System

Abstract

An object of a system according to an embodiment is to efficiently perform an attendance report and stamping using a voice input.SOLUTION: A system includes a voice input analysis unit, an attendance report unit, and a time-stamping unit. The voice input analysis unit analyzes a user's voice. The attendance report unit reports attendance on the basis of the voice data analyzed by the voice input analysis unit. The time-stamping unit stamps a time in the attendance management system based on the voice data analyzed by the voice input analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology requires employees to manually report attendance and clock in, which is inefficient.

[0005] The system according to the embodiment aims to efficiently report attendance and clock in using voice input. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice input analysis unit, an attendance reporting unit, and a time-stamping unit. The voice input analysis unit analyzes the user's voice. The time-stamping unit reports attendance based on the voice data analyzed by the voice input analysis unit. The time-stamping unit time-stamps the attendance management system based on the voice data analyzed by the voice input analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows for efficient attendance reporting and clocking in using voice input. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 AI ​​system according to the embodiment of the present invention is a system that uses simple voice input to report attendance to a communication tool and clock in to an attendance management system. This allows the AI ​​system to automatically report attendance and clock in based on the user's voice input.

[0029] The AI ​​system according to the embodiment includes a voice input analysis unit, an attendance reporting unit, and a time-stamping unit. The voice input analysis unit analyzes a user's voice. For example, the voice input analysis unit converts the voice into text data using a generation AI (e.g., a text generation AI or a multimodal generation AI). The voice input analysis unit can also analyze the voice using voice recognition technology. For example, the generation AI analyzes the user's voice and converts it into text data. The generation AI analyzes the content of the voice and converts it into text data using voice recognition technology. The attendance reporting unit makes an attendance report based on the voice data analyzed by the voice input analysis unit. For example, the attendance reporting unit uses the generation AI to send an attendance report to a communication tool based on the analyzed voice data. The attendance reporting unit can also automatically generate an attendance report based on the analyzed voice data using the generation AI. For example, the attendance reporting unit uses the generation AI to send a message to a communication tool based on the analyzed voice data. The time-stamping unit clocks in to the attendance management system based on the voice data analyzed by the voice input analysis unit. For example, the time-stamping unit uses a generation AI to clock in to the attendance management system based on the analyzed voice data. The time-stamping unit can also clock in automatically through the attendance management system's API. For example, the time-stamping unit uses a generation AI to clock in to the attendance management system based on the analyzed voice data. This allows the AI ​​system according to the embodiment to automatically report attendance and clock in based on the user's voice input. For example, if the user simply inputs "Good morning. I'm at work," the report to the communication tool and the clock in to the attendance management system are simultaneously performed.

[0030] The voice input analysis unit can analyze the tone and speed of a user's voice to estimate stress and fatigue levels and reflect them in the attendance report. For example, when a user provides voice input, the voice input analysis unit uses a generation AI to analyze the tone and speed of the voice to estimate stress and fatigue levels. For example, if the voice is low and the speed is slow, it determines that the fatigue level is high and adds that information to the attendance report. The voice input analysis unit can also use a generation AI to analyze the tone and speed of the voice to estimate stress and fatigue levels. For example, if the voice is high and the speed is fast, it determines that stress is high and adds that information to the attendance report. The voice input analysis unit can also use a generation AI to analyze the tone and speed of the voice to estimate stress and fatigue levels. For example, if the voice is low and the speed is slow, it determines that the fatigue level is high and adds that information to the attendance report. This allows for more detailed attendance management by reflecting the user's stress and fatigue levels in the attendance report.

[0031] The voice input analysis unit can analyze background sounds of voice input, estimate the user's work location from environmental sounds, and add it to the report. For example, when a user makes a voice input, the voice input analysis unit uses a generation AI to analyze the background sounds and estimate the work location. For example, it detects noise in the office or the quietness of home and adds that information to the attendance report. The voice input analysis unit can also use a generation AI to analyze background sounds and estimate the work location. For example, it detects noise when out and about or sounds in a cafe and adds that information to the attendance report. The voice input analysis unit can also use a generation AI to analyze background sounds and estimate the work location. For example, it detects noise in the office or the quietness of home and adds that information to the attendance report. This allows the user's work location to be automatically estimated and reflected in the attendance report, enabling more accurate attendance management.

[0032] The voice input analysis unit can analyze multiple languages ​​simultaneously, building a multilingual system that can be used by international teams. For example, when a user provides voice input, the voice input analysis unit uses a generation AI to analyze multiple languages ​​simultaneously, building a multilingual system that can be used by international teams. For example, voices in English, Japanese, French, etc. can be analyzed simultaneously. The voice input analysis unit can also analyze multiple languages ​​simultaneously using a generation AI to build a multilingual system that can be used by international teams. For example, voices in English, Chinese, Spanish, etc. can be analyzed simultaneously. The voice input analysis unit can also analyze multiple languages ​​simultaneously using a generation AI to build a multilingual system that can be used by international teams. For example, voices in English, Japanese, French, etc. can be analyzed simultaneously. This makes it possible to provide a multilingual system that can be used by international teams by analyzing multiple languages ​​simultaneously.

[0033] The voice input analysis unit can link the analysis results of the voice input with the user's individual calendar or task management tool, thereby automating schedule management. For example, when a user provides voice input, the generation AI links the analysis results with the user's calendar or task management tool, thereby automating schedule management. For example, the voice input unit can provide voice input such as "Please add a meeting appointment." The voice input analysis unit can also use the generation AI to link the analysis results with the user's calendar or task management tool, thereby automating schedule management. For example, the voice input unit can provide voice input such as "Please check tomorrow's schedule." The voice input analysis unit can also use the generation AI to link the analysis results with the user's calendar or task management tool, thereby automating schedule management. For example, the voice input unit can provide voice input such as "Please add a meeting appointment." In this way, the voice input analysis results can be linked with the calendar or task management tool, thereby automating schedule management.

[0034] The attendance reporting unit can refer to the user's past attendance data and send an alert if an abnormal pattern is detected. For example, when a user reports their attendance, the generation AI in the attendance reporting unit refers to the past attendance data and sends an alert if an abnormal pattern is detected. For example, an alert is sent if a later-than-usual arrival time is detected. The attendance reporting unit can also use the generation AI to refer to the past attendance data and send an alert if an abnormal pattern is detected. For example, an alert is sent if there is a high frequency of absences. The attendance reporting unit can also use the generation AI to refer to the past attendance data and send an alert if an abnormal pattern is detected. For example, an alert is sent if a later-than-usual arrival time is detected. In this way, by detecting abnormal attendance patterns and sending an alert, it is possible to detect and address problems early.

[0035] The attendance reporting unit can compare the contents of the attendance report with the attendance status of the entire team and visualize the team's attendance status in real time. For example, when a user submits an attendance report, the generation AI compares it with the attendance status of the entire team and visualizes the attendance status in real time. For example, it displays the attendance status of team members in a graph. The attendance reporting unit can also use the generation AI to compare the contents of the attendance report with the attendance status of the entire team and visualize the attendance status in real time. For example, it displays the team's attendance status on a dashboard. The attendance reporting unit can also use the generation AI to compare the contents of the attendance report with the attendance status of the entire team and visualize the attendance status in real time. For example, it displays the team members' attendance status in a graph. This makes it easier to manage the team's attendance by visualizing the attendance status of the entire team in real time.

[0036] The attendance reporting unit can automatically acquire the user's location information when reporting attendance and add the work location to the report. For example, when a user reports attendance, the generation AI in the attendance reporting unit automatically acquires the location information and adds the work location to the report. For example, GPS data is used to identify the location of the office or home. The attendance reporting unit can also automatically acquire the location information and add the work location to the report using the generation AI. For example, Wi-Fi location information is used to identify the work location. The attendance reporting unit can also automatically acquire the location information and add the work location to the report using the generation AI. For example, GPS data is used to identify the location of the office or home. This enables more accurate attendance management by automatically acquiring the user's location information and adding the work location to the report.

[0037] The attendance reporting unit can link the contents of the attendance report with the project management tool and associate it with the progress of the project. For example, when a user submits an attendance report, the generation AI in the attendance reporting unit links with the project management tool and associates the contents of the attendance report with the progress of the project. For example, it associates the arrival time with a task in the project. The attendance reporting unit can also link with the project management tool using the generation AI and associate the contents of the attendance report with the progress of the project. For example, it reflects the completion status of a task in the attendance report. The attendance reporting unit can also link with the project management tool using the generation AI and associate the contents of the attendance report with the progress of the project. For example, it associates the arrival time with a task in the project. In this way, by linking the contents of the attendance report with the project management tool and associating it with the progress of the project, project management becomes easier.

[0038] The time-stamping unit can strengthen security by verifying the user's identity using biometric authentication. For example, when a user clocks in, the time-stamping unit has the generation AI use voiceprint authentication to verify the user's identity, thereby strengthening security. For example, the user's voiceprint data can be registered in advance and compared at the time of clocking in. The time-stamping unit can also strengthen security by using the generation AI to verify the user's identity using voiceprint authentication. For example, the user's voiceprint data can be registered in advance and compared at the time of clocking in. In this way, security can be strengthened by verifying the user's identity using biometric authentication.

[0039] The time-stamping unit can link the time-stamping data with the user's health data, integrating health management and attendance management. For example, when a user clocks in, the generation AI links the time-stamping data with the health data, integrating health management and attendance management. For example, the number of steps and heart rate are added to the attendance data. The time-stamping unit can also use the generation AI to link the time-stamping data with the health data, integrating health management and attendance management. For example, sleep data and calorie consumption are added to the attendance data. The time-stamping unit can also use the generation AI to link the time-stamping data with the health data, integrating health management and attendance management. For example, the number of steps and heart rate are added to the attendance data. In this way, by integrating the health data and attendance data, the user's health management and attendance management can be centralized.

[0040] The time-stamping unit can automatically acquire data from the user's device and add it to the time-stamp information. For example, when a user clocks in, the generation AI in the time-stamping unit automatically acquires data from a smartphone or smartwatch and adds it to the time-stamp information. For example, location information and step count data are reflected in the time-stamp information. The time-stamping unit can also use the generation AI to automatically acquire data from a smartphone or smartwatch and add it to the time-stamp information. For example, heart rate and calorie consumption are reflected in the time-stamp information. The time-stamping unit can also use the generation AI to automatically acquire data from a smartphone or smartwatch and add it to the time-stamp information. For example, location information and step count data are reflected in the time-stamp information. This enables more detailed attendance management by automatically acquiring data from the user's device and adding it to the time-stamp information.

[0041] The time-stamping unit can link the time-stamping data with the payroll system to automate payroll calculations. For example, when a user clocks in, the time-stamping unit uses a generation AI to link the time-stamping data with the payroll system to automate payroll calculations. For example, the arrival time and departure time are reflected in the payroll calculations. The time-stamping unit can also use a generation AI to link the time-stamping data with the payroll system to automate payroll calculations. For example, overtime hours and break times are reflected in the payroll calculations. The time-stamping unit can also use a generation AI to link the time-stamping data with the payroll system to automate payroll calculations. For example, the arrival time and departure time are reflected in the payroll calculations. In this way, by linking the time-stamping data with the payroll system and automating payroll calculations, the efficiency of payroll calculations can be improved.

[0042] The attendance information confirmation unit can refer to the user's past correction history when confirming the attendance information, identify patterns that frequently require correction, and make improvement suggestions. For example, when a user confirms their attendance information, the generation AI in the attendance information confirmation unit refers to the past correction history, identifies patterns that frequently require correction, and makes improvement suggestions. For example, an improvement suggestion is made if there are many late arrivals on specific days of the week. The attendance information confirmation unit can also use the generation AI to refer to the past correction history, identify patterns that frequently require correction, and make improvement suggestions. For example, an improvement suggestion is made if there are many late arrivals on specific days of the week. The attendance information confirmation unit can also use the generation AI to refer to the past correction history, identify patterns that frequently require correction, and make improvement suggestions. For example, an improvement suggestion is made if there are many late arrivals on specific days of the week. In this way, by referring to the past correction history, identifying patterns that frequently require correction, and making improvement suggestions, the accuracy of attendance management can be improved.

[0043] The attendance information confirmation unit can link with the user's schedule and task management data when correcting attendance information and automatically suggest the optimal correction. For example, when a user corrects attendance information, the attendance information confirmation unit uses the generation AI to link with the schedule and task management data and automatically suggest the optimal correction. For example, correcting the arrival time to match a scheduled meeting. The attendance information confirmation unit can also use the generation AI to link with the schedule and task management data and automatically suggest the optimal correction. For example, suggesting a correction based on task priority. The attendance information confirmation unit can also use the generation AI to link with the schedule and task management data and automatically suggest the optimal correction. For example, correcting the arrival time to match a scheduled meeting. This makes it easier to correct attendance information by linking with the schedule and task management data and automatically suggesting the optimal correction.

[0044] The attendance information confirmation unit can refer to the user's location information when confirming the attendance information, and can simultaneously confirm the work location. For example, when the user confirms the attendance information, the generation AI can refer to the location information and simultaneously confirm the work location. For example, GPS data can be used to identify the location of the office or home and reflect this in the attendance information. The attendance information confirmation unit can also use the generation AI to refer to the location information and simultaneously confirm the work location. For example, Wi-Fi location information can be used to identify the work location and reflect this in the attendance information. The attendance information confirmation unit can also use the generation AI to refer to the location information and simultaneously confirm the work location. For example, GPS data can be used to identify the location of the office or home and reflect this in the attendance information. This enables more accurate attendance management by referring to the user's location information and simultaneously confirming the work location.

[0045] The attendance information confirmation unit can compare the attendance information with that of other team members when correcting the attendance information and propose a correction proposal that takes into consideration the balance of the entire team. For example, when a user corrects their attendance information, the attendance information confirmation unit uses the generation AI to compare the attendance information with that of other team members and propose a correction proposal that takes into consideration the balance of the entire team. For example, the correction proposal may be proposed based on the arrival times of other members. The attendance information confirmation unit can also use the generation AI to compare the attendance information with that of other team members and propose a correction proposal that takes into consideration the balance of the entire team. For example, the correction proposal may be proposed based on the balance of tasks. The attendance information confirmation unit can also use the generation AI to compare the attendance information with that of other team members and propose a correction proposal that takes into consideration the balance of the entire team. For example, the correction proposal may be proposed based on the arrival times of other members. In this way, by comparing the attendance information with that of other team members and proposing a correction proposal that takes into consideration the balance of the entire team, the efficiency of the entire team can be improved.

[0046] The attendance data management unit can enhance security by encrypting the data when saving the attendance data. For example, when saving a user's attendance data, the attendance data management unit uses a generation AI to encrypt the data, thereby enhancing security. For example, the data is encrypted using the AES encryption algorithm. The attendance data management unit can also use a generation AI to encrypt the data, thereby enhancing security. For example, the data is encrypted using the RSA encryption algorithm. The attendance data management unit can also use a generation AI to encrypt the data, thereby enhancing security. For example, the data is encrypted using the AES encryption algorithm. In this way, security can be enhanced by encrypting the data when saving the attendance data.

[0047] The attendance data management unit can analyze a user's past attendance patterns when managing attendance data, and send an alert if an abnormal pattern is detected. For example, when managing a user's attendance data, the attendance data management unit uses a generation AI to analyze past attendance patterns and send an alert if an abnormal pattern is detected. For example, an alert is sent if a later-than-usual arrival time is detected. The attendance data management unit can also use a generation AI to analyze past attendance patterns and send an alert if an abnormal pattern is detected. For example, an alert is sent if there is a high frequency of absences. The attendance data management unit can also use a generation AI to analyze past attendance patterns and send an alert if an abnormal pattern is detected. For example, an alert is sent if a later-than-usual arrival time is detected. In this way, by detecting abnormal patterns when managing attendance data and sending an alert, problems can be detected and addressed early.

[0048] The attendance data management unit can work with cloud storage when saving attendance data and automate data backups. For example, when saving a user's attendance data, the generation AI works with cloud storage to automate data backups. For example, data is saved to the cloud on a regular basis. The attendance data management unit can also work with cloud storage using the generation AI to automate data backups. For example, the data saving frequency can be set and backups can be performed automatically. The attendance data management unit can also work with cloud storage using the generation AI to automate data backups. For example, data is saved to the cloud on a regular basis. In this way, by working with cloud storage and automating data backups, data safety can be ensured.

[0049] The attendance data management unit can integrate attendance data with other business data when managing it, thereby realizing comprehensive business management. For example, when managing a user's attendance data, the attendance data management unit uses a generation AI to integrate it with other business data, thereby realizing comprehensive business management. For example, it integrates project progress data with attendance data. The attendance data management unit can also use a generation AI to integrate it with other business data, thereby realizing comprehensive business management. For example, it integrates task data with attendance data. The attendance data management unit can also use a generation AI to integrate it with other business data, thereby realizing comprehensive business management. For example, it integrates project progress data with attendance data. This allows it to be integrated with other business data and realize comprehensive business management, thereby improving business efficiency.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The voice input analysis unit can analyze background sounds during voice input, estimate the user's work location from environmental sounds, and add it to the report. For example, when a user makes a voice input, the generation AI analyzes the background sounds and estimates the work location. For example, it can detect noise in the office or the quietness of home and add that information to the attendance report. The voice input analysis unit can also use the generation AI to analyze background sounds and estimate the work location. For example, it can detect noise when out and about or sounds in a cafe and add that information to the attendance report. This allows the user's work location to be automatically estimated and reflected in the attendance report, enabling more accurate attendance management.

[0052] The voice input analysis unit can analyze multiple languages ​​simultaneously, building a multilingual system that can be used by international teams. For example, when a user provides voice input, the generation AI can analyze multiple languages ​​simultaneously, building a multilingual system that can be used by international teams. For example, voices in English, Japanese, French, etc. can be analyzed simultaneously. The voice input analysis unit can also use the generation AI to analyze multiple languages ​​simultaneously, building a multilingual system that can be used by international teams. For example, voices in English, Chinese, Spanish, etc. can be analyzed simultaneously. This allows for the simultaneous analysis of multiple languages ​​to provide a multilingual system that can be used by international teams.

[0053] The voice input analysis unit can link the analysis results of the voice input with the user's individual calendar or task management tool to automate schedule management. For example, when a user provides voice input, the generation AI links the analysis results with the user's calendar or task management tool to automate schedule management. For example, a voice input such as "Please add a meeting appointment" can be made. The voice input analysis unit can also use the generation AI to link the analysis results with the user's calendar or task management tool to automate schedule management. For example, a voice input such as "Please check tomorrow's schedule." This allows the voice input analysis results to be linked with the calendar or task management tool to automate schedule management.

[0054] The attendance reporting unit can refer to the user's past attendance data and send an alert if an abnormal pattern is detected. For example, when a user reports their attendance, the generation AI refers to the past attendance data and sends an alert if an abnormal pattern is detected. For example, an alert is sent if a later-than-usual arrival time is detected. The attendance reporting unit can also use the generation AI to refer to the past attendance data and send an alert if an abnormal pattern is detected. For example, an alert is sent if there is a high frequency of absences. In this way, by detecting abnormal attendance patterns and sending alerts, it becomes possible to detect and address problems early.

[0055] The attendance reporting unit can compare the contents of the attendance report with the attendance status of the entire team and visualize the team's attendance status in real time. For example, when a user submits an attendance report, the generation AI compares it with the attendance status of the entire team and visualizes the attendance status in real time. For example, it can display the attendance status of team members in a graph. The attendance reporting unit can also use the generation AI to compare the contents of the attendance report with the attendance status of the entire team and visualize the attendance status in real time. For example, it can display the team's attendance status on a dashboard. This makes it easier to manage team attendance by visualizing the attendance status of the entire team in real time.

[0056] The time-stamping unit can strengthen security by verifying the user's identity using biometric authentication. For example, when a user clocks in, the generation AI can verify the user's identity using voiceprint authentication, thereby strengthening security. For example, the user's voiceprint data can be registered in advance and verified at the time of clocking in. The time-stamping unit can also strengthen security by using the generation AI to verify the user's identity using voiceprint authentication. For example, identity can be verified using fingerprint authentication or facial authentication. In this way, identity verification using biometric authentication can strengthen security.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The voice input analysis unit analyzes the user's voice. For example, the voice input analysis unit converts the voice into text data using a generation AI (e.g., a text generation AI or a multimodal generation AI). The voice can also be analyzed using voice recognition technology. Step 2: The attendance reporting unit makes an attendance report based on the voice data analyzed by the voice input analysis unit. For example, the attendance reporting unit uses a generation AI to send an attendance report to a communication tool based on the analyzed voice data. It can also automatically generate attendance reports based on the analyzed voice data. Step 3: The time-stamping unit clocks in to the attendance management system based on the voice data analyzed by the voice input analysis unit. For example, the time-stamping unit uses a generation AI to clock in to the attendance management system based on the analyzed voice data. It is also possible to clock in automatically via the attendance management system's API.

[0059] (Example 2) The AI ​​system according to the embodiment of the present invention is a system that uses simple voice input to report attendance to a communication tool and clock in to an attendance management system. This allows the AI ​​system to automatically report attendance and clock in based on the user's voice input.

[0060] The AI ​​system according to the embodiment includes a voice input analysis unit, an attendance reporting unit, and a time-stamping unit. The voice input analysis unit analyzes a user's voice. For example, the voice input analysis unit converts the voice into text data using a generation AI (e.g., a text generation AI or a multimodal generation AI). The voice input analysis unit can also analyze the voice using voice recognition technology. For example, the generation AI analyzes the user's voice and converts it into text data. The generation AI analyzes the content of the voice and converts it into text data using voice recognition technology. The attendance reporting unit makes an attendance report based on the voice data analyzed by the voice input analysis unit. For example, the attendance reporting unit uses the generation AI to send an attendance report to a communication tool based on the analyzed voice data. The attendance reporting unit can also automatically generate an attendance report based on the analyzed voice data using the generation AI. For example, the attendance reporting unit uses the generation AI to send a message to a communication tool based on the analyzed voice data. The time-stamping unit clocks in to the attendance management system based on the voice data analyzed by the voice input analysis unit. For example, the time-stamping unit uses a generation AI to clock in to the attendance management system based on the analyzed voice data. The time-stamping unit can also clock in automatically through the attendance management system's API. For example, the time-stamping unit uses a generation AI to clock in to the attendance management system based on the analyzed voice data. This allows the AI ​​system according to the embodiment to automatically report attendance and clock in based on the user's voice input. For example, if the user simply inputs "Good morning. I'm at work," the report to the communication tool and the clock in to the attendance management system are simultaneously performed.

[0061] The voice input analysis unit can analyze the tone and speed of a user's voice to estimate stress and fatigue levels and reflect them in the attendance report. For example, when a user provides voice input, the voice input analysis unit uses a generation AI to analyze the tone and speed of the voice to estimate stress and fatigue levels. For example, if the voice is low and the speed is slow, it determines that the fatigue level is high and adds that information to the attendance report. The voice input analysis unit can also use a generation AI to analyze the tone and speed of the voice to estimate stress and fatigue levels. For example, if the voice is high and the speed is fast, it determines that stress is high and adds that information to the attendance report. The voice input analysis unit can also use a generation AI to analyze the tone and speed of the voice to estimate stress and fatigue levels. For example, if the voice is low and the speed is slow, it determines that the fatigue level is high and adds that information to the attendance report. This allows for more detailed attendance management by reflecting the user's stress and fatigue levels in the attendance report.

[0062] The voice input analysis unit can analyze background sounds of voice input, estimate the user's work location from environmental sounds, and add it to the report. For example, when a user makes a voice input, the voice input analysis unit uses a generation AI to analyze the background sounds and estimate the work location. For example, it detects noise in the office or the quietness of home and adds that information to the attendance report. The voice input analysis unit can also use a generation AI to analyze background sounds and estimate the work location. For example, it detects noise when out and about or sounds in a cafe and adds that information to the attendance report. The voice input analysis unit can also use a generation AI to analyze background sounds and estimate the work location. For example, it detects noise in the office or the quietness of home and adds that information to the attendance report. This allows the user's work location to be automatically estimated and reflected in the attendance report, enabling more accurate attendance management.

[0063] The voice input analysis unit can analyze the user's emotional state using the emotion estimation function, and automatically generate an encouraging message if the user has a positive emotion. For example, when a user performs voice input, the voice input analysis unit uses the emotion estimation function to analyze the user's emotional state, and if the user has a positive emotion, it generates an encouraging message. For example, it displays a message such as, "You look good today!". The voice input analysis unit can also use the emotion estimation function to analyze the user's emotional state using the emotion estimation function, and if the user has a positive emotion, it generates an encouraging message. For example, it displays a message such as, "You look good today!". The voice input analysis unit can also use the emotion estimation function to analyze the user's emotional state using the emotion estimation function, and if the user has a positive emotion, it generates an encouraging message. For example, it displays a message such as, "You look good today!". In this way, by generating an encouraging message according to the user's emotional state, it is possible to improve the user's motivation.

[0064] The voice input analysis unit can analyze multiple languages ​​simultaneously, building a multilingual system that can be used by international teams. For example, when a user provides voice input, the voice input analysis unit uses a generation AI to analyze multiple languages ​​simultaneously, building a multilingual system that can be used by international teams. For example, voices in English, Japanese, French, etc. can be analyzed simultaneously. The voice input analysis unit can also analyze multiple languages ​​simultaneously using a generation AI to build a multilingual system that can be used by international teams. For example, voices in English, Chinese, Spanish, etc. can be analyzed simultaneously. The voice input analysis unit can also analyze multiple languages ​​simultaneously using a generation AI to build a multilingual system that can be used by international teams. For example, voices in English, Japanese, French, etc. can be analyzed simultaneously. This makes it possible to provide a multilingual system that can be used by international teams by analyzing multiple languages ​​simultaneously.

[0065] The voice input analysis unit can link the analysis results of the voice input with the user's individual calendar or task management tool, thereby automating schedule management. For example, when a user provides voice input, the generation AI links the analysis results with the user's calendar or task management tool, thereby automating schedule management. For example, the voice input unit can provide voice input such as "Please add a meeting appointment." The voice input analysis unit can also use the generation AI to link the analysis results with the user's calendar or task management tool, thereby automating schedule management. For example, the voice input unit can provide voice input such as "Please check tomorrow's schedule." The voice input analysis unit can also use the generation AI to link the analysis results with the user's calendar or task management tool, thereby automating schedule management. For example, the voice input unit can provide voice input such as "Please add a meeting appointment." In this way, the voice input analysis results can be linked with the calendar or task management tool, thereby automating schedule management.

[0066] The voice input analysis unit can use the emotion estimation function to analyze the user's emotional state and generate a customized attendance report format. For example, when a user provides voice input, the voice input analysis unit uses the emotion estimation function to analyze the user's emotional state and generate a customized attendance report format. For example, if the user has positive emotions, a cheerful format is used. The voice input analysis unit can also use the emotion estimation function to analyze the user's emotional state and generate a customized attendance report format. For example, if the user has negative emotions, a calm format is used. The voice input analysis unit can also use the emotion estimation function to analyze the user's emotional state and generate a customized attendance report format. For example, if the user has positive emotions, a cheerful format is used. This enables more individualized attendance management by generating a customized attendance report format according to the user's emotional state.

[0067] The attendance reporting unit can refer to the user's past attendance data and send an alert if an abnormal pattern is detected. For example, when a user reports their attendance, the generation AI in the attendance reporting unit refers to the past attendance data and sends an alert if an abnormal pattern is detected. For example, an alert is sent if a later-than-usual arrival time is detected. The attendance reporting unit can also use the generation AI to refer to the past attendance data and send an alert if an abnormal pattern is detected. For example, an alert is sent if there is a high frequency of absences. The attendance reporting unit can also use the generation AI to refer to the past attendance data and send an alert if an abnormal pattern is detected. For example, an alert is sent if a later-than-usual arrival time is detected. In this way, by detecting abnormal attendance patterns and sending an alert, it is possible to detect and address problems early.

[0068] The attendance reporting unit can compare the contents of the attendance report with the attendance status of the entire team and visualize the team's attendance status in real time. For example, when a user submits an attendance report, the generation AI compares it with the attendance status of the entire team and visualizes the attendance status in real time. For example, it displays the attendance status of team members in a graph. The attendance reporting unit can also use the generation AI to compare the contents of the attendance report with the attendance status of the entire team and visualize the attendance status in real time. For example, it displays the team's attendance status on a dashboard. The attendance reporting unit can also use the generation AI to compare the contents of the attendance report with the attendance status of the entire team and visualize the attendance status in real time. For example, it displays the team members' attendance status in a graph. This makes it easier to manage the team's attendance by visualizing the attendance status of the entire team in real time.

[0069] The attendance reporting unit can use the emotion estimation function to analyze the user's emotional state, automatically generate an encouraging or warning message, and send it to the communication tool. For example, when a user reports their attendance, the attendance reporting unit uses the emotion estimation function to analyze the user's emotional state, automatically generate an encouraging or warning message, and send it to the communication tool. For example, it sends a message such as, "You look good today!". The attendance reporting unit can also use the emotion estimation function to analyze the user's emotional state, automatically generate an encouraging or warning message, and send it to the communication tool. For example, it sends a message such as, "You look good today!". The attendance reporting unit can also use the emotion estimation function to analyze the user's emotional state, automatically generate an encouraging or warning message, and send it to the communication tool. For example, it sends a message such as, "You look good today!". In this way, by automatically generating a message according to the user's emotional state and sending it to the communication tool, it is possible to improve the user's motivation.

[0070] The attendance reporting unit can automatically acquire the user's location information when reporting attendance and add the work location to the report. For example, when a user reports attendance, the generation AI in the attendance reporting unit automatically acquires the location information and adds the work location to the report. For example, GPS data is used to identify the location of the office or home. The attendance reporting unit can also automatically acquire the location information and add the work location to the report using the generation AI. For example, Wi-Fi location information is used to identify the work location. The attendance reporting unit can also automatically acquire the location information and add the work location to the report using the generation AI. For example, GPS data is used to identify the location of the office or home. This enables more accurate attendance management by automatically acquiring the user's location information and adding the work location to the report.

[0071] The attendance reporting unit can link the contents of the attendance report with the project management tool and associate it with the progress of the project. For example, when a user submits an attendance report, the generation AI in the attendance reporting unit links with the project management tool and associates the contents of the attendance report with the progress of the project. For example, it associates the arrival time with a task in the project. The attendance reporting unit can also link with the project management tool using the generation AI and associate the contents of the attendance report with the progress of the project. For example, it reflects the completion status of a task in the attendance report. The attendance reporting unit can also link with the project management tool using the generation AI and associate the contents of the attendance report with the progress of the project. For example, it associates the arrival time with a task in the project. In this way, by linking the contents of the attendance report with the project management tool and associating it with the progress of the project, project management becomes easier.

[0072] The attendance reporting unit can use the emotion estimation function to analyze the user's emotional state and provide a customized attendance report template. For example, when a user reports their attendance, the generation AI uses the emotion estimation function to analyze the user's emotional state and provide a customized attendance report template. For example, if the user has positive emotions, a bright template is used. The attendance reporting unit can also use the generation AI to analyze the user's emotional state using the emotion estimation function and provide a customized attendance report template. For example, if the user has negative emotions, a calm template is used. The attendance reporting unit can also use the generation AI to analyze the user's emotional state using the emotion estimation function and provide a customized attendance report template. For example, if the user has positive emotions, a bright template is used. This enables more individualized attendance management by providing a customized attendance report template based on the user's emotional state.

[0073] The time-stamping unit can strengthen security by verifying the user's identity using biometric authentication. For example, when a user clocks in, the time-stamping unit has the generation AI use voiceprint authentication to verify the user's identity, thereby strengthening security. For example, the user's voiceprint data can be registered in advance and compared at the time of clocking in. The time-stamping unit can also strengthen security by using the generation AI to verify the user's identity using voiceprint authentication. For example, the user's voiceprint data can be registered in advance and compared at the time of clocking in. In this way, security can be strengthened by verifying the user's identity using biometric authentication.

[0074] The time-stamping unit can link the time-stamping data with the user's health data, integrating health management and attendance management. For example, when a user clocks in, the generation AI links the time-stamping data with the health data, integrating health management and attendance management. For example, the number of steps and heart rate are added to the attendance data. The time-stamping unit can also use the generation AI to link the time-stamping data with the health data, integrating health management and attendance management. For example, sleep data and calorie consumption are added to the attendance data. The time-stamping unit can also use the generation AI to link the time-stamping data with the health data, integrating health management and attendance management. For example, the number of steps and heart rate are added to the attendance data. In this way, by integrating the health data and attendance data, the user's health management and attendance management can be centralized.

[0075] The time-stamping unit can analyze the user's emotional state using the emotion estimation function, generate a time-stamping message, and send it to the attendance management system. For example, when a user clocks in, the time-stamping unit uses the generation AI to analyze the user's emotional state using the emotion estimation function, generate a time-stamping message, and send it to the attendance management system. For example, it sends a message such as, "You look good today!" The time-stamping unit can also use the generation AI to analyze the user's emotional state using the emotion estimation function, generate a time-stamping message, and send it to the attendance management system. For example, it sends a message such as, "You look good today!" The time-stamping unit can also use the generation AI to analyze the user's emotional state using the emotion estimation function, generate a time-stamping message, and send it to the attendance management system. For example, it sends a message such as, "You look good today!" In this way, by generating a time-stamping message according to the user's emotional state and sending it to the attendance management system, it is possible to improve the user's motivation.

[0076] The time-stamping unit can automatically acquire data from the user's device and add it to the time-stamp information. For example, when a user clocks in, the generation AI in the time-stamping unit automatically acquires data from a smartphone or smartwatch and adds it to the time-stamp information. For example, location information and step count data are reflected in the time-stamp information. The time-stamping unit can also use the generation AI to automatically acquire data from a smartphone or smartwatch and add it to the time-stamp information. For example, heart rate and calorie consumption are reflected in the time-stamp information. The time-stamping unit can also use the generation AI to automatically acquire data from a smartphone or smartwatch and add it to the time-stamp information. For example, location information and step count data are reflected in the time-stamp information. This enables more detailed attendance management by automatically acquiring data from the user's device and adding it to the time-stamp information.

[0077] The time-stamping unit can link the time-stamping data with the payroll system to automate payroll calculations. For example, when a user clocks in, the time-stamping unit uses a generation AI to link the time-stamping data with the payroll system to automate payroll calculations. For example, the arrival time and departure time are reflected in the payroll calculations. The time-stamping unit can also use a generation AI to link the time-stamping data with the payroll system to automate payroll calculations. For example, overtime hours and break times are reflected in the payroll calculations. The time-stamping unit can also use a generation AI to link the time-stamping data with the payroll system to automate payroll calculations. For example, the arrival time and departure time are reflected in the payroll calculations. In this way, by linking the time-stamping data with the payroll system and automating payroll calculations, the efficiency of payroll calculations can be improved.

[0078] The time-stamping unit can use the emotion estimation function to analyze the user's emotional state and provide a customized time-stamping message. For example, when a user clocks in, the time-stamping unit uses the generation AI to analyze the user's emotional state using the emotion estimation function and provide a customized time-stamping message. For example, if the user has positive emotions, an upbeat message is displayed. The time-stamping unit can also use the generation AI to analyze the user's emotional state using the emotion estimation function and provide a customized time-stamping message. For example, if the user has negative emotions, an encouraging message is displayed. The time-stamping unit can also use the generation AI to analyze the user's emotional state using the emotion estimation function and provide a customized time-stamping message. For example, if the user has positive emotions, an upbeat message is displayed. This makes it possible to improve the user's motivation by providing a customized time-stamping message based on the user's emotional state.

[0079] The attendance information confirmation unit can refer to the user's past correction history when confirming the attendance information, identify patterns that frequently require correction, and make improvement suggestions. For example, when a user confirms their attendance information, the generation AI in the attendance information confirmation unit refers to the past correction history, identifies patterns that frequently require correction, and makes improvement suggestions. For example, an improvement suggestion is made if there are many late arrivals on specific days of the week. The attendance information confirmation unit can also use the generation AI to refer to the past correction history, identify patterns that frequently require correction, and make improvement suggestions. For example, an improvement suggestion is made if there are many late arrivals on specific days of the week. The attendance information confirmation unit can also use the generation AI to refer to the past correction history, identify patterns that frequently require correction, and make improvement suggestions. For example, an improvement suggestion is made if there are many late arrivals on specific days of the week. In this way, by referring to the past correction history, identifying patterns that frequently require correction, and making improvement suggestions, the accuracy of attendance management can be improved.

[0080] The attendance information confirmation unit can link with the user's schedule and task management data when correcting attendance information and automatically suggest the optimal correction. For example, when a user corrects attendance information, the attendance information confirmation unit uses the generation AI to link with the schedule and task management data and automatically suggest the optimal correction. For example, correcting the arrival time to match a scheduled meeting. The attendance information confirmation unit can also use the generation AI to link with the schedule and task management data and automatically suggest the optimal correction. For example, suggesting a correction based on task priority. The attendance information confirmation unit can also use the generation AI to link with the schedule and task management data and automatically suggest the optimal correction. For example, correcting the arrival time to match a scheduled meeting. This makes it easier to correct attendance information by linking with the schedule and task management data and automatically suggesting the optimal correction.

[0081] The attendance information confirmation unit can use the emotion estimation function to analyze the user's emotional state, generate suggested revisions, and make suggestions to reduce stress. For example, when a user modifies their attendance information, the attendance information confirmation unit uses the generation AI to analyze the user's emotional state using the emotion estimation function and generate suggested revisions to reduce stress. For example, if the user has positive emotions, a simple suggested revision is made. The attendance information confirmation unit can also use the generation AI to analyze the user's emotional state using the emotion estimation function and generate suggested revisions to reduce stress. For example, if the user has negative emotions, a suggestion to reduce stress is made. The attendance information confirmation unit can also use the generation AI to analyze the user's emotional state using the emotion estimation function and generate suggested revisions to reduce stress. For example, if the user has positive emotions, a simple suggested revision is made. In this way, suggested revisions are generated according to the user's emotional state, and suggestions to reduce stress are made, thereby reducing the burden on the user.

[0082] The attendance information confirmation unit can refer to the user's location information when confirming the attendance information, and can simultaneously confirm the work location. For example, when the user confirms the attendance information, the generation AI can refer to the location information and simultaneously confirm the work location. For example, GPS data can be used to identify the location of the office or home and reflect this in the attendance information. The attendance information confirmation unit can also use the generation AI to refer to the location information and simultaneously confirm the work location. For example, Wi-Fi location information can be used to identify the work location and reflect this in the attendance information. The attendance information confirmation unit can also use the generation AI to refer to the location information and simultaneously confirm the work location. For example, GPS data can be used to identify the location of the office or home and reflect this in the attendance information. This enables more accurate attendance management by referring to the user's location information and simultaneously confirming the work location.

[0083] The attendance information confirmation unit can compare the attendance information with that of other team members when correcting the attendance information and propose a correction proposal that takes into consideration the balance of the entire team. For example, when a user corrects their attendance information, the attendance information confirmation unit uses the generation AI to compare the attendance information with that of other team members and propose a correction proposal that takes into consideration the balance of the entire team. For example, the correction proposal may be proposed based on the arrival times of other members. The attendance information confirmation unit can also use the generation AI to compare the attendance information with that of other team members and propose a correction proposal that takes into consideration the balance of the entire team. For example, the correction proposal may be proposed based on the balance of tasks. The attendance information confirmation unit can also use the generation AI to compare the attendance information with that of other team members and propose a correction proposal that takes into consideration the balance of the entire team. For example, the correction proposal may be proposed based on the arrival times of other members. In this way, by comparing the attendance information with that of other team members and proposing a correction proposal that takes into consideration the balance of the entire team, the efficiency of the entire team can be improved.

[0084] The attendance information confirmation unit can use the emotion estimation function to analyze the user's emotional state and provide customized correction suggestions. For example, when a user corrects their attendance information, the generation AI in the attendance information confirmation unit uses the emotion estimation function to analyze the user's emotional state and provide customized correction suggestions. For example, if the user has positive emotions, a simple correction suggestion is proposed. The attendance information confirmation unit can also use the generation AI to analyze the user's emotional state using the emotion estimation function and provide customized correction suggestions. For example, if the user has negative emotions, a correction suggestion to reduce stress is proposed. The attendance information confirmation unit can also use the generation AI to analyze the user's emotional state using the emotion estimation function and provide customized correction suggestions. For example, if the user has positive emotions, a simple correction suggestion is proposed. This reduces the burden on the user by providing customized correction suggestions based on the user's emotional state.

[0085] The attendance data management unit can enhance security by encrypting the data when saving the attendance data. For example, when saving a user's attendance data, the attendance data management unit uses a generation AI to encrypt the data, thereby enhancing security. For example, the data is encrypted using the AES encryption algorithm. The attendance data management unit can also use a generation AI to encrypt the data, thereby enhancing security. For example, the data is encrypted using the RSA encryption algorithm. The attendance data management unit can also use a generation AI to encrypt the data, thereby enhancing security. For example, the data is encrypted using the AES encryption algorithm. In this way, security can be enhanced by encrypting the data when saving the attendance data.

[0086] The attendance data management unit can analyze a user's past attendance patterns when managing attendance data, and send an alert if an abnormal pattern is detected. For example, when managing a user's attendance data, the attendance data management unit uses a generation AI to analyze past attendance patterns and send an alert if an abnormal pattern is detected. For example, an alert is sent if a later-than-usual arrival time is detected. The attendance data management unit can also use a generation AI to analyze past attendance patterns and send an alert if an abnormal pattern is detected. For example, an alert is sent if there is a high frequency of absences. The attendance data management unit can also use a generation AI to analyze past attendance patterns and send an alert if an abnormal pattern is detected. For example, an alert is sent if a later-than-usual arrival time is detected. In this way, by detecting abnormal patterns when managing attendance data and sending an alert, problems can be detected and addressed early.

[0087] The attendance data management unit can use the emotion estimation function to analyze the user's emotional state and suggest a method for managing attendance data, thereby reducing stress. For example, when managing the user's attendance data, the attendance data management unit uses the generation AI to analyze the user's emotional state using the emotion estimation function and suggest a method for managing attendance data that reduces stress. For example, if the user has positive emotions, it can suggest a simple management method. The attendance data management unit can also use the generation AI to analyze the user's emotional state using the emotion estimation function and suggest a method for managing attendance data that reduces stress. For example, if the user has negative emotions, it can suggest a method for reducing stress. The attendance data management unit can also use the generation AI to analyze the user's emotional state using the emotion estimation function and suggest a method for managing attendance data that reduces stress. For example, if the user has positive emotions, it can suggest a simple management method. In this way, it is possible to suggest a method for managing attendance data based on the user's emotional state, thereby reducing stress and reducing the burden on the user.

[0088] The attendance data management unit can work with cloud storage when saving attendance data and automate data backups. For example, when saving a user's attendance data, the generation AI works with cloud storage to automate data backups. For example, data is saved to the cloud on a regular basis. The attendance data management unit can also work with cloud storage using the generation AI to automate data backups. For example, the data saving frequency can be set and backups can be performed automatically. The attendance data management unit can also work with cloud storage using the generation AI to automate data backups. For example, data is saved to the cloud on a regular basis. In this way, by working with cloud storage and automating data backups, data safety can be ensured.

[0089] The attendance data management unit can integrate attendance data with other business data when managing it, thereby realizing comprehensive business management. For example, when managing a user's attendance data, the attendance data management unit uses a generation AI to integrate it with other business data, thereby realizing comprehensive business management. For example, it integrates project progress data with attendance data. The attendance data management unit can also use a generation AI to integrate it with other business data, thereby realizing comprehensive business management. For example, it integrates task data with attendance data. The attendance data management unit can also use a generation AI to integrate it with other business data, thereby realizing comprehensive business management. For example, it integrates project progress data with attendance data. This allows it to be integrated with other business data and realize comprehensive business management, thereby improving business efficiency.

[0090] The attendance data management unit can use the emotion estimation function to analyze the user's emotional state and provide a customized attendance data management method. For example, when managing the user's attendance data, the attendance data management unit uses the generation AI to analyze the user's emotional state using the emotion estimation function and provides a customized attendance data management method. For example, if the user has positive emotions, a simple management method is suggested. The attendance data management unit can also use the generation AI to analyze the user's emotional state using the emotion estimation function and provide a customized attendance data management method. For example, if the user has negative emotions, a management method that reduces stress is suggested. The attendance data management unit can also use the generation AI to analyze the user's emotional state using the emotion estimation function and provide a customized attendance data management method. For example, if the user has positive emotions, a simple management method is suggested. This reduces the burden on the user by providing a customized attendance data management method based on the user's emotional state.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The voice input analysis unit can analyze the tone and speed of the user's voice to estimate stress and fatigue levels and reflect these in the attendance report. For example, when a user makes a voice input, the generation AI analyzes the tone and speed of the voice to estimate stress and fatigue levels. For example, if the voice is low and the speed is slow, it will determine that the fatigue level is high and add this information to the attendance report. The voice input analysis unit can also use the generation AI to analyze the tone and speed of the voice to estimate stress and fatigue levels. For example, if the voice tone is high and the speed is fast, it will determine that the stress level is high and add this information to the attendance report. This allows the user's stress and fatigue levels to be reflected in the attendance report, enabling more detailed attendance management.

[0093] The voice input analysis unit can analyze background sounds during voice input, estimate the user's work location from environmental sounds, and add it to the report. For example, when a user makes a voice input, the generation AI analyzes the background sounds and estimates the work location. For example, it can detect noise in the office or the quietness of home and add that information to the attendance report. The voice input analysis unit can also use the generation AI to analyze background sounds and estimate the work location. For example, it can detect noise when out and about or sounds in a cafe and add that information to the attendance report. This allows the user's work location to be automatically estimated and reflected in the attendance report, enabling more accurate attendance management.

[0094] The voice input analysis unit can use the emotion estimation function to analyze the user's emotional state, and automatically generate an encouraging message if the emotion is positive. For example, when a user provides voice input, the generation AI can analyze the user's emotional state using the emotion estimation function, and generate an encouraging message if the emotion is positive. For example, a message such as "You look good today!" can be displayed. The voice input analysis unit can also use the generation AI to analyze the user's emotional state using the emotion estimation function, and generate an encouraging message if the emotion is positive. For example, a message such as "You look good today!" can be displayed. In this way, by generating an encouraging message that matches the user's emotional state, it is possible to improve the user's motivation.

[0095] The voice input analysis unit can analyze multiple languages ​​simultaneously, building a multilingual system that can be used by international teams. For example, when a user provides voice input, the generation AI can analyze multiple languages ​​simultaneously, building a multilingual system that can be used by international teams. For example, voices in English, Japanese, French, etc. can be analyzed simultaneously. The voice input analysis unit can also use the generation AI to analyze multiple languages ​​simultaneously, building a multilingual system that can be used by international teams. For example, voices in English, Chinese, Spanish, etc. can be analyzed simultaneously. This allows for the simultaneous analysis of multiple languages ​​to provide a multilingual system that can be used by international teams.

[0096] The voice input analysis unit can link the analysis results of the voice input with the user's individual calendar or task management tool to automate schedule management. For example, when a user provides voice input, the generation AI links the analysis results with the user's calendar or task management tool to automate schedule management. For example, a voice input such as "Please add a meeting appointment" can be made. The voice input analysis unit can also use the generation AI to link the analysis results with the user's calendar or task management tool to automate schedule management. For example, a voice input such as "Please check tomorrow's schedule." This allows the voice input analysis results to be linked with the calendar or task management tool to automate schedule management.

[0097] The attendance reporting unit can refer to the user's past attendance data and send an alert if an abnormal pattern is detected. For example, when a user reports their attendance, the generation AI refers to the past attendance data and sends an alert if an abnormal pattern is detected. For example, an alert is sent if a later-than-usual arrival time is detected. The attendance reporting unit can also use the generation AI to refer to the past attendance data and send an alert if an abnormal pattern is detected. For example, an alert is sent if there is a high frequency of absences. In this way, by detecting abnormal attendance patterns and sending alerts, it becomes possible to detect and address problems early.

[0098] The attendance reporting unit can use the emotion estimation function to analyze the user's emotional state, automatically generate an encouraging or warning message, and send it to the communication tool. For example, when a user reports their attendance, the generation AI can use the emotion estimation function to analyze the user's emotional state, automatically generate an encouraging or warning message, and send it to the communication tool. For example, it can send a message such as, "You look good today!". The attendance reporting unit can also use the generation AI to analyze the user's emotional state using the emotion estimation function, automatically generate an encouraging or warning message, and send it to the communication tool. For example, it can send a message such as, "You look good today!". In this way, by automatically generating a message according to the user's emotional state and sending it to the communication tool, it is possible to improve the user's motivation.

[0099] The attendance reporting unit can compare the contents of the attendance report with the attendance status of the entire team and visualize the team's attendance status in real time. For example, when a user submits an attendance report, the generation AI compares it with the attendance status of the entire team and visualizes the attendance status in real time. For example, it can display the attendance status of team members in a graph. The attendance reporting unit can also use the generation AI to compare the contents of the attendance report with the attendance status of the entire team and visualize the attendance status in real time. For example, it can display the team's attendance status on a dashboard. This makes it easier to manage team attendance by visualizing the attendance status of the entire team in real time.

[0100] The attendance report unit can use the emotion estimation function to analyze the user's emotional state and generate a customized attendance report format. For example, when a user provides voice input, the generation AI can use the emotion estimation function to analyze the user's emotional state and generate a customized attendance report format. For example, if the user has positive emotions, a cheerful format is used. The voice input analysis unit can also use the generation AI to analyze the user's emotional state using the emotion estimation function and generate a customized attendance report format. For example, if the user has negative emotions, a calm format is used. This allows for more individualized attendance management by generating a customized attendance report format according to the user's emotional state.

[0101] The time-stamping unit can strengthen security by verifying the user's identity using biometric authentication. For example, when a user clocks in, the generation AI can verify the user's identity using voiceprint authentication, thereby strengthening security. For example, the user's voiceprint data can be registered in advance and verified at the time of clocking in. The time-stamping unit can also strengthen security by using the generation AI to verify the user's identity using voiceprint authentication. For example, identity can be verified using fingerprint authentication or facial authentication. In this way, identity verification using biometric authentication can strengthen security.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The voice input analysis unit analyzes the user's voice. For example, the voice input analysis unit converts the voice into text data using a generation AI (e.g., a text generation AI or a multimodal generation AI). The voice can also be analyzed using voice recognition technology. Step 2: The attendance reporting unit makes an attendance report based on the voice data analyzed by the voice input analysis unit. For example, the attendance reporting unit uses a generation AI to send an attendance report to a communication tool based on the analyzed voice data. It can also automatically generate attendance reports based on the analyzed voice data. Step 3: The time-stamping unit clocks in to the attendance management system based on the voice data analyzed by the voice input analysis unit. For example, the time-stamping unit uses a generation AI to clock in to the attendance management system based on the analyzed voice data. It is also possible to clock in automatically via the attendance management system's API.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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 voice input analysis unit that analyzes the voice of a user; an attendance reporting unit that reports attendance based on the voice data analyzed by the voice input analysis unit; and a time-stamping unit that stamps the time into the attendance management system based on the voice data analyzed by the voice input analysis unit. A system characterized by:

2. The voice input analysis unit Analyze background sounds from voice input, estimate the user's work location from the environmental sounds, and add it to the report 2. The system of claim 1.

3. The voice input analysis unit Analyze multiple languages ​​simultaneously and build a multilingual system that can be used by international teams 2. The system of claim 1.

4. The attendance reporting unit Look up a user's past attendance data and send an alert if any unusual patterns are detected 2. The system of claim 1.

5. The stamping unit includes: Enhance security by verifying user biometrics 2. The system of claim 1.

6. The attendance information confirmation department Analyze the user's emotional state using emotion estimation, generate corrections, and make suggestions to reduce stress 2. The system of claim 1.

7. The attendance data management department Encryption of attendance data when it is stored to enhance security 2. The system of claim 1.

8. The voice input analysis unit Analyzes the user's emotional state using emotion estimation functionality and automatically generates encouraging messages if the user has positive emotions.

2. The system of claim 1.

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