information, and select training data based on these estimated emotions. For example, if an employee is relaxed, it can select detailed training data. If an employee is in a hurry, the learning unit can select training data that focuses on the essentials. Furthermore, if an employee is excited, it can select visually stimulating training data. This allows for more efficient learning by selecting training data based on employee emotions.

[0126] The generation unit can estimate the emotions of the company's employees when automatically generating training content based on learned information, and adjust the presentation of the training content based on these estimated emotions. For example, if employees are relaxed, the generation unit can adopt a visually relaxing presentation. If employees are in a hurry, the generation unit can adopt a concise presentation that gets straight to the point. Furthermore, if employees are excited, it can adopt a visually stimulating presentation. In this way, by adjusting the presentation of training content based on employees' emotions, more effective training can be provided.

[0127] When managing generated training content on the cloud, the management department can estimate the emotions of the company's employees and adjust how the training content is managed based on those estimates. For example, if an employee is stressed, a simple management interface can be provided. If an employee is relaxed, the management department can provide more detailed management options. Furthermore, if an employee is in a hurry, a management method that allows for quick access can be provided. This enables efficient management by adjusting how training content is managed based on employee emotions.

[0128] The service provider can select the optimal delivery method when providing managed training content to companies by referring to the past usage history of company employees. For example, the optimal delivery method can be selected based on the past usage history of employees. The service provider can extract areas for improvement in the delivery method from the past usage history of employees and optimize it. Furthermore, it can improve the accuracy of the delivery method by referring to the past usage history of employees. In this way, the optimal delivery method can be selected by referring to the past usage history of employees.

[0129] The following briefly describes the processing flow for example form 2.

[0130] Step 1: The data collection department collects information about the company's operations. The data collection department can collect, for example, sales data, customer information, and financial data. The data collection department can collect information using methods such as database extraction and surveys. For example, the data collection department can extract sales data from the company's database and collect customer information. The data collection department can also conduct surveys to collect information about the company's operations. Step 2: The learning unit trains the AI ​​with the information collected by the collection unit. The learning unit can train the information using machine learning algorithms such as deep learning or supervised learning. The learning unit preprocesses the collected information and trains the AI. For example, the learning unit cleans the collected data and inputs it into the AI. The learning unit can also extract features from the data and train the AI ​​with them. Step 3: The generation unit automatically generates training content based on the information learned by the learning unit. The generation unit can generate training content using algorithms such as natural language generation or template-based generation. Based on the learned information, the generation unit automatically generates company-specific training content. For example, the generation unit generates training content related to business operations such as accounting, finance, SCM, and CRM. The generation unit can also learn from customer support inquiry FAQ data and generate training content. Step 4: The management department manages the training content generated by the generation department on the cloud. The management department can manage the training content using, for example, a cloud service. The management department stores the generated training content on the cloud and makes it accessible to the company. For example, the management department can store the training content using cloud storage and make it easily accessible to the company. The management department can also implement data security measures to safely manage the training content. Step 5: The delivery department provides the training content managed by the administration department for use by the company. The delivery department can provide the training content, for example, through a web portal or mobile app. The delivery department provides the training content in a way that makes it easy for the company to access and use. For example, the delivery department can provide the training content through a web portal, allowing the company to access it from a browser. Alternatively, the delivery department can provide the training content through a mobile app, allowing the company to access it from a smartphone or tablet.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the collection unit, learning unit, generation unit, management unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect business content and information of a company and transmits it to the data processing unit 12 via the control unit 46A. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and trains the AI ​​with the collected information. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and automatically generates training content based on the learned information. The management unit is implemented in the specific processing unit 290 of the data processing unit 12 and manages the generated training content on the cloud. The provision unit is implemented in the specific processing unit 46A of the smart device 14 and provides the training content so that the company can easily access and use it. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0135] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the collection unit, learning unit, generation unit, management unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect business content and information of a company and transmits it to the data processing unit 12 via the control unit 46A. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and trains the AI ​​with the collected information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and automatically generates training content based on the learned information. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and manages the generated training content on the cloud. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides the training content so that companies can easily access and use it. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0151] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the collection unit, learning unit, generation unit, management unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect business content and information of a company and transmits it to the data processing unit 12 by the control unit 46A. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and trains the AI ​​with the collected information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and automatically generates training content based on the learned information. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and manages the generated training content on the cloud. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, and provides the training content so that companies can easily access and use it. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0167] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] Each of the multiple elements described above, including the collection unit, learning unit, generation unit, management unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect business content and information of a company and transmits it to the data processing unit 12 via the control unit 46A. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and trains the AI ​​with the collected information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and automatically generates training content based on the learned information. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and manages the generated training content on the cloud. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides the training content so that the company can easily access and use it. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] (Note 1) The collection department gathers information about the company's operations and, A learning unit that learns the information collected by the aforementioned collection unit, A generation unit that automatically generates training content based on the information learned by the learning unit, A management unit manages the training content generated by the aforementioned generation unit on the cloud, The system comprises a provisioning unit that provides training content managed by the aforementioned management unit so that companies can use it. A system characterized by the following features. (Note 2) The aforementioned collection unit is Gather information about the company's operations and activities. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning unit, The collected information is used to train the AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Automatically generate training content based on learned information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, Manage the generated training content on the cloud. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Providing managed training content for companies to use. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is Learn from customer support inquiry FAQ data and generate training content. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is Generate training content with role-playing and scoring functions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned supply unit is, We provide training content that companies can easily access and use. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Analyze the company's past business data and select the optimal information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, filter it based on the company's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When gathering information, prioritize collecting highly relevant information by considering the geographical location of companies. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is When gathering information, we analyze the company's social media activities and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning unit, During training, different learning algorithms are applied to each company's business category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning unit, During training, the training data is weighted based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned learning unit, During the learning process, we improve the accuracy of the learning by referring to relevant company literature. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is The system estimates user emotions and adjusts the presentation of training content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, adjust the level of detail based on the importance of the training content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, different generation algorithms are applied depending on the category of the training content. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is The system estimates the user's emotions and adjusts the length of the training content based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is Prioritize training content during generation based on submission deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is During generation, the order is adjusted based on the relevance of the training content. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, We estimate user emotions and adjust how training content is managed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, During management, past management data is referenced to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, When managing a company, different management methods should be applied to each business category. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned management department, It estimates user sentiment and adjusts the frequency of training content management based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned management department, During management, weight management data based on the submission timing of training content. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned management department, During management, we refer to relevant company literature to improve the accuracy of management. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, The system estimates user emotions and adjusts the delivery method of training content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the company's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing the service, different delivery methods will be applied depending on the company's business category. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned supply unit is, The system estimates user sentiment and adjusts the frequency of training content delivery based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned supply unit is, When providing the training content, the provided data will be weighted based on when the training content was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned supply unit is, When providing information, we refer to relevant company literature to improve the accuracy of the information provided. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned supply unit is, When providing the training content, we will ensure that companies can easily access and use it. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0203] 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. The collection department gathers information about the company's operations and, A learning unit that learns the information collected by the aforementioned collection unit, A generation unit that automatically generates training content based on the information learned by the learning unit, A management unit manages the training content generated by the aforementioned generation unit on the cloud, The system comprises a provisioning unit that provides training content managed by the aforementioned management unit so that companies can use it. A system characterized by the following features.

2. The aforementioned collection unit is Gather information about the company's operations and activities. The system according to feature 1.

3. The aforementioned learning unit, The collected information is used to train the AI. The system according to feature 1.

4. The generating unit is Automatically generate training content based on learned information. The system according to feature 1.

5. The aforementioned management department, Manage the generated training content on the cloud. The system according to feature 1.

6. The aforementioned supply unit is, Providing managed training content for companies to use. The system according to feature 1.

7. The generating unit is Learn from customer support inquiry FAQ data and generate training content. The system according to feature 1.

8. The generating unit is Generate training content with role-playing and scoring functions. The system according to feature 1.

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