System

Abstract

An object of a system according to an embodiment is to provide auxiliary information in order to improve work efficiency.SOLUTION: A system includes an imaging unit, an analysis unit, a display unit, a feedback collection unit, and an update unit. The photographing unit photographs a state of work. The analysis part analyzes the data photographed by the photographing part. The display unit displays auxiliary information based on the analysis result obtained by the analysis unit. The feedback collection unit collects feedback of the user on the basis of the auxiliary information displayed by the display unit. The update unit updates the auxiliary information on the basis of the feedback collected by the feedback collection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not provide sufficient auxiliary information to improve work efficiency, and there is room for improvement.

[0005] The system according to the embodiment aims to provide auxiliary information to improve work efficiency. [Means for solving the problem]

[0006] The system according to the embodiment includes an imaging unit, an analysis unit, a display unit, a feedback collection unit, and an update unit. The imaging unit captures images of the work being performed. The analysis unit analyzes the data captured by the imaging unit. The display unit displays auxiliary information based on the analysis results obtained by the analysis unit. The feedback collection unit collects user feedback based on the auxiliary information displayed by the display unit. The update unit updates the auxiliary information based on the feedback collected by the feedback collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide auxiliary information to improve the efficiency of the work. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The manual assistance system according to an embodiment of the present invention automatically learns and efficiently supports users in areas where it takes a long time to understand. In this system, a camera mounted on smart glasses captures the user's work in real time, and AI analyzes the captured images to identify areas where the user is having difficulty understanding. For example, the AI ​​automatically learns the parts that the user is confused about, such as how to install a particular part or an operation procedure. Then, based on the information learned by the AI, appropriate auxiliary information is displayed on the smart glasses' display. For example, animations showing how to install a part or text guides explaining the operation procedure are displayed. This allows users to receive appropriate support in real time and improve work efficiency. Furthermore, the AI ​​continuously improves the auxiliary information based on user feedback. For example, the AI ​​evaluates the results of the user's work while looking at the auxiliary information and updates the auxiliary information as necessary. This allows the system to continuously evolve to meet the user's needs. This makes the manual assistance system particularly effective in complex work environments, such as manufacturing and maintenance. For example, when a new worker performs a task for the first time, receiving appropriate support through smart glasses can reduce errors and accelerate their mastery of the task. It also helps experienced workers adapt to new procedures and equipment.

[0029] A manual assistance system according to an embodiment includes a camera, an analyzer, a display, a feedback collector, and an updater. The camera captures images of a user performing a task. For example, the camera can collect hand and eye movements using a camera mounted on smart glasses. The camera can also detect the illuminance and sound environment of the work environment and automatically set optimal shooting conditions. The camera can also have a zoom function to focus on specific parts or steps. The analyzer analyzes the data captured by the camera. For example, the analyzer can use AI to identify which parts the user is taking time to recognize. The analyzer can also have a function to detect anomalies by comparing the captured data with past work data. The analyzer can also evaluate the efficiency of the task and suggest areas for improvement. The display displays auxiliary information based on the analysis results obtained by the analyzer. For example, the display can display animations and audio guides. The display can also estimate the user's emotions and customize the display content based on the estimated user emotions. Furthermore, the display unit can dynamically change the display content depending on the progress of the work. The feedback collection unit collects user feedback based on the auxiliary information displayed by the display unit. For example, the feedback collection unit can collect the user's work results. The feedback collection unit can also estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The feedback collection unit can also collect data on the user's work environment and perform a comprehensive evaluation. The update unit updates the auxiliary information based on the feedback collected by the feedback collection unit. For example, the update unit can update the auxiliary information based on the collected feedback. The update unit can also estimate the user's emotions and adjust the update content based on the estimated user emotions. The update unit can also select an optimal update method by referring to past feedback data. As a result, the manual assistance system according to the embodiment can automatically learn parts that take a long time for the user to recognize and provide efficient support.

[0030] The camera unit can collect hand movements or gaze movements during work. The camera unit collects hand movements using, for example, motion capture technology. For example, the camera unit tracks hand movements with high accuracy and saves them as data. The camera unit can also analyze hand movements using image analysis technology. For example, the camera unit extracts and analyzes hand movements from video captured by a camera. The camera unit can also collect gaze movements using an eye-tracking device. For example, the camera unit uses an eye-tracking device to track a user's gaze movements in real time and saves them as data. The camera unit can also analyze gaze movements using camera analysis technology. For example, the camera unit extracts and analyzes gaze movements from video captured by a camera. This enables more detailed analysis by collecting hand movements and gaze movements during work. Some or all of the above-described processing in the camera unit may be performed using, for example, AI, or may be performed without using AI.

[0031] The display unit can display animation or audio guidance. The display unit can display, for example, 2D animation. For example, the display unit can display 2D animation showing how to install a part, thereby providing a visual guide to the user. The display unit can also display 3D animation. For example, the display unit can display 3D animation showing an operation procedure, thereby providing a three-dimensional guide to the user. The display unit can also display audio guidance. For example, the display unit can use text-to-speech technology to provide audio guidance for the operation procedure. The display unit can also play recorded audio. For example, the display unit can play pre-recorded audio guidance to provide auditory guidance to the user. In this way, displaying animation or audio guidance effectively provides auxiliary information to the user. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI.

[0032] The feedback collection unit can collect the results of the user's work. The feedback collection unit, for example, collects the user's work completion status. For example, the feedback collection unit checks whether the work is completed and saves it as data. The feedback collection unit can also collect whether the user made any errors in the work. For example, the feedback collection unit records any errors that occurred during the work and saves it as data. The feedback collection unit can also collect the user's work time. For example, the feedback collection unit measures the time from the start to the end of the work and saves it as data. By collecting the results of the user's work, the accuracy of the auxiliary information can be improved. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI.

[0033] The update unit can update the auxiliary information based on the collected feedback. The update unit, for example, updates an algorithm based on the collected feedback. For example, the update unit analyzes user feedback and improves the display method of the auxiliary information. The update unit can also update a database. For example, the update unit adds or modifies the content of the auxiliary information based on the collected feedback. The update unit can also adjust the priority of the auxiliary information based on user feedback. For example, the update unit sets the display priority to information frequently referenced by the user. In this way, updating the auxiliary information based on the collected feedback improves the accuracy of the system. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI.

[0034] The image capturing unit can detect the illuminance and sound environment of the work environment and automatically set appropriate image capturing conditions. The image capturing unit detects the illuminance of the work environment using, for example, an illuminance sensor. For example, if the illuminance is low, the image capturing unit increases the sensitivity of the camera to capture brighter images. Furthermore, if the illuminance is too high, the image capturing unit can adjust the exposure of the camera to achieve appropriate brightness. The image capturing unit can also detect the sound environment of the work environment using a microphone. For example, if there is a lot of noise, the image capturing unit can perform noise cancellation on the audio data. Furthermore, the image capturing unit can adjust the recording level of the audio data according to the sound environment. This automatically sets optimal image capturing conditions according to the work environment, thereby improving the quality of the captured data. Some or all of the above-described processing in the image capturing unit may be performed using, for example, AI, or may be performed without using AI.

[0035] The photographing unit may be added with a zoom function to focus on specific parts or procedures. For example, the photographing unit may automatically zoom in when installing small parts. For example, the photographing unit may use the zoom function of the camera to capture details of small parts. The photographing unit may also take close-up photographs of the hands during complex procedures. For example, the photographing unit may use the zoom function of the camera to capture detailed images of the movements of the hands. The photographing unit may also repeatedly zoom in and out to emphasize important work points. For example, the photographing unit may use the zoom function of the camera to capture images that emphasize important points. This enables detailed work analysis by focusing on specific parts or procedures. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI.

[0036] The imaging unit can perform noise removal and image correction on the imaging data in real time. The imaging unit, for example, uses filtering technology to remove noise generated during imaging in real time. For example, the imaging unit uses a noise reduction algorithm to remove noise from the imaging data. The imaging unit can also adjust the brightness and contrast of the image in real time using image correction technology. For example, the imaging unit uses an image correction algorithm to adjust the brightness and contrast of the imaging data. The imaging unit can also correct camera shake that occurs during imaging in real time using image stabilization technology. For example, the imaging unit uses an image stabilization algorithm to correct camera shake in the imaging data. This improves the quality of the imaging data by performing noise removal and image correction in real time. Some or all of the above-mentioned processing in the imaging unit may be performed using, for example, AI, or may be performed without using AI.

[0037] The imaging unit can adjust the imaging frame rate according to the user's working speed. The imaging unit measures the user's working speed, for example, using motion analysis technology. For example, the imaging unit analyzes the user's hand movements and the progress of the work to calculate the working speed. The imaging unit can also adjust the imaging frame rate according to the user's working speed. For example, if the user is working quickly, the frame rate can be increased to capture smoother images. If the user is working slowly, the frame rate can be decreased to capture images with reduced power consumption. The frame rate can also be adjusted in real time if the user's working speed changes. This improves the quality of the captured data by adjusting the frame rate according to the user's working speed. Some or all of the above-mentioned processing in the imaging unit may be performed using, for example, AI, or may be performed without using AI.

[0038] The imaging unit can upload the imaging data to the cloud in real time, allowing experts in remote locations to provide advice. The imaging unit uploads the imaging data to the cloud, for example, using a cloud storage service. For example, the imaging unit can upload the imaging data to the cloud in real time, allowing experts to provide advice in real time. The imaging unit can also allow experts in remote locations to check the imaging data and send necessary instructions in real time. For example, the imaging unit can share the imaging data on the cloud, allowing multiple experts to provide advice simultaneously. This allows experts in remote locations to provide advice in real time, improving the accuracy of the work. Some or all of the above-described processing in the imaging unit may be performed, for example, using AI, or may be performed without using AI.

[0039] The imaging unit can perform 3D modeling on the imaging data, enabling three-dimensional work analysis. The imaging unit generates a 3D model from the imaging data, for example, using point cloud data. For example, the imaging unit generates a 3D model based on the imaging data and performs three-dimensional work analysis. The imaging unit can also generate a 3D model using mesh generation technology. For example, the imaging unit generates a mesh based on the imaging data and performs three-dimensional work analysis. The imaging unit can also simulate work procedures using the 3D model. For example, the imaging unit evaluates the efficiency and safety of work based on the 3D model. Thus, three-dimensional work analysis becomes possible by performing 3D modeling. Some or all of the above-described processing in the imaging unit may be performed using, for example, AI, or may be performed without using AI.

[0040] The analysis unit can be added with a function to detect anomalies by comparing with past work data. The analysis unit, for example, detects abnormal movements based on past work data. For example, the analysis unit detects abnormal movements by comparing with past work data. The analysis unit can also detect abnormal time consumption based on past work data. For example, the analysis unit detects abnormal time consumption by comparing with past work data. The analysis unit can also detect abnormal differences in procedures based on past work data. For example, the analysis unit detects abnormal differences in procedures by comparing with past work data. This improves the accuracy of work by detecting anomalies by comparing with past work data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0041] The analysis unit can evaluate the efficiency of the work and suggest improvements. For example, the analysis unit can evaluate the efficiency of the work and suggest ways to reduce unnecessary movements. For example, the analysis unit can evaluate the efficiency of the work and suggest ways to reduce unnecessary movements. The analysis unit can also evaluate the efficiency of the work and suggest optimal procedures. For example, the analysis unit can evaluate the efficiency of the work and suggest optimal procedures. The analysis unit can also evaluate the efficiency of the work and suggest the placement of necessary tools and parts. For example, the analysis unit can evaluate the efficiency of the work and suggest the placement of necessary tools and parts. In this way, the efficiency of the work is evaluated and improvements are suggested, thereby improving the efficiency of the work. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0042] The analysis unit can evaluate the safety of the work and predict risks. The analysis unit, for example, evaluates the safety of the work and warns of dangerous movements. For example, the analysis unit evaluates the safety of the work and warns of dangerous movements. The analysis unit can also evaluate the safety of the work and make suggestions to improve high-risk procedures. For example, the analysis unit can evaluate the safety of the work and make suggestions to improve high-risk procedures. The analysis unit can also evaluate the safety of the work and suggest the use of necessary safety equipment. For example, the analysis unit can evaluate the safety of the work and suggest the use of necessary safety equipment. In this way, the safety of the work is improved by evaluating the safety of the work and predicting risks. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0043] The analysis unit can improve the analysis accuracy by taking into account data on the work environment. The analysis unit improves the analysis accuracy by taking into account, for example, temperature data of the work environment. For example, the analysis unit uses a temperature sensor to collect temperature data of the work environment and reflects it in the analysis. The analysis unit can also improve the analysis accuracy by taking into account humidity data of the work environment. For example, the analysis unit uses a humidity sensor to collect humidity data of the work environment and reflects it in the analysis. The analysis unit can also improve the analysis accuracy by taking into account illuminance data of the work environment. For example, the analysis unit uses an illuminance sensor to collect illuminance data of the work environment and reflects it in the analysis. In this way, by taking into account the data on the work environment, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0044] The analysis unit can share the analysis results with other workers to improve the work efficiency of the entire team. The analysis unit shares the analysis results with other workers, for example, using a cloud storage service. For example, the analysis unit uploads the analysis results to the cloud and shares them with other workers. The analysis unit can also share the analysis results in real time to improve the work efficiency of the entire team. For example, the analysis unit can share the analysis results in real time to improve the work efficiency of the entire team. The analysis unit can also discuss areas for improvement with the entire team based on the analysis results. For example, the analysis unit discusses areas for improvement with the entire team based on the analysis results. In this way, by sharing the analysis results, the work efficiency of the entire team improves. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0045] The analysis unit can automatically generate a work procedure based on the analysis results. The analysis unit, for example, automatically generates an optimal work procedure based on the analysis results. For example, the analysis unit automatically generates an optimal work procedure based on the analysis results. The analysis unit can also automatically generate an efficient work procedure based on the analysis results. For example, the analysis unit automatically generates an efficient work procedure based on the analysis results. The analysis unit can also automatically generate a safe work procedure based on the analysis results. For example, the analysis unit automatically generates a safe work procedure based on the analysis results. As a result, the efficiency of the work is improved by automatically generating a work procedure based on the analysis results. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0046] The display unit can dynamically change the display content according to the progress of the work. The display unit, for example, displays the next step according to the progress of the work. For example, the display unit displays the next step according to the progress of the work. The display unit can also display necessary tools and parts according to the progress of the work. For example, the display unit displays necessary tools and parts according to the progress of the work. The display unit can also display cautions and warnings according to the progress of the work. For example, the display unit displays cautions and warnings according to the progress of the work. This allows the display content to be dynamically changed according to the progress of the work, thereby providing effective support to the user. Some or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI.

[0047] The display unit can determine an appropriate display position using the user's gaze tracking data. The display unit, for example, uses an gaze tracking device to collect the user's gaze tracking data. For example, the display unit uses the gaze tracking device to track the user's gaze movements in real time and save the data. The display unit can also analyze the gaze tracking data using a gaze analysis algorithm. For example, the display unit can identify the user's gaze focus point using the gaze analysis algorithm. The display unit can also determine an optimal display position based on the user's gaze tracking data. For example, the display unit can highlight and display important information based on the user's gaze focus point. The display unit can also display information at a position with high visibility based on the user's gaze movement. In this way, the optimal display position is determined by using the user's gaze tracking data, improving visibility. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI or without AI.

[0048] The display unit can provide an individually optimized guide by referring to the user's work history. The display unit, for example, uses log data to collect the user's work history. For example, the display unit uses the log data to collect the user's work history and saves it as data. The display unit can also refer to the user's work history by using a history database. For example, the display unit can refer to the user's past work history by using a history database. The display unit can also provide an individually optimized guide based on the user's work history. For example, the display unit can provide an optimal guide based on the user's work history. The display unit can also provide an efficient guide based on the user's work history. The display unit can also provide a safe guide based on the user's work history. As a result, an individually optimized guide is provided by referring to the user's work history, improving work efficiency. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI.

[0049] The display unit can adjust the brightness and contrast of the display according to the user's work environment. The display unit detects the illuminance of the work environment using, for example, an illuminance sensor. For example, in a low-illuminance environment, the display unit increases the brightness of the display to improve visibility. In a high-illuminance environment, the display unit can also enhance the contrast of the display to improve visibility. In an environment where the illuminance fluctuates, the display unit can also adjust the brightness and contrast in real time. In this way, visibility is improved by adjusting the brightness and contrast of the display according to the work environment. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI.

[0050] The display unit can display content in multiple languages ​​and accommodate users who speak different languages. The display unit automatically translates the display content using, for example, a translation algorithm. For example, the display unit automatically translates the display content based on the user's language setting. The display unit can also provide a language switching function. For example, the display unit provides a language switching function when a user uses multiple languages. Furthermore, when a specific language is selected, the display unit can provide the display content in that language. This makes it possible to accommodate multiple languages ​​and accommodate users who speak different languages. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI.

[0051] The display unit can link the displayed content with an audio guide to provide both visual and audio support. The display unit can, for example, use text-to-speech technology to provide an audio guide corresponding to the displayed content. For example, the display unit can use text-to-speech technology to provide an audio guide corresponding to the displayed content. The display unit can also play recorded audio. For example, the display unit can play recorded audio to provide both visual and audio support. The display unit can also use an audio guide to highlight important information. For example, the display unit can use an audio guide to highlight important information. The display unit can also synchronize the audio guide with the displayed content to help the user's understanding. For example, the display unit synchronizes the audio guide with the displayed content to help the user's understanding. This provides both visual and audio support, thereby deepening the user's understanding. Some or all of the above-described processing in the display unit may be performed, for example, using AI, or may be performed without using AI.

[0052] The feedback collection unit can evaluate the user's work performance and provide detailed feedback when collecting feedback. The feedback collection unit, for example, evaluates the user's work performance and provides feedback on areas for improvement. For example, the feedback collection unit evaluates the user's work performance and provides feedback on areas for improvement. The feedback collection unit can also evaluate the user's work performance and suggest an efficient work method. For example, the feedback collection unit can evaluate the user's work performance and suggest an efficient work method. The feedback collection unit can also evaluate the user's work performance and suggest a safe work method. For example, the feedback collection unit can evaluate the user's work performance and suggest a safe work method. In this way, detailed feedback can be provided by evaluating the user's work performance. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI.

[0053] The feedback collection unit can also collect data on the user's working environment when collecting feedback and perform a comprehensive evaluation. The feedback collection unit, for example, uses a temperature sensor to collect temperature data on the working environment. For example, the feedback collection unit uses the temperature sensor to collect temperature data on the working environment and perform a comprehensive evaluation. The feedback collection unit can also use a humidity sensor to collect humidity data on the working environment. For example, the feedback collection unit uses the humidity sensor to collect humidity data on the working environment and perform a comprehensive evaluation. The feedback collection unit can also use an illuminance sensor to collect illuminance data on the working environment. For example, the feedback collection unit uses an illuminance sensor to collect illuminance data on the working environment and perform a comprehensive evaluation. In this way, by also collecting data on the user's working environment, a comprehensive evaluation is possible. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI.

[0054] When collecting feedback, the feedback collection unit can present individually optimized questions by referring to the user's work history. The feedback collection unit collects the user's work history by using, for example, log data. For example, the feedback collection unit collects the user's work history by using the log data and stores it as data. The feedback collection unit can also refer to the user's work history by using a history database. For example, the feedback collection unit can refer to the user's past work history by using the history database. The feedback collection unit can also present individually optimized questions based on the user's work history. For example, the feedback collection unit presents optimal questions based on the user's work history. The feedback collection unit can also present efficient questions based on the user's work history. The feedback collection unit can also present safe questions based on the user's work history. In this way, individually optimized questions can be presented by referring to the user's work history. Some or all of the above-described processing in the feedback collection unit may be performed by using, for example, AI, or may be performed without using AI.

[0055] When collecting feedback, the feedback collection unit can analyze the user's social media activity and collect related feedback. The feedback collection unit, for example, analyzes the content of social media posts to understand the user's activity. For example, the feedback collection unit collects related feedback based on the content of the user's social media posts. The feedback collection unit can also analyze followers to evaluate the user's influence. For example, the feedback collection unit collects feedback with reference to the activities of the user's followers. The feedback collection unit can also collect feedback with reference to the activities of the user's friends on social media. For example, the feedback collection unit collects related feedback based on the content of posts by the user's friends. In this way, related feedback can be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the feedback collection unit may be performed, for example, using AI or without using AI.

[0056] The feedback collection unit can identify region-specific problems by taking into account the user's geographical location information when collecting feedback. The feedback collection unit can collect the user's geographical location information by using, for example, GPS data. For example, the feedback collection unit can collect the user's geographical location information by using GPS data and store it as data. The feedback collection unit can also collect the user's geographical location information by using a location information service. For example, the feedback collection unit can collect the user's geographical location information by using a location information service and store it as data. The feedback collection unit can also identify region-specific problems based on the user's geographical location information. For example, the feedback collection unit can identify region-specific problems by taking into account the climatic conditions and cultural background of the region. The feedback collection unit can also suggest region-specific improvements. For example, the feedback collection unit can suggest region-specific improvements by taking into account the climatic conditions and cultural background of the region. The feedback collection unit can also predict region-specific risks. For example, the feedback collection unit can predict region-specific risks by taking into account the climatic conditions and cultural background of the region. In this way, region-specific problems can be identified by taking into account the user's geographical location information. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or may be performed without using AI.

[0057] When collecting feedback, the feedback collection unit can select the optimal collection method by taking into account device information of the user. The feedback collection unit collects, for example, the device type and OS version. For example, the feedback collection unit collects the device type and OS version used by the user and stores them as data. The feedback collection unit can also evaluate device performance and select the optimal collection method. For example, the feedback collection unit provides the optimal collection method depending on the device performance. The feedback collection unit can also adjust the collection method by taking into account device usage. For example, if the user uses a smartphone, the feedback collection unit can provide the optimal collection method for the smartphone. Also, if the user uses a tablet, the feedback collection unit can provide the optimal collection method for the tablet. Also, if the user uses a smartwatch, the feedback collection unit can provide the optimal collection method for the smartwatch. In this way, the optimal collection method can be selected by taking into account device information of the user. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI.

[0058] The update unit can select an optimal update method by referring to past feedback data during an update. The update unit selects the optimal update method based on, for example, past feedback data. For example, the update unit selects the optimal update method based on past feedback data. The update unit can also select an efficient update method based on past feedback data. For example, the update unit selects an efficient update method based on past feedback data. The update unit can also select a safe update method based on past feedback data. For example, the update unit selects a safe update method based on past feedback data. In this way, the optimal update method can be selected by referring to past feedback data. Some or all of the above-described processing in the update unit may be performed, for example, using AI or may be performed without using AI.

[0059] The update unit can evaluate the user's work performance during an update and perform an individually optimized update. The update unit, for example, evaluates the user's work performance and performs an optimal update. For example, the update unit evaluates the user's work performance and performs an optimal update. The update unit can also evaluate the user's work performance and perform an efficient update. For example, the update unit evaluates the user's work performance and performs an efficient update. The update unit can also evaluate the user's work performance and perform a safe update. For example, the update unit evaluates the user's work performance and performs a safe update. In this way, an individually optimized update can be performed by evaluating the user's work performance. Some or all of the above-described processing in the update unit may be performed, for example, using AI, or may be performed without using AI.

[0060] The update unit can perform a comprehensive update by also taking into account the user's work environment data when updating. The update unit, for example, uses a temperature sensor to collect temperature data of the work environment. For example, the update unit uses the temperature sensor to collect temperature data of the work environment and perform a comprehensive update. The update unit can also use a humidity sensor to collect humidity data of the work environment. For example, the update unit uses a humidity sensor to collect humidity data of the work environment and perform a comprehensive update. The update unit can also use an illuminance sensor to collect illuminance data of the work environment. For example, the update unit uses an illuminance sensor to collect illuminance data of the work environment and perform a comprehensive update. This makes it possible to perform a comprehensive update by also taking into account the user's work environment data. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI.

[0061] The update unit can analyze the user's social media activity at the time of updating and reflect related information in the update. The update unit, for example, analyzes the content of social media posts to understand the user's activity. For example, the update unit reflects related information in the update based on the content of the user's social media posts. The update unit can also analyze followers and evaluate the user's influence. For example, the update unit adjusts the update content based on the activity of the user's followers. The update unit can also adjust the update content based on the activity of the user's friends on social media. For example, the update unit reflects related information in the update based on the content posted by the user's friends. In this way, by analyzing the user's social media activity, related information can be reflected in the update. Some or all of the above-mentioned processing in the update unit may be performed, for example, using AI or without using AI.

[0062] During an update, the update unit may perform an update to solve region-specific problems by taking into account the user's geographical location information. The update unit may, for example, use GPS data to collect the user's geographical location information. For example, the update unit may use the GPS data to collect the user's geographical location information and store it as data. The update unit may also use a location information service to collect the user's geographical location information. For example, the update unit may use a location information service to collect the user's geographical location information and store it as data. The update unit may also perform an update to solve region-specific problems based on the user's geographical location information. For example, the update unit may perform an update to solve region-specific problems by taking into account the climatic conditions and cultural background of the region. The update unit may also perform an update to reflect region-specific improvements. For example, the update unit may perform an update to reflect region-specific improvements by taking into account the climatic conditions and cultural background of the region. The update unit may also predict region-specific risks and perform an update. For example, the update unit may predict region-specific risks and perform an update by taking into account the climatic conditions and cultural background of the region. This allows updates to be performed to solve problems specific to a region by taking into account the user's geographical location information. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI.

[0063] During an update, the update unit can select the optimal update method by taking into account the user's device information. The update unit collects, for example, the device type and OS version. For example, the update unit collects the device type and OS version used by the user and stores them as data. The update unit can also evaluate the device performance and select the optimal update method. For example, the update unit provides the optimal update method according to the device performance. The update unit can also adjust the update method by taking into account the device usage status. For example, if the user uses a smartphone, the update unit can provide the optimal update method for the smartphone. Also, if the user uses a tablet, the update unit can provide the optimal update method for the tablet. Also, if the user uses a smartwatch, the update unit can provide the optimal update method for the smartwatch. In this way, the optimal update method can be selected by taking into account the user's device information. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI.

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

[0065] The analysis unit can evaluate the user's work performance in real time and dynamically adjust the auxiliary information based on the evaluation results. For example, the analysis unit can evaluate the user's work speed and accuracy, and if the user is slow, display more detailed auxiliary information. Also, if the user is working accurately, the analysis unit can simplify the auxiliary information. Furthermore, the analysis unit can predict the next step based on the user's work performance and provide auxiliary information at an appropriate time. This enables flexible support according to the user's work performance.

[0066] The display unit can provide individually optimized guidance based on the user's work history. For example, the display unit can refer to past work data and highlight and display parts where the user previously struggled. It can also provide simplified guidance for work that the user is good at. Furthermore, the display unit can provide an option to skip certain work steps based on the user's work history. This enables efficient support based on the user's past experience.

[0067] The feedback collection unit can also collect data on the user's work environment and perform a comprehensive evaluation. For example, the feedback collection unit can use a temperature sensor to collect temperature data of the work environment and evaluate the impact on work efficiency. It can also use a humidity sensor to collect humidity data of the work environment and evaluate the comfort of the work. Furthermore, it can use an illuminance sensor to collect illuminance data of the work environment and use this data to improve visibility. This enables a comprehensive evaluation that takes the entire work environment into consideration.

[0068] The display unit can determine an appropriate display position using the user's eye-tracking data. For example, the display unit can use an eye-tracking device to track the user's eye movements in real time and display important information at the point where the user's eyes are focused. It can also display information at a highly visible position based on the eye movements. Furthermore, it can dynamically change the display content according to the user's eye movements. This makes it possible to provide optimal information based on the user's eye movements.

[0069] When updating auxiliary information based on collected feedback, the update unit can select the optimal update method by referring to past feedback data. For example, it can select the most effective way to display auxiliary information based on past feedback data. It can also prioritize the display of information frequently referenced by users based on past feedback data. It can also add or modify the content of auxiliary information based on past feedback data. This makes it possible to perform optimal updates by utilizing past data.

[0070] The analysis unit can evaluate the efficiency of work and suggest areas for improvement. For example, the analysis unit can evaluate the efficiency of work and suggest ways to reduce unnecessary movements. It can also suggest optimal procedures. It can also suggest the placement of necessary tools and parts. In this way, by evaluating the efficiency of work and suggesting areas for improvement, work efficiency can be improved.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The camera captures the user's work. For example, a camera mounted on smart glasses can be used to collect hand and eye movements. The camera can also detect the lighting and sound environment of the work environment and automatically set the optimal shooting conditions. It can also have a zoom function to focus on specific parts or steps. Step 2: The analysis unit analyzes the data captured by the imaging unit. For example, it can use AI to identify which parts the user is taking a long time to recognize. It can also be equipped with a function to detect anomalies by comparing them with past work data. It can also evaluate the efficiency of work and suggest areas for improvement. Step 3: The display unit displays auxiliary information based on the analysis results obtained by the analysis unit. For example, it can display animations or audio guides. It can also estimate the user's emotions and customize the display content based on the estimated user emotions. It can also dynamically change the display content according to the progress of the task. Step 4: The feedback collection unit collects user feedback based on the auxiliary information displayed by the display unit. For example, the feedback collection unit can collect the user's work results. It can also estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. It can also collect data on the user's work environment and perform a comprehensive evaluation. Step 5: The update unit updates the auxiliary information based on the feedback collected by the feedback collection unit. For example, the auxiliary information can be updated based on the collected feedback. In addition, the update unit can estimate the user's emotion and adjust the update content based on the estimated user's emotion. Furthermore, the update unit can select the optimal update method by referring to past feedback data.

[0073] (Example 2) The manual assistance system according to an embodiment of the present invention automatically learns and efficiently supports users in areas where it takes a long time to understand. In this system, a camera mounted on smart glasses captures the user's work in real time, and AI analyzes the captured images to identify areas where the user is having difficulty understanding. For example, the AI ​​automatically learns the parts that the user is confused about, such as how to install a particular part or an operation procedure. Then, based on the information learned by the AI, appropriate auxiliary information is displayed on the smart glasses' display. For example, animations showing how to install a part or text guides explaining the operation procedure are displayed. This allows users to receive appropriate support in real time and improve work efficiency. Furthermore, the AI ​​continuously improves the auxiliary information based on user feedback. For example, the AI ​​evaluates the results of the user's work while looking at the auxiliary information and updates the auxiliary information as necessary. This allows the system to continuously evolve to meet the user's needs. This makes the manual assistance system particularly effective in complex work environments, such as manufacturing and maintenance. For example, when a new worker performs a task for the first time, receiving appropriate support through smart glasses can reduce errors and accelerate their mastery of the task. It also helps experienced workers adapt to new procedures and equipment.

[0074] A manual assistance system according to an embodiment includes a camera, an analyzer, a display, a feedback collector, and an updater. The camera captures images of a user performing a task. For example, the camera can collect hand and eye movements using a camera mounted on smart glasses. The camera can also detect the illuminance and sound environment of the work environment and automatically set optimal shooting conditions. The camera can also have a zoom function to focus on specific parts or steps. The analyzer analyzes the data captured by the camera. For example, the analyzer can use AI to identify which parts the user is taking time to recognize. The analyzer can also have a function to detect anomalies by comparing the captured data with past work data. The analyzer can also evaluate the efficiency of the task and suggest areas for improvement. The display displays auxiliary information based on the analysis results obtained by the analyzer. For example, the display can display animations and audio guides. The display can also estimate the user's emotions and customize the display content based on the estimated user emotions. Furthermore, the display unit can dynamically change the display content depending on the progress of the work. The feedback collection unit collects user feedback based on the auxiliary information displayed by the display unit. For example, the feedback collection unit can collect the user's work results. The feedback collection unit can also estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The feedback collection unit can also collect data on the user's work environment and perform a comprehensive evaluation. The update unit updates the auxiliary information based on the feedback collected by the feedback collection unit. For example, the update unit can update the auxiliary information based on the collected feedback. The update unit can also estimate the user's emotions and adjust the update content based on the estimated user emotions. The update unit can also select an optimal update method by referring to past feedback data. As a result, the manual assistance system according to the embodiment can automatically learn parts that take a long time for the user to recognize and provide efficient support.

[0075] The camera unit can collect hand movements or gaze movements during work. The camera unit collects hand movements using, for example, motion capture technology. For example, the camera unit tracks hand movements with high accuracy and saves them as data. The camera unit can also analyze hand movements using image analysis technology. For example, the camera unit extracts and analyzes hand movements from video captured by a camera. The camera unit can also collect gaze movements using an eye-tracking device. For example, the camera unit uses an eye-tracking device to track a user's gaze movements in real time and saves them as data. The camera unit can also analyze gaze movements using camera analysis technology. For example, the camera unit extracts and analyzes gaze movements from video captured by a camera. This enables more detailed analysis by collecting hand movements and gaze movements during work. Some or all of the above-described processing in the camera unit may be performed using, for example, AI, or may be performed without using AI.

[0076] The display unit can display animation or audio guidance. The display unit can display, for example, 2D animation. For example, the display unit can display 2D animation showing how to install a part, thereby providing a visual guide to the user. The display unit can also display 3D animation. For example, the display unit can display 3D animation showing an operation procedure, thereby providing a three-dimensional guide to the user. The display unit can also display audio guidance. For example, the display unit can use text-to-speech technology to provide audio guidance for the operation procedure. The display unit can also play recorded audio. For example, the display unit can play pre-recorded audio guidance to provide auditory guidance to the user. In this way, displaying animation or audio guidance effectively provides auxiliary information to the user. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI.

[0077] The feedback collection unit can collect the results of the user's work. The feedback collection unit, for example, collects the user's work completion status. For example, the feedback collection unit checks whether the work is completed and saves it as data. The feedback collection unit can also collect whether the user made any errors in the work. For example, the feedback collection unit records any errors that occurred during the work and saves it as data. The feedback collection unit can also collect the user's work time. For example, the feedback collection unit measures the time from the start to the end of the work and saves it as data. By collecting the results of the user's work, the accuracy of the auxiliary information can be improved. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI.

[0078] The update unit can update the auxiliary information based on the collected feedback. The update unit, for example, updates an algorithm based on the collected feedback. For example, the update unit analyzes user feedback and improves the display method of the auxiliary information. The update unit can also update a database. For example, the update unit adds or modifies the content of the auxiliary information based on the collected feedback. The update unit can also adjust the priority of the auxiliary information based on user feedback. For example, the update unit sets the display priority to information frequently referenced by the user. In this way, updating the auxiliary information based on the collected feedback improves the accuracy of the system. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI.

[0079] The image capture unit can estimate the user's emotions and adjust the timing of capturing images based on the estimated user emotions. The image capture unit can estimate the user's emotions using, for example, facial expression analysis technology. For example, the image capture unit can analyze the user's facial expressions captured by a camera and calculate an emotion score. The image capture unit can also estimate the user's emotions using voice analysis technology. For example, the image capture unit can analyze the tone and speed of the user's voice and calculate an emotion score. The image capture unit can also adjust the timing of capturing images based on the estimated user emotions. For example, if the user is nervous, the image capture unit can delay capturing images until the user relaxes. If the user is concentrating, the image capture unit can start capturing images so as not to miss important moments in the work. If the user is tired, the image capture unit can resume capturing images after a break. This allows for more appropriate data collection by adjusting the timing of capturing images according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the imaging unit may be performed using AI, for example, or may be performed without using AI.

[0080] The image capturing unit can detect the illuminance and sound environment of the work environment and automatically set appropriate image capturing conditions. The image capturing unit detects the illuminance of the work environment using, for example, an illuminance sensor. For example, if the illuminance is low, the image capturing unit increases the sensitivity of the camera to capture brighter images. Furthermore, if the illuminance is too high, the image capturing unit can adjust the exposure of the camera to achieve appropriate brightness. The image capturing unit can also detect the sound environment of the work environment using a microphone. For example, if there is a lot of noise, the image capturing unit can perform noise cancellation on the audio data. Furthermore, the image capturing unit can adjust the recording level of the audio data according to the sound environment. This automatically sets optimal image capturing conditions according to the work environment, thereby improving the quality of the captured data. Some or all of the above-described processing in the image capturing unit may be performed using, for example, AI, or may be performed without using AI.

[0081] The photographing unit may be added with a zoom function to focus on specific parts or procedures. For example, the photographing unit may automatically zoom in when installing small parts. For example, the photographing unit may use the zoom function of the camera to capture details of small parts. The photographing unit may also take close-up photographs of the hands during complex procedures. For example, the photographing unit may use the zoom function of the camera to capture detailed images of the movements of the hands. The photographing unit may also repeatedly zoom in and out to emphasize important work points. For example, the photographing unit may use the zoom function of the camera to capture images that emphasize important points. This enables detailed work analysis by focusing on specific parts or procedures. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI.

[0082] The imaging unit can perform noise removal and image correction on the imaging data in real time. The imaging unit, for example, uses filtering technology to remove noise generated during imaging in real time. For example, the imaging unit uses a noise reduction algorithm to remove noise from the imaging data. The imaging unit can also adjust the brightness and contrast of the image in real time using image correction technology. For example, the imaging unit uses an image correction algorithm to adjust the brightness and contrast of the imaging data. The imaging unit can also correct camera shake that occurs during imaging in real time using image stabilization technology. For example, the imaging unit uses an image stabilization algorithm to correct camera shake in the imaging data. This improves the quality of the imaging data by performing noise removal and image correction in real time. Some or all of the above-mentioned processing in the imaging unit may be performed using, for example, AI, or may be performed without using AI.

[0083] The image capturing unit can estimate the user's emotions and change the viewpoint of the image capture based on the estimated user's emotions. The image capturing unit can estimate the user's emotions using, for example, facial expression analysis technology. For example, the image capturing unit can analyze the user's facial expressions captured by a camera and calculate an emotion score. The image capturing unit can also estimate the user's emotions using voice analysis technology. For example, the image capturing unit can analyze the tone and speed of the user's voice and calculate an emotion score. The image capturing unit can also change the viewpoint of the image capture based on the estimated user's emotions. For example, if the user is nervous, the image capturing unit can capture the entire image with a wide angle. If the user is relaxed, the image capturing unit can capture a close-up of the user's hands. If the user is concentrating, the image capturing unit can change the viewpoint to match the user's line of sight. This allows for more appropriate data to be collected by changing the viewpoint of the image capture based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the imaging unit may be performed using AI, for example, or may be performed without using AI.

[0084] The imaging unit can adjust the imaging frame rate according to the user's working speed. The imaging unit measures the user's working speed, for example, using motion analysis technology. For example, the imaging unit analyzes the user's hand movements and the progress of the work to calculate the working speed. The imaging unit can also adjust the imaging frame rate according to the user's working speed. For example, if the user is working quickly, the frame rate can be increased to capture smoother images. If the user is working slowly, the frame rate can be decreased to capture images with reduced power consumption. The frame rate can also be adjusted in real time if the user's working speed changes. This improves the quality of the captured data by adjusting the frame rate according to the user's working speed. Some or all of the above-mentioned processing in the imaging unit may be performed using, for example, AI, or may be performed without using AI.

[0085] The imaging unit can upload the imaging data to the cloud in real time, allowing experts in remote locations to provide advice. The imaging unit uploads the imaging data to the cloud, for example, using a cloud storage service. For example, the imaging unit can upload the imaging data to the cloud in real time, allowing experts to provide advice in real time. The imaging unit can also allow experts in remote locations to check the imaging data and send necessary instructions in real time. For example, the imaging unit can share the imaging data on the cloud, allowing multiple experts to provide advice simultaneously. This allows experts in remote locations to provide advice in real time, improving the accuracy of the work. Some or all of the above-described processing in the imaging unit may be performed, for example, using AI, or may be performed without using AI.

[0086] The imaging unit can perform 3D modeling on the imaging data, enabling three-dimensional work analysis. The imaging unit generates a 3D model from the imaging data, for example, using point cloud data. For example, the imaging unit generates a 3D model based on the imaging data and performs three-dimensional work analysis. The imaging unit can also generate a 3D model using mesh generation technology. For example, the imaging unit generates a mesh based on the imaging data and performs three-dimensional work analysis. The imaging unit can also simulate work procedures using the 3D model. For example, the imaging unit evaluates the efficiency and safety of work based on the 3D model. Thus, three-dimensional work analysis becomes possible by performing 3D modeling. Some or all of the above-described processing in the imaging unit may be performed using, for example, AI, or may be performed without using AI.

[0087] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. The analysis unit can estimate the user's emotions using, for example, facial expression analysis technology. For example, the analysis unit can analyze the user's facial expressions captured by a camera and calculate an emotion score. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice and calculate an emotion score. The analysis unit can also determine the analysis priority based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can prioritize analysis of important parts. If the user is relaxed, the analysis unit can analyze the entire text evenly. If the user is concentrating, the analysis unit can prioritize analysis of details. This enables more appropriate analysis by determining the analysis priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0088] The analysis unit can be added with a function to detect anomalies by comparing with past work data. The analysis unit, for example, detects abnormal movements based on past work data. For example, the analysis unit detects abnormal movements by comparing with past work data. The analysis unit can also detect abnormal time consumption based on past work data. For example, the analysis unit detects abnormal time consumption by comparing with past work data. The analysis unit can also detect abnormal differences in procedures based on past work data. For example, the analysis unit detects abnormal differences in procedures by comparing with past work data. This improves the accuracy of work by detecting anomalies by comparing with past work data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0089] The analysis unit can evaluate the efficiency of the work and suggest improvements. For example, the analysis unit can evaluate the efficiency of the work and suggest ways to reduce unnecessary movements. For example, the analysis unit can evaluate the efficiency of the work and suggest ways to reduce unnecessary movements. The analysis unit can also evaluate the efficiency of the work and suggest optimal procedures. For example, the analysis unit can evaluate the efficiency of the work and suggest optimal procedures. The analysis unit can also evaluate the efficiency of the work and suggest the placement of necessary tools and parts. For example, the analysis unit can evaluate the efficiency of the work and suggest the placement of necessary tools and parts. In this way, the efficiency of the work is evaluated and improvements are suggested, thereby improving the efficiency of the work. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0090] The analysis unit can evaluate the safety of the work and predict risks. The analysis unit, for example, evaluates the safety of the work and warns of dangerous movements. For example, the analysis unit evaluates the safety of the work and warns of dangerous movements. The analysis unit can also evaluate the safety of the work and make suggestions to improve high-risk procedures. For example, the analysis unit can evaluate the safety of the work and make suggestions to improve high-risk procedures. The analysis unit can also evaluate the safety of the work and suggest the use of necessary safety equipment. For example, the analysis unit can evaluate the safety of the work and suggest the use of necessary safety equipment. In this way, the safety of the work is improved by evaluating the safety of the work and predicting risks. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0091] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. The analysis unit can estimate the user's emotion using, for example, facial expression analysis technology. For example, the analysis unit can analyze the user's facial expression captured with a camera and calculate an emotion score. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice and calculate an emotion score. The analysis unit can also adjust the display method of the analysis results based on the estimated user's emotion. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. This allows for more appropriate information to be provided by adjusting the display method of the analysis results according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0092] The analysis unit can improve the analysis accuracy by taking into account data on the work environment. The analysis unit improves the analysis accuracy by taking into account, for example, temperature data of the work environment. For example, the analysis unit uses a temperature sensor to collect temperature data of the work environment and reflects it in the analysis. The analysis unit can also improve the analysis accuracy by taking into account humidity data of the work environment. For example, the analysis unit uses a humidity sensor to collect humidity data of the work environment and reflects it in the analysis. The analysis unit can also improve the analysis accuracy by taking into account illuminance data of the work environment. For example, the analysis unit uses an illuminance sensor to collect illuminance data of the work environment and reflects it in the analysis. In this way, by taking into account the data on the work environment, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0093] The analysis unit can share the analysis results with other workers to improve the work efficiency of the entire team. The analysis unit shares the analysis results with other workers, for example, using a cloud storage service. For example, the analysis unit uploads the analysis results to the cloud and shares them with other workers. The analysis unit can also share the analysis results in real time to improve the work efficiency of the entire team. For example, the analysis unit can share the analysis results in real time to improve the work efficiency of the entire team. The analysis unit can also discuss areas for improvement with the entire team based on the analysis results. For example, the analysis unit discusses areas for improvement with the entire team based on the analysis results. In this way, by sharing the analysis results, the work efficiency of the entire team improves. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0094] The analysis unit can automatically generate a work procedure based on the analysis results. The analysis unit, for example, automatically generates an optimal work procedure based on the analysis results. For example, the analysis unit automatically generates an optimal work procedure based on the analysis results. The analysis unit can also automatically generate an efficient work procedure based on the analysis results. For example, the analysis unit automatically generates an efficient work procedure based on the analysis results. The analysis unit can also automatically generate a safe work procedure based on the analysis results. For example, the analysis unit automatically generates a safe work procedure based on the analysis results. As a result, the efficiency of the work is improved by automatically generating a work procedure based on the analysis results. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0095] The display unit can estimate the user's emotion and customize the display content based on the estimated user's emotion. The display unit estimates the user's emotion using, for example, facial expression analysis technology. For example, the display unit analyzes the user's facial expression captured by a camera and calculates an emotion score. The display unit can also estimate the user's emotion using voice analysis technology. For example, the display unit analyzes the tone and speed of the user's voice and calculates an emotion score. The display unit can also customize the display content based on the estimated user's emotion. For example, if the user is nervous, the display unit can provide simple, highly visible display content. If the user is relaxed, the display unit can provide display content including detailed information. If the user is in a hurry, the display unit can provide display content that focuses on the main points. This allows the display content to be customized according to the user's emotion, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using AI, for example, or may be performed without using AI.

[0096] The display unit can dynamically change the display content according to the progress of the work. The display unit, for example, displays the next step according to the progress of the work. For example, the display unit displays the next step according to the progress of the work. The display unit can also display necessary tools and parts according to the progress of the work. For example, the display unit displays necessary tools and parts according to the progress of the work. The display unit can also display cautions and warnings according to the progress of the work. For example, the display unit displays cautions and warnings according to the progress of the work. This allows the display content to be dynamically changed according to the progress of the work, thereby providing effective support to the user. Some or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI.

[0097] The display unit can determine an appropriate display position using the user's gaze tracking data. The display unit, for example, uses an gaze tracking device to collect the user's gaze tracking data. For example, the display unit uses the gaze tracking device to track the user's gaze movements in real time and save the data. The display unit can also analyze the gaze tracking data using a gaze analysis algorithm. For example, the display unit can identify the user's gaze focus point using the gaze analysis algorithm. The display unit can also determine an optimal display position based on the user's gaze tracking data. For example, the display unit can highlight and display important information based on the user's gaze focus point. The display unit can also display information at a position with high visibility based on the user's gaze movement. In this way, the optimal display position is determined by using the user's gaze tracking data, improving visibility. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI or without AI.

[0098] The display unit can provide an individually optimized guide by referring to the user's work history. The display unit, for example, uses log data to collect the user's work history. For example, the display unit uses the log data to collect the user's work history and saves it as data. The display unit can also refer to the user's work history by using a history database. For example, the display unit can refer to the user's past work history by using a history database. The display unit can also provide an individually optimized guide based on the user's work history. For example, the display unit can provide an optimal guide based on the user's work history. The display unit can also provide an efficient guide based on the user's work history. The display unit can also provide a safe guide based on the user's work history. As a result, an individually optimized guide is provided by referring to the user's work history, improving work efficiency. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI.

[0099] The display unit can estimate the user's emotions and determine the priority of information to display based on the estimated user's emotions. The display unit estimates the user's emotions using, for example, facial expression analysis technology. For example, the display unit analyzes the user's facial expressions captured by a camera and calculates an emotion score. The display unit can also estimate the user's emotions using voice analysis technology. For example, the display unit analyzes the tone and speed of the user's voice and calculates an emotion score. The display unit can also determine the priority of information to display based on the estimated user's emotions. For example, if the user is nervous, the most important information can be displayed preferentially. If the user is relaxed, detailed information can be displayed preferentially. If the user is in a hurry, information that highlights the main points can be displayed preferentially. This enables more appropriate information to be provided by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using AI, for example, or may be performed without using AI.

[0100] The display unit can adjust the brightness and contrast of the display according to the user's work environment. The display unit detects the illuminance of the work environment using, for example, an illuminance sensor. For example, in a low-illuminance environment, the display unit increases the brightness of the display to improve visibility. In a high-illuminance environment, the display unit can also enhance the contrast of the display to improve visibility. In an environment where the illuminance fluctuates, the display unit can also adjust the brightness and contrast in real time. In this way, visibility is improved by adjusting the brightness and contrast of the display according to the work environment. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI.

[0101] The display unit can display content in multiple languages ​​and accommodate users who speak different languages. The display unit automatically translates the display content using, for example, a translation algorithm. For example, the display unit automatically translates the display content based on the user's language setting. The display unit can also provide a language switching function. For example, the display unit provides a language switching function when a user uses multiple languages. Furthermore, when a specific language is selected, the display unit can provide the display content in that language. This makes it possible to accommodate multiple languages ​​and accommodate users who speak different languages. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI.

[0102] The display unit can link the displayed content with an audio guide to provide both visual and audio support. The display unit can, for example, use text-to-speech technology to provide an audio guide corresponding to the displayed content. For example, the display unit can use text-to-speech technology to provide an audio guide corresponding to the displayed content. The display unit can also play recorded audio. For example, the display unit can play recorded audio to provide both visual and audio support. The display unit can also use an audio guide to highlight important information. For example, the display unit can use an audio guide to highlight important information. The display unit can also synchronize the audio guide with the displayed content to help the user's understanding. For example, the display unit synchronizes the audio guide with the displayed content to help the user's understanding. This provides both visual and audio support, thereby deepening the user's understanding. Some or all of the above-described processing in the display unit may be performed, for example, using AI, or may be performed without using AI.

[0103] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user's emotions. The feedback collection unit can estimate the user's emotions using, for example, facial expression analysis technology. For example, the feedback collection unit can analyze the user's facial expressions captured by a camera and calculate an emotion score. The feedback collection unit can also estimate the user's emotions using voice analysis technology. For example, the feedback collection unit can analyze the tone and speed of the user's voice and calculate an emotion score. The feedback collection unit can also adjust the feedback collection method based on the estimated user's emotions. For example, if the user is nervous, feedback can be collected in the form of simple questions. On the other hand, if the user is relaxed, feedback can be collected in the form of detailed questions. On the other hand, if the user is in a hurry, feedback can be provided in a form that can be answered in a short time. In this way, by adjusting the feedback collection method according to the user's emotions, more appropriate feedback can be collected. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or may be performed without using AI.

[0104] The feedback collection unit can evaluate the user's work performance and provide detailed feedback when collecting feedback. The feedback collection unit, for example, evaluates the user's work performance and provides feedback on areas for improvement. For example, the feedback collection unit evaluates the user's work performance and provides feedback on areas for improvement. The feedback collection unit can also evaluate the user's work performance and suggest an efficient work method. For example, the feedback collection unit can evaluate the user's work performance and suggest an efficient work method. The feedback collection unit can also evaluate the user's work performance and suggest a safe work method. For example, the feedback collection unit can evaluate the user's work performance and suggest a safe work method. In this way, detailed feedback can be provided by evaluating the user's work performance. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI.

[0105] The feedback collection unit can also collect data on the user's working environment when collecting feedback and perform a comprehensive evaluation. The feedback collection unit, for example, uses a temperature sensor to collect temperature data on the working environment. For example, the feedback collection unit uses the temperature sensor to collect temperature data on the working environment and perform a comprehensive evaluation. The feedback collection unit can also use a humidity sensor to collect humidity data on the working environment. For example, the feedback collection unit uses the humidity sensor to collect humidity data on the working environment and perform a comprehensive evaluation. The feedback collection unit can also use an illuminance sensor to collect illuminance data on the working environment. For example, the feedback collection unit uses an illuminance sensor to collect illuminance data on the working environment and perform a comprehensive evaluation. In this way, by also collecting data on the user's working environment, a comprehensive evaluation is possible. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI.

[0106] When collecting feedback, the feedback collection unit can present individually optimized questions by referring to the user's work history. The feedback collection unit collects the user's work history by using, for example, log data. For example, the feedback collection unit collects the user's work history by using the log data and stores it as data. The feedback collection unit can also refer to the user's work history by using a history database. For example, the feedback collection unit can refer to the user's past work history by using the history database. The feedback collection unit can also present individually optimized questions based on the user's work history. For example, the feedback collection unit presents optimal questions based on the user's work history. The feedback collection unit can also present efficient questions based on the user's work history. The feedback collection unit can also present safe questions based on the user's work history. In this way, individually optimized questions can be presented by referring to the user's work history. Some or all of the above-described processing in the feedback collection unit may be performed by using, for example, AI, or may be performed without using AI.

[0107] The feedback collection unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. The feedback collection unit estimates the user's emotions using, for example, facial expression analysis technology. For example, the feedback collection unit analyzes the user's facial expressions captured by a camera and calculates an emotion score. The feedback collection unit can also estimate the user's emotions using voice analysis technology. For example, the feedback collection unit analyzes the tone and speed of the user's voice and calculates an emotion score. The feedback collection unit can also determine the priority of feedback based on the estimated user's emotions. For example, if the user is nervous, important feedback can be collected preferentially. If the user is relaxed, detailed feedback can be collected preferentially. If the user is in a hurry, feedback that highlights the main points can be collected preferentially. In this way, by determining the priority of feedback according to the user's emotions, more important feedback can be collected preferentially. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or may be performed without using AI.

[0108] When collecting feedback, the feedback collection unit can analyze the user's social media activity and collect related feedback. The feedback collection unit, for example, analyzes the content of social media posts to understand the user's activity. For example, the feedback collection unit collects related feedback based on the content of the user's social media posts. The feedback collection unit can also analyze followers to evaluate the user's influence. For example, the feedback collection unit collects feedback with reference to the activities of the user's followers. The feedback collection unit can also collect feedback with reference to the activities of the user's friends on social media. For example, the feedback collection unit collects related feedback based on the content of posts by the user's friends. In this way, related feedback can be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the feedback collection unit may be performed, for example, using AI or without using AI.

[0109] The feedback collection unit can identify region-specific problems by taking into account the user's geographical location information when collecting feedback. The feedback collection unit can collect the user's geographical location information by using, for example, GPS data. For example, the feedback collection unit can collect the user's geographical location information by using GPS data and store it as data. The feedback collection unit can also collect the user's geographical location information by using a location information service. For example, the feedback collection unit can collect the user's geographical location information by using a location information service and store it as data. The feedback collection unit can also identify region-specific problems based on the user's geographical location information. For example, the feedback collection unit can identify region-specific problems by taking into account the climatic conditions and cultural background of the region. The feedback collection unit can also suggest region-specific improvements. For example, the feedback collection unit can suggest region-specific improvements by taking into account the climatic conditions and cultural background of the region. The feedback collection unit can also predict region-specific risks. For example, the feedback collection unit can predict region-specific risks by taking into account the climatic conditions and cultural background of the region. In this way, region-specific problems can be identified by taking into account the user's geographical location information. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or may be performed without using AI.

[0110] When collecting feedback, the feedback collection unit can select the optimal collection method by taking into account device information of the user. The feedback collection unit collects, for example, the device type and OS version. For example, the feedback collection unit collects the device type and OS version used by the user and stores them as data. The feedback collection unit can also evaluate device performance and select the optimal collection method. For example, the feedback collection unit provides the optimal collection method depending on the device performance. The feedback collection unit can also adjust the collection method by taking into account device usage. For example, if the user uses a smartphone, the feedback collection unit can provide the optimal collection method for the smartphone. Also, if the user uses a tablet, the feedback collection unit can provide the optimal collection method for the tablet. Also, if the user uses a smartwatch, the feedback collection unit can provide the optimal collection method for the smartwatch. In this way, the optimal collection method can be selected by taking into account device information of the user. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI.

[0111] The update unit can estimate the user's emotion and adjust the update content based on the estimated user's emotion. The update unit can estimate the user's emotion using, for example, facial expression analysis technology. For example, the update unit can analyze the user's facial expression captured by a camera and calculate an emotion score. The update unit can also estimate the user's emotion using voice analysis technology. For example, the update unit can analyze the tone and speed of the user's voice and calculate an emotion score. The update unit can also adjust the update content based on the estimated user's emotion. For example, if the user is nervous, the update unit can provide simple, highly visible update content. If the user is relaxed, the update unit can provide update content including detailed information. If the user is in a hurry, the update unit can provide update content that focuses on the main points. This allows for more appropriate information to be provided by adjusting the update content according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the update unit may be performed using AI, for example, or may be performed without using AI.

[0112] The update unit can select an optimal update method by referring to past feedback data during an update. The update unit selects the optimal update method based on, for example, past feedback data. For example, the update unit selects the optimal update method based on past feedback data. The update unit can also select an efficient update method based on past feedback data. For example, the update unit selects an efficient update method based on past feedback data. The update unit can also select a safe update method based on past feedback data. For example, the update unit selects a safe update method based on past feedback data. In this way, the optimal update method can be selected by referring to past feedback data. Some or all of the above-described processing in the update unit may be performed, for example, using AI or may be performed without using AI.

[0113] The update unit can evaluate the user's work performance during an update and perform an individually optimized update. The update unit, for example, evaluates the user's work performance and performs an optimal update. For example, the update unit evaluates the user's work performance and performs an optimal update. The update unit can also evaluate the user's work performance and perform an efficient update. For example, the update unit evaluates the user's work performance and performs an efficient update. The update unit can also evaluate the user's work performance and perform a safe update. For example, the update unit evaluates the user's work performance and performs a safe update. In this way, an individually optimized update can be performed by evaluating the user's work performance. Some or all of the above-described processing in the update unit may be performed, for example, using AI, or may be performed without using AI.

[0114] The update unit can perform a comprehensive update by also taking into account the user's work environment data when updating. The update unit, for example, uses a temperature sensor to collect temperature data of the work environment. For example, the update unit uses the temperature sensor to collect temperature data of the work environment and perform a comprehensive update. The update unit can also use a humidity sensor to collect humidity data of the work environment. For example, the update unit uses a humidity sensor to collect humidity data of the work environment and perform a comprehensive update. The update unit can also use an illuminance sensor to collect illuminance data of the work environment. For example, the update unit uses an illuminance sensor to collect illuminance data of the work environment and perform a comprehensive update. This makes it possible to perform a comprehensive update by also taking into account the user's work environment data. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI.

[0115] The update unit can analyze the user's social media activity at the time of updating and reflect related information in the update. The update unit, for example, analyzes the content of social media posts to understand the user's activity. For example, the update unit reflects related information in the update based on the content of the user's social media posts. The update unit can also analyze followers and evaluate the user's influence. For example, the update unit adjusts the update content based on the activity of the user's followers. The update unit can also adjust the update content based on the activity of the user's friends on social media. For example, the update unit reflects related information in the update based on the content posted by the user's friends. In this way, by analyzing the user's social media activity, related information can be reflected in the update. Some or all of the above-mentioned processing in the update unit may be performed, for example, using AI or without using AI.

[0116] During an update, the update unit may perform an update to solve region-specific problems by taking into account the user's geographical location information. The update unit may, for example, use GPS data to collect the user's geographical location information. For example, the update unit may use the GPS data to collect the user's geographical location information and store it as data. The update unit may also use a location information service to collect the user's geographical location information. For example, the update unit may use a location information service to collect the user's geographical location information and store it as data. The update unit may also perform an update to solve region-specific problems based on the user's geographical location information. For example, the update unit may perform an update to solve region-specific problems by taking into account the climatic conditions and cultural background of the region. The update unit may also perform an update to reflect region-specific improvements. For example, the update unit may perform an update to reflect region-specific improvements by taking into account the climatic conditions and cultural background of the region. The update unit may also predict region-specific risks and perform an update. For example, the update unit may predict region-specific risks and perform an update by taking into account the climatic conditions and cultural background of the region. This allows updates to be performed to solve problems specific to a region by taking into account the user's geographical location information. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI.

[0117] During an update, the update unit can select the optimal update method by taking into account the user's device information. The update unit collects, for example, the device type and OS version. For example, the update unit collects the device type and OS version used by the user and stores them as data. The update unit can also evaluate the device performance and select the optimal update method. For example, the update unit provides the optimal update method according to the device performance. The update unit can also adjust the update method by taking into account the device usage status. For example, if the user uses a smartphone, the update unit can provide the optimal update method for the smartphone. Also, if the user uses a tablet, the update unit can provide the optimal update method for the tablet. Also, if the user uses a smartwatch, the update unit can provide the optimal update method for the smartwatch. In this way, the optimal update method can be selected by taking into account the user's device information. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned photographing unit, analysis unit, display unit, feedback collection unit, and update unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the photographing unit can photograph a user's work using the camera 42 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the photographed data. The display unit displays auxiliary information using the display 40A of the smart device 14. The feedback collection unit is realized by the control unit 46A of the smart device 14 and collects user feedback. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the auxiliary information based on the collected feedback. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned photographing unit, analysis unit, display unit, feedback collection unit, and update unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the photographing unit can photograph the user's work using the camera 42 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the photographed data. The display unit displays auxiliary information using the display of the smart glasses 214. The feedback collection unit is realized by the control unit 46A of the smart glasses 214 and collects user feedback. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the auxiliary information based on the collected feedback. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned photographing unit, analysis unit, display unit, feedback collection unit, and update unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the photographing unit can photograph the user's work using the camera 42 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the photographed data. The display unit displays auxiliary information using the display 343 of the headset type terminal 314. The feedback collection unit is realized by the control unit 46A of the headset type terminal 314 and collects user feedback. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the auxiliary information based on the collected feedback. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned photographing unit, analysis unit, display unit, feedback collection unit, and update unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit can photograph the user's work using the camera 42 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the photographed data. The display unit displays auxiliary information using the display of the robot 414. The feedback collection unit is realized by the control unit 46A of the robot 414 and collects user feedback. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the auxiliary information based on the collected feedback.

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

[0119] The analysis unit can evaluate the user's work performance in real time and dynamically adjust the auxiliary information based on the evaluation results. For example, the analysis unit can evaluate the user's work speed and accuracy, and if the user is slow, display more detailed auxiliary information. Also, if the user is working accurately, the analysis unit can simplify the auxiliary information. Furthermore, the analysis unit can predict the next step based on the user's work performance and provide auxiliary information at an appropriate time. This enables flexible support according to the user's work performance.

[0120] The display unit can provide individually optimized guidance based on the user's work history. For example, the display unit can refer to past work data and highlight and display parts where the user previously struggled. It can also provide simplified guidance for work that the user is good at. Furthermore, the display unit can provide an option to skip certain work steps based on the user's work history. This enables efficient support based on the user's past experience.

[0121] The feedback collection unit can also collect data on the user's work environment and perform a comprehensive evaluation. For example, the feedback collection unit can use a temperature sensor to collect temperature data of the work environment and evaluate the impact on work efficiency. It can also use a humidity sensor to collect humidity data of the work environment and evaluate the comfort of the work. Furthermore, it can use an illuminance sensor to collect illuminance data of the work environment and use this data to improve visibility. This enables a comprehensive evaluation that takes the entire work environment into consideration.

[0122] When updating the auxiliary information based on the collected feedback, the update unit can estimate the user's emotions and adjust the update content based on the estimated emotions. For example, if the user is feeling stressed, the update unit can provide simple, highly visible auxiliary information. If the user is relaxed, the update unit can also provide auxiliary information including detailed information. Furthermore, if the user is in a hurry, the update unit can also provide auxiliary information that focuses on the main points. This enables flexible updates according to the user's emotions.

[0123] The camera unit can estimate the user's emotions and adjust the timing of capturing images based on the estimated emotions. For example, if the user is nervous, the camera can delay capturing images until the user is relaxed. Also, if the user is concentrating, the camera can start capturing images so as not to miss important moments in the work. Furthermore, if the user is tired, the camera can resume capturing images after a break. This makes it possible to capture images at appropriate times according to the user's emotions.

[0124] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is nervous, it can prioritize analysis of important parts. If the user is relaxed, it can analyze the entire image evenly. Furthermore, if the user is concentrating, it can prioritize analysis of details. This allows for flexible analysis according to the user's emotions.

[0125] The display unit can determine an appropriate display position using the user's eye-tracking data. For example, the display unit can use an eye-tracking device to track the user's eye movements in real time and display important information at the point where the user's eyes are focused. It can also display information at a highly visible position based on the eye movements. Furthermore, it can dynamically change the display content according to the user's eye movements. This makes it possible to provide optimal information based on the user's eye movements.

[0126] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is nervous, feedback can be collected in the form of simple questions. If the user is relaxed, feedback can be collected in the form of detailed questions. Furthermore, if the user is in a hurry, a feedback format that can be answered in a short time can be provided. This enables flexible feedback collection according to the user's emotions.

[0127] When updating auxiliary information based on collected feedback, the update unit can select the optimal update method by referring to past feedback data. For example, it can select the most effective way to display auxiliary information based on past feedback data. It can also prioritize the display of information frequently referenced by users based on past feedback data. It can also add or modify the content of auxiliary information based on past feedback data. This makes it possible to perform optimal updates by utilizing past data.

[0128] The analysis unit can evaluate the efficiency of work and suggest areas for improvement. For example, the analysis unit can evaluate the efficiency of work and suggest ways to reduce unnecessary movements. It can also suggest optimal procedures. It can also suggest the placement of necessary tools and parts. In this way, by evaluating the efficiency of work and suggesting areas for improvement, work efficiency can be improved.

[0129] The processing flow of the second embodiment will be briefly explained below.

[0130] Step 1: The camera captures the user's work. For example, a camera mounted on smart glasses can be used to collect hand and eye movements. The camera can also detect the lighting and sound environment of the work environment and automatically set the optimal shooting conditions. It can also have a zoom function to focus on specific parts or steps. Step 2: The analysis unit analyzes the data captured by the imaging unit. For example, it can use AI to identify which parts the user is taking a long time to recognize. It can also be equipped with a function to detect anomalies by comparing them with past work data. It can also evaluate the efficiency of work and suggest areas for improvement. Step 3: The display unit displays auxiliary information based on the analysis results obtained by the analysis unit. For example, it can display animations or audio guides. It can also estimate the user's emotions and customize the display content based on the estimated user emotions. It can also dynamically change the display content according to the progress of the task. Step 4: The feedback collection unit collects user feedback based on the auxiliary information displayed by the display unit. For example, the feedback collection unit can collect the user's work results. It can also estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. It can also collect data on the user's work environment and perform a comprehensive evaluation. Step 5: The update unit updates the auxiliary information based on the feedback collected by the feedback collection unit. For example, the auxiliary information can be updated based on the collected feedback. In addition, the update unit can estimate the user's emotion and adjust the update content based on the estimated user's emotion. Furthermore, the update unit can select the optimal update method by referring to past feedback data.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

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

[0136] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0149] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0152] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0154] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0158] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0161] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0163] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0165] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0168] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0170] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

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

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

[0174] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0175] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0176] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0177] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0178] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0179] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0180] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0181] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0182] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0183] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0184] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0185] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0186] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0187] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0188] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0189] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0190] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0191] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0192] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0193] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0194] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0195] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0196] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0197] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0198] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0199] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0200] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0201] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0202] [Explanation of symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A photography department that takes pictures of the work, an analysis unit that analyzes the data captured by the imaging unit; a display unit that displays auxiliary information based on the analysis result obtained by the analysis unit; a feedback collection unit that collects user feedback based on the auxiliary information displayed by the display unit; an update unit that updates the auxiliary information based on the feedback collected by the feedback collection unit; Equipped with A system characterized by:

2. The imaging unit is Collect hand or eye movements during work 2. The system of claim 1.

3. The display unit Display animation or audio guide 2. The system of claim 1.

4. The feedback collection unit: Collecting user work results 2. The system of claim 1.

5. The update unit Update supporting information based on collected feedback 2. The system of claim 1.

6. The imaging unit is Estimate the user's emotions and adjust the timing of taking photos based on the estimated user emotions.

2. The system of claim 1.

7. The imaging unit is Detects the lighting and sound environment of the work environment and automatically sets appropriate shooting conditions 2. The system of claim 1.

8. The imaging unit is Add zoom functionality to focus on specific parts or steps 2. The system of claim 1.

9. The imaging unit is Performs real-time noise reduction and image correction on captured data 2. The system of claim 1.

10. The imaging unit is Estimate the user's emotions and change the viewpoint of the camera based on the estimated user emotions.

2. The system of claim 1.

Images