system

Abstract

The system according to this embodiment aims to generate optimal work procedures and provide training by utilizing accumulated work procedure manuals and on-site feedback. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a training unit. The collection unit collects accumulated work procedures and on-site feedback. The analysis unit analyzes the information collected by the collection unit and generates the optimal work procedure. The training unit provides training based on the work procedure generated by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

[0007] The system according to this embodiment can generate optimal work procedures and provide training by utilizing accumulated work procedure manuals and on-site feedback. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The work procedure automation system according to an embodiment of the present invention is a system that automates work procedures based on accumulated work procedure manuals and on-site feedback, and provides on-demand training to part-time operators. The work procedure automation system collects accumulated work procedure manuals and on-site feedback, and an AI analyzes them to automatically generate the optimal work procedure. Furthermore, it provides on-demand training to part-time operators based on the generated work procedure. This mechanism aims to improve the efficiency of work procedures and enhance the skills of operators. For example, work procedure manuals contain detailed procedures and points to note, and on-site feedback includes problems and areas for improvement that occurred during actual work. For example, a work procedure manual states, "When attaching part A, tighten the screws securely," and on-site feedback includes information such as, "The screws tend to loosen, so they need to be checked regularly." Next, the AI ​​analyzes the collected information. The AI ​​automatically generates the optimal work procedure based on the work procedure manuals and on-site feedback. For example, the AI ​​combines information such as "When installing part A, tighten the screws securely" and "The screws tend to loosen, so they need to be checked regularly" to generate the optimal work procedure: "When installing part A, tighten the screws securely and check them regularly." Furthermore, based on the generated work procedure, on-demand training is provided to part-time operators. For example, the work procedure generated by the AI ​​can be provided to operators in the form of videos or simulations, which can be used to help them in their actual work. This allows operators to receive training at their own pace and improve their skills. This system improves the efficiency of work procedures and enhances the skills of operators. By automatically generating work procedures with AI, procedures can be reviewed and improved quickly, leading to increased work efficiency. In addition, on-demand training makes it easier for part-time operators to acquire the necessary skills. For example, even if a new work procedure is introduced, operators can receive training immediately, improving their ability to respond on-site. In this way, the automated work procedure system can achieve both the efficiency of work procedures and the skill improvement of operators.

[0029] The automated work procedure system according to this embodiment comprises a collection unit, an analysis unit, and a training unit. The collection unit collects accumulated work procedure manuals and on-site feedback. The collection unit can, for example, scan paper work procedure manuals and convert them into digital data. The collection unit can also directly collect work procedure manuals in digital format. Furthermore, the collection unit can collect on-site feedback as questionnaires or digital comments. For example, the collection unit scans work procedure manuals with a high-resolution scanner and converts them into text information using OCR technology. Digital work procedure manuals can be directly collected if submitted in a specific file format. On-site feedback is collected as questionnaire forms or digital comments. The analysis unit analyzes the information collected by the collection unit and generates the optimal work procedure. The analysis unit can, for example, use AI to analyze work procedure manuals and on-site feedback and automatically generate the optimal work procedure. For example, the analysis unit combines information such as "When installing part A, tighten the screws securely" and "The screws tend to loosen, so they need to be checked regularly" to generate the optimal work procedure, "When installing part A, tighten the screws securely and check them regularly." The analysis unit can use AI to generate the optimal work procedure based on work procedure manuals and on-site feedback. The training unit provides training based on the work procedure generated by the analysis unit. The training unit provides the AI-generated work procedure to operators in video or simulation format, for example. For example, the training unit provides training to operators in video format based on the AI-generated work procedure. The training unit can also provide training to operators in simulation format. For example, the training unit provides training to operators in simulation format based on the AI-generated work procedure. As a result, the work procedure automation system according to this embodiment can automate work procedures based on accumulated work procedure manuals and on-site feedback, and provide on-demand training to part-time operators.

[0030] The data collection unit collects accumulated work procedures and on-site feedback. For example, the unit can scan paper work procedures and convert them into digital data. It can also directly collect digital work procedures. Specifically, it scans paper work procedures with a high-resolution scanner and converts them into text information using OCR (Optical Character Recognition) technology. OCR technology recognizes characters from scanned images and extracts them as text data, and can handle both handwritten and printed characters. Digital work procedures can be directly collected if submitted in specific file formats such as PDF, Word, or Excel. This allows the data collection unit to efficiently collect both paper and digital work procedures and manage them centrally as digital data. Furthermore, the data collection unit can collect on-site feedback as questionnaires and digital comments. For example, it can provide on-site operators with online questionnaire forms to collect opinions and suggestions for improvement regarding work procedures. Additionally, operators can record points they noticed or problems they encountered during work in real time as digital comments. This allows the data collection unit to gather feedback that reflects actual field conditions and operator opinions, helping to improve work procedures. The collected data is stored in a central database, making it accessible to the analysis and training units. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the data collection unit and generates the optimal work procedure. For example, the analysis unit uses AI to analyze work procedure manuals and on-site feedback to automatically generate the optimal work procedure. Specifically, the AI ​​uses natural language processing (NLP) technology to understand the content of the work procedure manual and extract important information. For example, by combining information such as "When installing part A, tighten the screws securely" and "The screws tend to loosen, so they need to be checked regularly," it generates the optimal work procedure "When installing part A, tighten the screws securely and check them regularly." Furthermore, the AI ​​uses machine learning algorithms to analyze the collected feedback data and identify areas for improvement and problems in the work procedure. For example, based on feedback from operators, if a particular work procedure is too time-consuming or prone to errors, it can revise the procedure and propose a more efficient and accurate procedure. By integrating this information and automatically generating the optimal work procedure, the analysis unit can improve work efficiency and quality. In addition, the analysis unit can continuously evaluate the generated work procedure and make corrections and improvements as needed. This allows the analysis unit to always provide optimal work procedures based on the latest information, improving the reliability and efficiency of the entire system.

[0032] The training department provides training based on work procedures generated by the analysis department. For example, the training department provides operators with AI-generated work procedures in the form of videos or simulations. Specifically, it provides operators with training in video format based on AI-generated work procedures. The videos visually demonstrate the actual work procedures, making it easier for operators to understand them. For example, the procedure for installing part A is shown in a video, with detailed explanations of how to tighten screws and how to check them. The training department can also provide operators with training in the form of simulations. Simulations reproduce the actual work procedures in a virtual environment, allowing operators to practice the work by actually moving their hands. For example, using virtual reality (VR) technology, operators can simulate installing part A in a virtual space and actually experience how to tighten screws and how to check them. This allows operators to receive training in a situation close to the actual work environment, enabling them to learn work procedures more effectively. Furthermore, the training department can evaluate the progress and results of the training and provide additional training as needed. For example, if an operator is having trouble with a particular procedure, additional training specifically for that procedure can be provided to resolve the problem. This allows the training department to provide operators with effective training and support them in mastering work procedures.

[0033] The data collection unit can collect work procedures and on-site feedback. For example, the data collection unit can scan paper work procedures and convert them into digital data. The data collection unit can also directly collect work procedures in digital format. Furthermore, the data collection unit can collect on-site feedback as questionnaires or digital comments. For example, the data collection unit can scan work procedures with a high-resolution scanner and convert them into text information using OCR technology. Digital work procedures can be directly collected if submitted in a specific file format. On-site feedback is collected as questionnaire forms or digital comments. This allows the data collection unit to efficiently gather the necessary information by collecting work procedures and on-site feedback. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input image data obtained by scanning work procedures into a generating AI and have the generating AI generate text data from the image data.

[0034] The analysis unit can analyze the collected information and generate the optimal work procedure. For example, the analysis unit can use AI to analyze work procedure manuals and on-site feedback to automatically generate the optimal work procedure. For example, the analysis unit can use AI to combine the information "When installing part A, tighten the screws securely" and "The screws tend to loosen, so they need to be checked regularly" to generate the optimal work procedure "When installing part A, tighten the screws securely and check them regularly." The analysis unit can use AI to generate the optimal work procedure based on work procedure manuals and on-site feedback. This improves work efficiency by having the analysis unit analyze the collected information and generate the optimal work procedure. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into a generation AI and have the generation AI execute the generation of the optimal work procedure.

[0035] The training department can provide on-demand training based on the generated work procedures. For example, the training department can provide the AI-generated work procedures to operators in video or simulation format. For example, the training department can provide training to operators in video format based on the AI-generated work procedures. The training department can also provide training to operators in simulation format. For example, the training department can provide training to operators in simulation format based on the AI-generated work procedures. This allows part-time operators to receive training efficiently by providing on-demand training based on the generated work procedures. Some or all of the above processes in the training department may be performed using AI, for example, or without AI. For example, the training department can input the generated work procedures into a generating AI and have the generating AI generate the training content.

[0036] The training unit can provide training in video or simulation format. For example, the training unit can provide operators with training in video format based on work procedures generated by AI. The training unit can also provide operators with training in simulation format. For example, the training unit can provide operators with training in simulation format based on work procedures generated by AI. This allows operators to utilize the training provided by the training unit in video or simulation format in their actual work. Some or all of the above processes in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input the generated work procedures into a generating AI and have the generating AI generate training content in video or simulation format.

[0037] The data collection unit can analyze the update frequency of work procedure manuals and prioritize the collection of the latest information. For example, the data collection unit can analyze the update history of work procedure manuals and prioritize the collection of manuals that are frequently updated. The data collection unit can also identify manuals with high update frequency and prioritize the collection of feedback related to those manuals. Furthermore, the data collection unit can collect the latest information from manuals with low update frequency as needed. In this way, by analyzing the update frequency of work procedure manuals, the latest information can be collected preferentially. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input work procedure manual update history data into a generating AI and have the generating AI perform an analysis of update frequency.

[0038] The data collection unit can evaluate the reliability of on-site feedback and prioritize the collection of reliable feedback. For example, the data collection unit can evaluate the past reliability of feedback submitters and prioritize the collection of feedback from reliable submitters. The data collection unit can also analyze the content of the feedback and prioritize the collection of specific and detailed feedback. Furthermore, the data collection unit can consider the frequency of feedback submission and prioritize the collection of frequently submitted feedback. By prioritizing the collection of reliable feedback, more accurate information can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input feedback reliability evaluation data into a generating AI and have the generating AI perform the reliability evaluation.

[0039] The data collection unit can collect data while considering the attribute information of the creator of the work procedure manual. For example, the data collection unit can consider the creator's area of ​​expertise and prioritize the collection of relevant feedback. It can also consider the creator's years of experience and prioritize the collection of feedback from experienced creators. Furthermore, it can consider the creator's job title and prioritize the collection of feedback from senior managers. By considering the creator's attribute information, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the attribute information of the creator of the work procedure manual into a generating AI and have the generating AI perform the data collection.

[0040] The data collection unit can collect data while considering the job title information of the person submitting the feedback at the time of collection. For example, the data collection unit can consider the job title information of the person submitting the feedback and prioritize the collection of feedback from higher-ranking officials. The data collection unit can also consider the area of ​​expertise of the person submitting the feedback and prioritize the collection of relevant feedback. Furthermore, the data collection unit can consider the years of experience of the person submitting the feedback and prioritize the collection of feedback from experienced submitters. This allows for the collection of more reliable information by considering the submitter's job title information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the job title information of the person submitting the feedback into a generating AI and have the generating AI perform the data collection.

[0041] The analysis unit can optimize the analysis algorithm by referring to past analysis results during the analysis. For example, the analysis unit can select the optimal analysis algorithm based on past analysis results. The analysis unit can also analyze past analysis results to improve the accuracy of the analysis algorithm. Furthermore, the analysis unit can adjust the parameters of the analysis algorithm by referring to past analysis results. In this way, the accuracy of the analysis algorithm can be improved by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis result data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0042] The analysis unit can improve the accuracy of its analysis by considering the interrelationship between work procedures and on-site feedback during the analysis. For example, the analysis unit can analyze the interrelationship between work procedures and on-site feedback to generate the optimal work procedure. Furthermore, the analysis unit can improve the accuracy of its analysis algorithm by considering the interrelationship between work procedures and on-site feedback. In addition, the analysis unit can improve the reliability of its analysis results by analyzing the interrelationship between work procedures and on-site feedback. Thus, by considering the interrelationship between work procedures and on-site feedback, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on work procedures and on-site feedback into a generating AI and have the generating AI perform the analysis of the interrelationships.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the work procedure manual during analysis. For example, the analysis unit can select the optimal analysis algorithm depending on the category of the work procedure manual. The analysis unit can also adjust the parameters of the analysis algorithm based on the category of the work procedure manual. Furthermore, the analysis unit can improve the accuracy of the analysis by applying different analysis algorithms depending on the category of the work procedure manual. In this way, the accuracy of the analysis can be improved by applying different analysis algorithms depending on the category of the work procedure manual. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the category data of the work procedure manual into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0044] The analysis unit can weight the analysis based on the timing of feedback submissions from the field. For example, the analysis unit can prioritize the analysis of the most recent feedback, taking into account the timing of feedback submissions. The analysis unit can also adjust the weighting of the analysis algorithm based on the timing of feedback submissions. Furthermore, the analysis unit can improve the reliability of the analysis results by taking into account the timing of feedback submissions. Thus, the reliability of the analysis results can be improved by weighting the analysis based on the timing of feedback submissions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input feedback submission timing data into a generating AI and have the generating AI perform the weighting adjustments.

[0045] The training unit can select the optimal training method by referring to the operator's past training history during training. For example, the training unit selects the optimal training method based on the operator's past training history. The training unit can also analyze the operator's past training history and customize the training content. Furthermore, the training unit can refer to the operator's past training history and adjust the training progress speed. In this way, the optimal training method can be provided by referring to the operator's past training history. Some or all of the above processes in the training unit may be performed using AI, for example, or not using AI. For example, the training unit can input the operator's past training history data into a generating AI and have the generating AI perform the selection of the training method.

[0046] The training unit can adjust the difficulty of training according to the operator's skill level during training. For example, the training unit can evaluate the operator's skill level and set the optimal training difficulty. The training unit can also customize the training content based on the operator's skill level. Furthermore, the training unit can adjust the training pace, taking the operator's skill level into consideration. This allows for more effective training by adjusting the difficulty of training according to the operator's skill level. Some or all of the above processes in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input operator skill level data into a generating AI and have the generating AI adjust the training difficulty.

[0047] The training department can provide an optimal training schedule by taking into account the operator's working hours during training. For example, the training department can set an optimal training schedule by considering the operator's working hours. Furthermore, the training department can customize the training content based on the operator's working hours. In addition, the training department can adjust the training pace by considering the operator's working hours. This allows the training department to provide an optimal training schedule by considering the operator's working hours. Some or all of the above processes in the training department may be performed using AI, for example, or without AI. For example, the training department can input operator working hour data into a generating AI and have the generating AI set the training schedule.

[0048] The training unit can select the optimal training format by considering the operator's device information during training. For example, if the operator is using a smartphone, the training unit can provide a training format optimized for smartphones. Furthermore, if the operator is using a tablet, the training unit can provide a training format optimized for tablets. In addition, if the operator is using a desktop PC, the training unit can provide a training format optimized for desktop PCs. This allows the optimal training format to be provided by considering the operator's device information. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input the operator's device information into a generating AI and have the generating AI select the training format.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The data collection unit can analyze the update frequency of work procedure manuals and prioritize the collection of the latest information. For example, the data collection unit can analyze the update history of work procedure manuals and prioritize the collection of manuals that are frequently updated. The data collection unit can also identify manuals with high update frequency and prioritize the collection of feedback related to those manuals. Furthermore, the data collection unit can collect the latest information from manuals with low update frequency as needed. In this way, by analyzing the update frequency of work procedure manuals, the latest information can be collected preferentially. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input work procedure manual update history data into a generating AI and have the generating AI perform an analysis of update frequency.

[0051] The data collection unit can evaluate the reliability of on-site feedback and prioritize the collection of reliable feedback. For example, the data collection unit can evaluate the past reliability of feedback submitters and prioritize the collection of feedback from reliable submitters. The data collection unit can also analyze the content of the feedback and prioritize the collection of specific and detailed feedback. Furthermore, the data collection unit can consider the frequency of feedback submission and prioritize the collection of frequently submitted feedback. By prioritizing the collection of reliable feedback, more accurate information can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input feedback reliability evaluation data into a generating AI and have the generating AI perform the reliability evaluation.

[0052] The analysis unit can optimize the analysis algorithm by referring to past analysis results during the analysis. For example, the analysis unit can select the optimal analysis algorithm based on past analysis results. The analysis unit can also analyze past analysis results to improve the accuracy of the analysis algorithm. Furthermore, the analysis unit can adjust the parameters of the analysis algorithm by referring to past analysis results. In this way, the accuracy of the analysis algorithm can be improved by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis result data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0053] The training unit can select the optimal training method by referring to the operator's past training history during training. For example, the training unit selects the optimal training method based on the operator's past training history. The training unit can also analyze the operator's past training history and customize the training content. Furthermore, the training unit can refer to the operator's past training history and adjust the training progress speed. In this way, the optimal training method can be provided by referring to the operator's past training history. Some or all of the above processes in the training unit may be performed using AI, for example, or not using AI. For example, the training unit can input the operator's past training history data into a generating AI and have the generating AI perform the selection of the training method.

[0054] The training unit can adjust the difficulty of training according to the operator's skill level during training. For example, the training unit can evaluate the operator's skill level and set the optimal training difficulty. The training unit can also customize the training content based on the operator's skill level. Furthermore, the training unit can adjust the training pace, taking the operator's skill level into consideration. This allows for more effective training by adjusting the difficulty of training according to the operator's skill level. Some or all of the above processes in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input operator skill level data into a generating AI and have the generating AI adjust the training difficulty.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The collection unit collects accumulated work procedures and on-site feedback. The collection unit can scan paper work procedures and convert them into digital data. It can also directly collect work procedures in digital format. Furthermore, the collection unit can collect on-site feedback as questionnaires or digital comments. For example, the collection unit scans work procedures with a high-resolution scanner and converts them into text information using OCR technology. Digital work procedures are collected directly if submitted in a specific file format. On-site feedback is collected as questionnaire forms or digital comments. Step 2: The analysis unit analyzes the information collected by the collection unit and generates the optimal work procedure. The analysis unit uses AI to analyze the work procedure manual and on-site feedback and automatically generates the optimal work procedure. For example, the analysis unit uses AI to combine the information that "when installing part A, tighten the screws securely" and "the screws tend to loosen, so they need to be checked regularly" to generate the optimal work procedure "when installing part A, tighten the screws securely and check them regularly." Step 3: The training unit provides training based on the work procedures generated by the analysis unit. The training unit provides the AI-generated work procedures to the operator in video or simulation format. For example, the training unit provides training to the operator in video format based on the AI-generated work procedures. The training unit can also provide training to the operator in simulation format.

[0057] (Example of form 2) The work procedure automation system according to an embodiment of the present invention is a system that automates work procedures based on accumulated work procedure manuals and on-site feedback, and provides on-demand training to part-time operators. The work procedure automation system collects accumulated work procedure manuals and on-site feedback, and an AI analyzes them to automatically generate the optimal work procedure. Furthermore, it provides on-demand training to part-time operators based on the generated work procedure. This mechanism aims to improve the efficiency of work procedures and enhance the skills of operators. For example, work procedure manuals contain detailed procedures and points to note, and on-site feedback includes problems and areas for improvement that occurred during actual work. For example, a work procedure manual states, "When attaching part A, tighten the screws securely," and on-site feedback includes information such as, "The screws tend to loosen, so they need to be checked regularly." Next, the AI ​​analyzes the collected information. The AI ​​automatically generates the optimal work procedure based on the work procedure manuals and on-site feedback. For example, the AI ​​combines information such as "When installing part A, tighten the screws securely" and "The screws tend to loosen, so they need to be checked regularly" to generate the optimal work procedure: "When installing part A, tighten the screws securely and check them regularly." Furthermore, based on the generated work procedure, on-demand training is provided to part-time operators. For example, the work procedure generated by the AI ​​can be provided to operators in the form of videos or simulations, which can be used to help them in their actual work. This allows operators to receive training at their own pace and improve their skills. This system improves the efficiency of work procedures and enhances the skills of operators. By automatically generating work procedures with AI, procedures can be reviewed and improved quickly, leading to increased work efficiency. In addition, on-demand training makes it easier for part-time operators to acquire the necessary skills. For example, even if a new work procedure is introduced, operators can receive training immediately, improving their ability to respond on-site. In this way, the automated work procedure system can achieve both the efficiency of work procedures and the skill improvement of operators.

[0058] The automated work procedure system according to this embodiment comprises a collection unit, an analysis unit, and a training unit. The collection unit collects accumulated work procedure manuals and on-site feedback. The collection unit can, for example, scan paper work procedure manuals and convert them into digital data. The collection unit can also directly collect work procedure manuals in digital format. Furthermore, the collection unit can collect on-site feedback as questionnaires or digital comments. For example, the collection unit scans work procedure manuals with a high-resolution scanner and converts them into text information using OCR technology. Digital work procedure manuals can be directly collected if submitted in a specific file format. On-site feedback is collected as questionnaire forms or digital comments. The analysis unit analyzes the information collected by the collection unit and generates the optimal work procedure. The analysis unit can, for example, use AI to analyze work procedure manuals and on-site feedback and automatically generate the optimal work procedure. For example, the analysis unit combines information such as "When installing part A, tighten the screws securely" and "The screws tend to loosen, so they need to be checked regularly" to generate the optimal work procedure, "When installing part A, tighten the screws securely and check them regularly." The analysis unit can use AI to generate the optimal work procedure based on work procedure manuals and on-site feedback. The training unit provides training based on the work procedure generated by the analysis unit. The training unit provides the AI-generated work procedure to operators in video or simulation format, for example. For example, the training unit provides training to operators in video format based on the AI-generated work procedure. The training unit can also provide training to operators in simulation format. For example, the training unit provides training to operators in simulation format based on the AI-generated work procedure. As a result, the work procedure automation system according to this embodiment can automate work procedures based on accumulated work procedure manuals and on-site feedback, and provide on-demand training to part-time operators.

[0059] The data collection unit collects accumulated work procedures and on-site feedback. For example, the unit can scan paper work procedures and convert them into digital data. It can also directly collect digital work procedures. Specifically, it scans paper work procedures with a high-resolution scanner and converts them into text information using OCR (Optical Character Recognition) technology. OCR technology recognizes characters from scanned images and extracts them as text data, and can handle both handwritten and printed characters. Digital work procedures can be directly collected if submitted in specific file formats such as PDF, Word, or Excel. This allows the data collection unit to efficiently collect both paper and digital work procedures and manage them centrally as digital data. Furthermore, the data collection unit can collect on-site feedback as questionnaires and digital comments. For example, it can provide on-site operators with online questionnaire forms to collect opinions and suggestions for improvement regarding work procedures. Additionally, operators can record points they noticed or problems they encountered during work in real time as digital comments. This allows the data collection unit to gather feedback that reflects actual field conditions and operator opinions, helping to improve work procedures. The collected data is stored in a central database, making it accessible to the analysis and training units. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0060] The analysis unit analyzes the information collected by the data collection unit and generates the optimal work procedure. For example, the analysis unit uses AI to analyze work procedure manuals and on-site feedback to automatically generate the optimal work procedure. Specifically, the AI ​​uses natural language processing (NLP) technology to understand the content of the work procedure manual and extract important information. For example, by combining information such as "When installing part A, tighten the screws securely" and "The screws tend to loosen, so they need to be checked regularly," it generates the optimal work procedure "When installing part A, tighten the screws securely and check them regularly." Furthermore, the AI ​​uses machine learning algorithms to analyze the collected feedback data and identify areas for improvement and problems in the work procedure. For example, based on feedback from operators, if a particular work procedure is too time-consuming or prone to errors, it can revise the procedure and propose a more efficient and accurate procedure. By integrating this information and automatically generating the optimal work procedure, the analysis unit can improve work efficiency and quality. In addition, the analysis unit can continuously evaluate the generated work procedure and make corrections and improvements as needed. This allows the analysis unit to always provide optimal work procedures based on the latest information, improving the reliability and efficiency of the entire system.

[0061] The training department provides training based on work procedures generated by the analysis department. For example, the training department provides operators with AI-generated work procedures in the form of videos or simulations. Specifically, it provides operators with training in video format based on AI-generated work procedures. The videos visually demonstrate the actual work procedures, making it easier for operators to understand them. For example, the procedure for installing part A is shown in a video, with detailed explanations of how to tighten screws and how to check them. The training department can also provide operators with training in the form of simulations. Simulations reproduce the actual work procedures in a virtual environment, allowing operators to practice the work by actually moving their hands. For example, using virtual reality (VR) technology, operators can simulate installing part A in a virtual space and actually experience how to tighten screws and how to check them. This allows operators to receive training in a situation close to the actual work environment, enabling them to learn work procedures more effectively. Furthermore, the training department can evaluate the progress and results of the training and provide additional training as needed. For example, if an operator is having trouble with a particular procedure, additional training specifically for that procedure can be provided to resolve the problem. This allows the training department to provide operators with effective training and support them in mastering work procedures.

[0062] The data collection unit can collect work procedures and on-site feedback. For example, the data collection unit can scan paper work procedures and convert them into digital data. The data collection unit can also directly collect work procedures in digital format. Furthermore, the data collection unit can collect on-site feedback as questionnaires or digital comments. For example, the data collection unit can scan work procedures with a high-resolution scanner and convert them into text information using OCR technology. Digital work procedures can be directly collected if submitted in a specific file format. On-site feedback is collected as questionnaire forms or digital comments. This allows the data collection unit to efficiently gather the necessary information by collecting work procedures and on-site feedback. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input image data obtained by scanning work procedures into a generating AI and have the generating AI generate text data from the image data.

[0063] The analysis unit can analyze the collected information and generate the optimal work procedure. For example, the analysis unit can use AI to analyze work procedure manuals and on-site feedback to automatically generate the optimal work procedure. For example, the analysis unit can use AI to combine the information "When installing part A, tighten the screws securely" and "The screws tend to loosen, so they need to be checked regularly" to generate the optimal work procedure "When installing part A, tighten the screws securely and check them regularly." The analysis unit can use AI to generate the optimal work procedure based on work procedure manuals and on-site feedback. This improves work efficiency by having the analysis unit analyze the collected information and generate the optimal work procedure. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into a generation AI and have the generation AI execute the generation of the optimal work procedure.

[0064] The training department can provide on-demand training based on the generated work procedures. For example, the training department can provide the AI-generated work procedures to operators in video or simulation format. For example, the training department can provide training to operators in video format based on the AI-generated work procedures. The training department can also provide training to operators in simulation format. For example, the training department can provide training to operators in simulation format based on the AI-generated work procedures. This allows part-time operators to receive training efficiently by providing on-demand training based on the generated work procedures. Some or all of the above processes in the training department may be performed using AI, for example, or without AI. For example, the training department can input the generated work procedures into a generating AI and have the generating AI generate the training content.

[0065] The training unit can provide training in video or simulation format. For example, the training unit can provide operators with training in video format based on work procedures generated by AI. The training unit can also provide operators with training in simulation format. For example, the training unit can provide operators with training in simulation format based on work procedures generated by AI. This allows operators to utilize the training provided by the training unit in video or simulation format in their actual work. Some or all of the above processes in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input the generated work procedures into a generating AI and have the generating AI generate training content in video or simulation format.

[0066] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting positive feedback to alleviate the stress. If the user is relaxed, the data collection unit will prioritize collecting detailed feedback to help improve work procedures. Furthermore, if the user is in a hurry, the data collection unit will prioritize collecting important feedback to respond quickly. This allows for the collection of more relevant information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0067] The data collection unit can analyze the update frequency of work procedure manuals and prioritize the collection of the latest information. For example, the data collection unit can analyze the update history of work procedure manuals and prioritize the collection of manuals that are frequently updated. The data collection unit can also identify manuals with high update frequency and prioritize the collection of feedback related to those manuals. Furthermore, the data collection unit can collect the latest information from manuals with low update frequency as needed. In this way, by analyzing the update frequency of work procedure manuals, the latest information can be collected preferentially. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input work procedure manual update history data into a generating AI and have the generating AI perform an analysis of update frequency.

[0068] The data collection unit can evaluate the reliability of on-site feedback and prioritize the collection of reliable feedback. For example, the data collection unit can evaluate the past reliability of feedback submitters and prioritize the collection of feedback from reliable submitters. The data collection unit can also analyze the content of the feedback and prioritize the collection of specific and detailed feedback. Furthermore, the data collection unit can consider the frequency of feedback submission and prioritize the collection of frequently submitted feedback. By prioritizing the collection of reliable feedback, more accurate information can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input feedback reliability evaluation data into a generating AI and have the generating AI perform the reliability evaluation.

[0069] The data collection unit can estimate the user's emotions and filter the information it collects based on those emotions. For example, if the user is stressed, the unit can collect only positive feedback to help reduce stress. If the user is relaxed, the unit can collect detailed feedback to help improve work procedures. Furthermore, if the user is in a hurry, the unit can collect only important feedback to respond quickly. This allows for the collection of more relevant information by filtering it based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform information filtering.

[0070] The data collection unit can collect data while considering the attribute information of the creator of the work procedure manual. For example, the data collection unit can consider the creator's area of ​​expertise and prioritize the collection of relevant feedback. It can also consider the creator's years of experience and prioritize the collection of feedback from experienced creators. Furthermore, it can consider the creator's job title and prioritize the collection of feedback from senior managers. By considering the creator's attribute information, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the attribute information of the creator of the work procedure manual into a generating AI and have the generating AI perform the data collection.

[0071] The data collection unit can collect data while considering the job title information of the person submitting the feedback at the time of collection. For example, the data collection unit can consider the job title information of the person submitting the feedback and prioritize the collection of feedback from higher-ranking officials. The data collection unit can also consider the area of ​​expertise of the person submitting the feedback and prioritize the collection of relevant feedback. Furthermore, the data collection unit can consider the years of experience of the person submitting the feedback and prioritize the collection of feedback from experienced submitters. This allows for the collection of more reliable information by considering the submitter's job title information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the job title information of the person submitting the feedback into a generating AI and have the generating AI perform the data collection.

[0072] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0073] The analysis unit can optimize the analysis algorithm by referring to past analysis results during the analysis. For example, the analysis unit can select the optimal analysis algorithm based on past analysis results. The analysis unit can also analyze past analysis results to improve the accuracy of the analysis algorithm. Furthermore, the analysis unit can adjust the parameters of the analysis algorithm by referring to past analysis results. In this way, the accuracy of the analysis algorithm can be improved by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis result data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0074] The analysis unit can improve the accuracy of its analysis by considering the interrelationship between work procedures and on-site feedback during the analysis. For example, the analysis unit can analyze the interrelationship between work procedures and on-site feedback to generate the optimal work procedure. Furthermore, the analysis unit can improve the accuracy of its analysis algorithm by considering the interrelationship between work procedures and on-site feedback. In addition, the analysis unit can improve the reliability of its analysis results by analyzing the interrelationship between work procedures and on-site feedback. Thus, by considering the interrelationship between work procedures and on-site feedback, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on work procedures and on-site feedback into a generating AI and have the generating AI perform the analysis of the interrelationships.

[0075] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize displaying positive analysis results to alleviate stress. If the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying important analysis results. This allows for the provision of more appropriate information by prioritizing analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis results.

[0076] The analysis unit can apply different analysis algorithms depending on the category of the work procedure manual during analysis. For example, the analysis unit can select the optimal analysis algorithm depending on the category of the work procedure manual. The analysis unit can also adjust the parameters of the analysis algorithm based on the category of the work procedure manual. Furthermore, the analysis unit can improve the accuracy of the analysis by applying different analysis algorithms depending on the category of the work procedure manual. In this way, the accuracy of the analysis can be improved by applying different analysis algorithms depending on the category of the work procedure manual. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the category data of the work procedure manual into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0077] The analysis unit can weight the analysis based on the timing of feedback submissions from the field. For example, the analysis unit can prioritize the analysis of the most recent feedback, taking into account the timing of feedback submissions. The analysis unit can also adjust the weighting of the analysis algorithm based on the timing of feedback submissions. Furthermore, the analysis unit can improve the reliability of the analysis results by taking into account the timing of feedback submissions. Thus, the reliability of the analysis results can be improved by weighting the analysis based on the timing of feedback submissions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input feedback submission timing data into a generating AI and have the generating AI perform the weighting adjustments.

[0078] The training unit can estimate the user's emotions and customize the training content based on those emotions. For example, if the user is nervous, the training unit can provide relaxing training content. If the user is relaxed, the training unit can provide detailed training content. Furthermore, if the user is in a hurry, the training unit can provide concise training content. By customizing the training content based on the user's emotions, more effective training can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the training unit may be performed using AI, or not using AI. For example, the training unit can input user emotion data into a generative AI and have the generative AI customize the training content.

[0079] The training unit can select the optimal training method by referring to the operator's past training history during training. For example, the training unit selects the optimal training method based on the operator's past training history. The training unit can also analyze the operator's past training history and customize the training content. Furthermore, the training unit can refer to the operator's past training history and adjust the training progress speed. In this way, the optimal training method can be provided by referring to the operator's past training history. Some or all of the above processes in the training unit may be performed using AI, for example, or not using AI. For example, the training unit can input the operator's past training history data into a generating AI and have the generating AI perform the selection of the training method.

[0080] The training unit can adjust the difficulty of training according to the operator's skill level during training. For example, the training unit can evaluate the operator's skill level and set the optimal training difficulty. The training unit can also customize the training content based on the operator's skill level. Furthermore, the training unit can adjust the training pace, taking the operator's skill level into consideration. This allows for more effective training by adjusting the difficulty of training according to the operator's skill level. Some or all of the above processes in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input operator skill level data into a generating AI and have the generating AI adjust the training difficulty.

[0081] The training unit can estimate the user's emotions and adjust the timing of training delivery based on the estimated emotions. For example, if the user is tense, the training unit can deliver training at a time when the user can relax. Also, if the user is relaxed, the training unit can provide detailed training content. Furthermore, if the user is in a hurry, the training unit can provide concise training content. In this way, by adjusting the timing of training delivery based on the user's emotions, more effective training can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the training unit may be performed using AI, for example, or not using AI. For example, the training unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of delivery timing.

[0082] The training department can provide an optimal training schedule by taking into account the operator's working hours during training. For example, the training department can set an optimal training schedule by considering the operator's working hours. Furthermore, the training department can customize the training content based on the operator's working hours. In addition, the training department can adjust the training pace by considering the operator's working hours. This allows the training department to provide an optimal training schedule by considering the operator's working hours. Some or all of the above processes in the training department may be performed using AI, for example, or without AI. For example, the training department can input operator working hour data into a generating AI and have the generating AI set the training schedule.

[0083] The training unit can select the optimal training format by considering the operator's device information during training. For example, if the operator is using a smartphone, the training unit can provide a training format optimized for smartphones. Furthermore, if the operator is using a tablet, the training unit can provide a training format optimized for tablets. In addition, if the operator is using a desktop PC, the training unit can provide a training format optimized for desktop PCs. This allows the optimal training format to be provided by considering the operator's device information. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input the operator's device information into a generating AI and have the generating AI select the training format.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting positive feedback to alleviate the stress. If the user is relaxed, the data collection unit will prioritize collecting detailed feedback to help improve work procedures. Furthermore, if the user is in a hurry, the data collection unit will prioritize collecting important feedback to respond quickly. This allows for the collection of more relevant information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0087] The training unit can estimate the user's emotions and customize the training content based on those emotions. For example, if the user is nervous, the training unit can provide relaxing training content. If the user is relaxed, the training unit can provide detailed training content. Furthermore, if the user is in a hurry, the training unit can provide concise training content. By customizing the training content based on the user's emotions, more effective training can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the training unit may be performed using AI, or not using AI. For example, the training unit can input user emotion data into a generative AI and have the generative AI customize the training content.

[0088] The training unit can estimate the user's emotions and adjust the timing of training delivery based on the estimated emotions. For example, if the user is tense, the training unit can deliver training at a time when the user can relax. Also, if the user is relaxed, the training unit can provide detailed training content. Furthermore, if the user is in a hurry, the training unit can provide concise training content. In this way, by adjusting the timing of training delivery based on the user's emotions, more effective training can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the training unit may be performed using AI, for example, or not using AI. For example, the training unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of delivery timing.

[0089] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize displaying positive analysis results to alleviate stress. If the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying important analysis results. This allows for the provision of more appropriate information by prioritizing analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis results.

[0090] The data collection unit can analyze the update frequency of work procedure manuals and prioritize the collection of the latest information. For example, the data collection unit can analyze the update history of work procedure manuals and prioritize the collection of manuals that are frequently updated. The data collection unit can also identify manuals with high update frequency and prioritize the collection of feedback related to those manuals. Furthermore, the data collection unit can collect the latest information from manuals with low update frequency as needed. In this way, by analyzing the update frequency of work procedure manuals, the latest information can be collected preferentially. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input work procedure manual update history data into a generating AI and have the generating AI perform an analysis of update frequency.

[0091] The data collection unit can evaluate the reliability of on-site feedback and prioritize the collection of reliable feedback. For example, the data collection unit can evaluate the past reliability of feedback submitters and prioritize the collection of feedback from reliable submitters. The data collection unit can also analyze the content of the feedback and prioritize the collection of specific and detailed feedback. Furthermore, the data collection unit can consider the frequency of feedback submission and prioritize the collection of frequently submitted feedback. By prioritizing the collection of reliable feedback, more accurate information can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input feedback reliability evaluation data into a generating AI and have the generating AI perform the reliability evaluation.

[0092] The analysis unit can optimize the analysis algorithm by referring to past analysis results during the analysis. For example, the analysis unit can select the optimal analysis algorithm based on past analysis results. The analysis unit can also analyze past analysis results to improve the accuracy of the analysis algorithm. Furthermore, the analysis unit can adjust the parameters of the analysis algorithm by referring to past analysis results. In this way, the accuracy of the analysis algorithm can be improved by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis result data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0093] The training unit can select the optimal training method by referring to the operator's past training history during training. For example, the training unit selects the optimal training method based on the operator's past training history. The training unit can also analyze the operator's past training history and customize the training content. Furthermore, the training unit can refer to the operator's past training history and adjust the training progress speed. In this way, the optimal training method can be provided by referring to the operator's past training history. Some or all of the above processes in the training unit may be performed using AI, for example, or not using AI. For example, the training unit can input the operator's past training history data into a generating AI and have the generating AI perform the selection of the training method.

[0094] The training unit can adjust the difficulty of training according to the operator's skill level during training. For example, the training unit can evaluate the operator's skill level and set the optimal training difficulty. The training unit can also customize the training content based on the operator's skill level. Furthermore, the training unit can adjust the training pace, taking the operator's skill level into consideration. This allows for more effective training by adjusting the difficulty of training according to the operator's skill level. Some or all of the above processes in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input operator skill level data into a generating AI and have the generating AI adjust the training difficulty.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The collection unit collects accumulated work procedures and on-site feedback. The collection unit can scan paper work procedures and convert them into digital data. It can also directly collect work procedures in digital format. Furthermore, the collection unit can collect on-site feedback as questionnaires or digital comments. For example, the collection unit scans work procedures with a high-resolution scanner and converts them into text information using OCR technology. Digital work procedures are collected directly if submitted in a specific file format. On-site feedback is collected as questionnaire forms or digital comments. Step 2: The analysis unit analyzes the information collected by the collection unit and generates the optimal work procedure. The analysis unit uses AI to analyze the work procedure manual and on-site feedback and automatically generates the optimal work procedure. For example, the analysis unit uses AI to combine the information that "when installing part A, tighten the screws securely" and "the screws tend to loosen, so they need to be checked regularly" to generate the optimal work procedure "when installing part A, tighten the screws securely and check them regularly." Step 3: The training unit provides training based on the work procedures generated by the analysis unit. The training unit provides the AI-generated work procedures to the operator in video or simulation format. For example, the training unit provides training to the operator in video format based on the AI-generated work procedures. The training unit can also provide training to the operator in simulation format.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0100] Each of the multiple elements described above, including the data collection unit, analysis unit, and training unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects work procedures and on-site feedback using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which uses AI to analyze the collected information and generate the optimal work procedure. The training unit is implemented in the control unit 46A of the smart device 14, which provides training to the operator in the form of video or simulation based on the generated work procedure. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0110] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0111] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0115] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the data collection unit, analysis unit, and training unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects work procedures and on-site feedback using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which analyzes the collected information using AI and generates the optimal work procedure. The training unit is implemented, for example, in the control unit 46A of the smart glasses 214, which provides training to the operator in the form of video or simulation based on the generated work procedure. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, and training unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects work procedures and on-site feedback using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which uses AI to analyze the collected information and generate the optimal work procedure. The training unit is implemented in the control unit 46A of the headset terminal 314, which provides training to the operator in the form of video or simulation based on the generated work procedure. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the data collection unit, analysis unit, and training unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects work procedures and on-site feedback using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which uses AI to analyze the collected information and generate the optimal work procedure. The training unit is implemented in the control unit 46A of the robot 414, which provides training to the operator in the form of video or simulation based on the generated work procedure. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0159] 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.

[0160] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) A collection unit that gathers accumulated work procedure manuals and on-site feedback, An analysis unit analyzes the information collected by the aforementioned collection unit and generates an optimal work procedure, The system includes a training unit that provides training based on the work procedures generated by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect work procedures and on-site feedback. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected information is analyzed to generate the optimal work procedure. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned training department We provide on-demand training based on the generated work procedures. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned training department Training is provided in the form of videos and simulations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the update frequency of work procedure manuals and prioritize collecting the latest information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Evaluate the reliability of on-site feedback and prioritize collecting reliable feedback. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and filters the information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, consider the attribute information of the creator of the work procedure manual. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting feedback from the field, we will take into account the job title of the person submitting the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The accuracy of the analysis is improved by considering the interrelationship between work procedures and on-site feedback during the analysis process. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the work procedure manual. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The analysis is weighted based on the timing of on-site feedback submissions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned training department It estimates the user's emotions and customizes the training content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned training department During training, the optimal training method is selected by referring to the operator's past training history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned training department The difficulty of training is adjusted according to the operator's skill level during training. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned training department It estimates the user's emotions and adjusts the timing of training delivery based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned training department We provide an optimal training schedule that takes into account the operator's working hours during training. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned training department During training, the optimal training format is selected considering the operator's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that gathers accumulated work procedure manuals and on-site feedback, An analysis unit analyzes the information collected by the aforementioned collection unit and generates an optimal work procedure, The system includes a training unit that provides training based on the work procedures generated by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect work procedures and on-site feedback. The system according to feature 1.

3. The aforementioned analysis unit, The collected information is analyzed to generate the optimal work procedure. The system according to feature 1.

4. The aforementioned training department We provide on-demand training based on the generated work procedures. The system according to feature 1.

5. The aforementioned training department Training is provided in the form of videos and simulations. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the update frequency of work procedure manuals and prioritize collecting the latest information. The system according to feature 1.

8. The aforementioned collection unit is Evaluate the reliability of on-site feedback and prioritize collecting reliable feedback. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and filters the information collected based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting data, consider the attribute information of the creator of the work procedure manual. The system according to feature 1.

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