system

Abstract

The system according to this embodiment aims to efficiently and automatically generate training programs for each company and update them in real time. [Solution] The system according to the embodiment comprises a provisioning unit, a generation unit, a management unit, an input unit, and an update unit. The provisioning unit provides an application specifically for training. The generation unit automatically generates company-specific training using the application provided by the provisioning unit. The management unit manages the training content generated by the generation unit in the cloud. The input unit receives training content managed by the management unit as input by the user. The update unit updates the training content in real time based on the information input by the input unit.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , ,

[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, and includes 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to efficiently and automatically generate training for each company and update it in real time.

[0005] The system according to the embodiment aims to efficiently and automatically generate training for each company and update it in real time.

Means for Solving the Problems

[0007] The system according to this embodiment can efficiently and automatically generate training programs for each company and update them in real time. [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 a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 reception 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 reception 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 AI ​​agent system according to an embodiment of the present invention is a system for solving the challenges of securing personnel in the spot work market. This AI agent system provides an application specifically for training and automatically generates company-specific training using multimodal AI. Furthermore, by offering a monthly subscription, cloud-based management, and easy operation via the application, it solves the problem at a low cost and with minimal effort. In addition, by incorporating an input function by the user (worker), the system maintains high accuracy despite being automated, as the work is updated in a timely and real-time manner. For example, the AI ​​agent system collects the work content and procedures provided by the company in the form of videos, audio, and documents, and the AI ​​analyzes this data to automatically generate training content. This allows companies to conduct training efficiently without maintaining specialized personnel or organizations. Next, by paying a monthly fee, the AI ​​agent system manages training content on the cloud and can be easily operated through the application. This allows for efficient training while keeping costs down. Furthermore, the AI ​​agent system allows workers to input points they notice or areas for improvement during their work into the application, and the AI ​​analyzes this information and updates the training content in real time. This ensures that training is always based on the latest information, making workers immediately productive. This enables AI agent systems to quickly integrate talent into the spot work market. Companies can conduct efficient training, and workers can quickly adapt to their tasks. This improves company productivity and increases worker retention rates. In short, AI agent systems can enable the immediate integration of talent into the spot work market, contributing to increased company productivity and higher worker retention rates.

[0029] The AI ​​agent system according to this embodiment comprises a provisioning unit, a generation unit, a management unit, an input unit, and an update unit. The provisioning unit provides an application specifically for training. For example, the provisioning unit can customize the training content required by a company and provide it as an application. The provisioning unit is provided, for example, on a monthly subscription basis. The generation unit automatically generates company-specific training using the application provided by the provisioning unit. For example, the generation unit uses multimodal AI to analyze the company's business operations and procedures and automatically generate training content. For example, the generation unit analyzes data such as videos, audio, and documents to generate training content. The management unit manages the training content generated by the generation unit in the cloud. For example, the management unit stores the training content in the cloud and manages access permissions. For example, the management unit performs version control and backup of the training content. The input unit allows users to input the training content managed by the management unit. For example, the input unit allows users to input points they noticed or suggestions for improvement during their work. The input unit inputs information using methods such as text input, voice input, or questionnaires. The update unit updates the training content in real time based on the information input by the input unit. The update unit analyzes the input information and automatically updates the training content, for example. The update unit analyzes the input information using AI and updates the training content, for example. As a result, the AI ​​agent system according to this embodiment enables efficient training by automatically generating training for each company, managing it in the cloud, and updating it in real time based on user input.

[0030] The service provider offers training-focused applications. For example, they can customize training content to meet a company's specific needs and deliver it as an application. Specifically, the service provider has the ability to flexibly customize training content according to a company's needs. For instance, they can create training programs to strengthen specific skills and knowledge based on a company's operations and goals. The service provider can regularly update training content to reflect the latest information and technologies, according to company requests. Furthermore, by making training content multilingual, the service provider can cater to global companies. The service provider is offered on a monthly subscription basis, for example. Monthly subscription plans are flexibly set according to the company's size and usage frequency. This allows companies to continuously receive necessary training while keeping costs down. In addition, the service provider collects user usage data and feedback to improve and customize training content. This enables the service provider to provide training applications optimized for each company, supporting their growth and development.

[0031] The generation unit automatically generates company-specific training using an application provided by the service provider. For example, the generation unit uses multimodal AI to analyze a company's business operations and procedures, and automatically generates training content. Specifically, the generation unit analyzes data such as business manuals, procedure documents, videos, audio, and other materials provided by the company to generate training content. Multimodal AI has the ability to comprehensively analyze data in different formats, such as text, images, and audio, accurately understanding the company's business operations. For example, the generation unit analyzes text data from business manuals to extract key points and procedures. It also analyzes videos of work in progress to generate content that visually demonstrates specific operating methods and points to note. Furthermore, it analyzes audio data to provide explanations and instructions related to the work as audio guides. This allows the generation unit to automatically generate training content tailored to the company's business operations, enabling efficient training.

[0032] The management department manages the training content generated by the generation department in the cloud. For example, the management department stores the training content on the cloud and manages access permissions. Specifically, the management department uses cloud storage to securely store the generated training content. Cloud storage has data redundancy and backup functions to prevent data loss or corruption. The management department also sets access permissions for each user and controls access to the training content. For example, administrators can access all content, but general users can only access content related to their own training. Furthermore, the management department provides version control of the training content and a function to revert to previous versions. This allows users to refer to past content even if there are changes or updates to the training content. In addition, the management department monitors the usage of the training content and understands which content is being used and to what extent. This allows them to evaluate the effectiveness of the training and improve or add content as needed.

[0033] The input section is where users input training content managed by the management section. For example, the input section allows users to input points they noticed or suggestions for improvement during their work. Specifically, the input section provides an interface that allows users to input questions and suggestions for improvement in real time during training. Users can input information through methods such as text input, voice input, and questionnaires. For example, text input provides a field where users can freely enter comments, and voice input provides a function to record voice memos using a microphone. The questionnaire format provides a format for users to input answers to pre-set questions. This allows the input section to efficiently collect user feedback and use it to improve training content. Furthermore, the input section has a function to automatically categorize user feedback and prioritize it according to importance and urgency. This allows the input section to effectively manage the collected information and enable quick responses.

[0034] The update unit updates training content in real time based on information input by the input unit. For example, the update unit analyzes the input information and automatically updates the training content. Specifically, the update unit uses AI to analyze the input information and automatically modify the content of the training. For instance, it may add or modify parts of the training content based on user feedback. The update unit can also generate new training content based on the input information. This allows the update unit to always provide training content that reflects the latest information, improving the quality of training. Furthermore, the update unit has the functionality to manage the update history of the training content and refer to past changes. This allows the update unit to track the change history of the training content and revert to previous versions as needed. The update unit can also identify areas for improvement in the training content based on user feedback and continuously improve it. This enables the update unit to respond flexibly to user needs and maximize the effectiveness of the training.

[0035] The service provider is offered on a monthly subscription basis. For example, a company can use the training application by paying a monthly fee. The service provider can, for example, set the monthly fee amount and payment method. The service provider can, for example, set a contract period and offer a monthly fee structure. This allows for efficient training while keeping costs down by offering the service on a monthly basis. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can have AI perform tasks such as setting monthly fees and managing payment methods.

[0036] The management department manages training content on the cloud. The management department, for example, uses cloud services to store training content and manage access permissions. The management department, for example, performs version control and backups of training content on the cloud. The management department can, for example, configure the type of cloud service and the method of data storage. The management department protects data by implementing security measures. This allows for efficient training by managing training content on the cloud. Some or all of the above processes performed by the management department may be performed using AI, for example, or not. For example, the management department can have AI perform data management and access permission settings on the cloud.

[0037] The input section allows users to input observations and areas for improvement during their work. For example, users can input suggestions for improving work efficiency or point out problems. The input section accepts information through methods such as text input, voice input, or questionnaires. The input section provides an interface that allows users to easily input observations and areas for improvement. The input section automatically analyzes the inputted information and reflects it in the training content. This allows the training content to be updated in real time as users input observations and areas for improvement during their work. Some or all of the above-described processes in the input section may be performed using AI, or not. For example, the input section can have AI analyze the information entered by the user and reflect it in the training content.

[0038] The update unit updates training content in real time based on the input information. The update unit can, for example, analyze the input information and automatically update the training content. The update unit can, for example, use AI to analyze the input information and update the training content. The update unit can, for example, set the data synchronization method and the timing of updates. The update unit can, for example, update the data in real time and reflect the latest information in the training content. This ensures that training is always based on the latest information by updating the training content in real time based on the input information. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can have AI analyze the input information and update the training content in real time.

[0039] The service provider customizes the applications they offer based on the company's business operations and training needs. For example, they might provide applications that focus on specific skill sets depending on the company's business operations. For example, they might add specific training modules based on the company's training needs. For example, they might customize the application interface to match the company's business processes. This allows for application customization to suit the company's business operations and training needs. Some or all of the above processes performed by the service provider may be carried out using AI, or not. For example, the service provider could input the company's business operations and training needs into an AI and have the AI ​​perform the application customization.

[0040] The service provider analyzes the company's past training data and selects the optimal method for delivering the app. For example, the service provider selects the most effective training method based on past training data. For example, the service provider analyzes past training data and delivers the app at a specific time. For example, the service provider refers to past training data and prioritizes the delivery of specific training modules. This enables effective training by selecting the optimal method for delivering the app based on the company's past training data. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input past training data into an AI and have the AI ​​select the optimal method for delivering the app.

[0041] The service provider prioritizes providing highly relevant apps, taking into account the company's geographical location. For example, the service provider provides region-specific training content based on the company's location. For example, the service provider provides information on nearby business partners, taking into account the company's geographical location. For example, the service provider provides training content on local laws and regulations based on the company's geographical location. This enables region-specific training by providing highly relevant apps, taking into account the company's geographical location. 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 company's geographical location into AI and have the AI ​​select highly relevant apps.

[0042] The service provider analyzes a company's social media activities and provides relevant applications. For example, the service provider analyzes a company's social media activities and provides training content tailored to current trends. For example, the service provider provides training content that aligns with a company's brand image based on its social media activities. For example, the service provider refers to a company's social media activities and provides training content based on customer feedback. This allows for trend-driven training by analyzing a company's social media activities. Some or all of the above processes performed by the service provider may be carried out using AI, for example, or not. For example, the service provider can input a company's social media activity data into an AI and have the AI ​​select relevant applications.

[0043] The generation unit analyzes a company's business operations and procedures in detail and generates optimal training content. For example, the generation unit analyzes a company's business operations in detail and generates training content that focuses on a specific skill set. For example, the generation unit analyzes a company's work procedures and generates training content that proposes efficient work methods. For example, the generation unit analyzes a company's business processes and generates training content that reflects areas for improvement. In this way, optimal training content can be generated by analyzing a company's business operations and procedures in detail. 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 data on a company's business operations and procedures into an AI and have the AI ​​generate optimal training content.

[0044] The generation unit improves the accuracy of the training content it generates by referring to the company's past training data. For example, the generation unit selects the most effective training method based on past training data. For example, the generation unit analyzes past training data and provides training content at specific time slots. For example, the generation unit refers to past training data and prioritizes the provision of specific training modules. In this way, the accuracy of the training content generated can be improved by referring to the company's past training data. 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 past training data into AI and have the AI ​​perform the task of improving the accuracy of the training content.

[0045] The generation unit generates highly relevant training content, taking into account the geographical location of the company. The generation unit generates region-specific training content, for example, based on the company's location. The generation unit provides information about nearby business partners, for example, taking into account the company's geographical location. The generation unit generates training content on local laws and regulations, for example, based on the company's geographical location. This enables region-specific training by generating highly relevant training content, taking into account the company's geographical location. 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 the company's geographical location into AI and have AI generate highly relevant training content.

[0046] The generation unit analyzes a company's social media activities and generates relevant training content. For example, the generation unit analyzes a company's social media activities and generates training content that aligns with trends. For example, the generation unit generates training content that matches the brand image based on a company's social media activities. For example, the generation unit references a company's social media activities and generates training content based on customer feedback. This makes it possible to conduct training that aligns with trends by analyzing a company's social media activities. 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 a company's social media activity data into an AI and have the AI ​​generate relevant training content.

[0047] The management department customizes the training content it manages based on the company's business operations and training needs. For example, the management department manages training content that focuses on specific skill sets according to the company's business operations. For example, the management department adds specific training modules based on the company's training needs. For example, the management department customizes how training content is managed to match the company's business processes. This enables the management of training content that is tailored to the company's business operations and training needs. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input the company's business operations and training needs into an AI and have the AI ​​perform the customization of training content.

[0048] The management department analyzes the company's past training data and selects the optimal management method. For example, the management department selects the most effective management method based on past training data. For example, the management department analyzes past training data and manages training content during specific time slots. For example, the management department refers to past training data and prioritizes the management of specific training modules. This enables effective training by selecting the optimal management method based on the company's past training data. 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 past training data into AI and have the AI ​​select the optimal management method.

[0049] The management department prioritizes and manages highly relevant training content, taking into account the company's geographical location. For example, the management department manages region-specific training content based on the company's location. For example, the management department provides information on nearby business partners, taking into account the company's geographical location. For example, the management department manages training content related to local laws and regulations based on the company's geographical location. This enables region-specific training by managing highly relevant training content while considering the company's geographical location. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input the company's geographical location into an AI and have the AI ​​manage highly relevant training content.

[0050] The management department analyzes the company's social media activities and manages related training content. For example, the management department analyzes the company's social media activities and manages training content that aligns with trends. For example, the management department manages training content that aligns with the brand image based on the company's social media activities. For example, the management department manages training content based on customer feedback by referring to the company's social media activities. This makes it possible to conduct training that aligns with trends by analyzing the company's social media activities. 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 the company's social media activity data into an AI and have the AI ​​manage related training content.

[0051] The input unit collects detailed information on points and areas for improvement noticed by the user during their work and selects the optimal input method. For example, the input unit provides an interface that allows users to easily input points they noticed during their work. For example, the input unit provides an option for users to add detailed explanations when inputting areas for improvement. For example, the input unit collects points and areas for improvement noticed by the user during their work via voice input. This allows for the selection of the optimal input method by collecting detailed information on points and areas for improvement noticed by the user during their work. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input points and areas for improvement noticed by the user during their work into an AI, and have the AI ​​select the optimal input method.

[0052] The input unit improves the accuracy of input by referring to the user's past input data. The input unit, for example, suggests the optimal input method based on the user's past input data. The input unit, for example, analyzes the user's past input data to improve the accuracy of input. The input unit, for example, refers to the user's past input data to preferentially suggest specific input patterns. In this way, the accuracy of input can be improved by referring to the user's past input data. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past input data into AI and have AI perform the input accuracy improvement.

[0053] The input unit prioritizes collecting highly relevant inputs, taking into account the user's geographical location. For example, the input unit collects region-specific inputs based on the user's location. For example, the input unit collects inputs about nearby business partners, taking into account the user's geographical location. For example, the input unit collects inputs about local laws and regulations based on the user's geographical location. This enables region-specific inputs by collecting highly relevant inputs while considering the user's geographical location. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's geographical location into AI and have AI collect highly relevant inputs.

[0054] The input unit analyzes the user's social media activity and collects relevant input. For example, the input unit analyzes the user's social media activity and collects trend-aligned input. For example, the input unit collects input that matches the brand image based on the user's social media activity. For example, the input unit refers to the user's social media activity and collects input based on customer feedback. This makes it possible to provide trend-aligned input by analyzing the user's social media activity. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's social media activity data into AI and have AI collect relevant input.

[0055] The update unit analyzes the input information in detail and selects the optimal update method. For example, the update unit selects the most effective update method based on the input information. For example, the update unit analyzes the input information and updates the training content at a specific time. For example, the update unit refers to the input information and prioritizes updating specific training modules. This allows the optimal update method to be selected by analyzing the input information in detail. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the input information into AI and have the AI ​​select the optimal update method.

[0056] The update unit improves the accuracy of the training content to be updated by referring to the company's past training data. For example, the update unit selects the most effective update method based on past training data. For example, the update unit analyzes past training data and updates the training content at specific time periods. For example, the update unit refers to past training data and prioritizes updating specific training modules. This allows the accuracy of the training content to be updated to be improved by referring to the company's past training data. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input past training data into AI and have the AI ​​perform the task of improving the accuracy of the training content.

[0057] The update unit prioritizes updating highly relevant training content, taking into account the company's geographical location. For example, the update unit updates region-specific training content based on the company's location. For example, the update unit provides information on nearby business partners, taking into account the company's geographical location. For example, the update unit updates training content on local laws and regulations based on the company's geographical location. This enables region-specific training by updating highly relevant training content while considering the company's geographical location. Some or all of the above processing in the update unit may be performed using AI, or not. For example, the update unit can input the company's geographical location into an AI and have the AI ​​perform the updating of highly relevant training content.

[0058] The update unit analyzes a company's social media activities and updates relevant training content. For example, the update unit analyzes a company's social media activities and updates training content to match current trends. For example, the update unit updates training content to match a company's brand image based on its social media activities. For example, the update unit refers to a company's social media activities and updates training content based on customer feedback. This makes it possible to provide training that is aligned with trends by analyzing a company's social media activities. Some or all of the above processes in the update unit may be performed using AI, for example, or not. For example, the update unit can input a company's social media activity data into an AI and have the AI ​​perform the update of relevant training content.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The service provider can also customize the applications they offer based on a company's business operations and training needs. For example, they can provide applications that focus on specific skill sets depending on the company's business operations. They can also add specific training modules based on the company's training needs. Furthermore, they can customize the application interface to match the company's business processes. This allows for the customization of applications to suit a company's business operations and training needs.

[0061] The service provider can analyze a company's past training data and select the optimal app delivery method. For example, they can select the most effective training method based on past training data. They can also analyze past training data and deliver the app at specific time slots. Furthermore, they can refer to past training data to prioritize the delivery of specific training modules. This allows for more effective training by selecting the optimal app delivery method based on a company's past training data.

[0062] The service provider can also prioritize providing highly relevant apps by considering the company's geographical location. For example, it can provide region-specific training content based on the company's location. It can also provide information about nearby business partners by considering the company's geographical location. Furthermore, it can provide training content on local laws and regulations based on the company's geographical location. In this way, providing highly relevant apps by considering the company's geographical location enables region-specific training.

[0063] The service provider can also analyze a company's social media activities and provide related applications. For example, they can analyze a company's social media activities and provide training content tailored to current trends. They can also provide training content that aligns with the brand image based on the company's social media activities. Furthermore, they can provide training content based on customer feedback, referencing the company's social media activities. This makes it possible to conduct training that is aligned with current trends by analyzing a company's social media activities.

[0064] The generation unit can also analyze a company's business operations and procedures in detail and generate optimal training content. For example, it can analyze a company's business operations in detail and generate training content that focuses on specific skill sets. It can also analyze a company's work procedures and generate training content that proposes efficient work methods. Furthermore, it can analyze a company's business processes and generate training content that reflects areas for improvement. In this way, by analyzing a company's business operations and procedures in detail, it is possible to generate optimal training content.

[0065] The generation unit can also improve the accuracy of the generated training content by referring to the company's past training data. For example, it can select the most effective training method based on past training data. It can also analyze past training data and provide training content at specific time slots. Furthermore, it can prioritize the provision of specific training modules by referring to past training data. In this way, the accuracy of the generated training content can be improved by referring to the company's past training data.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The service provider will offer an app specifically designed for training. For example, the service provider can customize the training content required by a company and provide it as an app. The service provider may offer it on a monthly subscription basis. Step 2: The generation unit automatically generates company-specific training using the application provided by the provider unit. The generation unit, for example, uses multimodal AI to analyze the company's business operations and procedures and automatically generates training content. The generation unit, for example, analyzes data such as videos, audio, and documents to generate training content. Step 3: The management department manages the training content generated by the generation department in the cloud. For example, the management department stores the training content in the cloud and manages access permissions. For example, the management department manages versions of the training content and performs backups. Step 4: The input section is where users input training content managed by the management section. For example, users can input points they noticed or suggestions for improvement during their work. The input section accepts information through methods such as text input, voice input, and questionnaires. Step 5: The update unit updates the training content in real time based on the information input by the input unit. The update unit, for example, analyzes the input information and automatically updates the training content. The update unit, for example, uses AI to analyze the input information and updates the training content.

[0068] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system for solving the challenges of securing personnel in the spot work market. This AI agent system provides an application specifically for training and automatically generates company-specific training using multimodal AI. Furthermore, by offering a monthly subscription, cloud-based management, and easy operation via the application, it solves the problem at a low cost and with minimal effort. In addition, by incorporating an input function by the user (worker), the system maintains high accuracy despite being automated, as the work is updated in a timely and real-time manner. For example, the AI ​​agent system collects the work content and procedures provided by the company in the form of videos, audio, and documents, and the AI ​​analyzes this data to automatically generate training content. This allows companies to conduct training efficiently without maintaining specialized personnel or organizations. Next, by paying a monthly fee, the AI ​​agent system manages training content on the cloud and can be easily operated through the application. This allows for efficient training while keeping costs down. Furthermore, the AI ​​agent system allows workers to input points they notice or areas for improvement during their work into the application, and the AI ​​analyzes this information and updates the training content in real time. This ensures that training is always based on the latest information, making workers immediately productive. This enables AI agent systems to quickly integrate talent into the spot work market. Companies can conduct efficient training, and workers can quickly adapt to their tasks. This improves company productivity and increases worker retention rates. In short, AI agent systems can enable the immediate integration of talent into the spot work market, contributing to increased company productivity and higher worker retention rates.

[0069] The AI ​​agent system according to this embodiment comprises a provisioning unit, a generation unit, a management unit, an input unit, and an update unit. The provisioning unit provides an application specifically for training. For example, the provisioning unit can customize the training content required by a company and provide it as an application. The provisioning unit is provided, for example, on a monthly subscription basis. The generation unit automatically generates company-specific training using the application provided by the provisioning unit. For example, the generation unit uses multimodal AI to analyze the company's business operations and procedures and automatically generate training content. For example, the generation unit analyzes data such as videos, audio, and documents to generate training content. The management unit manages the training content generated by the generation unit in the cloud. For example, the management unit stores the training content in the cloud and manages access permissions. For example, the management unit performs version control and backup of the training content. The input unit allows users to input the training content managed by the management unit. For example, the input unit allows users to input points they noticed or suggestions for improvement during their work. The input unit inputs information using methods such as text input, voice input, or questionnaires. The update unit updates the training content in real time based on the information input by the input unit. The update unit analyzes the input information and automatically updates the training content, for example. The update unit analyzes the input information using AI and updates the training content, for example. As a result, the AI ​​agent system according to this embodiment enables efficient training by automatically generating training for each company, managing it in the cloud, and updating it in real time based on user input.

[0070] The service provider offers training-focused applications. For example, they can customize training content to meet a company's specific needs and deliver it as an application. Specifically, the service provider has the ability to flexibly customize training content according to a company's needs. For instance, they can create training programs to strengthen specific skills and knowledge based on a company's operations and goals. The service provider can regularly update training content to reflect the latest information and technologies, according to company requests. Furthermore, by making training content multilingual, the service provider can cater to global companies. The service provider is offered on a monthly subscription basis, for example. Monthly subscription plans are flexibly set according to the company's size and usage frequency. This allows companies to continuously receive necessary training while keeping costs down. In addition, the service provider collects user usage data and feedback to improve and customize training content. This enables the service provider to provide training applications optimized for each company, supporting their growth and development.

[0071] The generation unit automatically generates company-specific training using an application provided by the service provider. For example, the generation unit uses multimodal AI to analyze a company's business operations and procedures, and automatically generates training content. Specifically, the generation unit analyzes data such as business manuals, procedure documents, videos, audio, and other materials provided by the company to generate training content. Multimodal AI has the ability to comprehensively analyze data in different formats, such as text, images, and audio, accurately understanding the company's business operations. For example, the generation unit analyzes text data from business manuals to extract key points and procedures. It also analyzes videos of work in progress to generate content that visually demonstrates specific operating methods and points to note. Furthermore, it analyzes audio data to provide explanations and instructions related to the work as audio guides. This allows the generation unit to automatically generate training content tailored to the company's business operations, enabling efficient training.

[0072] The management department manages the training content generated by the generation department in the cloud. For example, the management department stores the training content on the cloud and manages access permissions. Specifically, the management department uses cloud storage to securely store the generated training content. Cloud storage has data redundancy and backup functions to prevent data loss or corruption. The management department also sets access permissions for each user and controls access to the training content. For example, administrators can access all content, but general users can only access content related to their own training. Furthermore, the management department provides version control of the training content and a function to revert to previous versions. This allows users to refer to past content even if there are changes or updates to the training content. In addition, the management department monitors the usage of the training content and understands which content is being used and to what extent. This allows them to evaluate the effectiveness of the training and improve or add content as needed.

[0073] The input section is where users input training content managed by the management section. For example, the input section allows users to input points they noticed or suggestions for improvement during their work. Specifically, the input section provides an interface that allows users to input questions and suggestions for improvement in real time during training. Users can input information through methods such as text input, voice input, and questionnaires. For example, text input provides a field where users can freely enter comments, and voice input provides a function to record voice memos using a microphone. The questionnaire format provides a format for users to input answers to pre-set questions. This allows the input section to efficiently collect user feedback and use it to improve training content. Furthermore, the input section has a function to automatically categorize user feedback and prioritize it according to importance and urgency. This allows the input section to effectively manage the collected information and enable quick responses.

[0074] The update unit updates training content in real time based on information input by the input unit. For example, the update unit analyzes the input information and automatically updates the training content. Specifically, the update unit uses AI to analyze the input information and automatically modify the content of the training. For instance, it may add or modify parts of the training content based on user feedback. The update unit can also generate new training content based on the input information. This allows the update unit to always provide training content that reflects the latest information, improving the quality of training. Furthermore, the update unit has the functionality to manage the update history of the training content and refer to past changes. This allows the update unit to track the change history of the training content and revert to previous versions as needed. The update unit can also identify areas for improvement in the training content based on user feedback and continuously improve it. This enables the update unit to respond flexibly to user needs and maximize the effectiveness of the training.

[0075] The service provider is offered on a monthly subscription basis. For example, a company can use the training application by paying a monthly fee. The service provider can, for example, set the monthly fee amount and payment method. The service provider can, for example, set a contract period and offer a monthly fee structure. This allows for efficient training while keeping costs down by offering the service on a monthly basis. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can have AI perform tasks such as setting monthly fees and managing payment methods.

[0076] The management department manages training content on the cloud. The management department, for example, uses cloud services to store training content and manage access permissions. The management department, for example, performs version control and backups of training content on the cloud. The management department can, for example, configure the type of cloud service and the method of data storage. The management department protects data by implementing security measures. This allows for efficient training by managing training content on the cloud. Some or all of the above processes performed by the management department may be performed using AI, for example, or not. For example, the management department can have AI perform data management and access permission settings on the cloud.

[0077] The input section allows users to input observations and areas for improvement during their work. For example, users can input suggestions for improving work efficiency or point out problems. The input section accepts information through methods such as text input, voice input, or questionnaires. The input section provides an interface that allows users to easily input observations and areas for improvement. The input section automatically analyzes the inputted information and reflects it in the training content. This allows the training content to be updated in real time as users input observations and areas for improvement during their work. Some or all of the above-described processes in the input section may be performed using AI, or not. For example, the input section can have AI analyze the information entered by the user and reflect it in the training content.

[0078] The update unit updates training content in real time based on the input information. The update unit can, for example, analyze the input information and automatically update the training content. The update unit can, for example, use AI to analyze the input information and update the training content. The update unit can, for example, set the data synchronization method and the timing of updates. The update unit can, for example, update the data in real time and reflect the latest information in the training content. This ensures that training is always based on the latest information by updating the training content in real time based on the input information. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can have AI analyze the input information and update the training content in real time.

[0079] The service provider estimates the user's emotions and adjusts the timing of app delivery based on the estimated emotions. For example, if the user is stressed, the service provider will deliver the app during a time when the user can relax. For example, if the user is concentrating, the service provider will deliver the app after work. For example, if the user is tired, the service provider will deliver the app during a break. By adjusting the timing of app delivery according to the user's emotions, more effective training becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. 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 user emotion data into an AI and have the AI ​​perform emotion estimation.

[0080] The service provider customizes the applications they offer based on the company's business operations and training needs. For example, they might provide applications that focus on specific skill sets depending on the company's business operations. For example, they might add specific training modules based on the company's training needs. For example, they might customize the application interface to match the company's business processes. This allows for application customization to suit the company's business operations and training needs. Some or all of the above processes performed by the service provider may be carried out using AI, or not. For example, the service provider could input the company's business operations and training needs into an AI and have the AI ​​perform the application customization.

[0081] The service provider analyzes the company's past training data and selects the optimal method for delivering the app. For example, the service provider selects the most effective training method based on past training data. For example, the service provider analyzes past training data and delivers the app at a specific time. For example, the service provider refers to past training data and prioritizes the delivery of specific training modules. This enables effective training by selecting the optimal method for delivering the app based on the company's past training data. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input past training data into an AI and have the AI ​​select the optimal method for delivering the app.

[0082] The service provider estimates the user's emotions and prioritizes the apps to be offered based on the estimated emotions. For example, if the user is stressed, the service provider prioritizes providing relaxing content. For example, if the user is focused, the service provider prioritizes providing content that helps improve skills. For example, if the user is tired, the service provider prioritizes providing light content. By prioritizing the apps offered according to the user's emotions, more effective training becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. 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 user emotion data into an AI and have the AI ​​perform emotion estimation.

[0083] The service provider prioritizes providing highly relevant apps, taking into account the company's geographical location. For example, the service provider provides region-specific training content based on the company's location. For example, the service provider provides information on nearby business partners, taking into account the company's geographical location. For example, the service provider provides training content on local laws and regulations based on the company's geographical location. This enables region-specific training by providing highly relevant apps, taking into account the company's geographical location. 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 company's geographical location into AI and have the AI ​​select highly relevant apps.

[0084] The service provider analyzes a company's social media activities and provides relevant applications. For example, the service provider analyzes a company's social media activities and provides training content tailored to current trends. For example, the service provider provides training content that aligns with a company's brand image based on its social media activities. For example, the service provider refers to a company's social media activities and provides training content based on customer feedback. This allows for trend-driven training by analyzing a company's social media activities. Some or all of the above processes performed by the service provider may be carried out using AI, for example, or not. For example, the service provider can input a company's social media activity data into an AI and have the AI ​​select relevant applications.

[0085] The generation unit estimates the user's emotions and adjusts the method of generating training content based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates training content that proceeds at a relaxed pace. For example, if the user is in a hurry, the generation unit generates training content that emphasizes the shortest route. For example, if the user is excited, the generation unit generates training content with visually stimulating effects. By adjusting the method of generating training content according to the user's emotions, more effective training becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. 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 user emotion data into an AI and have the AI ​​perform emotion estimation.

[0086] The generation unit analyzes a company's business operations and procedures in detail and generates optimal training content. For example, the generation unit analyzes a company's business operations in detail and generates training content that focuses on a specific skill set. For example, the generation unit analyzes a company's work procedures and generates training content that proposes efficient work methods. For example, the generation unit analyzes a company's business processes and generates training content that reflects areas for improvement. In this way, optimal training content can be generated by analyzing a company's business operations and procedures in detail. 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 data on a company's business operations and procedures into an AI and have the AI ​​generate optimal training content.

[0087] The generation unit improves the accuracy of the training content it generates by referring to the company's past training data. For example, the generation unit selects the most effective training method based on past training data. For example, the generation unit analyzes past training data and provides training content at specific time slots. For example, the generation unit refers to past training data and prioritizes the provision of specific training modules. In this way, the accuracy of the training content generated can be improved by referring to the company's past training data. 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 past training data into AI and have the AI ​​perform the task of improving the accuracy of the training content.

[0088] The generation unit estimates the user's emotions and determines the priority of training content to generate based on the estimated user emotions. For example, if the user is stressed, the generation unit prioritizes generating relaxing content. For example, if the user is focused, the generation unit prioritizes generating content that helps improve skills. For example, if the user is tired, the generation unit prioritizes generating lighthearted content. By prioritizing the training content generated according to the user's emotions, more effective training becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. 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 emotion data into an AI and have the AI ​​perform emotion estimation.

[0089] The generation unit generates highly relevant training content, taking into account the geographical location of the company. The generation unit generates region-specific training content, for example, based on the company's location. The generation unit provides information about nearby business partners, for example, taking into account the company's geographical location. The generation unit generates training content on local laws and regulations, for example, based on the company's geographical location. This enables region-specific training by generating highly relevant training content, taking into account the company's geographical location. 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 the company's geographical location into AI and have AI generate highly relevant training content.

[0090] The generation unit analyzes a company's social media activities and generates relevant training content. For example, the generation unit analyzes a company's social media activities and generates training content that aligns with trends. For example, the generation unit generates training content that matches the brand image based on a company's social media activities. For example, the generation unit references a company's social media activities and generates training content based on customer feedback. This makes it possible to conduct training that aligns with trends by analyzing a company's social media activities. 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 a company's social media activity data into an AI and have the AI ​​generate relevant training content.

[0091] The management department estimates the user's emotions and adjusts the management method of training content based on the estimated user emotions. For example, if the user is stressed, the management department provides a simple management interface. For example, if the user is focused, the management department provides detailed management options. For example, if the user is tired, the management department provides a visually easy-to-understand management method. This allows for more effective training by adjusting the management method of training content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. 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 emotion data into an AI and have the AI ​​perform emotion estimation.

[0092] The management department customizes the training content it manages based on the company's business operations and training needs. For example, the management department manages training content that focuses on specific skill sets according to the company's business operations. For example, the management department adds specific training modules based on the company's training needs. For example, the management department customizes how training content is managed to match the company's business processes. This enables the management of training content that is tailored to the company's business operations and training needs. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input the company's business operations and training needs into an AI and have the AI ​​perform the customization of training content.

[0093] The management department analyzes the company's past training data and selects the optimal management method. For example, the management department selects the most effective management method based on past training data. For example, the management department analyzes past training data and manages training content during specific time slots. For example, the management department refers to past training data and prioritizes the management of specific training modules. This enables effective training by selecting the optimal management method based on the company's past training data. 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 past training data into AI and have the AI ​​select the optimal management method.

[0094] The management department estimates the user's emotions and prioritizes the training content to be managed based on the estimated emotions. For example, if the user is stressed, the management department prioritizes content that helps them relax. For example, if the user is focused, the management department prioritizes content that helps them improve their skills. For example, if the user is tired, the management department prioritizes content with lighter content. By prioritizing the training content to be managed according to the user's emotions, more effective training becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the management department may be performed using AI, for example, or not using AI. For example, the management department can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0095] The management department prioritizes and manages highly relevant training content, taking into account the company's geographical location. For example, the management department manages region-specific training content based on the company's location. For example, the management department provides information on nearby business partners, taking into account the company's geographical location. For example, the management department manages training content related to local laws and regulations based on the company's geographical location. This enables region-specific training by managing highly relevant training content while considering the company's geographical location. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input the company's geographical location into an AI and have the AI ​​manage highly relevant training content.

[0096] The management department analyzes the company's social media activities and manages related training content. For example, the management department analyzes the company's social media activities and manages training content that aligns with trends. For example, the management department manages training content that aligns with the brand image based on the company's social media activities. For example, the management department manages training content based on customer feedback by referring to the company's social media activities. This makes it possible to conduct training that aligns with trends by analyzing the company's social media activities. 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 the company's social media activity data into an AI and have the AI ​​manage related training content.

[0097] The input unit estimates the user's emotions and adjusts the input method based on the estimated emotions. For example, if the user is stressed, the input unit provides a simple interface and minimizes the input steps. For example, if the user is relaxed, the input unit provides detailed input options and suggests a customizable input method. For example, if the user is in a hurry, the input unit prioritizes voice input to allow for quick input. This allows for more effective input by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the input unit may be performed using AI or not using AI. For example, the input unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0098] The input unit collects detailed information on points and areas for improvement noticed by the user during their work and selects the optimal input method. For example, the input unit provides an interface that allows users to easily input points they noticed during their work. For example, the input unit provides an option for users to add detailed explanations when inputting areas for improvement. For example, the input unit collects points and areas for improvement noticed by the user during their work via voice input. This allows for the selection of the optimal input method by collecting detailed information on points and areas for improvement noticed by the user during their work. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input points and areas for improvement noticed by the user during their work into an AI, and have the AI ​​select the optimal input method.

[0099] The input unit improves the accuracy of input by referring to the user's past input data. The input unit, for example, suggests the optimal input method based on the user's past input data. The input unit, for example, analyzes the user's past input data to improve the accuracy of input. The input unit, for example, refers to the user's past input data to preferentially suggest specific input patterns. In this way, the accuracy of input can be improved by referring to the user's past input data. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past input data into AI and have AI perform the input accuracy improvement.

[0100] The input unit estimates the user's emotions and prioritizes inputs based on the estimated emotions. For example, if the user is stressed, the input unit prioritizes collecting important inputs. For example, if the user is focused, the input unit prioritizes collecting detailed inputs. For example, if the user is tired, the input unit prioritizes collecting simple inputs. This allows for more effective input by prioritizing inputs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AIs include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI or not. For example, the input unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0101] The input unit prioritizes collecting highly relevant inputs, taking into account the user's geographical location. For example, the input unit collects region-specific inputs based on the user's location. For example, the input unit collects inputs about nearby business partners, taking into account the user's geographical location. For example, the input unit collects inputs about local laws and regulations based on the user's geographical location. This enables region-specific inputs by collecting highly relevant inputs while considering the user's geographical location. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's geographical location into AI and have AI collect highly relevant inputs.

[0102] The input unit analyzes the user's social media activity and collects relevant input. For example, the input unit analyzes the user's social media activity and collects trend-aligned input. For example, the input unit collects input that matches the brand image based on the user's social media activity. For example, the input unit refers to the user's social media activity and collects input based on customer feedback. This makes it possible to provide trend-aligned input by analyzing the user's social media activity. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's social media activity data into AI and have AI collect relevant input.

[0103] The update unit estimates the user's emotions and adjusts how the training content is updated based on the estimated emotions. For example, if the user is relaxed, the update unit updates the training content to proceed at a relaxed pace. If the user is in a hurry, the update unit updates the training content to emphasize the shortest route. If the user is excited, the update unit updates the training content to include visually stimulating effects. By adjusting how the training content is updated according to the user's emotions, more effective training becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0104] The update unit analyzes the input information in detail and selects the optimal update method. For example, the update unit selects the most effective update method based on the input information. For example, the update unit analyzes the input information and updates the training content at a specific time. For example, the update unit refers to the input information and prioritizes updating specific training modules. This allows the optimal update method to be selected by analyzing the input information in detail. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the input information into AI and have the AI ​​select the optimal update method.

[0105] The update unit improves the accuracy of the training content to be updated by referring to the company's past training data. For example, the update unit selects the most effective update method based on past training data. For example, the update unit analyzes past training data and updates the training content at specific time periods. For example, the update unit refers to past training data and prioritizes updating specific training modules. This allows the accuracy of the training content to be updated to be improved by referring to the company's past training data. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input past training data into AI and have the AI ​​perform the task of improving the accuracy of the training content.

[0106] The update unit estimates the user's emotions and determines the priority of training content to update based on the estimated emotions. For example, if the user is stressed, the update unit prioritizes updating content that promotes relaxation. For example, if the user is focused, the update unit prioritizes updating content that helps improve skills. For example, if the user is tired, the update unit prioritizes updating content with lighter content. This allows for more effective training by prioritizing training content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 update unit may be performed using AI or not. For example, the update unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0107] The update unit prioritizes updating highly relevant training content, taking into account the company's geographical location. For example, the update unit updates region-specific training content based on the company's location. For example, the update unit provides information on nearby business partners, taking into account the company's geographical location. For example, the update unit updates training content on local laws and regulations based on the company's geographical location. This enables region-specific training by updating highly relevant training content while considering the company's geographical location. Some or all of the above processing in the update unit may be performed using AI, or not. For example, the update unit can input the company's geographical location into an AI and have the AI ​​perform the updating of highly relevant training content.

[0108] The update unit analyzes a company's social media activities and updates relevant training content. For example, the update unit analyzes a company's social media activities and updates training content to match current trends. For example, the update unit updates training content to match a company's brand image based on its social media activities. For example, the update unit refers to a company's social media activities and updates training content based on customer feedback. This makes it possible to provide training that is aligned with trends by analyzing a company's social media activities. Some or all of the above processes in the update unit may be performed using AI, for example, or not. For example, the update unit can input a company's social media activity data into an AI and have the AI ​​perform the update of relevant training content.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The service provider can estimate the user's emotions and adjust the timing of app delivery based on those estimates. For example, if a user is feeling stressed, the app can be delivered during a time when they can relax. If a user is concentrating, the app can be delivered after work. Furthermore, if a user is tired, the app can be delivered during a break. By adjusting the timing of app delivery according to the user's emotions, more effective training becomes possible.

[0111] The service provider can also customize the applications they offer based on a company's business operations and training needs. For example, they can provide applications that focus on specific skill sets depending on the company's business operations. They can also add specific training modules based on the company's training needs. Furthermore, they can customize the application interface to match the company's business processes. This allows for the customization of applications to suit a company's business operations and training needs.

[0112] The service provider can analyze a company's past training data and select the optimal app delivery method. For example, they can select the most effective training method based on past training data. They can also analyze past training data and deliver the app at specific time slots. Furthermore, they can refer to past training data to prioritize the delivery of specific training modules. This allows for more effective training by selecting the optimal app delivery method based on a company's past training data.

[0113] The service provider can also estimate the user's emotions and prioritize the apps they offer based on those emotions. For example, if a user is stressed, they can prioritize relaxing content. If a user is focused, they can prioritize content that helps them improve their skills. Furthermore, if a user is tired, they can prioritize lighter content. By prioritizing apps according to the user's emotions, more effective training becomes possible.

[0114] The service provider can also prioritize providing highly relevant apps by considering the company's geographical location. For example, it can provide region-specific training content based on the company's location. It can also provide information about nearby business partners by considering the company's geographical location. Furthermore, it can provide training content on local laws and regulations based on the company's geographical location. In this way, providing highly relevant apps by considering the company's geographical location enables region-specific training.

[0115] The service provider can also analyze a company's social media activities and provide related applications. For example, they can analyze a company's social media activities and provide training content tailored to current trends. They can also provide training content that aligns with the brand image based on the company's social media activities. Furthermore, they can provide training content based on customer feedback, referencing the company's social media activities. This makes it possible to conduct training that is aligned with current trends by analyzing a company's social media activities.

[0116] The generation unit can also estimate the user's emotions and adjust the method of generating training content based on those emotions. For example, if the user is relaxed, it can generate training content that proceeds at a leisurely pace. If the user is in a hurry, it can generate training content that emphasizes the shortest route. Furthermore, if the user is excited, it can generate training content with visually stimulating effects. By adjusting the method of generating training content according to the user's emotions, more effective training becomes possible.

[0117] The generation unit can also analyze a company's business operations and procedures in detail and generate optimal training content. For example, it can analyze a company's business operations in detail and generate training content that focuses on specific skill sets. It can also analyze a company's work procedures and generate training content that proposes efficient work methods. Furthermore, it can analyze a company's business processes and generate training content that reflects areas for improvement. In this way, by analyzing a company's business operations and procedures in detail, it is possible to generate optimal training content.

[0118] The generation unit can also improve the accuracy of the generated training content by referring to the company's past training data. For example, it can select the most effective training method based on past training data. It can also analyze past training data and provide training content at specific time slots. Furthermore, it can prioritize the provision of specific training modules by referring to past training data. In this way, the accuracy of the generated training content can be improved by referring to the company's past training data.

[0119] The generation unit can also estimate the user's emotions and determine the priority of the training content to be generated based on those emotions. For example, if the user is feeling stressed, it can prioritize generating content that promotes relaxation. If the user is concentrating, it can prioritize generating content that helps improve their skills. Furthermore, if the user is tired, it can prioritize generating lighter content. By prioritizing the training content generated according to the user's emotions, more effective training becomes possible.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The service provider will offer an app specifically designed for training. For example, the service provider can customize the training content required by a company and provide it as an app. The service provider may offer it on a monthly subscription basis. Step 2: The generation unit automatically generates company-specific training using the application provided by the provider unit. The generation unit, for example, uses multimodal AI to analyze the company's business operations and procedures and automatically generates training content. The generation unit, for example, analyzes data such as videos, audio, and documents to generate training content. Step 3: The management department manages the training content generated by the generation department in the cloud. For example, the management department stores the training content in the cloud and manages access permissions. For example, the management department manages versions of the training content and performs backups. Step 4: The input section is where users input training content managed by the management section. For example, users can input points they noticed or suggestions for improvement during their work. The input section accepts information through methods such as text input, voice input, and questionnaires. Step 5: The update unit updates the training content in real time based on the information input by the input unit. The update unit, for example, analyzes the input information and automatically updates the training content. The update unit, for example, uses AI to analyze the input information and updates the training content.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the provision unit, generation unit, management unit, input unit, and update unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the provision unit is implemented by the control unit 46A of the smart device 14 and provides an application specifically for training. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates company-specific training using multimodal AI. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages training content on the cloud. The input unit is implemented by the control unit 46A of the smart device 14 and allows users to input points they noticed or suggestions for improvement during their work. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates the training content in real time based on the input information. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the provision unit, generation unit, management unit, input unit, and update unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the provision unit is implemented by the control unit 46A of the smart glasses 214 and provides an application specifically for training. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates company-specific training using multimodal AI. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages training content on the cloud. The input unit is implemented by the control unit 46A of the smart glasses 214 and allows users to input points they noticed or suggestions for improvement during their work. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates the training content in real time based on the input information. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the provision unit, generation unit, management unit, input unit, and update unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the provision unit is implemented by the control unit 46A of the headset terminal 314 and provides an application specifically for training. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates company-specific training using multimodal AI. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages training content on the cloud. The input unit is implemented by, for example, the control unit 46A of the headset terminal 314 and allows users to input points they noticed or suggestions for improvement during their work. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and updates the training content in real time based on the input information. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the provision unit, generation unit, management unit, input unit, and update unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the provision unit is implemented by the control unit 46A of the robot 414 and provides an application specifically for training. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates company-specific training using multimodal AI. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages training content on the cloud. The input unit is implemented by, for example, the control unit 46A of the robot 414 and allows users to input points they noticed or suggestions for improvement during their work. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and updates the training content in real time based on the input information. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) The service department provides apps specifically for training, A generation unit that automatically generates training for each company using the application provided by the aforementioned provision unit, A management unit manages the training content generated by the aforementioned generation unit in the cloud, The system includes an input unit where users input training content managed by the aforementioned management unit, The system includes an update unit that updates the training content in real time based on the information input by the input unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, It is offered on a monthly subscription basis. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned management department, Manage training content on the cloud The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned input unit is, Users input points they noticed or suggestions for improvement during their work. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned update unit is The training content is updated in real time based on the input information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We estimate the user's emotions and adjust the timing of app release based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, We customize the applications we provide based on the company's business operations and training needs. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, We analyze past training data from companies to select the optimal method for providing the app. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the apps to offer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned supply unit is, Prioritize providing highly relevant apps by considering the company's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned supply unit is, We analyze a company's social media activity and provide relevant apps. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is We estimate user emotions and adjust the training content generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is We analyze a company's business operations and procedures in detail to generate optimal training content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is By referencing past training data from companies, we can improve the accuracy of the training content we generate. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates user emotions and determines the priority of training content to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is Generate highly relevant training content by considering the company's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is Analyze a company's social media activity and generate relevant training content. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned management department, We customize the training content we manage based on the company's business operations and training needs. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, We analyze past training data from companies and select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, It estimates user emotions and prioritizes training content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, Prioritize the management of highly relevant training content by considering the company's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, Analyze a company's social media activity and manage relevant training content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned input unit is, It estimates the user's emotions and adjusts the input method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned input unit is, We collect detailed information about points and areas for improvement that users notice during their work, and select the most suitable input method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned input unit is, Referencing the user's past input data improves the accuracy of input. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned input unit is, It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned input unit is, Prioritize collecting highly relevant inputs, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned input unit is, Analyze users' social media activity and collect relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned update unit is We estimate user sentiment and adjust how training content is updated based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned update unit is The input information is analyzed in detail, and the optimal update method is selected. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned update unit is By referring to past training data from the company, we can improve the accuracy of the training content we update. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned update unit is It estimates user sentiment and prioritizes training content updates based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned update unit is Prioritize updating highly relevant training content by taking into account the company's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned update unit is Analyze companies' social media activities and update relevant training content. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0194] 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 service department provides apps specifically for training, A generation unit that automatically generates training for each company using the application provided by the aforementioned provision unit, A management unit manages the training content generated by the aforementioned generation unit in the cloud, The system includes an input unit where users input training content managed by the aforementioned management unit, The system includes an update unit that updates the training content in real time based on the information input by the input unit. A system characterized by the following features.

2. The aforementioned supply unit is, It is offered on a monthly subscription basis. The system according to feature 1.

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

4. The aforementioned input unit is, Users input points they noticed or suggestions for improvement during their work. The system according to feature 1.

5. The aforementioned update unit is, The training content is updated in real time based on the input information. The system according to feature 1.

6. The aforementioned supply unit is, We estimate the user's emotions and adjust the timing of app release based on those estimated emotions. The system according to feature 1.

7. The aforementioned supply unit is, We customize the applications we provide based on the company's business operations and training needs. The system according to feature 1.

8. The aforementioned supply unit is, We analyze past training data from companies to select the optimal method for providing the app. The system according to feature 1.

9. The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the apps to offer based on those estimated emotions. The system according to feature 1.

10. The aforementioned supply unit is, Prioritize providing highly relevant apps by considering the company's geographical location. The system according to feature 1.

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