System

Abstract

An object of a system according to an embodiment is to efficiently grasp a progress status of a site and report the progress status to a head office.SOLUTION: A system includes a progress confirmation unit, an analysis unit, and a report unit. The progress confirmation unit autonomously confirms the progress status with respect to the work-site side. The analysis unit analyzes the progress status confirmed by the progress confirmation unit. The report part reports the result analyzed by the analysis part to the head office side.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of efficiently grasping the progress status of the field and reporting it to headquarters is complicated, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently grasp the progress status of the site and report it to the head office. [Means for solving the problem]

[0006] The system according to the embodiment includes a progress confirmation unit, an analysis unit, and a reporting unit. The progress confirmation unit autonomously confirms the progress status of the field side. The analysis unit analyzes the progress status confirmed by the progress confirmation unit. The reporting unit reports the results of the analysis by the analysis unit to the head office side. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently grasp the progress status of the site and report it to the head office. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The on-site connection system according to the embodiment of the present invention is a system that autonomously checks the progress status of the on-site side through chat and reports the results to the head office side in a consolidated manner. This eliminates the need to allocate man-hours to check progress between the on-site and head office, and allows progress to be shared in real time.

[0029] The on-site connection system according to the embodiment includes a progress confirmation unit, an analysis unit, and a reporting unit. The progress confirmation unit autonomously confirms the progress status from the on-site side. For example, the generation AI sends a question such as, "Please tell me the progress of the current work." The generation AI can also analyze the response from the on-site side to grasp the progress status. Furthermore, the generation AI can periodically send chats to confirm the progress status and automatically update the progress status. The analysis unit analyzes the progress status confirmed by the progress confirmation unit. For example, the generation AI analyzes the progress status obtained from the on-site side and reports the results to the headquarters side. The generation AI can also display the progress status in graphs or charts so that the headquarters side can grasp the progress status at a glance. The reporting unit reports the results analyzed by the analysis unit to the headquarters side. For example, the generation AI reports, "The progress status at site A is 80%." The generation AI can also immediately notify the headquarters side of the progress status obtained from the on-site side. Furthermore, the generation AI can visualize the progress status and display it in graphs or charts. As a result, the on-site connection system according to the embodiment eliminates the need to allocate man-hours to confirm progress between the on-site and the head office, and progress can be shared in real time. For example, it eliminates the need for on-site personnel to report progress status, and the head office can efficiently grasp the progress status. This improves work efficiency and speeds up information sharing.

[0030] The progress confirmation unit can learn the past response history of on-site personnel and generate individually optimized questions. In the progress confirmation unit, for example, the generation AI analyzes the past response history of on-site personnel and generates optimal questions for each personnel. For example, detailed progress confirmation is performed for work that has been frequently delayed in the past. The generation AI also learns the response patterns of personnel and adjusts the content of questions based on their response tendencies. For example, it sends short questions to personnel who prefer concise answers. The generation AI also generates questions based on the personnel's work style and progress status based on past response data. For example, if progress is delayed, it sends questions that ask about specific problems. This allows the unit to generate optimal questions for on-site personnel and improves the accuracy of progress confirmation.

[0031] The progress confirmation unit can analyze environmental sounds and background noise at the work site and provide a complementary check of the progress of work. In the progress confirmation unit, for example, the generation AI analyzes environmental sounds at the work site and provides a complementary check of the progress of work. For example, it detects changes in machine sounds and work sounds to estimate progress. The generation AI also analyzes background noise and checks the progress of work. For example, if the noise level at the work site is high, it determines that work is progressing. The generation AI also provides a complementary check of the progress of work based on environmental sound data. For example, if a specific sound is heard, it determines that the work has progressed to a specific stage. This improves the accuracy of progress confirmation by analyzing environmental sounds and background noise.

[0032] The progress confirmation unit can analyze images and videos of the work site and confirm the visual progress status. In the progress confirmation unit, for example, the generation AI analyzes images of the work site and confirms the visual progress status. For example, it analyzes photos of the work site to determine the progress of the work. The generation AI also analyzes videos of the work site and confirms the visual progress status. For example, it analyzes videos of the work being done to understand the progress of the work. The generation AI also uses image recognition technology to analyze visual information of the work site and confirm the progress status. For example, it determines from images whether a specific task has been completed. This makes it possible to visually confirm the progress status by analyzing images and videos.

[0033] The progress confirmation unit can collect on-site sensor information (temperature, humidity, vibration, etc.) and use it to check the progress status. In the progress confirmation unit, for example, the generation AI collects on-site temperature sensor information and uses it to check the progress status. For example, it estimates progress based on temperature changes in the work environment. The generation AI also collects on-site humidity sensor information and uses it to check the progress status. For example, it determines the progress of work based on changes in humidity. The generation AI also collects on-site vibration sensor information and uses it to check the progress status. For example, it analyzes vibration data from machines to understand the progress of work. In this way, collecting sensor information improves the accuracy of checking the progress status.

[0034] The progress confirmation unit can send questions at the optimal timing, taking into account the schedule of the on-site person in charge. In the progress confirmation unit, for example, the generation AI analyzes the schedule of the on-site person in charge and sends questions at the optimal timing. For example, questions are sent avoiding times when the person in charge is busy. In addition, a system is built in which the generation AI sends questions at the optimal timing based on the schedule data of the person in charge. For example, questions are sent during the person in charge's break time. In addition, the generation AI takes into account the schedule of the on-site person in charge and sends questions at the optimal timing. For example, questions are sent immediately after the person in charge has finished their work. In this way, the timing of sending questions is optimized by taking into account the schedule of the on-site person in charge.

[0035] The progress confirmation unit can send questions to multiple on-site staff simultaneously to check the collective progress status. In the progress confirmation unit, for example, the generation AI sends questions to multiple on-site staff simultaneously to check the collective progress status. For example, it aggregates everyone's responses to understand the progress. In addition, the generation AI can send questions to multiple staff simultaneously to build a system to check the collective progress status. For example, it can integrate the responses of each staff member to report the progress. In addition, the generation AI can send questions to multiple on-site staff simultaneously to check the collective progress status. For example, it can understand the overall picture of progress based on everyone's responses. This makes it possible to check the collective progress status by sending questions to multiple on-site staff simultaneously.

[0036] The analysis unit can analyze progress status data from multiple angles and automatically detect outliers and trends. In the analysis unit, for example, the generation AI analyzes progress status data from multiple angles and automatically detects outliers. For example, it issues an alert if progress suddenly falls behind. In addition, the generation AI analyzes progress data and builds a system that automatically detects trends. For example, it reports trends if progress falls behind in a certain pattern. In addition, the generation AI analyzes progress status data from multiple angles and automatically detects outliers and trends. For example, it notifies if progress is progressing faster than expected. In this way, the accuracy of progress management is improved by analyzing progress status data from multiple angles and automatically detecting outliers and trends.

[0037] The analysis unit can compare it with past progress data and predict delays or accelerations in progress. For example, the generation AI analyzes past progress data and compares it with current progress to predict delays or accelerations. For example, it predicts delays in progress based on past data. In addition, a system is constructed in which the generation AI compares past progress data with current data to predict accelerations in progress. For example, it notifies users when progress is progressing faster than expected. In addition, the generation AI predicts delays or accelerations in progress based on past progress data. For example, it analyzes past data to understand progress trends and makes predictions. This makes it possible to predict delays or accelerations in progress by comparing it with past progress data.

[0038] The reporting unit can report the progress status in different formats (text, graph, audio) and provide it in a format that suits the recipient's preferences. For example, the reporting unit generates a detailed text report. Also, a system is constructed in which the generation AI reports the progress status in a graph and provides it in a format that suits the recipient's preferences. For example, the progress transition is displayed in a graph. Also, the generation AI reports the progress status in an audio format and provides it in a format that suits the recipient's preferences. For example, an audio report is generated to report the progress. In this way, the progress status can be reported in different formats and provided in a format that suits the recipient's preferences.

[0039] The reporting department can link the progress status with other business systems and provide integrated reporting. For example, the generation AI in the reporting department links the progress status with other business systems and provides integrated reporting. For example, it links with a project management system to report progress. In addition, the generation AI integrates progress data with other business systems to build a system that provides integrated reporting. For example, it links with an ERP system to report progress. In addition, the generation AI links the progress status with other business systems and provides integrated reporting. For example, it links with a CRM system to report progress. In this way, integrated reporting becomes possible by linking the progress status with other business systems.

[0040] The reporting unit can automatically summarize the progress report content and extract and report only the important points. For example, the generation AI in the reporting unit automatically summarizes the progress report content and extracts and reports only the important points. For example, it summarizes and reports delays in progress and problems. In addition, a system can be built in which the generation AI analyzes progress data, extracts important points, and reports a summary. For example, it reports progress highlights. In addition, the generation AI automatically summarizes the progress report content and extracts and reports only the important points. For example, it summarizes and reports the degree of progress achieved and the next steps. In this way, the efficiency of reporting is improved by automatically summarizing the progress report content and extracting and reporting only the important points.

[0041] The reporting department can compare the contents of the report with past data and point out any abnormalities or areas for improvement. In the reporting department, for example, the generation AI compares the contents of the report with past data and points out any abnormalities or areas for improvement. For example, if progress is slow, it identifies the cause. The generation AI also analyzes the contents of the report based on past data and builds a system that points out any abnormalities or areas for improvement. For example, it predicts and reports delays in progress. The generation AI also compares the contents of the report with past data and points out any abnormalities or areas for improvement. For example, it notifies the user if progress is progressing faster than expected. In this way, by comparing the contents of the report with past data, it is possible to point out any abnormalities or areas for improvement.

[0042] The reporting department can automatically translate the report content into different languages, making it possible to support international teams. For example, the generation AI can automatically translate the report content into different languages, making it possible to support international teams. For example, it can translate into English, French, Chinese, etc. The generation AI can also integrate the report content into a multilingual system, building a system for reporting to international teams. For example, it can generate reports in each language. The generation AI can also automatically translate the report content into different languages, making it possible to support international teams. For example, it can automatically send the translated report. This allows the report content to be automatically translated into different languages, making it possible to support international teams.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The progress confirmation unit can send questions at the optimal timing, taking into account the schedule of the on-site person in charge. For example, the generation AI analyzes the schedule of the on-site person in charge and sends questions at the optimal timing. For example, questions are sent avoiding times when the person in charge is busy. In addition, a system can be built in which the generation AI sends questions at the optimal timing based on the person in charge's schedule data. For example, questions are sent during the person in charge's break time. In addition, the generation AI takes into account the schedule of the on-site person in charge and sends questions at the optimal timing. For example, questions are sent immediately after the person in charge has finished their work. In this way, the timing of sending questions is optimized by taking into account the schedule of the on-site person in charge.

[0045] The progress confirmation unit can analyze the environmental sounds and background noise at the work site to provide a complementary check of the progress of work. For example, the generation AI analyzes the environmental sounds at the work site to provide a complementary check of the progress of work. For example, it detects changes in machine sounds and work sounds to estimate progress. The generation AI also analyzes background noise to check the progress of work. For example, if the noise level at the work site is high, it determines that work is progressing. The generation AI also provides a complementary check of the progress of work based on environmental sound data. For example, if a specific sound is heard, it determines that the work has progressed to a specific stage. This improves the accuracy of progress confirmation by analyzing environmental sounds and background noise.

[0046] The progress confirmation unit can analyze images and videos of the work site to visually confirm the progress. For example, the generation AI analyzes images of the work site to visually confirm the progress. For example, it analyzes photos of the work site to determine the progress of the work. The generation AI also analyzes videos of the work site to visually confirm the progress. For example, it analyzes videos of the work being done to understand the progress of the work. The generation AI also uses image recognition technology to analyze visual information on the site and confirm the progress. For example, it determines from images whether a specific task has been completed. This makes it possible to visually confirm the progress by analyzing images and videos.

[0047] The progress confirmation unit can collect on-site sensor information (temperature, humidity, vibration, etc.) and use it to check progress. For example, the generation AI collects on-site temperature sensor information and uses it to check progress. For example, it can estimate progress based on temperature changes in the work environment. The generation AI can also collect on-site humidity sensor information and use it to check progress. For example, it can determine the progress of work based on changes in humidity. The generation AI can also collect on-site vibration sensor information and use it to check progress. For example, it can analyze machine vibration data to understand the progress of work. In this way, collecting sensor information improves the accuracy of progress confirmation.

[0048] The progress confirmation unit can send questions to multiple on-site staff simultaneously to check collective progress. For example, the generation AI can send questions to multiple on-site staff simultaneously to check collective progress. For example, it can aggregate everyone's responses to understand progress. In addition, a system can be built in which the generation AI sends questions to multiple staff simultaneously to check collective progress. For example, it can integrate each staff member's responses to report progress. In addition, the generation AI can send questions to multiple on-site staff simultaneously to check collective progress. For example, it can understand the overall picture of progress based on everyone's responses. This makes it possible to check collective progress by sending questions to multiple on-site staff simultaneously.

[0049] The analysis unit can analyze progress data from multiple angles and automatically detect outliers and trends. For example, the generation AI analyzes progress data from multiple angles and automatically detects outliers. For example, it issues an alert if progress suddenly falls behind. In addition, a system can be built in which the generation AI analyzes progress data and automatically detects trends. For example, it reports trends if progress falls behind in a certain pattern. In addition, the generation AI analyzes progress data from multiple angles and automatically detects outliers and trends. For example, it notifies users if progress is progressing faster than expected. In this way, the accuracy of progress management is improved by analyzing progress data from multiple angles and automatically detecting outliers and trends.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The progress confirmation unit autonomously checks the progress status with the on-site side. For example, the generation AI sends a question to the on-site person in charge, such as "Please tell us the progress of the current work." The generation AI can also analyze the answers from the on-site person in charge and grasp the progress status. Furthermore, the generation AI can also periodically send chats to check the progress status and automatically update the progress status. Step 2: The analysis unit analyzes the progress confirmed by the progress confirmation unit. For example, the generation AI analyzes the progress obtained from the field side and reports the results to the head office side. The generation AI can also display the progress in graphs or charts so that the head office side can understand the progress at a glance. Step 3: The reporting unit reports the results of the analysis by the analysis unit to the headquarters. For example, the generation AI may report in the form of "Progress at site A is 80%." The generation AI can also immediately notify the headquarters of the progress status obtained from the site. Furthermore, the generation AI can visualize the progress status and display it in graphs and charts.

[0052] (Example 2) The on-site connection system according to the embodiment of the present invention is a system that autonomously checks the progress status of the on-site side through chat and reports the results to the head office side in a consolidated manner. This eliminates the need to allocate man-hours to check progress between the on-site and head office, and allows progress to be shared in real time.

[0053] The on-site connection system according to the embodiment includes a progress confirmation unit, an analysis unit, and a reporting unit. The progress confirmation unit autonomously confirms the progress status from the on-site side. For example, the generation AI sends a question such as, "Please tell me the progress of the current work." The generation AI can also analyze the response from the on-site side to grasp the progress status. Furthermore, the generation AI can periodically send chats to confirm the progress status and automatically update the progress status. The analysis unit analyzes the progress status confirmed by the progress confirmation unit. For example, the generation AI analyzes the progress status obtained from the on-site side and reports the results to the headquarters side. The generation AI can also display the progress status in graphs or charts so that the headquarters side can grasp the progress status at a glance. The reporting unit reports the results analyzed by the analysis unit to the headquarters side. For example, the generation AI reports, "The progress status at site A is 80%." The generation AI can also immediately notify the headquarters side of the progress status obtained from the on-site side. Furthermore, the generation AI can visualize the progress status and display it in graphs or charts. As a result, the on-site connection system according to the embodiment eliminates the need to allocate man-hours to confirm progress between the on-site and the head office, and progress can be shared in real time. For example, it eliminates the need for on-site personnel to report progress status, and the head office can efficiently grasp the progress status. This improves work efficiency and speeds up information sharing.

[0054] The progress confirmation unit can learn the past response history of on-site personnel and generate individually optimized questions. In the progress confirmation unit, for example, the generation AI analyzes the past response history of on-site personnel and generates optimal questions for each personnel. For example, detailed progress confirmation is performed for work that has been frequently delayed in the past. The generation AI also learns the response patterns of personnel and adjusts the content of questions based on their response tendencies. For example, it sends short questions to personnel who prefer concise answers. The generation AI also generates questions based on the personnel's work style and progress status based on past response data. For example, if progress is delayed, it sends questions that ask about specific problems. This allows the unit to generate optimal questions for on-site personnel and improves the accuracy of progress confirmation.

[0055] The progress confirmation unit can analyze environmental sounds and background noise at the work site and provide a complementary check of the progress of work. In the progress confirmation unit, for example, the generation AI analyzes environmental sounds at the work site and provides a complementary check of the progress of work. For example, it detects changes in machine sounds and work sounds to estimate progress. The generation AI also analyzes background noise and checks the progress of work. For example, if the noise level at the work site is high, it determines that work is progressing. The generation AI also provides a complementary check of the progress of work based on environmental sound data. For example, if a specific sound is heard, it determines that the work has progressed to a specific stage. This improves the accuracy of progress confirmation by analyzing environmental sounds and background noise.

[0056] The progress confirmation unit can use the emotion estimation function to estimate the emotional state of the on-site staff member and ask questions that take stress and fatigue levels into consideration. In the progress confirmation unit, for example, the generation AI estimates the emotional state of the on-site staff member and asks questions that take stress and fatigue levels into consideration. For example, if fatigue is observed, a simple question is sent. The emotion estimation function is also used to analyze the emotional state of the on-site staff member in real time and generate appropriate questions. For example, if stress is high, a question including an encouraging message is sent. The generation AI also asks questions that correspond to the emotional state of the on-site staff member based on the emotion estimation data. For example, if positive emotions are observed, a question that asks a detailed progress check is sent. In this way, the quality of progress checks is improved by asking questions that take the emotional state of the on-site staff member into consideration.

[0057] The progress confirmation unit can analyze images and videos of the work site and confirm the visual progress status. In the progress confirmation unit, for example, the generation AI analyzes images of the work site and confirms the visual progress status. For example, it analyzes photos of the work site to determine the progress of the work. The generation AI also analyzes videos of the work site and confirms the visual progress status. For example, it analyzes videos of the work being done to understand the progress of the work. The generation AI also uses image recognition technology to analyze visual information of the work site and confirm the progress status. For example, it determines from images whether a specific task has been completed. This makes it possible to visually confirm the progress status by analyzing images and videos.

[0058] The progress confirmation unit can collect on-site sensor information (temperature, humidity, vibration, etc.) and use it to check the progress status. In the progress confirmation unit, for example, the generation AI collects on-site temperature sensor information and uses it to check the progress status. For example, it estimates progress based on temperature changes in the work environment. The generation AI also collects on-site humidity sensor information and uses it to check the progress status. For example, it determines the progress of work based on changes in humidity. The generation AI also collects on-site vibration sensor information and uses it to check the progress status. For example, it analyzes vibration data from machines to understand the progress of work. In this way, collecting sensor information improves the accuracy of checking the progress status.

[0059] The progress confirmation unit can use the emotion estimation function to analyze in real time the emotions of the on-site staff when they answer questions and provide appropriate feedback. The progress confirmation unit, for example, uses the emotion estimation function to analyze in real time the emotions of the on-site staff when they answer questions and provide appropriate feedback. For example, if negative emotions are observed, an encouraging message is sent. The generation AI also provides feedback according to the on-site staff's emotional state based on the emotion estimation data. For example, if positive emotions are observed, a detailed progress confirmation is performed. A system is also constructed that uses the emotion estimation function to analyze the on-site staff's emotions in real time and provide appropriate feedback. For example, if stress is high, a message encouraging them to relax is sent. In this way, the quality of progress confirmation is improved by analyzing the on-site staff's emotions in real time and providing appropriate feedback.

[0060] The progress confirmation unit can send questions at the optimal timing, taking into account the schedule of the on-site person in charge. In the progress confirmation unit, for example, the generation AI analyzes the schedule of the on-site person in charge and sends questions at the optimal timing. For example, questions are sent avoiding times when the person in charge is busy. In addition, a system is built in which the generation AI sends questions at the optimal timing based on the schedule data of the person in charge. For example, questions are sent during the person in charge's break time. In addition, the generation AI takes into account the schedule of the on-site person in charge and sends questions at the optimal timing. For example, questions are sent immediately after the person in charge has finished their work. In this way, the timing of sending questions is optimized by taking into account the schedule of the on-site person in charge.

[0061] The progress confirmation unit can use the emotion estimation function to generate prompts according to the emotional state of the on-site staff, thereby improving the quality of the answers. The progress confirmation unit, for example, uses the emotion estimation function to generate prompts according to the emotional state of the on-site staff, thereby improving the quality of the answers. For example, if positive emotions are observed, a detailed question is sent. Furthermore, a system is constructed in which the generation AI generates prompts according to the emotional state of the on-site staff based on the emotion estimation data. For example, if stress is high, a simple question is sent. Furthermore, the emotion estimation function is used to generate prompts according to the emotional state of the on-site staff, thereby improving the quality of the answers. For example, if fatigue is observed, a question including an encouraging message is sent. In this way, by generating prompts according to the emotional state of the on-site staff, the quality of the answers is improved.

[0062] The progress confirmation unit can send questions to multiple on-site staff simultaneously to check the collective progress status. In the progress confirmation unit, for example, the generation AI sends questions to multiple on-site staff simultaneously to check the collective progress status. For example, it aggregates everyone's responses to understand the progress. In addition, the generation AI can send questions to multiple staff simultaneously to build a system to check the collective progress status. For example, it can integrate the responses of each staff member to report the progress. In addition, the generation AI can send questions to multiple on-site staff simultaneously to check the collective progress status. For example, it can understand the overall picture of progress based on everyone's responses. This makes it possible to check the collective progress status by sending questions to multiple on-site staff simultaneously.

[0063] The progress confirmation unit can use the emotion estimation function to analyze the emotions of the field staff when they answer questions, and generate prompts that elicit positive emotions. The progress confirmation unit, for example, uses the emotion estimation function to analyze the emotions of the field staff when they answer questions, and generate prompts that elicit positive emotions. For example, it sends questions that include encouraging messages. A system is also constructed in which the generation AI generates prompts according to the emotional state of the field staff based on the emotion estimation data. For example, it sends questions that elicit positive emotions. The emotion estimation function is also used to analyze the emotions of the field staff, and generate prompts that elicit positive emotions. For example, it sends questions that cite success stories. In this way, the quality of answers is improved by analyzing the emotions of the field staff and eliciting positive emotions.

[0064] The analysis unit can analyze progress status data from multiple angles and automatically detect outliers and trends. In the analysis unit, for example, the generation AI analyzes progress status data from multiple angles and automatically detects outliers. For example, it issues an alert if progress suddenly falls behind. In addition, the generation AI analyzes progress data and builds a system that automatically detects trends. For example, it reports trends if progress falls behind in a certain pattern. In addition, the generation AI analyzes progress status data from multiple angles and automatically detects outliers and trends. For example, it notifies if progress is progressing faster than expected. In this way, the accuracy of progress management is improved by analyzing progress status data from multiple angles and automatically detecting outliers and trends.

[0065] The analysis unit can compare it with past progress data and predict delays or accelerations in progress. For example, the generation AI analyzes past progress data and compares it with current progress to predict delays or accelerations. For example, it predicts delays in progress based on past data. In addition, a system is constructed in which the generation AI compares past progress data with current data to predict accelerations in progress. For example, it notifies users when progress is progressing faster than expected. In addition, the generation AI predicts delays or accelerations in progress based on past progress data. For example, it analyzes past data to understand progress trends and makes predictions. This makes it possible to predict delays or accelerations in progress by comparing it with past progress data.

[0066] The analysis unit uses the emotion estimation function to generate progress reports that take into account the emotional state of the on-site staff, thereby improving the reliability of the reports. The analysis unit, for example, uses the emotion estimation function to generate progress reports that take into account the emotional state of the on-site staff. For example, if stress is high, a detailed report is provided. In addition, a system is constructed in which the generation AI generates progress reports that take into account the emotional state of the on-site staff based on the emotion estimation data. For example, if positive emotions are observed, a concise report is provided. In addition, the emotion estimation function is used to generate progress reports that take into account the emotional state of the on-site staff, improving the reliability of the reports. For example, if fatigue is observed, a report that includes an encouraging message is provided. In this way, by generating progress reports that take into account the emotional state of the on-site staff, the reliability of the reports is improved.

[0067] The reporting unit can report the progress status in different formats (text, graph, audio) and provide it in a format that suits the recipient's preferences. For example, the reporting unit generates a detailed text report. Also, a system is constructed in which the generation AI reports the progress status in a graph and provides it in a format that suits the recipient's preferences. For example, the progress transition is displayed in a graph. Also, the generation AI reports the progress status in an audio format and provides it in a format that suits the recipient's preferences. For example, an audio report is generated to report the progress. In this way, the progress status can be reported in different formats and provided in a format that suits the recipient's preferences.

[0068] The reporting department can link the progress status with other business systems and provide integrated reporting. For example, the generation AI in the reporting department links the progress status with other business systems and provides integrated reporting. For example, it links with a project management system to report progress. In addition, the generation AI integrates progress data with other business systems to build a system that provides integrated reporting. For example, it links with an ERP system to report progress. In addition, the generation AI links the progress status with other business systems and provides integrated reporting. For example, it links with a CRM system to report progress. In this way, integrated reporting becomes possible by linking the progress status with other business systems.

[0069] The reporting department can use the emotion estimation function to analyze the emotional reactions of the headquarters side that receive the report and adjust the content of the report as appropriate. For example, the reporting department uses the emotion estimation function to analyze the emotional reactions of the headquarters side that receive the report and adjust the content of the report as appropriate. For example, if a negative reaction is observed, a detailed report will be made. In addition, a system is constructed in which the generation AI analyzes the emotional reactions of the headquarters side based on the emotion estimation data and adjusts the content of the report. For example, if a positive reaction is observed, a concise report will be made. In addition, the emotion estimation function is used to analyze the emotional reactions of the headquarters side that receive the report and adjust the content of the report as appropriate. For example, if stress is high, a report that includes an encouraging message will be made. In this way, by analyzing the emotional reactions of the headquarters side and adjusting the content of the report as appropriate, the ease with which the report is accepted is improved.

[0070] The reporting unit can automatically summarize the progress report content and extract and report only the important points. For example, the generation AI in the reporting unit automatically summarizes the progress report content and extracts and reports only the important points. For example, it summarizes and reports delays in progress and problems. In addition, a system can be built in which the generation AI analyzes progress data, extracts important points, and reports a summary. For example, it reports progress highlights. In addition, the generation AI automatically summarizes the progress report content and extracts and reports only the important points. For example, it summarizes and reports the degree of progress achieved and the next steps. In this way, the efficiency of reporting is improved by automatically summarizing the progress report content and extracting and reporting only the important points.

[0071] The reporting department can compare the contents of the report with past data and point out any abnormalities or areas for improvement. In the reporting department, for example, the generation AI compares the contents of the report with past data and points out any abnormalities or areas for improvement. For example, if progress is slow, it identifies the cause. The generation AI also analyzes the contents of the report based on past data and builds a system that points out any abnormalities or areas for improvement. For example, it predicts and reports delays in progress. The generation AI also compares the contents of the report with past data and points out any abnormalities or areas for improvement. For example, it notifies the user if progress is progressing faster than expected. In this way, by comparing the contents of the report with past data, it is possible to point out any abnormalities or areas for improvement.

[0072] The reporting department can use the emotion estimation function to adjust the tone and content of the report, taking into account the emotional state of the headquarters side receiving the report. For example, the reporting department uses the emotion estimation function to adjust the tone and content of the report, taking into account the emotional state of the headquarters side receiving the report. For example, if negative emotions are observed, a detailed report will be provided. In addition, a system is built in which the generation AI uses emotion estimation data to provide reports that take into account the emotional state of the headquarters side. For example, if positive emotions are observed, a concise report will be provided. In addition, the emotion estimation function is used to adjust the tone and content of the report, taking into account the emotional state of the headquarters side receiving the report. For example, if stress is high, a report will be provided that includes an encouraging message. In this way, by taking into account the emotional state of the headquarters side and adjusting the tone and content of the report, the ease with which the report is received is improved.

[0073] The reporting department can automatically translate the report content into different languages, making it possible to support international teams. For example, the generation AI can automatically translate the report content into different languages, making it possible to support international teams. For example, it can translate into English, French, Chinese, etc. The generation AI can also integrate the report content into a multilingual system, building a system for reporting to international teams. For example, it can generate reports in each language. The generation AI can also automatically translate the report content into different languages, making it possible to support international teams. For example, it can automatically send the translated report. This allows the report content to be automatically translated into different languages, making it possible to support international teams.

[0074] The reporting department can use the emotion estimation function to analyze the emotional reactions of the headquarters side that receive the report in real time and adjust the content of the report as appropriate. For example, the reporting department uses the emotion estimation function to analyze the emotional reactions of the headquarters side that receive the report in real time and adjust the content of the report as appropriate. For example, if a negative reaction is observed, a detailed report is made. In addition, a system is constructed in which the generation AI analyzes the emotional reactions of the headquarters side in real time based on the emotion estimation data and adjusts the content of the report. For example, if a positive reaction is observed, a concise report is made. In addition, the emotion estimation function is used to analyze the emotional reactions of the headquarters side that receive the report in real time and adjust the content of the report as appropriate. For example, if stress is high, a report that includes an encouraging message is made. In this way, by analyzing the emotional reactions of the headquarters side in real time and adjusting the content of the report as appropriate, the ease of acceptance of the report is improved.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The progress confirmation unit can send questions at the optimal timing, taking into account the schedule of the on-site person in charge. For example, the generation AI analyzes the schedule of the on-site person in charge and sends questions at the optimal timing. For example, questions are sent avoiding times when the person in charge is busy. In addition, a system can be built in which the generation AI sends questions at the optimal timing based on the person in charge's schedule data. For example, questions are sent during the person in charge's break time. In addition, the generation AI takes into account the schedule of the on-site person in charge and sends questions at the optimal timing. For example, questions are sent immediately after the person in charge has finished their work. In this way, the timing of sending questions is optimized by taking into account the schedule of the on-site person in charge.

[0077] The progress confirmation unit can analyze the environmental sounds and background noise at the work site to provide a complementary check of the progress of work. For example, the generation AI analyzes the environmental sounds at the work site to provide a complementary check of the progress of work. For example, it detects changes in machine sounds and work sounds to estimate progress. The generation AI also analyzes background noise to check the progress of work. For example, if the noise level at the work site is high, it determines that work is progressing. The generation AI also provides a complementary check of the progress of work based on environmental sound data. For example, if a specific sound is heard, it determines that the work has progressed to a specific stage. This improves the accuracy of progress confirmation by analyzing environmental sounds and background noise.

[0078] The progress confirmation unit can use the emotion estimation function to estimate the emotional state of the on-site staff member and ask questions that take stress and fatigue levels into consideration. For example, the generation AI estimates the emotional state of the on-site staff member and asks questions that take stress and fatigue levels into consideration. For example, if fatigue is observed, a simple question is sent. The emotion estimation function can also be used to analyze the emotional state of the on-site staff member in real time and generate appropriate questions. For example, if stress is high, a question containing an encouraging message is sent. The generation AI can also ask questions that correspond to the emotional state of the on-site staff member based on the emotion estimation data. For example, if positive emotions are observed, a question that asks a detailed progress confirmation is sent. This improves the quality of progress confirmation by asking questions that take the emotional state of the on-site staff member into consideration.

[0079] The progress confirmation unit can analyze images and videos of the work site to visually confirm the progress. For example, the generation AI analyzes images of the work site to visually confirm the progress. For example, it analyzes photos of the work site to determine the progress of the work. The generation AI also analyzes videos of the work site to visually confirm the progress. For example, it analyzes videos of the work being done to understand the progress of the work. The generation AI also uses image recognition technology to analyze visual information on the site and confirm the progress. For example, it determines from images whether a specific task has been completed. This makes it possible to visually confirm the progress by analyzing images and videos.

[0080] The progress confirmation unit can use the emotion estimation function to analyze the emotions of on-site personnel when they answer questions in real time and provide appropriate feedback. For example, the emotion estimation function can be used to analyze the emotions of on-site personnel when they answer questions in real time and provide appropriate feedback. For example, if negative emotions are observed, an encouraging message can be sent. The generation AI also provides feedback according to the emotional state of the on-site personnel based on the emotion estimation data. For example, if positive emotions are observed, a detailed progress confirmation can be performed. Furthermore, a system can be built that uses the emotion estimation function to analyze the emotions of on-site personnel in real time and provide appropriate feedback. For example, if stress is high, a message encouraging them to relax can be sent. This allows for real-time analysis of on-site personnel's emotions and appropriate feedback to be provided, improving the quality of progress confirmation.

[0081] The progress confirmation unit can collect on-site sensor information (temperature, humidity, vibration, etc.) and use it to check progress. For example, the generation AI collects on-site temperature sensor information and uses it to check progress. For example, it can estimate progress based on temperature changes in the work environment. The generation AI can also collect on-site humidity sensor information and use it to check progress. For example, it can determine the progress of work based on changes in humidity. The generation AI can also collect on-site vibration sensor information and use it to check progress. For example, it can analyze machine vibration data to understand the progress of work. In this way, collecting sensor information improves the accuracy of progress confirmation.

[0082] The progress confirmation unit can use the emotion estimation function to generate prompts according to the emotional state of the on-site staff, thereby improving the quality of the answers. For example, the emotion estimation function is used to generate prompts according to the emotional state of the on-site staff, thereby improving the quality of the answers. For example, if positive emotions are observed, detailed questions are sent. In addition, a system is constructed in which the generation AI generates prompts according to the emotional state of the on-site staff based on the emotion estimation data. For example, if stress is high, a simple question is sent. In addition, the emotion estimation function is used to generate prompts according to the emotional state of the on-site staff, thereby improving the quality of the answers. For example, if fatigue is observed, a question including an encouraging message is sent. In this way, by generating prompts according to the emotional state of the on-site staff, the quality of the answers is improved.

[0083] The progress confirmation unit can send questions to multiple on-site staff simultaneously to check collective progress. For example, the generation AI can send questions to multiple on-site staff simultaneously to check collective progress. For example, it can aggregate everyone's responses to understand progress. In addition, a system can be built in which the generation AI sends questions to multiple staff simultaneously to check collective progress. For example, it can integrate each staff member's responses to report progress. In addition, the generation AI can send questions to multiple on-site staff simultaneously to check collective progress. For example, it can understand the overall picture of progress based on everyone's responses. This makes it possible to check collective progress by sending questions to multiple on-site staff simultaneously.

[0084] The analysis unit can analyze progress data from multiple angles and automatically detect outliers and trends. For example, the generation AI analyzes progress data from multiple angles and automatically detects outliers. For example, it issues an alert if progress suddenly falls behind. In addition, a system can be built in which the generation AI analyzes progress data and automatically detects trends. For example, it reports trends if progress falls behind in a certain pattern. In addition, the generation AI analyzes progress data from multiple angles and automatically detects outliers and trends. For example, it notifies users if progress is progressing faster than expected. In this way, the accuracy of progress management is improved by analyzing progress data from multiple angles and automatically detecting outliers and trends.

[0085] The analysis unit uses the emotion estimation function to generate progress reports that take into account the emotional state of the on-site staff, thereby improving the reliability of the reports. For example, the emotion estimation function is used to generate progress reports that take into account the emotional state of the on-site staff. For example, if stress is high, a detailed report is provided. In addition, a system is constructed in which the generation AI generates progress reports that take into account the emotional state of the on-site staff based on the emotion estimation data. For example, if positive emotions are observed, a concise report is provided. In addition, the emotion estimation function is used to generate progress reports that take into account the emotional state of the on-site staff, thereby improving the reliability of the reports. For example, if fatigue is observed, a report that includes an encouraging message is provided. In this way, by generating progress reports that take into account the emotional state of the on-site staff, the reliability of the reports is improved.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The progress confirmation unit autonomously checks the progress status with the on-site side. For example, the generation AI sends a question to the on-site person in charge, such as "Please tell us the progress of the current work." The generation AI can also analyze the answers from the on-site person in charge and grasp the progress status. Furthermore, the generation AI can also periodically send chats to check the progress status and automatically update the progress status. Step 2: The analysis unit analyzes the progress confirmed by the progress confirmation unit. For example, the generation AI analyzes the progress obtained from the field side and reports the results to the head office side. The generation AI can also display the progress in graphs or charts so that the head office side can understand the progress at a glance. Step 3: The reporting unit reports the results of the analysis by the analysis unit to the headquarters. For example, the generation AI may report in the form of "Progress at site A is 80%." The generation AI can also immediately notify the headquarters of the progress status obtained from the site. Furthermore, the generation AI can visualize the progress status and display it in graphs and charts.

[0088] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0089] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0093] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0094] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0095] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0096] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0097] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0098] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0099] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0101] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0102] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0129] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0138] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0139] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0140] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0141] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0142] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0143] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0144] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0145] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0146] 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.

[0147] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0148] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0149] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0150] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0151] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0152] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0153] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0154] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0155] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A progress confirmation department that autonomously checks the progress status with the on-site side, an analysis unit that analyzes the progress status confirmed by the progress confirmation unit; a reporting unit that reports the results of the analysis by the analysis unit to the head office side. A system characterized by:

2. The progress confirmation unit Learns from the past answers of field personnel and generates individually optimized questions 2. The system of claim 1.

3. The progress confirmation unit Analyze the environmental sounds and background noises at the site to check the progress of work.

2. The system of claim 1.

4. The progress confirmation unit Estimate the emotional state of field personnel and ask questions that take into account stress and fatigue levels 2. The system of claim 1.

5. The progress confirmation unit Analyze on-site images and videos to visually monitor progress 2. The system of claim 1.

Images