System

Abstract

An object of a system according to an embodiment is to enable a trainee delivery company to efficiently learn a correct packing method and handling method.SOLUTION: A system includes a reception unit, an analysis unit, a provision unit, and a monitoring unit. The reception part inputs information of the apprentice delivery company. The analysis unit analyzes the information input by the reception unit. The providing unit provides a packing method or a handling method based on a result analyzed by the analyzing unit. The monitoring unit monitors the learning progress based on the content provided by the providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult for trainee delivery personnel to efficiently learn the correct packing and handling methods.

[0005] The system according to the embodiment aims to enable trainee delivery personnel to efficiently learn the correct packing and handling methods. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a monitoring unit. The reception unit inputs information about the trainee delivery person. The analysis unit analyzes the information input by the reception unit. The provision unit provides a packing method or handling method based on the results of the analysis by the analysis unit. The monitoring unit monitors the learning progress based on the content provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment allows trainee delivery personnel to efficiently learn the correct packing and handling methods. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention provides delivery workers with the correct packaging and handling methods to avoid breakage, along with specific scenarios. This system supports efficient learning by adapting the content to the trainee's level of understanding and progress. For example, a trainee logs in to the system and inputs their level of understanding and progress. The generation AI then analyzes the information and provides appropriate packaging and handling methods along with specific scenarios. For example, training can be based on specific scenarios, such as how to pack fragile glass products or how to lift heavy loads. Furthermore, the generation AI monitors the trainee's learning progress in real time and adjusts the content as needed. For example, if a trainee does not understand a particular packaging method, the system adjusts the training to focus on that aspect. As the training progresses, the system also allows trainees to progress to more advanced content. This allows trainees to efficiently learn the correct packaging and handling methods and acquire the skills to avoid breakage. For example, when learning how to pack glass products, specific steps and precautions are provided using videos and diagrams, making them useful for actual work. Furthermore, when learning how to lift heavy loads, the system can teach the correct posture and how to apply force through simulations. In this way, by utilizing generative AI, it becomes possible for trainee delivery workers to learn efficiently according to their level of understanding and progress, and they can effectively acquire the skills to avoid damage.

[0029] A learning support system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a monitoring unit. The reception unit inputs information about the apprentice delivery person. The information about the apprentice delivery person includes, but is not limited to, years of experience, job content, and skill level. The reception unit, for example, allows the apprentice delivery person to log in to the system and input their level of understanding and progress. The analysis unit analyzes the information input by the reception unit. The analysis may be performed using, for example, but is not limited to, data mining, statistical analysis, or a machine learning algorithm. For example, the analysis unit analyzes the apprentice delivery person's level of understanding and progress and determines appropriate learning content. The provision unit provides packaging or handling methods based on the results of the analysis by the analysis unit. The provided content includes, for example, but is not limited to, types of packaging materials, packing procedures, and handling precautions. For example, the provision unit provides learning content based on specific scenarios, such as how to pack fragile glass products or how to lift heavy loads. The monitoring unit monitors the learning progress based on the content provided by the provision unit. The monitoring of learning progress is performed based on criteria such as, but not limited to, the level of learning achievement, progress status, and evaluation method. For example, the monitoring unit monitors the learning progress of the apprentice delivery company in real time and adjusts the content of the providing unit as necessary. As a result, the learning support system according to the embodiment can support efficient learning by providing appropriate packaging and handling methods based on the information of the apprentice delivery company and monitoring the learning progress.

[0030] The providing unit cooperates with a scenario unit that provides specific scenarios. The scenario unit provides the specific scenarios. Specific scenarios include, but are not limited to, how to pack fragile glass products and how to lift heavy loads. For example, the scenario unit provides a scenario that specifically shows the steps and precautions that a trainee delivery person should take when packing glass products. The scenario unit can also provide a scenario that shows the correct posture and how to apply force when lifting heavy loads. By providing specific scenarios, the trainee delivery person can deepen their understanding. Some or all of the above-described processing in the scenario unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario unit can provide a scenario using an AI model that inputs information about the trainee delivery person and outputs an appropriate scenario.

[0031] The provision unit works in cooperation with a media unit that provides videos or illustrations. The media unit provides the videos or illustrations. Examples of the videos and illustrations provided include, but are not limited to, videos showing steps for packaging and illustrations showing handling precautions. For example, the media unit provides a video showing how to package glass products. The media unit can also provide illustrations showing how to lift heavy packages. In this way, providing videos and illustrations can support learning that is easy to understand visually. Some or all of the above-described processing in the media unit may be performed using, for example, AI, or may be performed without using AI. For example, the media unit can provide media using an AI model that inputs information about trainee delivery personnel and outputs appropriate videos and illustrations.

[0032] The providing unit cooperates with a simulation unit that provides simulations. The simulation unit provides the simulations. The provided simulations include, but are not limited to, simulations of how to lift heavy loads and how to pack glass products. For example, the simulation unit simulates the correct posture and force to apply when lifting heavy loads. The simulation unit can also simulate how to pack glass products. This allows for providing simulations to support practical learning. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit may provide a simulation using an AI model that inputs information about a trainee delivery person and outputs an appropriate simulation.

[0033] The monitoring unit monitors the learning progress and adjusts the content of the provision unit as necessary. The monitoring unit monitors the learning progress. Monitoring of the learning progress includes, but is not limited to, criteria such as the level of learning achievement, progress status, and evaluation method. For example, the monitoring unit monitors the learning progress of the apprentice delivery company in real time and adjusts the content of the provision unit as necessary. For example, if the apprentice delivery company does not understand a specific packaging method, the monitoring unit adjusts the learning so that that part is focused on. Furthermore, as the learning progresses, the apprentice delivery company can move on to more advanced content. This makes it possible to support efficient learning by monitoring the learning progress in real time and providing appropriate content. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can monitor the learning progress using an AI model that inputs the apprentice delivery company's learning progress data and outputs appropriate feedback.

[0034] The reception unit analyzes the apprentice delivery agent's past learning history and suggests an input method. The reception unit analyzes the apprentice delivery agent's past learning history. The past learning history includes, but is not limited to, the type of learning history, analysis method, and evaluation criteria. For example, the reception unit automatically displays information frequently input by the apprentice delivery agent in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the apprentice delivery agent has used in the past. The reception unit can also predict and suggest information that the apprentice delivery agent will use during a specific time period based on the apprentice delivery agent's past input history. This allows for efficient input by suggesting the optimal input method based on the apprentice delivery agent's past learning history. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can suggest an input method using an AI model that inputs the apprentice delivery agent's past learning history data and outputs an appropriate input method.

[0035] The reception unit customizes the input content based on the apprentice delivery agent's current work environment. The reception unit customizes the input content based on the apprentice delivery agent's current work environment. The current work environment includes, but is not limited to, the type of work environment, the customization method, and the evaluation criteria. For example, if the apprentice delivery agent is working outdoors, the reception unit uses highly visible fonts and large buttons. The reception unit can also prioritize voice input if the apprentice delivery agent is in a noisy environment. The reception unit can also adjust the sensitivity of the touchscreen if the apprentice delivery agent is wearing gloves. This allows for efficient input by providing input content appropriate to the current work environment. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can customize the input content using an AI model that inputs the apprentice delivery agent's work environment data and outputs appropriate input content.

[0036] The reception unit analyzes the input data of the trainee delivery agent in real time and provides immediate feedback. The reception unit analyzes the input data of the trainee delivery agent in real time. Real-time analysis includes, but is not limited to, a real-time time range, an analysis method, and a feedback method. For example, the reception unit immediately displays an error message if there is an error in the data entered by the trainee delivery agent. The reception unit can also display a guide to the next step when the trainee delivery agent completes input. The reception unit can also provide advice based on the input content in real time while the trainee delivery agent is inputting data. This allows for efficient learning by providing feedback in real time. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can provide feedback using an AI model that uses the input data of the trainee delivery agent as input and outputs appropriate feedback.

[0037] The reception unit proposes a region-specific packing method based on the geographical location information of the apprentice delivery company. The reception unit proposes a region-specific packing method taking into account the geographical location information of the apprentice delivery company. Geographical location information includes, but is not limited to, methods for acquiring location information, methods for using location information, and evaluation criteria. For example, if the apprentice delivery company is in a cold region, the reception unit proposes a method using cold-resistant packing materials. Furthermore, if the apprentice delivery company is in a humid region, the reception unit can also propose a moisture-proof packing method. Furthermore, if the apprentice delivery company is in a hot region, the reception unit can also propose a method using heat-resistant packing materials. In this way, by proposing a packing method based on the geographical location information, appropriate packing can be supported. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may propose a packing method using an AI model that inputs the geographical location information of the apprentice delivery company and outputs an appropriate packing method.

[0038] The reception unit analyzes the apprentice delivery agent's social media activity and acquires related input data. The reception unit analyzes the apprentice delivery agent's social media activity. Social media activity includes, but is not limited to, analysis methods, types of acquired data, and evaluation criteria. For example, the reception unit automatically sets input data based on the location where the apprentice delivery agent checked in on social media. The reception unit can also analyze the apprentice delivery agent's social media posts and input related data. The reception unit can also input related data based on the apprentice delivery agent's social media activities. This allows for efficient input support by acquiring data based on social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can acquire input data using an AI model that inputs the apprentice delivery agent's social media data and outputs appropriate input data.

[0039] The reception unit customizes the input interface by reflecting the apprentice delivery agent's past feedback. The reception unit customizes the input interface by reflecting the apprentice delivery agent's past feedback. Past feedback includes, but is not limited to, the type of feedback, the customization method, and the evaluation criteria. For example, the reception unit adjusts the interface layout based on the apprentice delivery agent's past feedback. The reception unit can also simplify the input procedure by reflecting the apprentice delivery agent's feedback. The reception unit can also adjust the color and font of the input interface based on the apprentice delivery agent's feedback. This allows for efficient input support by customizing the interface based on the apprentice delivery agent's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can customize the interface using an AI model that inputs the apprentice delivery agent's feedback data and outputs an appropriate interface.

[0040] The analysis unit improves the analysis accuracy by referring to the apprentice delivery company's past learning data during analysis. The analysis unit improves the analysis accuracy by referring to the apprentice delivery company's past learning data during analysis. The past learning data includes, but is not limited to, the type of learning data, the reference method, and the evaluation criteria. For example, the analysis unit adjusts the analysis algorithm based on the apprentice delivery company's past learning data. The analysis unit can also improve the accuracy of the analysis results by referring to the apprentice delivery company's past learning data. The analysis unit can also analyze the apprentice delivery company's past learning data and select an optimal analysis method. In this way, the analysis accuracy can be improved by referring to the past learning data. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can improve the analysis accuracy by using an AI model that inputs the apprentice delivery company's past learning data and outputs an appropriate analysis algorithm.

[0041] The analysis unit takes into account the attribute information of the apprentice delivery company when performing the analysis. The analysis unit takes into account the attribute information of the apprentice delivery company when performing the analysis. Attribute information includes, for example, the type of attribute information, analysis method, evaluation criteria, etc., but is not limited to these examples. For example, the analysis unit adjusts the analysis algorithm taking into account the age and years of experience of the apprentice delivery company. The analysis unit can also customize the analysis results based on the attribute information of the apprentice delivery company. The analysis unit can also select the optimal analysis method by referring to the attribute information of the apprentice delivery company. This makes it possible to provide highly accurate results through analysis that takes attribute information into account. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs the attribute information of the apprentice delivery company and outputs an appropriate analysis algorithm.

[0042] During analysis, the analysis unit weights the analysis based on the apprentice delivery agent's learning progress. During analysis, the analysis unit weights the analysis based on the apprentice delivery agent's learning progress. Learning progress includes, but is not limited to, the level of learning achievement, progress status, and weighting method. For example, the analysis unit adjusts the weighting of the analysis algorithm based on the apprentice delivery agent's learning progress. The analysis unit can also improve the accuracy of the analysis results by referring to the apprentice delivery agent's learning progress. The analysis unit can also select an optimal analysis method taking into account the apprentice delivery agent's learning progress. This enables efficient analysis through weighting based on learning progress. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can weight the analysis using an AI model that inputs the apprentice delivery agent's learning progress data and outputs appropriate weighting.

[0043] The analysis unit performs the analysis based on the geographic distribution of the apprentice delivery companies. The analysis unit performs the analysis while taking into account the geographic distribution of the apprentice delivery companies. Geographic distribution includes, but is not limited to, the type of distribution, the analysis method, and the evaluation criteria. For example, if apprentice delivery companies are concentrated in a specific area, the analysis unit performs the analysis while taking into account the characteristics of that area. The analysis unit can also customize the analysis results based on the geographic distribution of the apprentice delivery companies. The analysis unit can also select the optimal analysis method by referring to the geographic distribution of the apprentice delivery companies. This allows for analysis that takes geographic distribution into account to provide highly accurate results. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs the geographic distribution data of the apprentice delivery companies and outputs an appropriate analysis algorithm.

[0044] The analysis unit refers to related literature during analysis to improve the accuracy of the analysis. The analysis unit refers to related literature during analysis. Related literature includes, but is not limited to, for example, the type of literature, the reference method, and the evaluation criteria. For example, the analysis unit adjusts the analysis algorithm based on the related literature. The analysis unit can also improve the accuracy of the analysis results by referring to the related literature. The analysis unit can also analyze the related literature and select an optimal analysis method. In this way, by referring to the related literature, the analysis accuracy can be improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can improve the analysis accuracy by using an AI model that inputs related literature data and outputs an appropriate analysis algorithm.

[0045] The analysis unit performs the analysis based on the market value of the apprentice delivery company during the analysis. The analysis unit takes into account the market value of the apprentice delivery company during the analysis. Market value includes, but is not limited to, the type of market value, evaluation criteria, and analysis method. For example, the analysis unit adjusts the analysis algorithm based on the market value of the apprentice delivery company. The analysis unit can also customize the analysis results by referring to the market value of the apprentice delivery company. The analysis unit can also select the optimal analysis method by taking into account the market value of the apprentice delivery company. This allows for analysis that takes market value into account to provide highly accurate results. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs market value data of the apprentice delivery company and outputs an appropriate analysis algorithm.

[0046] The providing unit adjusts the level of detail of the provided content based on the priority of the packaging method and handling method when providing the information. The providing unit adjusts the level of detail of the provided content based on the priority of the packaging method and handling method when providing the information. Priority includes, but is not limited to, the type of priority, evaluation criteria, and adjustment method. For example, the providing unit provides detailed procedures and precautions for important packaging methods. The providing unit can also provide concise procedures for general packaging methods. The providing unit can also provide detailed illustrations and videos for special packaging methods. This makes it possible to provide appropriate information by adjusting the level of detail based on the priority. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can adjust the level of detail using an AI model that inputs priority data of the packaging method and handling method and outputs an appropriate level of detail.

[0047] The providing unit applies different provision algorithms depending on the category of the packing method or handling method when providing the information. The providing unit applies different provision algorithms depending on the category of the packing method or handling method when providing the information. The categories include, but are not limited to, the type of category, provision algorithms, and evaluation criteria. For example, the providing unit applies a specific algorithm to provide packaging methods for glass products. The providing unit can also apply a different algorithm to provide packaging methods for heavy luggage. The providing unit can also apply a dedicated algorithm to provide packaging methods for electronic devices. This makes it possible to provide appropriate information by applying an algorithm depending on the category. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can apply a provision algorithm using an AI model that inputs category data of the packing method or handling method and outputs an appropriate provision algorithm.

[0048] The provision unit improves the accuracy of the provided content by referring to the apprentice delivery company's past provision results when providing the content. The provision unit improves the accuracy of the provided content by referring to the apprentice delivery company's past provision results when providing the content. Past provision results include, but are not limited to, the type of provision result, the reference method, and the evaluation criteria. For example, the provision unit customizes the provided content based on the content learned by the apprentice delivery company in the past. The provision unit can also analyze the apprentice delivery company's past provision results to improve the accuracy of the provided content. The provision unit can also select the optimal delivery method by referring to the apprentice delivery company's past provision results. In this way, the accuracy of the provided content can be improved by referring to the past provision results. Some or all of the above-described processing by the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can improve the accuracy of the provided content by using an AI model that inputs the apprentice delivery company's past provision result data and outputs appropriate provided content.

[0049] The providing unit determines the priority of the provided content based on the time when the packaging method or handling information was submitted. The providing unit determines the priority of the provided content based on the time when the packaging method or handling information was submitted. The submission time includes, but is not limited to, the type of time, evaluation criteria, and a method for determining the priority. For example, the providing unit prioritizes the provision of packaging methods with high urgency. The providing unit can also set a high priority for packaging methods with an approaching submission deadline. The providing unit can also postpone packaging methods with more time to submit. This makes it possible to provide appropriate information by setting the priority based on the submission time. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can determine the priority using an AI model that inputs data on the submission time of the packaging method or handling information and outputs an appropriate priority.

[0050] The providing unit adjusts the order of the provided content based on the relevance of the packing method and handling method when providing the content. The providing unit adjusts the order of the provided content based on the relevance of the packing method and handling method when providing the content. The relevance includes, but is not limited to, the type of relevance, evaluation criteria, and order adjustment method. For example, the providing unit provides highly relevant packing methods consecutively. The providing unit can also postpone less relevant packing methods. The providing unit can also prioritize providing highly relevant content according to the apprentice delivery person's learning progress. This makes it possible to support efficient learning by adjusting the order based on the relevance. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can adjust the order using an AI model that inputs relevance data of packing methods and handling methods and outputs an appropriate order.

[0051] The providing unit adjusts the use of technical terms in the provided content according to the expertise level of the apprentice delivery agent when providing the content. The providing unit adjusts the use of technical terms in the provided content according to the expertise level of the apprentice delivery agent when providing the content. Expertise level includes, but is not limited to, the type of knowledge level, evaluation criteria, and adjustment method. For example, if the apprentice delivery agent is a beginner, the providing unit may avoid technical terms and provide explanations in simple terms. Furthermore, if the apprentice delivery agent is an intermediate level apprentice, the providing unit may provide explanations using technical terms appropriately. Furthermore, if the apprentice delivery agent is an advanced level apprentice, the providing unit may provide detailed explanations using a lot of technical terms. This allows appropriate information to be provided by using technical terms appropriate to the expertise level. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may adjust the use of technical terms using an AI model that inputs the expertise level data of the apprentice delivery agent and outputs appropriate use of technical terms.

[0052] The monitoring unit improves monitoring accuracy based on the apprentice delivery agent's past learning progress during monitoring. The monitoring unit references the apprentice delivery agent's past learning progress during monitoring. Past learning progress includes, but is not limited to, the type of learning progress, the reference method, and the evaluation criteria. For example, the monitoring unit adjusts the monitoring algorithm based on the apprentice delivery agent's past learning progress. The monitoring unit can also improve the accuracy of the monitoring results by referencing the apprentice delivery agent's past learning progress. The monitoring unit can also analyze the apprentice delivery agent's past learning progress and select an optimal monitoring method. In this way, by referencing the past learning progress, monitoring accuracy can be improved. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can improve monitoring accuracy using an AI model that inputs the apprentice delivery agent's past learning progress data and outputs an appropriate monitoring algorithm.

[0053] The monitoring unit performs monitoring while taking into account the attribute information of the apprentice delivery company. The monitoring unit performs monitoring while taking into account the attribute information of the apprentice delivery company. The attribute information includes, but is not limited to, the type of attribute information, analysis method, and evaluation criteria. For example, the monitoring unit adjusts the monitoring algorithm while taking into account the age and years of experience of the apprentice delivery company. The monitoring unit can also customize the monitoring results based on the attribute information of the apprentice delivery company. The monitoring unit can also select the optimal monitoring method by referring to the attribute information of the apprentice delivery company. This allows for monitoring that takes the attribute information into account to provide highly accurate results. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can perform monitoring using an AI model that inputs the attribute information of the apprentice delivery company and outputs an appropriate monitoring algorithm.

[0054] The monitoring unit weights the monitoring based on the learning progress of the apprentice delivery agent during monitoring. The monitoring unit weights the monitoring based on the learning progress of the apprentice delivery agent during monitoring. Learning progress includes, but is not limited to, the level of learning achievement, progress status, and weighting method. For example, the monitoring unit adjusts the weighting of the monitoring algorithm based on the learning progress of the apprentice delivery agent. The monitoring unit can also improve the accuracy of the monitoring results by referring to the learning progress of the apprentice delivery agent. The monitoring unit can also select an optimal monitoring method taking into account the learning progress of the apprentice delivery agent. This enables efficient monitoring through weighting based on learning progress. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can weight the monitoring using an AI model that inputs learning progress data of the apprentice delivery agent and outputs appropriate weighting.

[0055] The monitoring unit performs monitoring based on the geographic distribution of apprentice delivery companies. The monitoring unit performs monitoring while taking into account the geographic distribution of apprentice delivery companies. Geographic distribution includes, but is not limited to, the type of distribution, analysis method, and evaluation criteria. For example, if apprentice delivery companies are concentrated in a specific area, the monitoring unit performs monitoring while taking into account the characteristics of that area. The monitoring unit can also customize the monitoring results based on the geographic distribution of apprentice delivery companies. The monitoring unit can also select the optimal monitoring method by referring to the geographic distribution of apprentice delivery companies. This allows for monitoring that takes geographic distribution into account, thereby providing highly accurate results. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can perform monitoring using an AI model that inputs geographic distribution data of apprentice delivery companies and outputs an appropriate monitoring algorithm.

[0056] The monitoring unit improves monitoring accuracy by referring to related literature during monitoring. The monitoring unit refers to related literature during monitoring. Related literature includes, but is not limited to, the type of literature, the reference method, and the evaluation criteria. For example, the monitoring unit adjusts the monitoring algorithm based on the related literature. The monitoring unit can also improve the accuracy of the monitoring results by referring to the related literature. The monitoring unit can also analyze the related literature and select an optimal monitoring method. In this way, by referring to the related literature, monitoring accuracy can be improved. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can improve monitoring accuracy by using an AI model that inputs related literature data and outputs an appropriate monitoring algorithm.

[0057] The monitoring unit performs monitoring based on the market value of the apprentice delivery company during monitoring. The monitoring unit performs monitoring while taking into account the market value of the apprentice delivery company during monitoring. Market value includes, but is not limited to, the type of market value, evaluation criteria, and analysis method. For example, the monitoring unit adjusts the monitoring algorithm based on the market value of the apprentice delivery company. The monitoring unit can also customize the monitoring results by referring to the market value of the apprentice delivery company. The monitoring unit can also select the optimal monitoring method by taking into account the market value of the apprentice delivery company. This enables monitoring that takes market value into account to provide highly accurate results. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can perform monitoring using an AI model that inputs market value data of the apprentice delivery company and outputs an appropriate monitoring algorithm.

[0058] When providing a scenario, the scenario unit provides the scenario by referring to the apprentice delivery agent's past scenario history. When providing a scenario, the scenario unit refers to the apprentice delivery agent's past scenario history. The past scenario history includes, for example, but is not limited to, the type of scenario history, the reference method, and the evaluation criteria. For example, the scenario unit provides an optimal scenario based on the apprentice delivery agent's past scenario history. The scenario unit can also customize the scenario content by referring to the apprentice delivery agent's past scenario history. The scenario unit can also analyze the apprentice delivery agent's past scenario history and select the optimal scenario. In this way, the optimal scenario can be provided by referring to the past scenario history. Some or all of the above-described processing in the scenario unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario unit can provide a scenario using an AI model that inputs the apprentice delivery agent's past scenario history data and outputs an appropriate scenario.

[0059] When providing a scenario, the scenario unit customizes the scenario based on the apprentice delivery agent's current work status. When providing a scenario, the scenario unit customizes the scenario based on the apprentice delivery agent's current work status. The current work status includes, but is not limited to, the type of work status, the customization method, and the evaluation criteria. For example, the scenario unit provides a scenario related to the work currently being performed by the apprentice delivery agent. The scenario unit can also adjust the scenario content based on the apprentice delivery agent's current work status. The scenario unit can also select an optimal scenario by referring to the apprentice delivery agent's current work status. This enables efficient learning by customizing the scenario based on the current work status. Some or all of the above-described processing in the scenario unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario unit can customize a scenario using an AI model that inputs the apprentice delivery agent's work status data and outputs an appropriate scenario.

[0060] When providing a scenario, the scenario unit provides an optimal scenario taking into account the geographical location information of the apprentice delivery agent. When providing a scenario, the scenario unit provides an optimal scenario taking into account the geographical location information of the apprentice delivery agent. Geographical location information includes, but is not limited to, methods for acquiring location information, methods for using location information, and evaluation criteria. For example, if the apprentice delivery agent is in a specific area, the scenario unit provides a scenario taking into account the characteristics of that area. The scenario unit can also customize the scenario content based on the geographical location information of the apprentice delivery agent. The scenario unit can also select an optimal scenario by referencing the geographical location information of the apprentice delivery agent. This makes it possible to provide appropriate information by providing a scenario based on geographical location information. Some or all of the above-described processing in the scenario unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario unit can provide a scenario using an AI model that inputs the geographical location information of the apprentice delivery agent and outputs an appropriate scenario.

[0061] When providing a scenario, the scenario unit analyzes the apprentice delivery agent's social media activity and provides a related scenario. When providing a scenario, the scenario unit analyzes the apprentice delivery agent's social media activity. Social media activity includes, but is not limited to, examples of analysis methods, types of acquired data, and evaluation criteria. For example, the scenario unit provides a scenario related to the locations where the apprentice delivery agent checked in on social media. The scenario unit can also analyze the apprentice delivery agent's social media posts and provide a related scenario. The scenario unit can also provide a related scenario based on the apprentice delivery agent's social media activities. This allows for efficient learning by providing a scenario based on social media activity. Some or all of the above-described processing in the scenario unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario unit can provide a scenario using an AI model that inputs the apprentice delivery agent's social media data and outputs an appropriate scenario.

[0062] When providing media, the media unit refers to the apprentice delivery agent's past media history to provide the media. When providing media, the media unit refers to the apprentice delivery agent's past media history. Past media history includes, but is not limited to, for example, the type of media history, the reference method, and the evaluation criteria. For example, the media unit provides optimal media based on the apprentice delivery agent's past media history. The media unit can also customize the media content by referring to the apprentice delivery agent's past media history. The media unit can also analyze the apprentice delivery agent's past media history and select optimal media. In this way, optimal media can be provided by referring to the past media history. Some or all of the above-described processing in the media unit may be performed using, for example, AI, or may be performed without using AI. For example, the media unit can provide media using an AI model that inputs the apprentice delivery agent's past media history data and outputs appropriate media.

[0063] The media unit customizes the media based on the apprentice delivery agent's current work status when providing the media. The media unit customizes the media based on the apprentice delivery agent's current work status when providing the media. The current work status includes, but is not limited to, the type of work status, the customization method, and the evaluation criteria. For example, the media unit provides media related to the work currently being performed by the apprentice delivery agent. The media unit can also adjust the media content based on the apprentice delivery agent's current work status. The media unit can also select the optimal media by referring to the apprentice delivery agent's current work status. This enables efficient learning by customizing the media based on the current work status. Some or all of the above-described processing in the media unit may be performed using, for example, AI, or may be performed without using AI. For example, the media unit can customize the media using an AI model that inputs the apprentice delivery agent's work status data and outputs appropriate media.

[0064] The media unit provides the optimal media by taking into consideration the geographic location information of the apprentice delivery agent when providing the media. The media unit provides the optimal media by taking into consideration the geographic location information of the apprentice delivery agent when providing the media. Geographic location information includes, but is not limited to, methods for acquiring location information, methods for using location information, and evaluation criteria. For example, if the apprentice delivery agent is in a specific area, the media unit provides media by taking into consideration the characteristics of that area. The media unit can also customize media content based on the geographic location information of the apprentice delivery agent. The media unit can also select the optimal media by referring to the geographic location information of the apprentice delivery agent. This allows appropriate information to be provided by providing media based on the geographic location information. Some or all of the above-described processing in the media unit may be performed using, for example, AI, or may be performed without using AI. For example, the media unit can provide media using an AI model that inputs the geographic location information of the apprentice delivery agent and outputs appropriate media.

[0065] The media unit analyzes the apprentice delivery agent's social media activity when providing the media and provides related media. The media unit analyzes the apprentice delivery agent's social media activity when providing the media. Social media activity includes, but is not limited to, analysis methods, types of acquired data, and evaluation criteria. For example, the media unit provides media related to locations where the apprentice delivery agent checked in on social media. The media unit can also analyze the apprentice delivery agent's social media posts and provide related media. The media unit can also provide related media based on the apprentice delivery agent's social media activities. This allows for efficient learning by providing media based on social media activity. Some or all of the above-described processing in the media unit may be performed using, for example, AI, or may be performed without AI. For example, the media unit can provide media using an AI model that inputs the apprentice delivery agent's social media data and outputs appropriate media.

[0066] When providing a simulation, the simulation unit provides the simulation by referring to the apprentice delivery agent's past simulation history. When providing a simulation, the simulation unit references the apprentice delivery agent's past simulation history. The past simulation history includes, for example, the type of simulation history, the reference method, and the evaluation criteria, but is not limited to these examples. For example, the simulation unit provides an optimal simulation based on the apprentice delivery agent's past simulation history. The simulation unit can also customize the simulation content by referring to the apprentice delivery agent's past simulation history. The simulation unit can also analyze the apprentice delivery agent's past simulation history and select the optimal simulation. In this way, by referring to the past simulation history, the optimal simulation can be provided. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can provide a simulation using an AI model that inputs the apprentice delivery agent's past simulation history data and outputs an appropriate simulation.

[0067] The simulation unit customizes the simulation based on the apprentice delivery agent's current work status when providing the simulation. The simulation unit customizes the simulation based on the apprentice delivery agent's current work status when providing the simulation. The current work status includes, but is not limited to, the type of work status, the customization method, and the evaluation criteria. For example, the simulation unit provides a simulation related to the work currently being performed by the apprentice delivery agent. The simulation unit can also adjust the simulation content based on the apprentice delivery agent's current work status. The simulation unit can also select the optimal simulation by referring to the apprentice delivery agent's current work status. This allows for efficient learning by customizing the simulation based on the current work status. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can customize the simulation using an AI model that inputs the apprentice delivery agent's work status data and outputs an appropriate simulation.

[0068] The simulation unit provides an optimal simulation by taking into account the geographical location information of the apprentice delivery agent when providing the simulation. The simulation unit provides an optimal simulation by taking into account the geographical location information of the apprentice delivery agent when providing the simulation. Geographical location information includes, but is not limited to, methods for acquiring location information, methods for using location information, and evaluation criteria. For example, if the apprentice delivery agent is in a specific area, the simulation unit provides a simulation by taking into account the characteristics of that area. The simulation unit can also customize the simulation content based on the geographical location information of the apprentice delivery agent. The simulation unit can also select an optimal simulation by referring to the geographical location information of the apprentice delivery agent. This makes it possible to provide appropriate information by providing a simulation based on the geographical location information. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can provide a simulation using an AI model that inputs the geographical location information of the apprentice delivery agent and outputs an appropriate simulation.

[0069] The simulation unit analyzes the apprentice delivery agent's social media activity when providing a simulation and provides a related simulation. The simulation unit analyzes the apprentice delivery agent's social media activity when providing a simulation. Social media activity includes, but is not limited to, analysis methods, types of acquired data, and evaluation criteria. For example, the simulation unit provides a simulation related to the locations where the apprentice delivery agent checked in on social media. The simulation unit can also analyze the apprentice delivery agent's social media posts and provide a related simulation. The simulation unit can also provide a related simulation based on the apprentice delivery agent's social media activities. This allows for efficient learning by providing a simulation based on social media activity. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without AI. For example, the simulation unit can provide a simulation using an AI model that inputs the apprentice delivery agent's social media data and outputs an appropriate simulation.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The analysis unit can also adjust the difficulty of the learning content based on the apprentice delivery worker's past learning data. For example, if the apprentice delivery worker did not previously understand a specific packaging method, the analysis unit can adjust the learning so that the apprentice re-learns that part. Also, if the apprentice delivery worker has already mastered a specific skill, the analysis unit can provide more advanced content related to that skill. Furthermore, the analysis unit can adjust the progress speed of the learning content according to the apprentice delivery worker's learning speed. This enables efficient learning that matches the individual learning pace of the apprentice delivery worker.

[0072] The media department can also suggest new media related to the apprentice delivery worker's viewing history based on the apprentice delivery worker's past media viewing history. For example, it can suggest new videos related to a video on packing methods that the apprentice delivery worker has previously viewed. It can also provide new illustrations related to illustrations that the apprentice delivery worker has previously viewed. Furthermore, it can prioritize suggesting media with high ratings based on the apprentice delivery worker's ratings of media that he or she has previously viewed. This makes it possible to support efficient learning by suggesting media based on past viewing history.

[0073] The monitoring unit can also predict learning progress based on the apprentice delivery agent's past learning progress data. For example, it can predict future learning progress based on the time it took the apprentice delivery agent to acquire a specific skill in the past. It can also analyze the apprentice delivery agent's past learning progress data to identify learning bottlenecks. Furthermore, it can optimize learning plans based on the apprentice delivery agent's past learning progress data. This makes it possible to support efficient learning through predictions and optimization based on past learning progress data.

[0074] The analysis unit can also optimize the order of learning content based on the apprentice delivery worker's past learning data. For example, if the apprentice delivery worker had difficulty mastering a particular skill in the past, content related to that skill can be provided early. It can also postpone content related to skills in which the apprentice delivery worker excelled in the past. Furthermore, the order of learning content can be dynamically adjusted based on the apprentice delivery worker's past learning data. This makes it possible to support efficient learning by optimizing the order based on past learning data.

[0075] The scenario unit can also adjust the speed at which a scenario progresses based on the apprentice delivery agent's past scenario history. For example, if the apprentice delivery agent has completed a particular scenario quickly in the past, the speed at which the next scenario progresses can be increased. Also, if the apprentice delivery agent has taken a long time to complete a particular scenario in the past, the speed at which the next scenario progresses can be decreased. Furthermore, the scenario unit can dynamically adjust the speed at which a scenario progresses based on the apprentice delivery agent's past scenario history. This can support efficient learning by adjusting the speed at which the scenario progresses based on the apprentice delivery agent's past scenario history.

[0076] The processing flow of the first embodiment will be briefly explained below.

[0077] Step 1: The reception unit inputs the apprentice delivery agent's information. The apprentice delivery agent's information includes, for example, years of experience, job description, skill level, etc. The reception unit allows the apprentice delivery agent to log in to the system and input their understanding level and progress. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit analyzes the apprentice delivery worker's level of understanding and progress, and determines the appropriate learning content. Step 3: The provision unit provides packing or handling methods based on the results of the analysis by the analysis unit. The provided content includes the type of packaging material, packing procedures, handling precautions, etc. For example, learning content is provided based on specific scenarios, such as how to pack fragile glass products or how to lift heavy luggage. Step 4: The monitoring department monitors the learning progress based on the content provided by the provision department. The monitoring of learning progress is based on criteria such as learning achievement level, progress status, and evaluation method. The monitoring department monitors the learning progress of the trainee delivery agent in real time and adjusts the content provided by the provision department as necessary.

[0078] (Example 2) A system according to an embodiment of the present invention provides delivery workers with the correct packaging and handling methods to avoid breakage, along with specific scenarios. This system supports efficient learning by adapting the content to the trainee's level of understanding and progress. For example, a trainee logs in to the system and inputs their level of understanding and progress. The generation AI then analyzes the information and provides appropriate packaging and handling methods along with specific scenarios. For example, training can be based on specific scenarios, such as how to pack fragile glass products or how to lift heavy loads. Furthermore, the generation AI monitors the trainee's learning progress in real time and adjusts the content as needed. For example, if a trainee does not understand a particular packaging method, the system adjusts the training to focus on that aspect. As the training progresses, the system also allows trainees to progress to more advanced content. This allows trainees to efficiently learn the correct packaging and handling methods and acquire the skills to avoid breakage. For example, when learning how to pack glass products, specific steps and precautions are provided using videos and diagrams, making them useful for actual work. Furthermore, when learning how to lift heavy loads, the system can teach the correct posture and how to apply force through simulations. In this way, by utilizing generative AI, it becomes possible for trainee delivery workers to learn efficiently according to their level of understanding and progress, and they can effectively acquire the skills to avoid damage.

[0079] A learning support system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a monitoring unit. The reception unit inputs information about the apprentice delivery person. The information about the apprentice delivery person includes, but is not limited to, years of experience, job content, and skill level. The reception unit, for example, allows the apprentice delivery person to log in to the system and input their level of understanding and progress. The analysis unit analyzes the information input by the reception unit. The analysis may be performed using, for example, but is not limited to, data mining, statistical analysis, or a machine learning algorithm. For example, the analysis unit analyzes the apprentice delivery person's level of understanding and progress and determines appropriate learning content. The provision unit provides packaging or handling methods based on the results of the analysis by the analysis unit. The provided content includes, for example, but is not limited to, types of packaging materials, packing procedures, and handling precautions. For example, the provision unit provides learning content based on specific scenarios, such as how to pack fragile glass products or how to lift heavy loads. The monitoring unit monitors the learning progress based on the content provided by the provision unit. The monitoring of learning progress is performed based on criteria such as, but not limited to, the level of learning achievement, progress status, and evaluation method. For example, the monitoring unit monitors the learning progress of the apprentice delivery company in real time and adjusts the content of the providing unit as necessary. As a result, the learning support system according to the embodiment can support efficient learning by providing appropriate packaging and handling methods based on the information of the apprentice delivery company and monitoring the learning progress.

[0080] The providing unit cooperates with a scenario unit that provides specific scenarios. The scenario unit provides the specific scenarios. Specific scenarios include, but are not limited to, how to pack fragile glass products and how to lift heavy loads. For example, the scenario unit provides a scenario that specifically shows the steps and precautions that a trainee delivery person should take when packing glass products. The scenario unit can also provide a scenario that shows the correct posture and how to apply force when lifting heavy loads. By providing specific scenarios, the trainee delivery person can deepen their understanding. Some or all of the above-described processing in the scenario unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario unit can provide a scenario using an AI model that inputs information about the trainee delivery person and outputs an appropriate scenario.

[0081] The provision unit works in cooperation with a media unit that provides videos or illustrations. The media unit provides the videos or illustrations. Examples of the videos and illustrations provided include, but are not limited to, videos showing steps for packaging and illustrations showing handling precautions. For example, the media unit provides a video showing how to package glass products. The media unit can also provide illustrations showing how to lift heavy packages. In this way, providing videos and illustrations can support learning that is easy to understand visually. Some or all of the above-described processing in the media unit may be performed using, for example, AI, or may be performed without using AI. For example, the media unit can provide media using an AI model that inputs information about trainee delivery personnel and outputs appropriate videos and illustrations.

[0082] The providing unit cooperates with a simulation unit that provides simulations. The simulation unit provides the simulations. The provided simulations include, but are not limited to, simulations of how to lift heavy loads and how to pack glass products. For example, the simulation unit simulates the correct posture and force to apply when lifting heavy loads. The simulation unit can also simulate how to pack glass products. This allows for providing simulations to support practical learning. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit may provide a simulation using an AI model that inputs information about a trainee delivery person and outputs an appropriate simulation.

[0083] The monitoring unit monitors the learning progress and adjusts the content of the provision unit as necessary. The monitoring unit monitors the learning progress. Monitoring of the learning progress includes, but is not limited to, criteria such as the level of learning achievement, progress status, and evaluation method. For example, the monitoring unit monitors the learning progress of the apprentice delivery company in real time and adjusts the content of the provision unit as necessary. For example, if the apprentice delivery company does not understand a specific packaging method, the monitoring unit adjusts the learning so that that part is focused on. Furthermore, as the learning progresses, the apprentice delivery company can move on to more advanced content. This makes it possible to support efficient learning by monitoring the learning progress in real time and providing appropriate content. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can monitor the learning progress using an AI model that inputs the apprentice delivery company's learning progress data and outputs appropriate feedback.

[0084] The reception unit estimates the emotion of the trainee delivery agent and adjusts the display method of the input interface based on the estimated emotion. The reception unit estimates the emotion of the trainee delivery agent. Emotion estimation includes, but is not limited to, emotion recognition technology, emotion types, and estimation algorithms. For example, if the trainee delivery agent is nervous, the reception unit provides a calming interface to reduce visual stress. If the trainee delivery agent is relaxed, the reception unit can provide a brightly colored interface to make input work more enjoyable. If the trainee delivery agent is tired, the reception unit can provide a simple, highly visible interface to make input work easier. This provides an interface that corresponds to the trainee delivery agent's emotion, reducing stress and supporting efficient input. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception department can adjust the interface using an AI model that takes the trainee delivery worker's emotional data as input and outputs an appropriate interface.

[0085] The reception unit analyzes the apprentice delivery agent's past learning history and suggests an input method. The reception unit analyzes the apprentice delivery agent's past learning history. The past learning history includes, but is not limited to, the type of learning history, analysis method, and evaluation criteria. For example, the reception unit automatically displays information frequently input by the apprentice delivery agent in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the apprentice delivery agent has used in the past. The reception unit can also predict and suggest information that the apprentice delivery agent will use during a specific time period based on the apprentice delivery agent's past input history. This allows for efficient input by suggesting the optimal input method based on the apprentice delivery agent's past learning history. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can suggest an input method using an AI model that inputs the apprentice delivery agent's past learning history data and outputs an appropriate input method.

[0086] The reception unit customizes the input content based on the apprentice delivery agent's current work environment. The reception unit customizes the input content based on the apprentice delivery agent's current work environment. The current work environment includes, but is not limited to, the type of work environment, the customization method, and the evaluation criteria. For example, if the apprentice delivery agent is working outdoors, the reception unit uses highly visible fonts and large buttons. The reception unit can also prioritize voice input if the apprentice delivery agent is in a noisy environment. The reception unit can also adjust the sensitivity of the touchscreen if the apprentice delivery agent is wearing gloves. This allows for efficient input by providing input content appropriate to the current work environment. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can customize the input content using an AI model that inputs the apprentice delivery agent's work environment data and outputs appropriate input content.

[0087] The reception unit analyzes the input data of the trainee delivery agent in real time and provides immediate feedback. The reception unit analyzes the input data of the trainee delivery agent in real time. Real-time analysis includes, but is not limited to, a real-time time range, an analysis method, and a feedback method. For example, the reception unit immediately displays an error message if there is an error in the data entered by the trainee delivery agent. The reception unit can also display a guide to the next step when the trainee delivery agent completes input. The reception unit can also provide advice based on the input content in real time while the trainee delivery agent is inputting data. This allows for efficient learning by providing feedback in real time. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can provide feedback using an AI model that uses the input data of the trainee delivery agent as input and outputs appropriate feedback.

[0088] The reception unit estimates the emotions of the trainee delivery agents and prioritizes input data based on the estimated emotions. The reception unit estimates the emotions of the trainee delivery agents. Emotion estimation includes, but is not limited to, emotion recognition technology, emotion types, and estimation algorithms. For example, if the trainee delivery agent is stressed, the reception unit prioritizes important data input and postpones other inputs. Furthermore, if the trainee delivery agent is relaxed, the reception unit can also perform all data input evenly. Furthermore, if the trainee delivery agent is in a hurry, the reception unit can also perform the most important data input first. This allows for efficient data input by setting priorities based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception department can determine priorities using an AI model that takes emotional data from trainee delivery workers as input and outputs appropriate priorities.

[0089] The reception unit proposes a region-specific packing method based on the geographical location information of the apprentice delivery company. The reception unit proposes a region-specific packing method taking into account the geographical location information of the apprentice delivery company. Geographical location information includes, but is not limited to, methods for acquiring location information, methods for using location information, and evaluation criteria. For example, if the apprentice delivery company is in a cold region, the reception unit proposes a method using cold-resistant packing materials. Furthermore, if the apprentice delivery company is in a humid region, the reception unit can also propose a moisture-proof packing method. Furthermore, if the apprentice delivery company is in a hot region, the reception unit can also propose a method using heat-resistant packing materials. In this way, by proposing a packing method based on the geographical location information, appropriate packing can be supported. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may propose a packing method using an AI model that inputs the geographical location information of the apprentice delivery company and outputs an appropriate packing method.

[0090] The reception unit analyzes the apprentice delivery agent's social media activity and acquires related input data. The reception unit analyzes the apprentice delivery agent's social media activity. Social media activity includes, but is not limited to, analysis methods, types of acquired data, and evaluation criteria. For example, the reception unit automatically sets input data based on the location where the apprentice delivery agent checked in on social media. The reception unit can also analyze the apprentice delivery agent's social media posts and input related data. The reception unit can also input related data based on the apprentice delivery agent's social media activities. This allows for efficient input support by acquiring data based on social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can acquire input data using an AI model that inputs the apprentice delivery agent's social media data and outputs appropriate input data.

[0091] The reception unit customizes the input interface by reflecting the apprentice delivery agent's past feedback. The reception unit customizes the input interface by reflecting the apprentice delivery agent's past feedback. Past feedback includes, but is not limited to, the type of feedback, the customization method, and the evaluation criteria. For example, the reception unit adjusts the interface layout based on the apprentice delivery agent's past feedback. The reception unit can also simplify the input procedure by reflecting the apprentice delivery agent's feedback. The reception unit can also adjust the color and font of the input interface based on the apprentice delivery agent's feedback. This allows for efficient input support by customizing the interface based on the apprentice delivery agent's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can customize the interface using an AI model that inputs the apprentice delivery agent's feedback data and outputs an appropriate interface.

[0092] The analysis unit estimates the emotions of the trainee delivery agent and adjusts the analysis algorithm based on the estimated emotions. The analysis unit estimates the emotions of the trainee delivery agent. Emotion estimation includes, but is not limited to, emotion recognition technology, emotion types, and estimation algorithms. For example, if the trainee delivery agent is nervous, the analysis unit simplifies the analysis algorithm to provide quick results. Furthermore, if the trainee delivery agent is relaxed, the analysis unit can perform a detailed analysis to provide highly accurate results. Furthermore, if the trainee delivery agent is tired, the analysis unit can optimize the analysis algorithm to reduce the burden. This allows for efficient analysis by adjusting the analysis algorithm based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can adjust the analysis algorithm using an AI model that takes the emotional data of trainee delivery workers as input and outputs an appropriate analysis algorithm.

[0093] The analysis unit improves the analysis accuracy by referring to the apprentice delivery company's past learning data during analysis. The analysis unit improves the analysis accuracy by referring to the apprentice delivery company's past learning data during analysis. The past learning data includes, but is not limited to, the type of learning data, the reference method, and the evaluation criteria. For example, the analysis unit adjusts the analysis algorithm based on the apprentice delivery company's past learning data. The analysis unit can also improve the accuracy of the analysis results by referring to the apprentice delivery company's past learning data. The analysis unit can also analyze the apprentice delivery company's past learning data and select an optimal analysis method. In this way, the analysis accuracy can be improved by referring to the past learning data. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can improve the analysis accuracy by using an AI model that inputs the apprentice delivery company's past learning data and outputs an appropriate analysis algorithm.

[0094] The analysis unit takes into account the attribute information of the apprentice delivery company when performing the analysis. The analysis unit takes into account the attribute information of the apprentice delivery company when performing the analysis. Attribute information includes, for example, the type of attribute information, analysis method, evaluation criteria, etc., but is not limited to these examples. For example, the analysis unit adjusts the analysis algorithm taking into account the age and years of experience of the apprentice delivery company. The analysis unit can also customize the analysis results based on the attribute information of the apprentice delivery company. The analysis unit can also select the optimal analysis method by referring to the attribute information of the apprentice delivery company. This makes it possible to provide highly accurate results through analysis that takes attribute information into account. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs the attribute information of the apprentice delivery company and outputs an appropriate analysis algorithm.

[0095] During analysis, the analysis unit weights the analysis based on the apprentice delivery agent's learning progress. During analysis, the analysis unit weights the analysis based on the apprentice delivery agent's learning progress. Learning progress includes, but is not limited to, the level of learning achievement, progress status, and weighting method. For example, the analysis unit adjusts the weighting of the analysis algorithm based on the apprentice delivery agent's learning progress. The analysis unit can also improve the accuracy of the analysis results by referring to the apprentice delivery agent's learning progress. The analysis unit can also select an optimal analysis method taking into account the apprentice delivery agent's learning progress. This enables efficient analysis through weighting based on learning progress. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can weight the analysis using an AI model that inputs the apprentice delivery agent's learning progress data and outputs appropriate weighting.

[0096] The analysis unit estimates the emotions of the apprentice delivery agent and adjusts the display method of the analysis results based on the estimated emotions. The analysis unit estimates the emotions of the apprentice delivery agent. Emotion estimation includes, but is not limited to, emotion recognition technology, emotion types, and estimation algorithms. For example, if the apprentice delivery agent is nervous, the analysis unit provides a simple, highly visible display method. If the apprentice delivery agent is relaxed, the analysis unit can provide a display method that includes detailed information. If the apprentice delivery agent is tired, the analysis unit can provide a display method that focuses on the main points. This allows for highly visible results to be provided by adjusting the display method based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can adjust the display method using an AI model that takes the emotional data of trainee delivery workers as input and outputs an appropriate display method.

[0097] The analysis unit performs the analysis based on the geographic distribution of the apprentice delivery companies. The analysis unit performs the analysis while taking into account the geographic distribution of the apprentice delivery companies. Geographic distribution includes, but is not limited to, the type of distribution, the analysis method, and the evaluation criteria. For example, if apprentice delivery companies are concentrated in a specific area, the analysis unit performs the analysis while taking into account the characteristics of that area. The analysis unit can also customize the analysis results based on the geographic distribution of the apprentice delivery companies. The analysis unit can also select the optimal analysis method by referring to the geographic distribution of the apprentice delivery companies. This allows for analysis that takes geographic distribution into account to provide highly accurate results. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs the geographic distribution data of the apprentice delivery companies and outputs an appropriate analysis algorithm.

[0098] The analysis unit refers to related literature during analysis to improve the accuracy of the analysis. The analysis unit refers to related literature during analysis. Related literature includes, but is not limited to, for example, the type of literature, the reference method, and the evaluation criteria. For example, the analysis unit adjusts the analysis algorithm based on the related literature. The analysis unit can also improve the accuracy of the analysis results by referring to the related literature. The analysis unit can also analyze the related literature and select an optimal analysis method. In this way, by referring to the related literature, the analysis accuracy can be improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can improve the analysis accuracy by using an AI model that inputs related literature data and outputs an appropriate analysis algorithm.

[0099] The analysis unit performs the analysis based on the market value of the apprentice delivery company during the analysis. The analysis unit takes into account the market value of the apprentice delivery company during the analysis. Market value includes, but is not limited to, the type of market value, evaluation criteria, and analysis method. For example, the analysis unit adjusts the analysis algorithm based on the market value of the apprentice delivery company. The analysis unit can also customize the analysis results by referring to the market value of the apprentice delivery company. The analysis unit can also select the optimal analysis method by taking into account the market value of the apprentice delivery company. This allows for analysis that takes market value into account to provide highly accurate results. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs market value data of the apprentice delivery company and outputs an appropriate analysis algorithm.

[0100] The providing unit estimates the emotion of the apprentice delivery agent and adjusts the expression method of the provided content based on the estimated emotion. The providing unit estimates the emotion of the apprentice delivery agent. Emotion estimation includes, but is not limited to, emotion recognition technology, emotion types, and estimation algorithms. For example, if the apprentice delivery agent is nervous, the providing unit provides a simple, highly visible expression method. Furthermore, if the apprentice delivery agent is relaxed, the providing unit can provide an expression method that includes detailed information. Furthermore, if the apprentice delivery agent is tired, the providing unit can provide an expression method that focuses on the main points. This allows for highly visible content to be provided by adjusting the expression method based on emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or without AI. For example, the providing unit can adjust the expression method using an AI model that inputs the emotional data of the trainee delivery person and outputs an appropriate expression method.

[0101] The providing unit adjusts the level of detail of the provided content based on the priority of the packaging method and handling method when providing the information. The providing unit adjusts the level of detail of the provided content based on the priority of the packaging method and handling method when providing the information. Priority includes, but is not limited to, the type of priority, evaluation criteria, and adjustment method. For example, the providing unit provides detailed procedures and precautions for important packaging methods. The providing unit can also provide concise procedures for general packaging methods. The providing unit can also provide detailed illustrations and videos for special packaging methods. This makes it possible to provide appropriate information by adjusting the level of detail based on the priority. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can adjust the level of detail using an AI model that inputs priority data of the packaging method and handling method and outputs an appropriate level of detail.

[0102] The providing unit applies different provision algorithms depending on the category of the packing method or handling method when providing the information. The providing unit applies different provision algorithms depending on the category of the packing method or handling method when providing the information. The categories include, but are not limited to, the type of category, provision algorithms, and evaluation criteria. For example, the providing unit applies a specific algorithm to provide packaging methods for glass products. The providing unit can also apply a different algorithm to provide packaging methods for heavy luggage. The providing unit can also apply a dedicated algorithm to provide packaging methods for electronic devices. This makes it possible to provide appropriate information by applying an algorithm depending on the category. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can apply a provision algorithm using an AI model that inputs category data of the packing method or handling method and outputs an appropriate provision algorithm.

[0103] The provision unit improves the accuracy of the provided content by referring to the apprentice delivery company's past provision results when providing the content. The provision unit improves the accuracy of the provided content by referring to the apprentice delivery company's past provision results when providing the content. Past provision results include, but are not limited to, the type of provision result, the reference method, and the evaluation criteria. For example, the provision unit customizes the provided content based on the content learned by the apprentice delivery company in the past. The provision unit can also analyze the apprentice delivery company's past provision results to improve the accuracy of the provided content. The provision unit can also select the optimal delivery method by referring to the apprentice delivery company's past provision results. In this way, the accuracy of the provided content can be improved by referring to the past provision results. Some or all of the above-described processing by the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can improve the accuracy of the provided content by using an AI model that inputs the apprentice delivery company's past provision result data and outputs appropriate provided content.

[0104] The providing unit estimates the emotion of the trainee delivery agent and adjusts the length of the content to be provided based on the estimated emotion. The providing unit estimates the emotion of the trainee delivery agent. Emotion estimation includes, but is not limited to, emotion recognition technology, emotion types, and estimation algorithms. For example, if the trainee delivery agent is in a hurry, the providing unit provides short, to-the-point content to the trainee delivery agent. Furthermore, if the trainee delivery agent is relaxed, the providing unit can provide longer content with detailed explanations. Furthermore, if the trainee delivery agent is excited, the providing unit can provide content with visually stimulating effects. This allows appropriate information to be provided by adjusting the length of the content based on the emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, 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-described processing in the providing unit may be performed using, for example, AI, or without AI. For example, the providing unit can adjust the length of the content using an AI model that takes the emotional data of the trainee delivery person as input and outputs an appropriate length of content.

[0105] The providing unit determines the priority of the provided content based on the time when the packaging method or handling information was submitted. The providing unit determines the priority of the provided content based on the time when the packaging method or handling information was submitted. The submission time includes, but is not limited to, the type of time, evaluation criteria, and a method for determining the priority. For example, the providing unit prioritizes the provision of packaging methods with high urgency. The providing unit can also set a high priority for packaging methods with an approaching submission deadline. The providing unit can also postpone packaging methods with more time to submit. This makes it possible to provide appropriate information by setting the priority based on the submission time. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can determine the priority using an AI model that inputs data on the submission time of the packaging method or handling information and outputs an appropriate priority.

[0106] The providing unit adjusts the order of the provided content based on the relevance of the packing method and handling method when providing the content. The providing unit adjusts the order of the provided content based on the relevance of the packing method and handling method when providing the content. The relevance includes, but is not limited to, the type of relevance, evaluation criteria, and order adjustment method. For example, the providing unit provides highly relevant packing methods consecutively. The providing unit can also postpone less relevant packing methods. The providing unit can also prioritize providing highly relevant content according to the apprentice delivery person's learning progress. This makes it possible to support efficient learning by adjusting the order based on the relevance. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can adjust the order using an AI model that inputs relevance data of packing methods and handling methods and outputs an appropriate order.

[0107] The providing unit adjusts the use of technical terms in the provided content according to the expertise level of the apprentice delivery agent when providing the content. The providing unit adjusts the use of technical terms in the provided content according to the expertise level of the apprentice delivery agent when providing the content. Expertise level includes, but is not limited to, the type of knowledge level, evaluation criteria, and adjustment method. For example, if the apprentice delivery agent is a beginner, the providing unit may avoid technical terms and provide explanations in simple terms. Furthermore, if the apprentice delivery agent is an intermediate level apprentice, the providing unit may provide explanations using technical terms appropriately. Furthermore, if the apprentice delivery agent is an advanced level apprentice, the providing unit may provide detailed explanations using a lot of technical terms. This allows appropriate information to be provided by using technical terms appropriate to the expertise level. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may adjust the use of technical terms using an AI model that inputs the expertise level data of the apprentice delivery agent and outputs appropriate use of technical terms.

[0108] The monitoring unit estimates the emotions of the apprentice delivery agent and adjusts the monitoring method based on the estimated emotions. The monitoring unit estimates the emotions of the apprentice delivery agent. Emotion estimation includes, but is not limited to, emotion recognition technology, emotion types, and estimation algorithms. For example, if the apprentice delivery agent is nervous, the monitoring unit reduces the monitoring frequency to reduce stress. Furthermore, if the apprentice delivery agent is relaxed, the monitoring unit can maintain a normal monitoring frequency. Furthermore, if the apprentice delivery agent is tired, the monitoring unit can reduce the monitoring frequency to encourage rest. This allows for adjustment of the monitoring method based on emotions to reduce stress and support efficient learning. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can adjust its monitoring methods using an AI model that takes emotional data from trainee delivery workers as input and outputs appropriate monitoring methods.

[0109] The monitoring unit improves monitoring accuracy based on the apprentice delivery agent's past learning progress during monitoring. The monitoring unit references the apprentice delivery agent's past learning progress during monitoring. Past learning progress includes, but is not limited to, the type of learning progress, the reference method, and the evaluation criteria. For example, the monitoring unit adjusts the monitoring algorithm based on the apprentice delivery agent's past learning progress. The monitoring unit can also improve the accuracy of the monitoring results by referencing the apprentice delivery agent's past learning progress. The monitoring unit can also analyze the apprentice delivery agent's past learning progress and select an optimal monitoring method. In this way, by referencing the past learning progress, monitoring accuracy can be improved. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can improve monitoring accuracy using an AI model that inputs the apprentice delivery agent's past learning progress data and outputs an appropriate monitoring algorithm.

[0110] The monitoring unit performs monitoring while taking into account the attribute information of the apprentice delivery company. The monitoring unit performs monitoring while taking into account the attribute information of the apprentice delivery company. The attribute information includes, but is not limited to, the type of attribute information, analysis method, and evaluation criteria. For example, the monitoring unit adjusts the monitoring algorithm while taking into account the age and years of experience of the apprentice delivery company. The monitoring unit can also customize the monitoring results based on the attribute information of the apprentice delivery company. The monitoring unit can also select the optimal monitoring method by referring to the attribute information of the apprentice delivery company. This allows for monitoring that takes the attribute information into account to provide highly accurate results. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can perform monitoring using an AI model that inputs the attribute information of the apprentice delivery company and outputs an appropriate monitoring algorithm.

[0111] The monitoring unit weights the monitoring based on the learning progress of the apprentice delivery agent during monitoring. The monitoring unit weights the monitoring based on the learning progress of the apprentice delivery agent during monitoring. Learning progress includes, but is not limited to, the level of learning achievement, progress status, and weighting method. For example, the monitoring unit adjusts the weighting of the monitoring algorithm based on the learning progress of the apprentice delivery agent. The monitoring unit can also improve the accuracy of the monitoring results by referring to the learning progress of the apprentice delivery agent. The monitoring unit can also select an optimal monitoring method taking into account the learning progress of the apprentice delivery agent. This enables efficient monitoring through weighting based on learning progress. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can weight the monitoring using an AI model that inputs learning progress data of the apprentice delivery agent and outputs appropriate weighting.

[0112] The monitoring unit estimates the emotions of the apprentice delivery agent and adjusts the display method of the monitoring results based on the estimated emotions. The monitoring unit estimates the emotions of the apprentice delivery agent. Emotion estimation includes, but is not limited to, emotion recognition technology, emotion types, and estimation algorithms. For example, if the apprentice delivery agent is nervous, the monitoring unit provides a simple, highly visible display method. If the apprentice delivery agent is relaxed, the monitoring unit can provide a display method that includes detailed information. If the apprentice delivery agent is tired, the monitoring unit can provide a display method that focuses on the main points. This allows for highly visible results to be provided by adjusting the display method based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can adjust the display method using an AI model that takes the emotional data of the trainee delivery person as input and outputs an appropriate display method.

[0113] The monitoring unit performs monitoring based on the geographic distribution of apprentice delivery companies. The monitoring unit performs monitoring while taking into account the geographic distribution of apprentice delivery companies. Geographic distribution includes, but is not limited to, the type of distribution, analysis method, and evaluation criteria. For example, if apprentice delivery companies are concentrated in a specific area, the monitoring unit performs monitoring while taking into account the characteristics of that area. The monitoring unit can also customize the monitoring results based on the geographic distribution of apprentice delivery companies. The monitoring unit can also select the optimal monitoring method by referring to the geographic distribution of apprentice delivery companies. This allows for monitoring that takes geographic distribution into account, thereby providing highly accurate results. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can perform monitoring using an AI model that inputs geographic distribution data of apprentice delivery companies and outputs an appropriate monitoring algorithm.

[0114] The monitoring unit improves monitoring accuracy by referring to related literature during monitoring. The monitoring unit refers to related literature during monitoring. Related literature includes, but is not limited to, the type of literature, the reference method, and the evaluation criteria. For example, the monitoring unit adjusts the monitoring algorithm based on the related literature. The monitoring unit can also improve the accuracy of the monitoring results by referring to the related literature. The monitoring unit can also analyze the related literature and select an optimal monitoring method. In this way, by referring to the related literature, monitoring accuracy can be improved. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can improve monitoring accuracy by using an AI model that inputs related literature data and outputs an appropriate monitoring algorithm.

[0115] The monitoring unit performs monitoring based on the market value of the apprentice delivery company during monitoring. The monitoring unit performs monitoring while taking into account the market value of the apprentice delivery company during monitoring. Market value includes, but is not limited to, the type of market value, evaluation criteria, and analysis method. For example, the monitoring unit adjusts the monitoring algorithm based on the market value of the apprentice delivery company. The monitoring unit can also customize the monitoring results by referring to the market value of the apprentice delivery company. The monitoring unit can also select the optimal monitoring method by taking into account the market value of the apprentice delivery company. This enables monitoring that takes market value into account to provide highly accurate results. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can perform monitoring using an AI model that inputs market value data of the apprentice delivery company and outputs an appropriate monitoring algorithm.

[0116] The scenario unit estimates the emotions of the apprentice delivery agent and adjusts the content of the scenario based on the estimated emotions. The scenario unit estimates the emotions of the apprentice delivery agent. Emotion estimation includes, but is not limited to, emotion recognition technology, emotion types, and estimation algorithms. For example, if the apprentice delivery agent is nervous, the scenario unit provides a simple, highly visible scenario. Furthermore, if the apprentice delivery agent is relaxed, the scenario unit can provide a scenario containing detailed information. Furthermore, if the apprentice delivery agent is tired, the scenario unit can provide a scenario that focuses on the main points. This allows for highly visible content to be provided by adjusting the scenario based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the scenario unit may be performed using, for example, AI, or without AI. For example, the scenario unit can adjust the content of the scenario using an AI model that takes the emotional data of trainee delivery workers as input and outputs an appropriate scenario.

[0117] When providing a scenario, the scenario unit provides the scenario by referring to the apprentice delivery agent's past scenario history. When providing a scenario, the scenario unit refers to the apprentice delivery agent's past scenario history. The past scenario history includes, for example, but is not limited to, the type of scenario history, the reference method, and the evaluation criteria. For example, the scenario unit provides an optimal scenario based on the apprentice delivery agent's past scenario history. The scenario unit can also customize the scenario content by referring to the apprentice delivery agent's past scenario history. The scenario unit can also analyze the apprentice delivery agent's past scenario history and select the optimal scenario. In this way, the optimal scenario can be provided by referring to the past scenario history. Some or all of the above-described processing in the scenario unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario unit can provide a scenario using an AI model that inputs the apprentice delivery agent's past scenario history data and outputs an appropriate scenario.

[0118] When providing a scenario, the scenario unit customizes the scenario based on the apprentice delivery agent's current work status. When providing a scenario, the scenario unit customizes the scenario based on the apprentice delivery agent's current work status. The current work status includes, but is not limited to, the type of work status, the customization method, and the evaluation criteria. For example, the scenario unit provides a scenario related to the work currently being performed by the apprentice delivery agent. The scenario unit can also adjust the scenario content based on the apprentice delivery agent's current work status. The scenario unit can also select an optimal scenario by referring to the apprentice delivery agent's current work status. This enables efficient learning by customizing the scenario based on the current work status. Some or all of the above-described processing in the scenario unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario unit can customize a scenario using an AI model that inputs the apprentice delivery agent's work status data and outputs an appropriate scenario.

[0119] The scenario unit estimates the emotions of the trainee delivery agent and determines the priority of scenarios based on the estimated emotions. The scenario unit estimates the emotions of the trainee delivery agent. Emotion estimation includes, but is not limited to, emotion recognition technology, emotion types, and estimation algorithms. For example, if the trainee delivery agent is nervous, the scenario unit may prioritize providing important scenarios. Alternatively, if the trainee delivery agent is relaxed, the scenario unit may provide all scenarios equally. Alternatively, if the trainee delivery agent is in a hurry, the scenario unit may provide the most important scenario first. This enables efficient learning by setting priorities based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the scenario unit may be performed using, for example, AI, or without AI. For example, the scenario department can determine priorities using an AI model that takes emotional data from trainee delivery workers as input and outputs appropriate priorities.

[0120] When providing a scenario, the scenario unit provides an optimal scenario taking into account the geographical location information of the apprentice delivery agent. When providing a scenario, the scenario unit provides an optimal scenario taking into account the geographical location information of the apprentice delivery agent. Geographical location information includes, but is not limited to, methods for acquiring location information, methods for using location information, and evaluation criteria. For example, if the apprentice delivery agent is in a specific area, the scenario unit provides a scenario taking into account the characteristics of that area. The scenario unit can also customize the scenario content based on the geographical location information of the apprentice delivery agent. The scenario unit can also select an optimal scenario by referencing the geographical location information of the apprentice delivery agent. This makes it possible to provide appropriate information by providing a scenario based on geographical location information. Some or all of the above-described processing in the scenario unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario unit can provide a scenario using an AI model that inputs the geographical location information of the apprentice delivery agent and outputs an appropriate scenario.

[0121] When providing a scenario, the scenario unit analyzes the apprentice delivery agent's social media activity and provides a related scenario. When providing a scenario, the scenario unit analyzes the apprentice delivery agent's social media activity. Social media activity includes, but is not limited to, examples of analysis methods, types of acquired data, and evaluation criteria. For example, the scenario unit provides a scenario related to the locations where the apprentice delivery agent checked in on social media. The scenario unit can also analyze the apprentice delivery agent's social media posts and provide a related scenario. The scenario unit can also provide a related scenario based on the apprentice delivery agent's social media activities. This allows for efficient learning by providing a scenario based on social media activity. Some or all of the above-described processing in the scenario unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario unit can provide a scenario using an AI model that inputs the apprentice delivery agent's social media data and outputs an appropriate scenario.

[0122] The media unit estimates the emotions of the delivery apprentice and adjusts the media display method based on the estimated emotions. The media unit estimates the emotions of the delivery apprentice. Emotion estimation includes, but is not limited to, emotion recognition technology, emotion types, and estimation algorithms. For example, if the delivery apprentice is nervous, the media unit provides a simple, highly visible display method. If the delivery apprentice is relaxed, the media unit can provide a display method that includes detailed information. If the delivery apprentice is tired, the media unit can provide a display method that focuses on the main points. This allows for highly visible content to be provided by adjusting the display method based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the media unit may be performed using, for example, AI, or without AI. For example, the media department can adjust the way it displays content using an AI model that takes the emotional data of trainee delivery workers as input and outputs appropriate display methods.

[0123] When providing media, the media unit refers to the apprentice delivery agent's past media history to provide the media. When providing media, the media unit refers to the apprentice delivery agent's past media history. Past media history includes, but is not limited to, for example, the type of media history, the reference method, and the evaluation criteria. For example, the media unit provides optimal media based on the apprentice delivery agent's past media history. The media unit can also customize the media content by referring to the apprentice delivery agent's past media history. The media unit can also analyze the apprentice delivery agent's past media history and select optimal media. In this way, optimal media can be provided by referring to the past media history. Some or all of the above-described processing in the media unit may be performed using, for example, AI, or may be performed without using AI. For example, the media unit can provide media using an AI model that inputs the apprentice delivery agent's past media history data and outputs appropriate media.

[0124] The media unit customizes the media based on the apprentice delivery agent's current work status when providing the media. The media unit customizes the media based on the apprentice delivery agent's current work status when providing the media. The current work status includes, but is not limited to, the type of work status, the customization method, and the evaluation criteria. For example, the media unit provides media related to the work currently being performed by the apprentice delivery agent. The media unit can also adjust the media content based on the apprentice delivery agent's current work status. The media unit can also select the optimal media by referring to the apprentice delivery agent's current work status. This enables efficient learning by customizing the media based on the current work status. Some or all of the above-described processing in the media unit may be performed using, for example, AI, or may be performed without using AI. For example, the media unit can customize the media using an AI model that inputs the apprentice delivery agent's work status data and outputs appropriate media.

[0125] The media unit estimates the emotions of the trainee delivery agent and prioritizes media based on the estimated emotions. The media unit estimates the emotions of the trainee delivery agent. Emotion estimation includes, but is not limited to, emotion recognition technology, emotion types, and estimation algorithms. For example, if the trainee delivery agent is nervous, the media unit may prioritize important media. Alternatively, if the trainee delivery agent is relaxed, the media unit may provide all media equally. Alternatively, if the trainee delivery agent is in a hurry, the media unit may provide the most important media first. This enables efficient learning by setting priorities based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the media unit may be performed using, for example, AI, or without AI. For example, the media department can determine priorities using an AI model that takes the emotional data of trainee delivery workers as input and outputs appropriate priorities.

[0126] The media unit provides the optimal media by taking into consideration the geographic location information of the apprentice delivery agent when providing the media. The media unit provides the optimal media by taking into consideration the geographic location information of the apprentice delivery agent when providing the media. Geographic location information includes, but is not limited to, methods for acquiring location information, methods for using location information, and evaluation criteria. For example, if the apprentice delivery agent is in a specific area, the media unit provides media by taking into consideration the characteristics of that area. The media unit can also customize media content based on the geographic location information of the apprentice delivery agent. The media unit can also select the optimal media by referring to the geographic location information of the apprentice delivery agent. This allows appropriate information to be provided by providing media based on the geographic location information. Some or all of the above-described processing in the media unit may be performed using, for example, AI, or may be performed without using AI. For example, the media unit can provide media using an AI model that inputs the geographic location information of the apprentice delivery agent and outputs appropriate media.

[0127] The media unit analyzes the apprentice delivery agent's social media activity when providing the media and provides related media. The media unit analyzes the apprentice delivery agent's social media activity when providing the media. Social media activity includes, but is not limited to, analysis methods, types of acquired data, and evaluation criteria. For example, the media unit provides media related to locations where the apprentice delivery agent checked in on social media. The media unit can also analyze the apprentice delivery agent's social media posts and provide related media. The media unit can also provide related media based on the apprentice delivery agent's social media activities. This allows for efficient learning by providing media based on social media activity. Some or all of the above-described processing in the media unit may be performed using, for example, AI, or may be performed without AI. For example, the media unit can provide media using an AI model that inputs the apprentice delivery agent's social media data and outputs appropriate media.

[0128] The simulation unit estimates the emotions of the apprentice delivery agent and adjusts the content of the simulation based on the estimated emotions. The simulation unit estimates the emotions of the apprentice delivery agent. Emotion estimation includes, but is not limited to, emotion recognition technology, emotion types, and estimation algorithms. For example, if the apprentice delivery agent is nervous, the simulation unit provides a simple, highly visible simulation. Furthermore, if the apprentice delivery agent is relaxed, the simulation unit can provide a simulation that includes detailed information. Furthermore, if the apprentice delivery agent is tired, the simulation unit can provide a simulation that focuses on the key points. This allows for adjustment of the simulation based on emotions to provide highly visible content. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or without AI. For example, the simulation unit can adjust the content of the simulation using an AI model that takes the emotional data of trainee delivery workers as input and outputs an appropriate simulation.

[0129] When providing a simulation, the simulation unit provides the simulation by referring to the apprentice delivery agent's past simulation history. When providing a simulation, the simulation unit references the apprentice delivery agent's past simulation history. The past simulation history includes, for example, the type of simulation history, the reference method, and the evaluation criteria, but is not limited to these examples. For example, the simulation unit provides an optimal simulation based on the apprentice delivery agent's past simulation history. The simulation unit can also customize the simulation content by referring to the apprentice delivery agent's past simulation history. The simulation unit can also analyze the apprentice delivery agent's past simulation history and select the optimal simulation. In this way, by referring to the past simulation history, the optimal simulation can be provided. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can provide a simulation using an AI model that inputs the apprentice delivery agent's past simulation history data and outputs an appropriate simulation.

[0130] The simulation unit customizes the simulation based on the apprentice delivery agent's current work status when providing the simulation. The simulation unit customizes the simulation based on the apprentice delivery agent's current work status when providing the simulation. The current work status includes, but is not limited to, the type of work status, the customization method, and the evaluation criteria. For example, the simulation unit provides a simulation related to the work currently being performed by the apprentice delivery agent. The simulation unit can also adjust the simulation content based on the apprentice delivery agent's current work status. The simulation unit can also select the optimal simulation by referring to the apprentice delivery agent's current work status. This allows for efficient learning by customizing the simulation based on the current work status. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can customize the simulation using an AI model that inputs the apprentice delivery agent's work status data and outputs an appropriate simulation.

[0131] The simulation unit estimates the emotions of the trainee delivery agent and determines the priority of the simulations based on the estimated emotions. The simulation unit estimates the emotions of the trainee delivery agent. Emotion estimation includes, but is not limited to, emotion recognition technology, emotion types, and estimation algorithms. For example, if the trainee delivery agent is nervous, the simulation unit may prioritize providing important simulations. Alternatively, if the trainee delivery agent is relaxed, the simulation unit may provide all simulations equally. Alternatively, if the trainee delivery agent is in a hurry, the simulation unit may provide the most important simulation first. This allows for efficient learning by setting priorities based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or without AI. For example, the simulation unit can determine priorities using an AI model that takes emotional data from trainee delivery workers as input and outputs appropriate priorities.

[0132] The simulation unit provides an optimal simulation by taking into account the geographical location information of the apprentice delivery agent when providing the simulation. The simulation unit provides an optimal simulation by taking into account the geographical location information of the apprentice delivery agent when providing the simulation. Geographical location information includes, but is not limited to, methods for acquiring location information, methods for using location information, and evaluation criteria. For example, if the apprentice delivery agent is in a specific area, the simulation unit provides a simulation by taking into account the characteristics of that area. The simulation unit can also customize the simulation content based on the geographical location information of the apprentice delivery agent. The simulation unit can also select an optimal simulation by referring to the geographical location information of the apprentice delivery agent. This makes it possible to provide appropriate information by providing a simulation based on the geographical location information. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can provide a simulation using an AI model that inputs the geographical location information of the apprentice delivery agent and outputs an appropriate simulation.

[0133] The simulation unit analyzes the apprentice delivery agent's social media activity when providing a simulation and provides a related simulation. The simulation unit analyzes the apprentice delivery agent's social media activity when providing a simulation. Social media activity includes, but is not limited to, analysis methods, types of acquired data, and evaluation criteria. For example, the simulation unit provides a simulation related to the locations where the apprentice delivery agent checked in on social media. The simulation unit can also analyze the apprentice delivery agent's social media posts and provide a related simulation. The simulation unit can also provide a related simulation based on the apprentice delivery agent's social media activities. This allows for efficient learning by providing a simulation based on social media activity. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without AI. For example, the simulation unit can provide a simulation using an AI model that inputs the apprentice delivery agent's social media data and outputs an appropriate simulation. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, provision unit, and monitoring unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input information about the apprentice delivery company using the reception device 38 of the smart device 14. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the information about the apprentice delivery company. For example, the provision unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides appropriate packaging and handling methods. For example, the monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the learning progress and adjusts the provision content as necessary. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, and monitoring unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input information about the apprentice delivery agent using the microphone 238 of the smart glasses 214. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the information about the apprentice delivery agent. For example, the provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides appropriate packaging and handling methods. For example, the monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the learning progress and adjusts the provision content as necessary. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and monitoring unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can input information about the apprentice delivery agent using the microphone 238 of the headset-type terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the information about the apprentice delivery agent. For example, the provision unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12 and provides appropriate packaging and handling methods. For example, the monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the learning progress and adjusts the provision content as necessary. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and monitoring unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input information about the apprentice delivery agent using the microphone 238 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the information about the apprentice delivery agent. For example, the provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and provides appropriate packaging and handling methods. For example, the monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the learning progress and adjusts the provision content as necessary.

[0134] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0135] The analysis unit can also adjust the difficulty of the learning content based on the apprentice delivery worker's past learning data. For example, if the apprentice delivery worker did not previously understand a specific packaging method, the analysis unit can adjust the learning so that the apprentice re-learns that part. Also, if the apprentice delivery worker has already mastered a specific skill, the analysis unit can provide more advanced content related to that skill. Furthermore, the analysis unit can adjust the progress speed of the learning content according to the apprentice delivery worker's learning speed. This enables efficient learning that matches the individual learning pace of the apprentice delivery worker.

[0136] The scenario unit can also estimate the emotions of the trainee delivery agent and adjust the difficulty of the scenario based on the estimated emotions. For example, if the trainee delivery agent is nervous, the scenario can start with an easy scenario and gradually increase in difficulty. Also, if the trainee delivery agent is relaxed, the scenario unit can provide a more difficult scenario. Furthermore, if the trainee delivery agent is tired, the scenario unit can provide a scenario that can be completed in a short time. This makes it possible to support efficient learning by adjusting the difficulty of the scenario based on emotions.

[0137] The media department can also suggest new media related to the apprentice delivery worker's viewing history based on the apprentice delivery worker's past media viewing history. For example, it can suggest new videos related to a video on packing methods that the apprentice delivery worker has previously viewed. It can also provide new illustrations related to illustrations that the apprentice delivery worker has previously viewed. Furthermore, it can prioritize suggesting media with high ratings based on the apprentice delivery worker's ratings of media that he or she has previously viewed. This makes it possible to support efficient learning by suggesting media based on past viewing history.

[0138] The simulation unit can also estimate the emotions of the trainee delivery agent and adjust the feedback method in the simulation based on the estimated emotions. For example, if the trainee delivery agent is nervous, it can provide more positive feedback. If the trainee delivery agent is relaxed, it can provide more detailed feedback. Furthermore, if the trainee delivery agent is tired, it can provide brief, to-the-point feedback. This makes it possible to support efficient learning by adjusting the feedback method based on emotions.

[0139] The monitoring unit can also predict learning progress based on the apprentice delivery agent's past learning progress data. For example, it can predict future learning progress based on the time it took the apprentice delivery agent to acquire a specific skill in the past. It can also analyze the apprentice delivery agent's past learning progress data to identify learning bottlenecks. Furthermore, it can optimize learning plans based on the apprentice delivery agent's past learning progress data. This makes it possible to support efficient learning through predictions and optimization based on past learning progress data.

[0140] The reception unit can also estimate the emotions of the delivery apprentice and adjust the voice guidance of the input interface based on the estimated emotions. For example, if the delivery apprentice is nervous, the reception unit can provide guidance in a calm voice. If the delivery apprentice is relaxed, the reception unit can provide guidance in a cheerful voice. Furthermore, if the delivery apprentice is tired, the reception unit can provide simple and easy-to-understand voice guidance. This makes it possible to support efficient input by adjusting the voice guidance based on emotions.

[0141] The analysis unit can also optimize the order of learning content based on the apprentice delivery worker's past learning data. For example, if the apprentice delivery worker had difficulty mastering a particular skill in the past, content related to that skill can be provided early. It can also postpone content related to skills in which the apprentice delivery worker excelled in the past. Furthermore, the order of learning content can be dynamically adjusted based on the apprentice delivery worker's past learning data. This makes it possible to support efficient learning by optimizing the order based on past learning data.

[0142] The providing unit can also estimate the emotions of the trainee delivery agent and adjust the interactivity of the provided content based on the estimated emotions. For example, if the trainee delivery agent is nervous, a simple and intuitive interaction can be provided. If the trainee delivery agent is relaxed, a detailed interaction can be provided. Furthermore, if the trainee delivery agent is tired, the interaction can be minimized. This makes it possible to support efficient learning by adjusting the interactivity based on emotions.

[0143] The scenario unit can also adjust the speed at which a scenario progresses based on the apprentice delivery agent's past scenario history. For example, if the apprentice delivery agent has completed a particular scenario quickly in the past, the speed at which the next scenario progresses can be increased. Also, if the apprentice delivery agent has taken a long time to complete a particular scenario in the past, the speed at which the next scenario progresses can be decreased. Furthermore, the scenario unit can dynamically adjust the speed at which a scenario progresses based on the apprentice delivery agent's past scenario history. This can support efficient learning by adjusting the speed at which the scenario progresses based on the apprentice delivery agent's past scenario history.

[0144] The media department can also estimate the emotions of the trainee delivery agent and adjust the volume of the media based on the estimated emotions. For example, if the trainee delivery agent is nervous, the media can be provided at a calm volume. If the trainee delivery agent is relaxed, the media can be provided at a normal volume. Furthermore, if the trainee delivery agent is tired, the media can be provided at a lower volume. This makes it possible to support efficient learning by adjusting the volume based on emotions.

[0145] The processing flow of the second embodiment will be briefly explained below.

[0146] Step 1: The reception unit inputs the apprentice delivery agent's information. The apprentice delivery agent's information includes, for example, years of experience, job description, skill level, etc. The reception unit allows the apprentice delivery agent to log in to the system and input their understanding level and progress. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit analyzes the apprentice delivery worker's level of understanding and progress, and determines the appropriate learning content. Step 3: The provision unit provides packing or handling methods based on the results of the analysis by the analysis unit. The provided content includes the type of packaging material, packing procedures, handling precautions, etc. For example, learning content is provided based on specific scenarios, such as how to pack fragile glass products or how to lift heavy luggage. Step 4: The monitoring department monitors the learning progress based on the content provided by the provision department. The monitoring of learning progress is based on criteria such as learning achievement level, progress status, and evaluation method. The monitoring department monitors the learning progress of the trainee delivery agent in real time and adjusts the content provided by the provision department as necessary.

[0147] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0151] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0152] 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.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0158] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0167] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0168] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0169] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0170] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0171] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0173] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0174] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0175] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0176] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0177] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0178] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0179] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0182] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0183] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0184] 7, a 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.

[0185] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0186] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0187] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0188] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0189] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0190] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0191] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0192] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0193] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0194] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0195] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0196] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0197] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0198] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0199] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0200] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0201] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0202] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0203] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0204] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0205] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0206] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0207] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0208] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0209] 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.

[0210] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0211] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0212] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0213] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0214] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0215] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0216] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0217] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0218] [Explanation of symbols]

[0219] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where trainee delivery personnel enter their information; an analysis unit that analyzes the information input by the reception unit; a providing unit that provides a packaging method or a handling method based on the analysis result by the analyzing unit; a monitoring unit that monitors the learning progress based on the content provided by the providing unit. A system characterized by:

2. The providing unit Collaborate with the scenario department to provide specific scenarios 2. The system of claim 1.

3. The providing unit Work with the media department to provide videos or illustrations 2. The system of claim 1.

4. The providing unit Collaborate with the Simulation Department, which provides simulations 2. The system of claim 1.

5. The monitoring unit Monitor learning progress and adjust offerings as needed 2. The system of claim 1.

6. The reception unit Estimate the emotions of trainee delivery drivers and adjust the display of the input interface based on the estimated emotions.

2. The system of claim 1.

7. The reception unit Analyze the past learning history of trainee delivery workers and suggest input methods 2. The system of claim 1.

8. The reception unit Customize input based on the apprentice carrier's current work environment 2. The system of claim 1.

9. The reception unit Analyze trainee delivery agent input data in real time and provide immediate feedback 2. The system of claim 1.

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