system

Abstract

The system according to this embodiment aims to consistently quantify the evaluation of 5S activities and visualize abnormal areas. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a monitoring unit, and a display unit. The data collection unit quantifies consistent 5S evaluations using image recognition AI. The analysis unit analyzes the data collected by the data collection unit. The monitoring unit constantly monitors the video from the in-warehouse camera. The analysis unit analyzes the video collected by the monitoring unit. The display unit visually displays the results analyzed by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the evaluation of 5S activities is subjective and it is difficult to quantitatively evaluate them consistently.

[0005] The system according to the embodiment aims to consistently quantify the evaluation of 5S activities and visualize abnormal locations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a monitoring unit, and a display unit. The data collection unit quantifies consistent 5S evaluations using image recognition AI. The analysis unit analyzes the data collected by the data collection unit. The monitoring unit constantly monitors the video feed from the in-warehouse camera. The analysis unit analyzes the video feed collected by the monitoring unit. The display unit visually displays the results analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can consistently quantify the evaluation of 5S activities and visualize abnormal areas. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The 5S activity support system according to an embodiment of the present invention is a system that scores 5S activities, visualizes abnormal areas, provides images after improvement, and quantifies the effects of improvement. The 5S activity support system uses image recognition AI to quantify consistent 5S evaluations. For example, a photograph of the work site is taken, and the AI ​​analyzes the photograph to calculate a score based on each 5S item (sorting, setting in order, shining, standardizing, and sustaining). This allows employees to quantitatively grasp the progress of their 5S activities. Next, the 5S activity support system performs continuous monitoring and AI analysis of in-warehouse cameras. For example, cameras installed in the warehouse constantly capture images, and the AI ​​analyzes these images to identify abnormal areas. The identified abnormal areas are displayed in heatmaps and graphs. This allows managers to quickly grasp abnormal areas and take appropriate countermeasures. Furthermore, the 5S activity support system provides AI-generated images after improvement. For example, the AI ​​analyzes a photograph of the current work site and generates an image after improvement. This image visually shows employees areas for improvement and increases their motivation to improve. Furthermore, by pointing out necessary improvements in writing and visually, the system presents concrete improvement measures. Finally, the 5S activity support system quantifies the specific effects of cost reduction and productivity improvement resulting from improvements. For example, AI analyzes data before and after improvements to calculate cost reductions and productivity improvement rates. This allows managers to concretely understand the effects of improvements and use this information to inform their business decisions. In this way, the 5S activity support system promotes improvements in the workplace and supports increased employee motivation and management decision-making by scoring 5S activities, visualizing abnormal areas, providing images of the improved state, and quantifying the effects of improvements.

[0029] The 5S activity support system according to this embodiment comprises a data collection unit, an analysis unit, a monitoring unit, and a display unit. The data collection unit quantifies consistent 5S evaluations using image recognition AI. For example, the data collection unit takes photographs of the work site, and the AI ​​analyzes the photographs to calculate a score based on each 5S item (sorting, setting in order, shining, standardizing, and sustaining). For example, the data collection unit can take photographs of the work site with a high-resolution camera, and the AI ​​can analyze the photographs to calculate a score. The data collection unit can also take photographs of the work site periodically, and the AI ​​can analyze the photographs to calculate a score. Furthermore, the data collection unit can take photographs of the work site from different angles, and the AI ​​can analyze the photographs to calculate a score. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses AI to analyze the photographic data collected by the data collection unit and calculates a score based on each 5S item. For example, the analysis unit uses AI to analyze the photographic data collected by the data collection unit and calculates a score. Furthermore, the analysis unit can, for example, use AI to analyze photo data collected by the collection unit and calculate a score. The monitoring unit constantly monitors the video from the cameras inside the warehouse. The monitoring unit can, for example, use cameras installed inside the warehouse to constantly capture video, and AI analyzes that video to identify abnormal areas. The monitoring unit can, for example, use cameras installed inside the warehouse to constantly capture video, and AI analyzes that video to identify abnormal areas. Furthermore, the monitoring unit can, for example, use cameras installed inside the warehouse to constantly capture video, and AI analyzes that video to identify abnormal areas. Furthermore, the monitoring unit can, for example, use cameras installed inside the warehouse to constantly capture video, and AI analyzes that video to identify abnormal areas. The display unit visually displays the results analyzed by the analysis unit. The display unit can, for example, display the results analyzed by the analysis unit as a heatmap or graph. Furthermore, the display unit can also display the results analyzed by the analysis unit, for example, as a heatmap or graph.Furthermore, the display unit can also display the results analyzed by the analysis unit as a heat map or graph, for example. This allows the 5S activity support system according to the embodiment to score 5S activities, visualize abnormal areas, provide an image of the improved state, and quantify the improvement effect.

[0030] The data collection unit uses image recognition AI to quantify consistent 5S evaluations. Specifically, it takes photos of the work site with a high-resolution camera, and the AI ​​analyzes these photos to calculate scores based on each 5S item (Sort, Set in order, Shine, Standardize, Sustain). The data collection unit can also, for example, take photos of the work site periodically, and the AI ​​will analyze these photos to calculate scores. This makes it possible to continuously monitor the progress of 5S activities over time. Furthermore, the data collection unit can take photos of the work site from different angles, and the AI ​​will analyze these photos to calculate scores. This allows for an overall understanding of the work site and evaluation including areas that are often overlooked. In addition, the data collection unit can evaluate the effectiveness of 5S activities in more detail by taking photos at specific times or when specific tasks are being performed. For example, by taking photos immediately after work is completed or immediately after cleaning is performed and evaluating the state, the effectiveness of 5S activities can be grasped more accurately. As a result, the data collection unit can consistently evaluate 5S activities and provide data to clarify areas for improvement.

[0031] The analysis unit analyzes the data collected by the collection unit. Specifically, the AI ​​analyzes the photographic data collected by the collection unit and calculates a score based on each item of 5S. The AI ​​uses image recognition technology to identify objects and conditions in the photographs and evaluates each item. For example, in the sorting item, it evaluates whether unnecessary items are scattered around, and in the setting item, it evaluates whether items are placed in their designated locations. In the cleaning item, it evaluates whether the work area is kept clean, and in the sanitizing item, it evaluates whether cleaning is carried out regularly. In the sustaining item, it evaluates whether 5S activities have been ingrained in the employees. Based on these evaluation results, the analysis unit calculates an overall 5S score. In addition, the analysis unit can identify the progress of 5S activities and areas for improvement by comparing them with past data. For example, by comparing past scores with current scores, it can clearly identify which items have improved and which items still have room for improvement. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to quickly and accurately evaluate 5S activities and provide data to clearly identify areas for improvement.

[0032] The monitoring unit continuously monitors the footage from the warehouse cameras. Specifically, cameras installed inside the warehouse constantly capture images, and AI analyzes these images to identify abnormal areas. For example, the monitoring unit can install cameras in specific areas within the warehouse and monitor the footage of those areas in real time. The AI ​​detects abnormal movements and object placements in the footage and identifies abnormal areas. For example, it can detect conditions that violate 5S activities, such as items not being placed in their designated locations or cleaning not being carried out. The monitoring unit can also issue alerts and notify the responsible person when an abnormality is detected. This enables a quick response and helps maintain the effectiveness of 5S activities. Furthermore, the monitoring unit can save past video data and review it later. This allows for the identification of the cause of the abnormality and the implementation of measures to prevent recurrence. Through these functions, the monitoring unit can efficiently monitor 5S activities and detect and respond to abnormalities at an early stage.

[0033] The display unit visually displays the results analyzed by the analysis unit. Specifically, it displays the results analyzed by the analysis unit using heat maps and graphs. The heat map shows the 5S score for each area of ​​the work site using different colors, allowing users to quickly see which areas have room for improvement. For example, areas with low scores are displayed in red, and areas with high scores are displayed in green. This allows for the rapid identification of areas that need improvement. The graphs show the trend of scores for each item, allowing for a visual understanding of the progress of 5S activities. For example, a line graph showing how the score for the sorting item changes over time, or a bar graph comparing the scores of each item, are possible. Furthermore, the display unit can also display the analysis results in a dashboard format, allowing the person in charge to grasp the overall situation at a glance. The dashboard displays the score for each item, the status of anomaly detection, and comparisons with past data. This allows the person in charge to quickly grasp the current status of 5S activities and take appropriate measures. Through these functions, the display unit can visually display the results of 5S activities and provide information to clarify areas for improvement.

[0034] The generation unit analyzes photographs of the current work site and generates an image of the improved state. The generation unit can, for example, have AI analyze photographs of the current work site and generate an image of the improved state. The generation unit can, for example, have AI analyze photographs of the current work site and generate an image of the improved state. The generation unit can also, for example, have AI analyze photographs of the current work site and generate an image of the improved state. Furthermore, the generation unit can, for example, have AI analyze photographs of the current work site and generate an image of the improved state. By generating an image of the improved state, the motivation of employees to improve is increased. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input photographs of the current work site into a generation AI and have the generation AI perform the generation of the improved state.

[0035] The service provider provides the generated image. The service provider provides the generated image in digital format, for example. The service provider can provide the generated image in digital format, for example. The service provider can also provide the generated image in digital format, for example. Furthermore, the service provider can also provide the generated image in digital format, for example. This allows the service provider to show employees specific areas for improvement by providing the generated image. Some or all of the above processing in the service provider may be performed using a generation AI, for example, or without a generation AI. For example, the service provider can input the generated image into a generation AI and have the generation AI perform the generation in the format to be provided.

[0036] The quantification unit analyzes data before and after the improvement and quantifies the effect. For example, the quantification unit can use AI to analyze data before and after the improvement and calculate cost reductions and productivity improvement rates. The quantification unit can use AI to analyze data before and after the improvement and calculate cost reductions and productivity improvement rates. Furthermore, the quantification unit can use AI to analyze data before and after the improvement and calculate cost reductions and productivity improvement rates. In this way, quantifying the improvement effect makes it more persuasive to management. Some or all of the above processing in the quantification unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the quantification unit can input data before and after the improvement into a generating AI and have the generating AI perform the quantification of the effect.

[0037] The data collection unit takes photographs of the work site from different angles and at different times of day to collect more multifaceted data. For example, the data collection unit can take photographs of the work site at different times of day, such as in the morning and at night, to collect data that takes into account the differences in lighting conditions. The data collection unit can also take photographs of the work site from different angles to collect data that helps understand the overall situation. Furthermore, the data collection unit can also take photographs of the work site periodically to collect data that tracks changes over time. This allows for the collection of more multifaceted data by taking photographs from different angles and at different times of day. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not. For example, the data collection unit can input photographic data taken from different angles and at different times of day into a generative AI and have the generative AI perform data analysis.

[0038] The data collection unit analyzes the collected data in real time and provides immediate feedback. For example, after taking a photograph of the work site, the data collection unit can immediately analyze it with AI and display the 5S evaluation results in real time. The data collection unit can also immediately notify the manager if it identifies an abnormal area, prompting a quick response. Furthermore, the data collection unit can upload the collected data to the cloud in real time and share the data in conjunction with other systems. This enables a quick response by analyzing the data in real time and providing immediate feedback. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the data collection unit can input the collected data into a generating AI and have the generating AI perform real-time analysis and provide feedback.

[0039] The data collection unit collects environmental data such as temperature and humidity simultaneously when taking photographs of the work site. For example, the data collection unit can collect environmental data using a temperature sensor when taking photographs of the work site. The data collection unit can also collect environmental data using a humidity sensor when taking photographs of the work site. Furthermore, the data collection unit can also collect environmental data using a barometric pressure sensor when taking photographs of the work site. By simultaneously collecting environmental data, more detailed data analysis becomes possible. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input environmental data into a generative AI and have the generative AI perform data analysis.

[0040] The data collection unit stores the collected data in the cloud and shares the data in conjunction with other systems. For example, the data collection unit can upload the collected data to the cloud and share it with other departments in real time. The data collection unit can also store the collected data in the cloud and link it with other systems via APIs. Furthermore, the data collection unit can store the collected data in the cloud and perform analysis in conjunction with data analysis tools. This makes data sharing more efficient by storing the data in the cloud and linking it with other systems. Some or all of the above processes in the data collection unit may be performed using, for example, generative AI, or not. For example, the data collection unit can input the collected data into the generative AI and have the generative AI perform cloud storage and linking with other systems.

[0041] The analysis unit identifies anomalies by comparing them with past data during analysis and issues alerts. For example, the analysis unit can identify anomalies by comparing them with past data during analysis and issue alerts to administrators. The analysis unit can also identify anomalies by comparing them with past data during analysis and issue alerts to administrators. Furthermore, the analysis unit can also identify anomalies by comparing them with past data during analysis and issue alerts to employees. In addition, the analysis unit can also identify anomalies by comparing them with past data during analysis and issue alerts to the system. This enables a rapid response by identifying anomalies by comparing them with past data. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input data for identifying anomalies by comparing it with past data into a generation AI and have the generation AI perform the anomaly identification and alert issuance.

[0042] The analysis unit proposes specific improvement measures based on the analysis results. For example, the analysis unit can propose specific improvement measures based on the analysis results and notify employees. The analysis unit can also propose specific improvement measures based on the analysis results and notify managers. Furthermore, the analysis unit can also propose specific improvement measures based on the analysis results and notify the system. This enables efficient improvement by proposing specific improvement measures based on the analysis results. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the analysis results into a generating AI and have the generating AI execute the proposal of specific improvement measures.

[0043] The analysis unit performs a comprehensive evaluation by referring to other relevant data during the analysis. For example, the analysis unit can perform a comprehensive evaluation by referring to work efficiency data during the analysis. The analysis unit can also perform a comprehensive evaluation by referring to work efficiency data during the analysis. Furthermore, the analysis unit can also perform a comprehensive evaluation by referring to accident rate data during the analysis. In addition, the analysis unit can also perform a comprehensive evaluation by referring to other relevant data during the analysis. This makes a comprehensive evaluation possible by referring to other relevant data. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input other relevant data into a generating AI and have the generating AI perform a comprehensive evaluation.

[0044] The analysis department shares the analysis results with other departments and collaborates to implement improvement measures. For example, the analysis department can share the analysis results with the safety management department and collaborate to implement improvement measures. The analysis department can also share the analysis results with the quality control department and collaborate to implement improvement measures. Furthermore, the analysis department can also share the analysis results with the production management department and collaborate to implement improvement measures. This makes it possible to implement improvement measures collaboratively by sharing with other departments. Some or all of the above-described processes in the analysis department may be performed using, for example, a generation AI, or without using a generation AI. For example, the analysis department can input the analysis results into a generation AI and have the generation AI perform the sharing and collaboration with other departments.

[0045] The monitoring unit performs monitoring with a focus on specific areas or time periods. For example, the monitoring unit can perform monitoring with a focus on specific areas. The monitoring unit can also perform monitoring with a focus on specific time periods. Furthermore, the monitoring unit can also perform monitoring with a focus on specific areas or time periods. This enables efficient monitoring by focusing on specific areas or time periods. Some or all of the above processing in the monitoring unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the monitoring unit can input data for specific areas or time periods into a generation AI and have the generation AI execute the monitoring focus.

[0046] The monitoring unit analyzes monitoring data in real time and immediately notifies when an anomaly occurs. For example, the monitoring unit can analyze monitoring data in real time and immediately notify administrators when an anomaly occurs. The monitoring unit can also analyze monitoring data in real time and immediately notify administrators when an anomaly occurs. Furthermore, the monitoring unit can also analyze monitoring data in real time and immediately notify employees when an anomaly occurs. In addition, the monitoring unit can analyze monitoring data in real time and immediately notify the system when an anomaly occurs. This enables a rapid response by analyzing in real time and providing immediate notification. Some or all of the above processing in the monitoring unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the monitoring unit can input monitoring data into a generation AI and have the generation AI perform real-time analysis and notification.

[0047] The monitoring unit adds sensors to detect anomalies such as sound and vibration during monitoring. For example, the monitoring unit can add a sound sensor to detect anomalies during monitoring. The monitoring unit can also add a vibration sensor to detect anomalies during monitoring. Furthermore, the monitoring unit can also add sound and vibration sensors to detect anomalies during monitoring. This improves the accuracy of anomaly detection by detecting anomalies such as sound and vibration. Some or all of the above processing in the monitoring unit may be performed using, for example, a generation AI, or without a generation AI. For example, the monitoring unit can input sound and vibration data into a generation AI and have the generation AI perform anomaly detection.

[0048] The monitoring unit identifies anomalies by linking monitoring data with other systems. For example, the monitoring unit identifies anomalies by linking monitoring data with an inventory management system. The monitoring unit can also identify anomalies by linking monitoring data with an inventory management system. Furthermore, the monitoring unit can also identify anomalies by linking monitoring data with a quality management system. In addition, the monitoring unit can also identify anomalies by linking monitoring data with a production management system. This improves the accuracy of anomaly detection by linking with other systems. Some or all of the above processing in the monitoring unit may be performed using, for example, a generation AI, or without a generation AI. For example, the monitoring unit can input monitoring data into a generation AI and have the generation AI perform the linking with other systems.

[0049] The display unit provides detailed information about the abnormal location when it is displayed. The display unit can, for example, display the cause of the abnormal location in detail and propose countermeasures. The display unit can, for example, display the cause of the abnormal location in detail and propose countermeasures by referring to similar past cases. Furthermore, the display unit can, for example, display the cause of the abnormal location and provide countermeasures step by step. This enables a rapid response by providing detailed information about the abnormal location. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input detailed information about the abnormal location into a generation AI and have the generation AI provide the information.

[0050] The display unit optimizes the displayed content according to the user's role and responsibilities. For example, the display unit can provide administrators with display content that allows them to grasp the overall situation. The display unit can also provide workers with display content that includes specific work instructions. Furthermore, the display unit can provide managers with display content that includes information useful for business decisions. By optimizing the displayed content according to the user's role and responsibilities, efficient information provision becomes possible. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input information according to the user's role and responsibilities into the generation AI and have the generation AI perform the optimization of the displayed content.

[0051] The display unit displays trends by referring to past historical data of the anomaly locations when displaying the data. The display unit can, for example, refer to past historical data of the anomaly locations and display trends. The display unit can, for example, refer to past historical data of the anomaly locations and display trends. The display unit can also, for example, refer to past historical data of the anomaly locations and display the frequency of occurrence. Furthermore, the display unit can, for example, refer to past historical data of the anomaly locations and display occurrence patterns. This makes it possible to grasp the trend of anomalies by referring to past historical data. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the display unit can input past historical data into a generation AI and have the generation AI perform the display of trends.

[0052] The display unit allows the displayed content to be viewed on other devices. For example, the display unit can allow the displayed content to be viewed on a smartphone. The display unit can also allow the displayed content to be viewed on a tablet. Furthermore, the display unit can also allow the displayed content to be viewed on a personal computer. This improves convenience by allowing viewing on other devices. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input the displayed content into a generation AI and have the generation AI perform the viewing on other devices.

[0053] The generation unit presents multiple different improvement scenarios during generation, allowing the user to select the optimal scenario. The generation unit can, for example, present multiple different improvement scenarios, allowing the user to select the optimal scenario. The generation unit can also, for example, present different improvement scenarios and display their respective advantages and disadvantages. Furthermore, the generation unit can, for example, present different improvement scenarios and allow the user to perform simulations. This allows the user to select the optimal improvement by presenting different improvement scenarios. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input different improvement scenarios into a generation AI and have the generation AI present the scenarios.

[0054] The generation unit adds specific improvement steps and a list of necessary materials to the generated image. The generation unit can, for example, add specific improvement steps to the generated image. The generation unit can, for example, add specific improvement steps to the generated image. The generation unit can also, for example, add a list of necessary materials to the generated image. Furthermore, the generation unit can, for example, integrate and display the improvement steps and material list on the generated image. This makes efficient improvement possible by adding specific improvement steps and material lists. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the improvement steps and material list into the generation AI and have the generation AI perform the addition of information.

[0055] The generation unit proposes the optimal improvement measures by considering other relevant data during generation. For example, the generation unit can propose the optimal improvement measures by considering cost data during generation. The generation unit can also propose the optimal improvement measures by considering time data during generation. Furthermore, the generation unit can also propose the optimal improvement measures by considering other relevant data during generation. In this way, the optimal improvement measures can be proposed by considering other relevant data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input other relevant data into the generation AI and have the generation AI execute the proposal of improvement measures.

[0056] The generation unit shares the generated images in cooperation with other systems. For example, the generation unit can share the generated images in cooperation with a project management system. The generation unit can also share the generated images in cooperation with a quality management system. Furthermore, the generation unit can also share the generated images in cooperation with a production management system. This makes the sharing of generated images more efficient by coordinating with other systems. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the generated images into a generation AI and have the generation AI perform the coordination with other systems.

[0057] The information provider provides optimal information by referring to the user's past feedback at the time of provision. The information provider can, for example, provide optimal information by referring to the user's past feedback at the time of provision. The information provider can also, for example, analyze the user's past feedback at the time of provision and customize the information. Furthermore, the information provider can also, for example, optimize the information based on the user's past feedback at the time of provision. This makes it possible to provide optimal information by referring to past feedback. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the information provider can input past feedback data into a generative AI and have the generative AI perform the information provision.

[0058] The service provider provides guidelines to enable users to take specific actions based on the information provided. For example, the service provider can provide guidelines to enable users to take specific actions based on the information provided. The service provider can also provide guidelines to enable users to take step-by-step actions based on the information provided. Furthermore, the service provider can provide guidelines to enable users to take efficient actions based on the information provided. By providing guidelines that enable users to take specific actions, efficient improvements become possible. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input the provided information into a generative AI and have the generative AI perform the task of providing guidelines.

[0059] The information provider will provide additional relevant information at the time of provision. For example, the information provider may provide industry best practices at the time of provision. The information provider may also provide additional industry best practices at the time of provision. Furthermore, the information provider may also provide additional relevant information at the time of provision. In addition, the information provider may provide past success stories at the time of provision. This improves the accuracy of the information provided by adding other relevant information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the information provider may input relevant information into a generative AI and have the generative AI perform the information provision.

[0060] The service provider makes the provided information viewable on other devices. For example, the service provider can make the provided information viewable on a smartphone. The service provider can also make the provided information viewable on a tablet. Furthermore, the service provider can also make the provided information viewable on a personal computer. This improves convenience by making the information viewable on other devices. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the service provider can input the provided information into a generating AI and have the generating AI perform the viewing on other devices.

[0061] The quantification unit presents multiple different indicators during the quantification process and performs a comprehensive evaluation. For example, the quantification unit can present the cost reduction amount during the quantification process and perform a comprehensive evaluation. The quantification unit can also present the productivity improvement rate during the quantification process and perform a comprehensive evaluation. Furthermore, the quantification unit can present other indicators during the quantification process and perform a comprehensive evaluation. This makes it possible to perform a comprehensive evaluation by presenting different indicators. Some or all of the above processing in the quantification unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the quantification unit can input different indicators into a generation AI and have the generation AI perform a comprehensive evaluation.

[0062] The quantification unit proposes specific improvement measures based on the quantification results. For example, the quantification unit proposes specific improvement measures based on the quantification results and notifies employees. The quantification unit can, for example, propose specific improvement measures based on the quantification results and notify employees. Furthermore, the quantification unit can, for example, propose specific improvement measures based on the quantification results and notify managers. In addition, the quantification unit can, for example, propose specific improvement measures based on the quantification results and notify the system. This enables efficient improvement by proposing specific improvement measures based on quantification results. Some or all of the above processing in the quantification unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the quantification unit can input the quantification results into a generation AI and have the generation AI execute the proposal of specific improvement measures.

[0063] The quantification unit performs comparative evaluation by referring to other relevant data during the quantification process. For example, the quantification unit can perform comparative evaluation by referring to industry average data during the quantification process. The quantification unit can also perform comparative evaluation by referring to industry average data during the quantification process. Furthermore, the quantification unit can also perform comparative evaluation by referring to competitor data during the quantification process. In addition, the quantification unit can also perform comparative evaluation by referring to other relevant data during the quantification process. This makes comparative evaluation possible by referring to other relevant data. Some or all of the above processing in the quantification unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the quantification unit can input other relevant data into a generation AI and have the generation AI perform the comparative evaluation.

[0064] The quantification unit shares the quantification results with other departments and collaborates to implement improvement measures. For example, the quantification unit can share the quantification results with the management planning department and collaborate to implement improvement measures. The quantification unit can also share the quantification results with the management planning department and collaborate to implement improvement measures. Furthermore, the quantification unit can also share the quantification results with the quality control department and collaborate to implement improvement measures. In addition, the quantification unit can also share the quantification results with the production management department and collaborate to implement improvement measures. This makes it possible to implement improvement measures collaboratively by sharing with other departments. Some or all of the above processing in the quantification unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the quantification unit can input the quantification results into a generation AI and have the generation AI perform the sharing and collaboration with other departments.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The data collection unit can capture more multifaceted data by taking photographs of the work site from different angles and at different times of day. For example, it can take photographs of the work site at different times of day, such as in the morning and evening, to collect data that takes into account the differences in lighting conditions. It can also take photographs of the work site from different angles to collect data that helps understand the overall situation. Furthermore, it can take photographs of the work site periodically to collect data that tracks changes over time. In this way, more multifaceted data can be collected by taking photographs from different angles and at different times of day.

[0067] The data collection unit can analyze collected data in real time and provide immediate feedback. For example, after taking photos of a work site, the AI ​​can immediately analyze them and display the 5S evaluation results in real time. Furthermore, if an anomaly is identified, it can immediately notify the manager, prompting a quick response. In addition, collected data can be uploaded to the cloud in real time and shared with other systems. This enables rapid response through real-time analysis and immediate feedback.

[0068] The data collection unit can simultaneously collect environmental data such as temperature and humidity when taking photographs of the work site. For example, when taking photographs of the work site, environmental data can be collected using a temperature sensor. Environmental data can also be collected using a humidity sensor. Furthermore, environmental data can be collected using a barometric pressure sensor. By collecting environmental data simultaneously, more detailed data analysis becomes possible.

[0069] The analysis unit can identify anomalies by comparing current data with past data during analysis and issue alerts. For example, it can identify anomalies by comparing current data with past data during analysis and issue alerts to administrators. It can also issue alerts to employees. Furthermore, it can issue alerts to the system. This allows for a rapid response by identifying anomalies by comparing current data with past data.

[0070] The analysis unit can propose specific improvement measures based on the analysis results. For example, it can propose specific improvement measures based on the analysis results and notify employees. It can also notify managers. Furthermore, it can notify the system. This enables efficient improvement by proposing specific improvement measures based on the analysis results.

[0071] The analysis unit can perform a comprehensive evaluation by referring to other relevant data during the analysis. For example, it can perform a comprehensive evaluation by referring to work efficiency data. It can also perform a comprehensive evaluation by referring to accident occurrence rate data. Furthermore, it can perform a comprehensive evaluation by referring to other relevant data. In this way, a comprehensive evaluation becomes possible by referring to other relevant data.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The collection unit uses image recognition AI to quantify consistent 5S evaluations. The collection unit takes photos of the work site, and the AI ​​analyzes these photos to calculate scores based on each 5S item (Sort, Set in order, Shine, Standardize, Sustain). For example, the AI ​​can analyze photos taken with a high-resolution camera and calculate scores. Photos taken regularly and photos taken from different angles are also subject to analysis. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the photo data collected by the collection unit and calculates a score based on each item of the 5S framework. This improves the accuracy and reliability of the collected data, enabling more accurate evaluation. Step 3: The monitoring unit continuously monitors the footage from the in-warehouse cameras. The monitoring unit uses AI to analyze the footage captured by cameras installed inside the warehouse and identify any abnormalities. This allows for real-time monitoring of the warehouse environment and enables a quick response if an anomaly occurs. Step 4: The display unit visually displays the results analyzed by the analysis unit. The display unit can display the results analyzed by the analysis unit as a heat map or graph. This allows users to grasp the current status of 5S activities at a glance and quickly identify areas for improvement.

[0074] (Example of form 2) The 5S activity support system according to an embodiment of the present invention is a system that scores 5S activities, visualizes abnormal areas, provides images after improvement, and quantifies the effects of improvement. The 5S activity support system uses image recognition AI to quantify consistent 5S evaluations. For example, a photograph of the work site is taken, and the AI ​​analyzes the photograph to calculate a score based on each 5S item (sorting, setting in order, shining, standardizing, and sustaining). This allows employees to quantitatively grasp the progress of their 5S activities. Next, the 5S activity support system performs continuous monitoring and AI analysis of in-warehouse cameras. For example, cameras installed in the warehouse constantly capture images, and the AI ​​analyzes these images to identify abnormal areas. The identified abnormal areas are displayed in heatmaps and graphs. This allows managers to quickly grasp abnormal areas and take appropriate countermeasures. Furthermore, the 5S activity support system provides AI-generated images after improvement. For example, the AI ​​analyzes a photograph of the current work site and generates an image after improvement. This image visually shows employees areas for improvement and increases their motivation to improve. Furthermore, by pointing out necessary improvements in writing and visually, the system presents concrete improvement measures. Finally, the 5S activity support system quantifies the specific effects of cost reduction and productivity improvement resulting from improvements. For example, AI analyzes data before and after improvements to calculate cost reductions and productivity improvement rates. This allows managers to concretely understand the effects of improvements and use this information to inform their business decisions. In this way, the 5S activity support system promotes improvements in the workplace and supports increased employee motivation and management decision-making by scoring 5S activities, visualizing abnormal areas, providing images of the improved state, and quantifying the effects of improvements.

[0075] The 5S activity support system according to this embodiment comprises a data collection unit, an analysis unit, a monitoring unit, and a display unit. The data collection unit quantifies consistent 5S evaluations using image recognition AI. For example, the data collection unit takes photographs of the work site, and the AI ​​analyzes the photographs to calculate a score based on each 5S item (sorting, setting in order, shining, standardizing, and sustaining). For example, the data collection unit can take photographs of the work site with a high-resolution camera, and the AI ​​can analyze the photographs to calculate a score. The data collection unit can also take photographs of the work site periodically, and the AI ​​can analyze the photographs to calculate a score. Furthermore, the data collection unit can take photographs of the work site from different angles, and the AI ​​can analyze the photographs to calculate a score. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses AI to analyze the photographic data collected by the data collection unit and calculates a score based on each 5S item. For example, the analysis unit uses AI to analyze the photographic data collected by the data collection unit and calculates a score. Furthermore, the analysis unit can, for example, use AI to analyze photo data collected by the collection unit and calculate a score. The monitoring unit constantly monitors the video from the cameras inside the warehouse. The monitoring unit can, for example, use cameras installed inside the warehouse to constantly capture video, and AI analyzes that video to identify abnormal areas. The monitoring unit can, for example, use cameras installed inside the warehouse to constantly capture video, and AI analyzes that video to identify abnormal areas. Furthermore, the monitoring unit can, for example, use cameras installed inside the warehouse to constantly capture video, and AI analyzes that video to identify abnormal areas. Furthermore, the monitoring unit can, for example, use cameras installed inside the warehouse to constantly capture video, and AI analyzes that video to identify abnormal areas. The display unit visually displays the results analyzed by the analysis unit. The display unit can, for example, display the results analyzed by the analysis unit as a heatmap or graph. Furthermore, the display unit can also display the results analyzed by the analysis unit, for example, as a heatmap or graph.Furthermore, the display unit can also display the results analyzed by the analysis unit as a heat map or graph, for example. This allows the 5S activity support system according to the embodiment to score 5S activities, visualize abnormal areas, provide an image of the improved state, and quantify the improvement effect.

[0076] The data collection unit uses image recognition AI to quantify consistent 5S evaluations. Specifically, it takes photos of the work site with a high-resolution camera, and the AI ​​analyzes these photos to calculate scores based on each 5S item (Sort, Set in order, Shine, Standardize, Sustain). The data collection unit can also, for example, take photos of the work site periodically, and the AI ​​will analyze these photos to calculate scores. This makes it possible to continuously monitor the progress of 5S activities over time. Furthermore, the data collection unit can take photos of the work site from different angles, and the AI ​​will analyze these photos to calculate scores. This allows for an overall understanding of the work site and evaluation including areas that are often overlooked. In addition, the data collection unit can evaluate the effectiveness of 5S activities in more detail by taking photos at specific times or when specific tasks are being performed. For example, by taking photos immediately after work is completed or immediately after cleaning is performed and evaluating the state, the effectiveness of 5S activities can be grasped more accurately. As a result, the data collection unit can consistently evaluate 5S activities and provide data to clarify areas for improvement.

[0077] The analysis unit analyzes the data collected by the collection unit. Specifically, the AI ​​analyzes the photographic data collected by the collection unit and calculates a score based on each item of 5S. The AI ​​uses image recognition technology to identify objects and conditions in the photographs and evaluates each item. For example, in the sorting item, it evaluates whether unnecessary items are scattered around, and in the setting item, it evaluates whether items are placed in their designated locations. In the cleaning item, it evaluates whether the work area is kept clean, and in the sanitizing item, it evaluates whether cleaning is carried out regularly. In the sustaining item, it evaluates whether 5S activities have been ingrained in the employees. Based on these evaluation results, the analysis unit calculates an overall 5S score. In addition, the analysis unit can identify the progress of 5S activities and areas for improvement by comparing them with past data. For example, by comparing past scores with current scores, it can clearly identify which items have improved and which items still have room for improvement. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to quickly and accurately evaluate 5S activities and provide data to clearly identify areas for improvement.

[0078] The monitoring unit continuously monitors the footage from the warehouse cameras. Specifically, cameras installed inside the warehouse constantly capture images, and AI analyzes these images to identify abnormal areas. For example, the monitoring unit can install cameras in specific areas within the warehouse and monitor the footage of those areas in real time. The AI ​​detects abnormal movements and object placements in the footage and identifies abnormal areas. For example, it can detect conditions that violate 5S activities, such as items not being placed in their designated locations or cleaning not being carried out. The monitoring unit can also issue alerts and notify the responsible person when an abnormality is detected. This enables a quick response and helps maintain the effectiveness of 5S activities. Furthermore, the monitoring unit can save past video data and review it later. This allows for the identification of the cause of the abnormality and the implementation of measures to prevent recurrence. Through these functions, the monitoring unit can efficiently monitor 5S activities and detect and respond to abnormalities at an early stage.

[0079] The display unit visually displays the results analyzed by the analysis unit. Specifically, it displays the results analyzed by the analysis unit using heat maps and graphs. The heat map shows the 5S score for each area of ​​the work site using different colors, allowing users to quickly see which areas have room for improvement. For example, areas with low scores are displayed in red, and areas with high scores are displayed in green. This allows for the rapid identification of areas that need improvement. The graphs show the trend of scores for each item, allowing for a visual understanding of the progress of 5S activities. For example, a line graph showing how the score for the sorting item changes over time, or a bar graph comparing the scores of each item, are possible. Furthermore, the display unit can also display the analysis results in a dashboard format, allowing the person in charge to grasp the overall situation at a glance. The dashboard displays the score for each item, the status of anomaly detection, and comparisons with past data. This allows the person in charge to quickly grasp the current status of 5S activities and take appropriate measures. Through these functions, the display unit can visually display the results of 5S activities and provide information to clarify areas for improvement.

[0080] The generation unit analyzes photographs of the current work site and generates an image of the improved state. The generation unit can, for example, have AI analyze photographs of the current work site and generate an image of the improved state. The generation unit can, for example, have AI analyze photographs of the current work site and generate an image of the improved state. The generation unit can also, for example, have AI analyze photographs of the current work site and generate an image of the improved state. Furthermore, the generation unit can, for example, have AI analyze photographs of the current work site and generate an image of the improved state. By generating an image of the improved state, the motivation of employees to improve is increased. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input photographs of the current work site into a generation AI and have the generation AI perform the generation of the improved state.

[0081] The service provider provides the generated image. The service provider provides the generated image in digital format, for example. The service provider can provide the generated image in digital format, for example. The service provider can also provide the generated image in digital format, for example. Furthermore, the service provider can also provide the generated image in digital format, for example. This allows the service provider to show employees specific areas for improvement by providing the generated image. Some or all of the above processing in the service provider may be performed using a generation AI, for example, or without a generation AI. For example, the service provider can input the generated image into a generation AI and have the generation AI perform the generation in the format to be provided.

[0082] The quantification unit analyzes data before and after the improvement and quantifies the effect. For example, the quantification unit can use AI to analyze data before and after the improvement and calculate cost reductions and productivity improvement rates. The quantification unit can use AI to analyze data before and after the improvement and calculate cost reductions and productivity improvement rates. Furthermore, the quantification unit can use AI to analyze data before and after the improvement and calculate cost reductions and productivity improvement rates. In this way, quantifying the improvement effect makes it more persuasive to management. Some or all of the above processing in the quantification unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the quantification unit can input data before and after the improvement into a generating AI and have the generating AI perform the quantification of the effect.

[0083] The data collection unit estimates the user's emotions and adjusts the timing of 5S evaluation data collection based on the estimated emotions. For example, if the user is feeling stressed, the data collection unit can delay the collection timing to reduce the user's burden. The data collection unit can also accelerate the collection timing to efficiently collect data if the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can optimize the collection timing to quickly collect data. This reduces the user's burden by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The data collection unit takes photographs of the work site from different angles and at different times of day to collect more multifaceted data. For example, the data collection unit can take photographs of the work site at different times of day, such as in the morning and at night, to collect data that takes into account the differences in lighting conditions. The data collection unit can also take photographs of the work site from different angles to collect data that helps understand the overall situation. Furthermore, the data collection unit can also take photographs of the work site periodically to collect data that tracks changes over time. This allows for the collection of more multifaceted data by taking photographs from different angles and at different times of day. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not. For example, the data collection unit can input photographic data taken from different angles and at different times of day into a generative AI and have the generative AI perform data analysis.

[0085] The data collection unit analyzes the collected data in real time and provides immediate feedback. For example, after taking a photograph of the work site, the data collection unit can immediately analyze it with AI and display the 5S evaluation results in real time. The data collection unit can also immediately notify the manager if it identifies an abnormal area, prompting a quick response. Furthermore, the data collection unit can upload the collected data to the cloud in real time and share the data in conjunction with other systems. This enables a quick response by analyzing the data in real time and providing immediate feedback. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the data collection unit can input the collected data into a generating AI and have the generating AI perform real-time analysis and provide feedback.

[0086] The data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important data. The data collection unit can postpone the collection of less important data if the user is stressed. The data collection unit can also prioritize the collection of highly important data if the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can quickly collect the most important data. This enables efficient data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The data collection unit collects environmental data such as temperature and humidity simultaneously when taking photographs of the work site. For example, the data collection unit can collect environmental data using a temperature sensor when taking photographs of the work site. The data collection unit can also collect environmental data using a humidity sensor when taking photographs of the work site. Furthermore, the data collection unit can also collect environmental data using a barometric pressure sensor when taking photographs of the work site. By simultaneously collecting environmental data, more detailed data analysis becomes possible. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input environmental data into a generative AI and have the generative AI perform data analysis.

[0088] The data collection unit stores the collected data in the cloud and shares the data in conjunction with other systems. For example, the data collection unit can upload the collected data to the cloud and share it with other departments in real time. The data collection unit can also store the collected data in the cloud and link it with other systems via APIs. Furthermore, the data collection unit can store the collected data in the cloud and perform analysis in conjunction with data analysis tools. This makes data sharing more efficient by storing the data in the cloud and linking it with other systems. Some or all of the above processes in the data collection unit may be performed using, for example, generative AI, or not. For example, the data collection unit can input the collected data into the generative AI and have the generative AI perform cloud storage and linking with other systems.

[0089] The analysis unit estimates the user's emotions and adjusts the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit simplifies the analysis algorithm and provides results quickly. The analysis unit can also perform a detailed analysis and provide highly accurate results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can optimize the analysis algorithm and provide results quickly. This allows for rapid and highly accurate analysis by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The analysis unit identifies anomalies by comparing them with past data during analysis and issues alerts. For example, the analysis unit can identify anomalies by comparing them with past data during analysis and issue alerts to administrators. The analysis unit can also identify anomalies by comparing them with past data during analysis and issue alerts to administrators. Furthermore, the analysis unit can also identify anomalies by comparing them with past data during analysis and issue alerts to employees. In addition, the analysis unit can also identify anomalies by comparing them with past data during analysis and issue alerts to the system. This enables a rapid response by identifying anomalies by comparing them with past data. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input data for identifying anomalies by comparing it with past data into a generation AI and have the generation AI perform the anomaly identification and alert issuance.

[0091] The analysis unit proposes specific improvement measures based on the analysis results. For example, the analysis unit can propose specific improvement measures based on the analysis results and notify employees. The analysis unit can also propose specific improvement measures based on the analysis results and notify managers. Furthermore, the analysis unit can also propose specific improvement measures based on the analysis results and notify the system. This enables efficient improvement by proposing specific improvement measures based on the analysis results. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the analysis results into a generating AI and have the generating AI execute the proposal of specific improvement measures.

[0092] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit provides a simple and highly visible display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, highly visible displays become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The analysis unit performs a comprehensive evaluation by referring to other relevant data during the analysis. For example, the analysis unit can perform a comprehensive evaluation by referring to work efficiency data during the analysis. The analysis unit can also perform a comprehensive evaluation by referring to work efficiency data during the analysis. Furthermore, the analysis unit can also perform a comprehensive evaluation by referring to accident rate data during the analysis. In addition, the analysis unit can also perform a comprehensive evaluation by referring to other relevant data during the analysis. This makes a comprehensive evaluation possible by referring to other relevant data. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input other relevant data into a generating AI and have the generating AI perform a comprehensive evaluation.

[0094] The analysis department shares the analysis results with other departments and collaborates to implement improvement measures. For example, the analysis department can share the analysis results with the safety management department and collaborate to implement improvement measures. The analysis department can also share the analysis results with the quality control department and collaborate to implement improvement measures. Furthermore, the analysis department can also share the analysis results with the production management department and collaborate to implement improvement measures. This makes it possible to implement improvement measures collaboratively by sharing with other departments. Some or all of the above-described processes in the analysis department may be performed using, for example, a generation AI, or without using a generation AI. For example, the analysis department can input the analysis results into a generation AI and have the generation AI perform the sharing and collaboration with other departments.

[0095] The monitoring unit estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. For example, if the user is stressed, the monitoring unit reduces the monitoring frequency to alleviate the user's burden. The monitoring unit can also reduce the user's burden by reducing the monitoring frequency if the user is stressed. Furthermore, if the user is relaxed, the monitoring unit can increase the monitoring frequency to collect more detailed data. In addition, if the user is in a hurry, the monitoring unit can optimize the monitoring frequency to quickly collect data. This reduces the user's burden by adjusting the monitoring frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] The monitoring unit performs monitoring with a focus on specific areas or time periods. For example, the monitoring unit can perform monitoring with a focus on specific areas. The monitoring unit can also perform monitoring with a focus on specific time periods. Furthermore, the monitoring unit can also perform monitoring with a focus on specific areas or time periods. This enables efficient monitoring by focusing on specific areas or time periods. Some or all of the above processing in the monitoring unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the monitoring unit can input data for specific areas or time periods into a generation AI and have the generation AI execute the monitoring focus.

[0097] The monitoring unit analyzes monitoring data in real time and immediately notifies when an anomaly occurs. For example, the monitoring unit can analyze monitoring data in real time and immediately notify administrators when an anomaly occurs. The monitoring unit can also analyze monitoring data in real time and immediately notify administrators when an anomaly occurs. Furthermore, the monitoring unit can also analyze monitoring data in real time and immediately notify employees when an anomaly occurs. In addition, the monitoring unit can analyze monitoring data in real time and immediately notify the system when an anomaly occurs. This enables a rapid response by analyzing in real time and providing immediate notification. Some or all of the above processing in the monitoring unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the monitoring unit can input monitoring data into a generation AI and have the generation AI perform real-time analysis and notification.

[0098] The monitoring unit estimates the user's emotions and adjusts the display method of the monitoring results based on the estimated user emotions. For example, if the user is stressed, the monitoring unit provides a simple and highly visible display method. The monitoring unit can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the monitoring unit can provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, highly visible displays become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The monitoring unit adds sensors to detect anomalies such as sound and vibration during monitoring. For example, the monitoring unit can add a sound sensor to detect anomalies during monitoring. The monitoring unit can also add a vibration sensor to detect anomalies during monitoring. Furthermore, the monitoring unit can also add sound and vibration sensors to detect anomalies during monitoring. This improves the accuracy of anomaly detection by detecting anomalies such as sound and vibration. Some or all of the above processing in the monitoring unit may be performed using, for example, a generation AI, or without a generation AI. For example, the monitoring unit can input sound and vibration data into a generation AI and have the generation AI perform anomaly detection.

[0100] The monitoring unit identifies anomalies by linking monitoring data with other systems. For example, the monitoring unit identifies anomalies by linking monitoring data with an inventory management system. The monitoring unit can also identify anomalies by linking monitoring data with an inventory management system. Furthermore, the monitoring unit can also identify anomalies by linking monitoring data with a quality management system. In addition, the monitoring unit can also identify anomalies by linking monitoring data with a production management system. This improves the accuracy of anomaly detection by linking with other systems. Some or all of the above processing in the monitoring unit may be performed using, for example, a generation AI, or without a generation AI. For example, the monitoring unit can input monitoring data into a generation AI and have the generation AI perform the linking with other systems.

[0101] The display unit estimates the user's emotions and customizes the displayed content based on the estimated emotions. For example, if the user is feeling stressed, the display unit provides simple and highly visible content. The display unit can also provide detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the display unit can provide concise and to the point. By customizing the displayed content according to the user's emotions, highly visible displays are possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0102] The display unit provides detailed information about the abnormal location when it is displayed. The display unit can, for example, display the cause of the abnormal location in detail and propose countermeasures. The display unit can, for example, display the cause of the abnormal location in detail and propose countermeasures by referring to similar past cases. Furthermore, the display unit can, for example, display the cause of the abnormal location and provide countermeasures step by step. This enables a rapid response by providing detailed information about the abnormal location. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input detailed information about the abnormal location into a generation AI and have the generation AI provide the information.

[0103] The display unit optimizes the displayed content according to the user's role and responsibilities. For example, the display unit can provide administrators with display content that allows them to grasp the overall situation. The display unit can also provide workers with display content that includes specific work instructions. Furthermore, the display unit can provide managers with display content that includes information useful for business decisions. By optimizing the displayed content according to the user's role and responsibilities, efficient information provision becomes possible. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input information according to the user's role and responsibilities into the generation AI and have the generation AI perform the optimization of the displayed content.

[0104] The display unit estimates the user's emotions and determines the display priority based on the estimated emotions. For example, if the user is feeling stressed, the display unit will prioritize displaying information of high importance. The display unit can prioritize displaying information of high importance if the user is feeling stressed. The display unit can also prioritize displaying detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the display unit can prioritize displaying concise information. In this way, by determining the display priority according to the user's emotions, important information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0105] The display unit displays trends by referring to past historical data of the anomaly locations when displaying the data. The display unit can, for example, refer to past historical data of the anomaly locations and display trends. The display unit can, for example, refer to past historical data of the anomaly locations and display trends. The display unit can also, for example, refer to past historical data of the anomaly locations and display the frequency of occurrence. Furthermore, the display unit can, for example, refer to past historical data of the anomaly locations and display occurrence patterns. This makes it possible to grasp the trend of anomalies by referring to past historical data. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the display unit can input past historical data into a generation AI and have the generation AI perform the display of trends.

[0106] The display unit allows the displayed content to be viewed on other devices. For example, the display unit can allow the displayed content to be viewed on a smartphone. The display unit can also allow the displayed content to be viewed on a tablet. Furthermore, the display unit can also allow the displayed content to be viewed on a personal computer. This improves convenience by allowing viewing on other devices. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input the displayed content into a generation AI and have the generation AI perform the viewing on other devices.

[0107] The generation unit estimates the user's emotions and adjusts the level of detail in the generated image based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a simple image. The generation unit can also generate a detailed image if the user is relaxed. Furthermore, if the user is in a hurry, the generation unit can generate a concise image. By adjusting the level of detail in the image according to the user's emotions, it is possible to provide highly visual images. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0108] The generation unit presents multiple different improvement scenarios during generation, allowing the user to select the optimal scenario. The generation unit can, for example, present multiple different improvement scenarios, allowing the user to select the optimal scenario. The generation unit can also, for example, present different improvement scenarios and display their respective advantages and disadvantages. Furthermore, the generation unit can, for example, present different improvement scenarios and allow the user to perform simulations. This allows the user to select the optimal improvement by presenting different improvement scenarios. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input different improvement scenarios into a generation AI and have the generation AI present the scenarios.

[0109] The generation unit adds specific improvement steps and a list of necessary materials to the generated image. The generation unit can, for example, add specific improvement steps to the generated image. The generation unit can, for example, add specific improvement steps to the generated image. The generation unit can also, for example, add a list of necessary materials to the generated image. Furthermore, the generation unit can, for example, integrate and display the improvement steps and material list on the generated image. This makes efficient improvement possible by adding specific improvement steps and material lists. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the improvement steps and material list into the generation AI and have the generation AI perform the addition of information.

[0110] The generation unit estimates the user's emotions and determines the priority of images to generate based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize generating images of high importance. The generation unit can also prioritize generating detailed images if the user is relaxed. Furthermore, if the user is in a hurry, the generation unit can prioritize generating concise images. This allows for the prioritization of important images by determining image priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0111] The generation unit proposes the optimal improvement measures by considering other relevant data during generation. For example, the generation unit can propose the optimal improvement measures by considering cost data during generation. The generation unit can also propose the optimal improvement measures by considering time data during generation. Furthermore, the generation unit can also propose the optimal improvement measures by considering other relevant data during generation. In this way, the optimal improvement measures can be proposed by considering other relevant data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input other relevant data into the generation AI and have the generation AI execute the proposal of improvement measures.

[0112] The generation unit shares the generated images in cooperation with other systems. For example, the generation unit can share the generated images in cooperation with a project management system. The generation unit can also share the generated images in cooperation with a quality management system. Furthermore, the generation unit can also share the generated images in cooperation with a production management system. This makes the sharing of generated images more efficient by coordinating with other systems. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the generated images into a generation AI and have the generation AI perform the coordination with other systems.

[0113] The information provider estimates the user's emotions and adjusts the format of the information provided based on the estimated emotions. For example, if the user is stressed, the information provider provides information in a simple format. The information provider can also provide information in a detailed format if the user is relaxed. Furthermore, if the user is in a hurry, the information provider can provide information in a concise format. By adjusting the format of information according to the user's emotions, it becomes possible to provide information that is easy to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0114] The information provider provides optimal information by referring to the user's past feedback at the time of provision. The information provider can, for example, provide optimal information by referring to the user's past feedback at the time of provision. The information provider can also, for example, analyze the user's past feedback at the time of provision and customize the information. Furthermore, the information provider can also, for example, optimize the information based on the user's past feedback at the time of provision. This makes it possible to provide optimal information by referring to past feedback. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the information provider can input past feedback data into a generative AI and have the generative AI perform the information provision.

[0115] The service provider provides guidelines to enable users to take specific actions based on the information provided. For example, the service provider can provide guidelines to enable users to take specific actions based on the information provided. The service provider can also provide guidelines to enable users to take step-by-step actions based on the information provided. Furthermore, the service provider can provide guidelines to enable users to take efficient actions based on the information provided. By providing guidelines that enable users to take specific actions, efficient improvements become possible. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input the provided information into a generative AI and have the generative AI perform the task of providing guidelines.

[0116] The information provider estimates the user's emotions and determines the priority of the information to be provided based on the estimated emotions. For example, if the user is feeling stressed, the information provider will prioritize providing information of high importance. The information provider can prioritize providing information of high importance if the user is feeling stressed. The information provider can also prioritize providing detailed information if the user is relaxed. Furthermore, the information provider can prioritize providing concise information if the user is in a hurry. In this way, by prioritizing information according to the user's emotions, important information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0117] The information provider will provide additional relevant information at the time of provision. For example, the information provider may provide industry best practices at the time of provision. The information provider may also provide additional industry best practices at the time of provision. Furthermore, the information provider may also provide additional relevant information at the time of provision. In addition, the information provider may provide past success stories at the time of provision. This improves the accuracy of the information provided by adding other relevant information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the information provider may input relevant information into a generative AI and have the generative AI perform the information provision.

[0118] The service provider makes the provided information viewable on other devices. For example, the service provider can make the provided information viewable on a smartphone. The service provider can also make the provided information viewable on a tablet. Furthermore, the service provider can also make the provided information viewable on a personal computer. This improves convenience by making the information viewable on other devices. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the service provider can input the provided information into a generating AI and have the generating AI perform the viewing on other devices.

[0119] The quantification unit estimates the user's emotions and adjusts the quantification criteria based on the estimated emotions. For example, if the user is stressed, the quantification unit simplifies the quantification criteria and provides results quickly. The quantification unit can also perform detailed quantification and provide highly accurate results if the user is relaxed. Furthermore, if the user is in a hurry, the quantification unit can optimize the quantification criteria and provide results quickly. This allows for rapid and highly accurate quantification by adjusting the quantification criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the quantification unit may be performed using AI, for example, or without AI. For example, the quantification unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0120] The quantification unit presents multiple different indicators during the quantification process and performs a comprehensive evaluation. For example, the quantification unit can present the cost reduction amount during the quantification process and perform a comprehensive evaluation. The quantification unit can also present the productivity improvement rate during the quantification process and perform a comprehensive evaluation. Furthermore, the quantification unit can present other indicators during the quantification process and perform a comprehensive evaluation. This makes it possible to perform a comprehensive evaluation by presenting different indicators. Some or all of the above processing in the quantification unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the quantification unit can input different indicators into a generation AI and have the generation AI perform a comprehensive evaluation.

[0121] The quantification unit proposes specific improvement measures based on the quantification results. For example, the quantification unit proposes specific improvement measures based on the quantification results and notifies employees. The quantification unit can, for example, propose specific improvement measures based on the quantification results and notify employees. Furthermore, the quantification unit can, for example, propose specific improvement measures based on the quantification results and notify managers. In addition, the quantification unit can, for example, propose specific improvement measures based on the quantification results and notify the system. This enables efficient improvement by proposing specific improvement measures based on quantification results. Some or all of the above processing in the quantification unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the quantification unit can input the quantification results into a generation AI and have the generation AI execute the proposal of specific improvement measures.

[0122] The quantification unit estimates the user's emotions and adjusts the display method of the quantification results based on the estimated user emotions. For example, if the user is feeling stressed, the quantification unit can provide a simple and highly visible display method. The quantification unit can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the quantification unit can provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, a highly visible display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the quantification unit may be performed using AI, for example, or without AI. For example, the quantification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0123] The quantification unit performs comparative evaluation by referring to other relevant data during the quantification process. For example, the quantification unit can perform comparative evaluation by referring to industry average data during the quantification process. The quantification unit can also perform comparative evaluation by referring to industry average data during the quantification process. Furthermore, the quantification unit can also perform comparative evaluation by referring to competitor data during the quantification process. In addition, the quantification unit can also perform comparative evaluation by referring to other relevant data during the quantification process. This makes comparative evaluation possible by referring to other relevant data. Some or all of the above processing in the quantification unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the quantification unit can input other relevant data into a generation AI and have the generation AI perform the comparative evaluation.

[0124] The quantification unit shares the quantification results with other departments and collaborates to implement improvement measures. For example, the quantification unit can share the quantification results with the management planning department and collaborate to implement improvement measures. The quantification unit can also share the quantification results with the management planning department and collaborate to implement improvement measures. Furthermore, the quantification unit can also share the quantification results with the quality control department and collaborate to implement improvement measures. In addition, the quantification unit can also share the quantification results with the production management department and collaborate to implement improvement measures. This makes it possible to implement improvement measures collaboratively by sharing with other departments. Some or all of the above processing in the quantification unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the quantification unit can input the quantification results into a generation AI and have the generation AI perform the sharing and collaboration with other departments.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] The data collection unit can estimate the user's emotions and adjust the timing of 5S evaluation data collection based on the estimated emotions. For example, if the user is stressed, the data collection timing can be delayed to reduce the user's burden. Conversely, if the user is relaxed, the data collection timing can be advanced to collect data more efficiently. Furthermore, if the user is in a hurry, the data collection timing can be optimized to collect data quickly. In this way, the burden on the user can be reduced by adjusting the data collection timing according to the user's emotions.

[0127] The data collection unit can capture more multifaceted data by taking photographs of the work site from different angles and at different times of day. For example, it can take photographs of the work site at different times of day, such as in the morning and evening, to collect data that takes into account the differences in lighting conditions. It can also take photographs of the work site from different angles to collect data that helps understand the overall situation. Furthermore, it can take photographs of the work site periodically to collect data that tracks changes over time. In this way, more multifaceted data can be collected by taking photographs from different angles and at different times of day.

[0128] The data collection unit can analyze collected data in real time and provide immediate feedback. For example, after taking photos of a work site, the AI ​​can immediately analyze them and display the 5S evaluation results in real time. Furthermore, if an anomaly is identified, it can immediately notify the manager, prompting a quick response. In addition, collected data can be uploaded to the cloud in real time and shared with other systems. This enables rapid response through real-time analysis and immediate feedback.

[0129] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on those emotions. For example, if the user is stressed, the collection of less important data can be postponed. Conversely, if the user is relaxed, the collection of highly important data can be prioritized. Furthermore, if the user is in a hurry, the most important data can be collected quickly. This enables efficient data collection by prioritizing data according to the user's emotions.

[0130] The data collection unit can simultaneously collect environmental data such as temperature and humidity when taking photographs of the work site. For example, when taking photographs of the work site, environmental data can be collected using a temperature sensor. Environmental data can also be collected using a humidity sensor. Furthermore, environmental data can be collected using a barometric pressure sensor. By collecting environmental data simultaneously, more detailed data analysis becomes possible.

[0131] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on those emotions. For example, if the user is stressed, the analysis algorithm can be simplified to provide results quickly. If the user is relaxed, a detailed analysis can be performed to provide highly accurate results. Furthermore, if the user is in a hurry, the analysis algorithm can be optimized to provide results quickly. In this way, by adjusting the analysis algorithm according to the user's emotions, rapid and highly accurate analysis becomes possible.

[0132] The analysis unit can identify anomalies by comparing current data with past data during analysis and issue alerts. For example, it can identify anomalies by comparing current data with past data during analysis and issue alerts to administrators. It can also issue alerts to employees. Furthermore, it can issue alerts to the system. This allows for a rapid response by identifying anomalies by comparing current data with past data.

[0133] The analysis unit can propose specific improvement measures based on the analysis results. For example, it can propose specific improvement measures based on the analysis results and notify employees. It can also notify managers. Furthermore, it can notify the system. This enables efficient improvement by proposing specific improvement measures based on the analysis results.

[0134] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that focuses on the essentials. In this way, by adjusting the display method according to the user's emotions, highly visible displays become possible.

[0135] The analysis unit can perform a comprehensive evaluation by referring to other relevant data during the analysis. For example, it can perform a comprehensive evaluation by referring to work efficiency data. It can also perform a comprehensive evaluation by referring to accident occurrence rate data. Furthermore, it can perform a comprehensive evaluation by referring to other relevant data. In this way, a comprehensive evaluation becomes possible by referring to other relevant data.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The collection unit uses image recognition AI to quantify consistent 5S evaluations. The collection unit takes photos of the work site, and the AI ​​analyzes these photos to calculate scores based on each 5S item (Sort, Set in order, Shine, Standardize, Sustain). For example, the AI ​​can analyze photos taken with a high-resolution camera and calculate scores. Photos taken regularly and photos taken from different angles are also subject to analysis. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the photo data collected by the collection unit and calculates a score based on each item of the 5S framework. This improves the accuracy and reliability of the collected data, enabling more accurate evaluation. Step 3: The monitoring unit continuously monitors the footage from the in-warehouse cameras. The monitoring unit uses AI to analyze the footage captured by cameras installed inside the warehouse and identify any abnormalities. This allows for real-time monitoring of the warehouse environment and enables a quick response if an anomaly occurs. Step 4: The display unit visually displays the results analyzed by the analysis unit. The display unit can display the results analyzed by the analysis unit as a heat map or graph. This allows users to grasp the current status of 5S activities at a glance and quickly identify areas for improvement.

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0140] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the collection unit, analysis unit, monitoring unit, display unit, generation unit, provision unit, and digitization unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit takes photographs of the work site using the camera 42 of the smart device 14, and these are analyzed by the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 and calculates a score based on each item of 5S. The monitoring unit constantly monitors the images inside the warehouse using the camera 42 of the smart device 14 and identifies abnormal areas using the identification processing unit 290 of the data processing unit 12. The display unit displays the results analyzed by the identification processing unit 290 of the data processing unit 12 as a heatmap or graph on the display 40A of the smart device 14. The generation unit analyzes the current work site photographs using the identification processing unit 290 of the data processing unit 12 and generates an improved image. The provision unit provides the generated image in digital format to the display 40A of the smart device 14. The digitization unit analyzes the data before and after the improvement using the specific processing unit 290 of the data processing device 12, and calculates the cost reduction amount and the productivity improvement rate. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the collection unit, analysis unit, monitoring unit, display unit, generation unit, provision unit, and digitization unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit takes photographs of the work site using the camera 42 of the smart glasses 214, and these are analyzed by the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 and calculates a score based on each item of 5S. The monitoring unit constantly monitors the images inside the warehouse using the camera 42 of the smart glasses 214 and identifies abnormal areas using the identification processing unit 290 of the data processing unit 12. The display unit displays the results analyzed by the identification processing unit 290 of the data processing unit 12 as a heat map or graph on the display of the smart glasses 214. The generation unit analyzes the current work site photographs using the identification processing unit 290 of the data processing unit 12 and generates an improved image. The provision unit provides the generated image in digital format to the display of the smart glasses 214. The digitization unit analyzes the data before and after the improvement using the specific processing unit 290 of the data processing device 12, and calculates the cost reduction amount and the productivity improvement rate. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the collection unit, analysis unit, monitoring unit, display unit, generation unit, provision unit, and digitization unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit takes photographs of the work site using the camera 42 of the headset terminal 314, and these are analyzed by the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 and calculates a score based on each item of 5S. The monitoring unit constantly monitors the inside of the warehouse using the camera 42 of the headset terminal 314 and identifies abnormal areas using the identification processing unit 290 of the data processing unit 12. The display unit displays the results analyzed by the identification processing unit 290 of the data processing unit 12 as a heat map or graph on the display 343 of the headset terminal 314. The generation unit analyzes the current work site photographs using the identification processing unit 290 of the data processing unit 12 and generates an improved image. The data provision unit provides the generated image in digital format to the display 343 of the headset terminal 314. The data processing unit analyzes the data before and after the improvement using the specific processing unit 290 of the data processing device 12 and calculates the cost reduction amount and productivity improvement rate. The correspondence between each unit and the device and control unit is not limited to the example described above and can be changed in various ways.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0176] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0177] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0178] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0179] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0180] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0181] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0182] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0183] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0184] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0185] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0186] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0187] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0188] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0189] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0190] Each of the multiple elements described above, including the collection unit, analysis unit, monitoring unit, display unit, generation unit, provision unit, and digitization unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit takes photographs of the work site using the camera 42 of the robot 414, and these are analyzed by the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 and calculates a score based on each item of 5S. The monitoring unit constantly monitors the images inside the warehouse using the camera 42 of the robot 414 and identifies abnormal areas using the identification processing unit 290 of the data processing unit 12. The display unit displays the results analyzed by the identification processing unit 290 of the data processing unit 12 as a heat map or graph on the display of the robot 414. The generation unit analyzes the photographs of the current work site using the identification processing unit 290 of the data processing unit 12 and generates an improved image. The provision unit provides the generated image in digital format to the display of the robot 414. The digitization unit analyzes the data before and after the improvement using the specific processing unit 290 of the data processing device 12, and calculates the cost reduction amount and the productivity improvement rate. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0191] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0192] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0193] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0194] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0195] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0196] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0197] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0198] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0199] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0200] 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.

[0201] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0202] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0203] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0204] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0205] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0206] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0207] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0208] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0209] (Note 1) A data collection unit that uses image recognition AI to quantify consistent 5S evaluations, An analysis unit analyzes the data collected by the aforementioned collection unit, A monitoring unit that constantly monitors the footage from the in-warehouse cameras, The analysis unit analyzes the video collected by the monitoring unit, The system includes a display unit that visually displays the results analyzed by the analysis unit. A system characterized by the following features. (Note 2) We will analyze the current work site photos, It includes a generation unit that generates an improved image. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a unit that provides the generated image. The system described in Appendix 2, characterized by the features described herein. (Note 4) We will analyze the data before and after the improvement. It includes a quantification unit that quantifies the effect. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is To estimate the user's emotions, Adjust the timing of 5S rating collection based on estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When taking photos of the work site, By taking photos from different angles and at different times of day, we collect more multifaceted data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The collected data is analyzed in real time, Provide immediate feedback The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is To estimate the user's emotions, Prioritize the data to collect based on estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When taking photos of the work site, Environmental data such as temperature and humidity are also collected simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The collected data is stored in the cloud. Share data by integrating with other systems. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, To estimate the user's emotions, The analysis algorithm is adjusted based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, outliers are identified by comparing them with past data. Issue an alert The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, Based on the analysis results, we propose specific improvement measures. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, To estimate the user's emotions, The way analysis results are displayed is adjusted based on the estimated user's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, a comprehensive evaluation is performed by referring to other relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Share the analysis results with other departments. We will work together to implement improvement measures. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned monitoring unit, To estimate the user's emotions, Adjust monitoring frequency based on estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned monitoring unit, During monitoring, focus your monitoring efforts on specific areas or time periods. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned monitoring unit, By analyzing monitoring data in real time, Immediate notification when an anomaly occurs. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned monitoring unit, To estimate the user's emotions, Adjust how monitoring results are displayed based on estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned monitoring unit, During monitoring, add sensors that can also detect anomalies such as sound and vibration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned monitoring unit, Identifying anomalies by linking monitoring data with other systems. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is To estimate the user's emotions, Customize the displayed content based on the estimated user's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is When displayed, it provides detailed information about the abnormal area. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is Optimize the displayed content according to the user's job title and responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is To estimate the user's emotions, Display priorities are determined based on estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is When displaying the data, the system references historical data of the abnormal areas to show trends. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is Make the displayed content viewable on other devices. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is To estimate the user's emotions, Adjust the level of detail in the generated images based on the estimated user's emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The generating unit is During generation, multiple different improvement scenarios are presented. To enable the selection of the optimal scenario The system described in Appendix 2, characterized by the features described herein. (Note 31) The generating unit is Add specific improvement steps and a list of necessary materials to the generated image. The system described in Appendix 2, characterized by the features described herein. (Note 32) The generating unit is To estimate the user's emotions, Prioritize the images to generate based on the estimated user's emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The generating unit is During generation, the system considers other relevant data to propose the optimal improvement measures. The system described in Appendix 2, characterized by the features described herein. (Note 34) The generating unit is Share the generated image in conjunction with other systems. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned supply unit is, To estimate the user's emotions, Adjust the format of the information provided based on the estimated user's sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing information, we refer to the user's past feedback to provide the most relevant information. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned supply unit is, Based on the information provided, guidelines are presented to enable users to take specific actions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned supply unit is, To estimate the user's emotions, Prioritize the information provided based on the estimated user's sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned supply unit is, When providing the information, we will add other relevant information. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned supply unit is, Make the provided information viewable on other devices. The system described in Appendix 3, characterized by the features described herein. (Note 41) The digitization unit is, To estimate the user's emotions, Adjust the quantification criteria based on estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 42) The digitization unit is, When quantifying, present multiple different indicators. Conduct an overall evaluation. The system described in Appendix 4, characterized by the features described herein. (Note 43) The digitization unit is, Based on the quantified results, we propose specific improvement measures. The system described in Appendix 4, characterized by the features described herein. (Note 44) The digitization unit is, To estimate the user's emotions, Adjust the display method of numerical results based on estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 45) The digitization unit is, When quantifying data, compare and evaluate it by referring to other relevant data. The system described in Appendix 4, characterized by the features described herein. (Note 46) The digitization unit is, Share the quantified results with other departments. We will work together to implement improvement measures. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0210] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that uses image recognition AI to quantify consistent 5S evaluations, An analysis unit analyzes the data collected by the aforementioned collection unit, A monitoring unit that constantly monitors the footage from the in-warehouse cameras, The analysis unit analyzes the video collected by the monitoring unit, The system includes a display unit that visually displays the results analyzed by the analysis unit. A system characterized by the following features.

2. We will analyze the current work site photos, It includes a generation unit that generates an improved image. The system according to feature 1.

3. It includes a unit that provides the generated image. The system according to feature 2.

4. We will analyze the data before and after the improvement. It includes a quantification unit that quantifies the effect. The system according to feature 1.

5. The aforementioned collection unit is To estimate the user's emotions, Adjust the timing of 5S rating collection based on estimated user sentiment. The system according to feature 1.

6. The aforementioned collection unit is When taking photos of the work site, By taking photos from different angles and at different times of day, we collect more multifaceted data. The system according to feature 1.

7. The aforementioned collection unit is The collected data is analyzed in real time, Provide immediate feedback The system according to feature 1.

8. The aforementioned collection unit is To estimate the user's emotions, Prioritize the data to collect based on estimated user sentiment. The system according to feature 1.

9. The aforementioned collection unit is When taking photos of the work site, Environmental data such as temperature and humidity are also collected simultaneously. The system according to feature 1.

Images