system

Abstract

The system according to this embodiment aims to streamline communication between superiors and subordinates and to facilitate effective information transmission. [Solution] The system according to the embodiment comprises a collection unit, a learning unit, an analysis unit, and a presentation unit. The collection unit collects data from the supervisor's daily work or meetings. The learning unit analyzes the data collected by the collection unit and learns the supervisor's thinking, judgment, and values. The analysis unit analyzes the case information input by the subordinate and extracts issues or points of concern. The presentation unit presents priorities for the issues or points of concern extracted by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that communication between superiors and subordinates cannot be efficiently carried out due to time constraints and physical constraints.

[0005] The system according to the embodiment aims to improve the communication between superiors and subordinates and effectively conduct information transmission.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a learning unit, an analysis unit, and a presentation unit. The collection unit collects data from the supervisor's daily work or meetings. The learning unit analyzes the data collected by the collection unit and learns the supervisor's thinking, judgment, and values. The analysis unit analyzes the case information input by the subordinate and extracts issues or points of concern. The presentation unit presents priorities for the issues or points of concern extracted by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline communication between superiors and subordinates and enable effective information transmission. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The human-digital twin system according to an embodiment of the present invention is a system that enables communication even when the person is not physically present. This system is particularly intended to streamline the reporting lines between superiors and subordinates within a company. Specifically, first, a human-digital twin, which is a surrogate of the superior, is created. This human-digital twin learns the superior's thinking, judgment, and values ​​using a generating AI to build a personalized model. Next, the subordinate inputs information about a matter they want to discuss with their superior into the human-digital twin. This information is analyzed by the generating AI, and issues and points of concern are extracted. Furthermore, the human-digital twin presents priorities for the extracted issues and points of concern based on the superior's personalized thinking, judgment, and values. As a result, the subordinate can refine the matter in advance and significantly reduce the time spent on meetings and consultations with their superior. This mechanism dramatically improves the time and workload involved in the reporting lines between superiors and subordinates, and accelerates the progress of each project and matter within the company. For example, data on the superior's daily work and meetings can be collected and input into the generating AI. The generating AI analyzes this data and learns the manager's thinking, judgment, and values. Next, the generating AI analyzes the case information input by the subordinate and extracts issues and concerns using natural language processing techniques. For example, text analysis and keyword extraction methods could be used. Finally, the human-digital twin, based on the manager's personalized model, presents priorities for the extracted issues and concerns. For example, prioritization could be based on importance or urgency. In this way, the human-digital twin system can streamline the reporting line between managers and subordinates and remove communication barriers.

[0029] The human-digital twin system according to this embodiment comprises a collection unit, a learning unit, an analysis unit, and a presentation unit. The collection unit collects data on the supervisor's daily work or meetings. For example, the collection unit can collect data on tasks the supervisor performs daily, such as checking emails, preparing for meetings, and writing reports. The collection unit can also collect meeting data, such as meeting minutes, audio recordings, and video recordings. For example, the collection unit can record audio from meetings the supervisor attends and collect that data. The collection unit can also collect reports created by the supervisor in digital format. Furthermore, the collection unit can collect data from schedule management tools that the supervisor uses daily. The learning unit analyzes the data collected by the collection unit and learns the supervisor's thinking, judgment, and values. For example, the learning unit analyzes the content of collected emails and learns the supervisor's decision-making process. The learning unit can also analyze meeting minutes and learn the supervisor's problem-solving methods. Furthermore, the learning unit can analyze the supervisor's reports and learn how the supervisor assesses risks and prioritizes tasks. For example, the learning unit learns the manager's thought patterns based on data from past decision-making. The analysis unit analyzes project information input by subordinates and extracts issues or concerns. For example, the analysis unit analyzes project details submitted by subordinates and extracts technical problems or resource shortages. It can also analyze progress reports submitted by subordinates and extract schedule delays or risk factors. Furthermore, the analysis unit can analyze problems submitted by subordinates and extract potential problems or external influences. For example, the analysis unit uses natural language processing technology to analyze text data submitted by subordinates and extract keywords. The presentation unit presents priorities for the issues or concerns extracted by the analysis unit based on the manager's personalized model. For example, the presentation unit ranks the extracted issues and concerns based on their importance and urgency. It can also evaluate the scope of impact of the issues and concerns based on the manager's values. Furthermore, the presentation unit can determine the priority of issues and concerns based on the manager's judgment criteria.For example, the presentation unit presents the priority of issues and concerns based on the supervisor's past decision-making data. This allows the human-digital twin system according to the embodiment to streamline the reporting line between supervisors and subordinates and remove communication barriers.

[0030] The data collection unit can collect data on the supervisor's daily work and meetings. For example, it can collect data on tasks such as checking emails, preparing for meetings, and writing reports. The data collection unit can also collect meeting data such as meeting minutes, audio recordings, and video recordings. For example, the data collection unit can record audio from meetings attended by the supervisor and collect that data. The data collection unit can also collect reports created by the supervisor in digital format. Furthermore, the data collection unit can collect data from schedule management tools that the supervisor uses on a daily basis. This allows for the efficient collection of data on the supervisor's daily work and meetings. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the supervisor's email data into a generating AI and analyze the content of the emails.

[0031] The learning unit can analyze collected data and learn the supervisor's thinking, judgment, and values. For example, the learning unit can analyze the content of collected emails to learn the supervisor's decision-making process. It can also analyze meeting minutes to learn the supervisor's problem-solving methods. Furthermore, it can analyze the supervisor's reports to learn how the supervisor assesses risks and prioritizes tasks. For example, the learning unit can learn the supervisor's thinking patterns based on data from past decision-making. This allows for efficient learning of the supervisor's thinking, judgment, and values. Some or all of the above processes in the learning unit may be performed using AI, for example, or not. For example, the learning unit can input collected data into a generating AI to learn the supervisor's thinking patterns.

[0032] The analysis unit can analyze project information input by subordinates and extract issues or points of concern using natural language processing technology. For example, the analysis unit can analyze detailed project information submitted by subordinates and extract technical problems or resource shortages. It can also analyze progress reports submitted by subordinates and extract schedule delays or risk factors. Furthermore, the analysis unit can analyze problems submitted by subordinates and extract potential problems or external influences. For example, the analysis unit can use natural language processing technology to analyze text data submitted by subordinates and extract keywords. This allows for efficient analysis of project information input by subordinates and the extraction of issues and points of concern. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input project information input by subordinates into a generating AI and extract issues and points of concern.

[0033] The presentation unit can present priorities to the extracted issues and concerns based on the supervisor's personalized model. For example, the presentation unit ranks the extracted issues and concerns based on their importance and urgency. It can also evaluate the scope of impact of the issues and concerns based on the supervisor's values. Furthermore, the presentation unit can determine the priority of the issues and concerns based on the supervisor's judgment criteria. For example, the presentation unit presents priorities to the issues and concerns based on the supervisor's past decision-making data. This allows the presentation unit to present priorities to the extracted issues and concerns based on the supervisor's personalized model. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can input the extracted issues and concerns into a generating AI and present priorities.

[0034] The data collection unit can analyze the supervisor's past work history and select the optimal data collection method. For example, the data collection unit can prioritize data collection methods that the supervisor has frequently used in the past. The data collection unit can also select the optimal data collection method for a specific time period based on the supervisor's work history. Furthermore, the data collection unit can analyze the supervisor's work history and select the most efficient data collection method. For example, the data collection unit can select the optimal data collection method based on data from projects the supervisor has worked on in the past. This allows the optimal data collection method to be selected based on the supervisor's past work history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the supervisor's past work history data into a generating AI and select the optimal data collection method.

[0035] The data collection unit can filter data based on the supervisor's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to projects the supervisor is currently working on. The data collection unit can also filter and collect highly relevant data based on the supervisor's areas of interest. Furthermore, if the supervisor is focused on a specific project, the data collection unit can collect only data related to that project. For example, the data collection unit can filter highly relevant data based on data from projects the supervisor is currently working on. This allows the data to be filtered based on the supervisor's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the supervisor's project data into a generating AI and perform filtering.

[0036] The data collection unit can prioritize the collection of highly relevant data by considering the supervisor's geographical location information during data collection. For example, if the supervisor is on a business trip, the data collection unit will prioritize the collection of data related to the business trip destination. Furthermore, if the supervisor is in a specific region, the data collection unit can prioritize the collection of data related to that region. Additionally, if the supervisor is in the office, the data collection unit can prioritize the collection of data related to the office. For example, the data collection unit can prioritize the collection of highly relevant data based on the supervisor's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the supervisor's geographical location information into a generating AI and collect highly relevant data.

[0037] The data collection unit can analyze the supervisor's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to topics mentioned by the supervisor on social media. The data collection unit can also collect relevant data based on the activity of accounts that the supervisor follows. Furthermore, the data collection unit can collect relevant data based on information shared by the supervisor on social media. For example, the data collection unit can collect relevant data based on the supervisor's social media activity. This allows for the collection of relevant data based on the supervisor's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the supervisor's social media data into a generating AI and collect relevant data.

[0038] The learning unit can adjust the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also adjust the parameters of the learning algorithm based on past learning data. Furthermore, the learning unit can analyze past learning data to improve the accuracy of the learning algorithm. For example, the learning unit optimizes the learning algorithm based on past learning data. This makes it possible to optimize the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and adjust the learning algorithm.

[0039] The learning unit can adjust the frequency of learning based on the supervisor's work patterns. For example, the learning unit can reduce the frequency of learning when the supervisor is busy, and increase it when the supervisor has more free time. Furthermore, the learning unit can set the optimal learning frequency based on the supervisor's work patterns. For example, the learning unit can adjust the learning frequency based on the supervisor's work schedule. This allows for more efficient learning by adjusting the learning frequency based on the supervisor's work patterns. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input supervisor work pattern data into a generating AI and adjust the learning frequency.

[0040] The learning unit can select optimal learning data during training, taking into account the supervisor's geographical location information. For example, if the supervisor is on a business trip, the learning unit will select learning data related to the business trip destination. Furthermore, if the supervisor is in a specific region, the learning unit can select learning data related to that region. Additionally, if the supervisor is in the office, the learning unit can select learning data related to the office. For example, the learning unit can select optimal learning data based on the supervisor's geographical location information. This enables efficient learning by selecting optimal learning data based on the supervisor's geographical location information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the supervisor's geographical location information into a generating AI to select optimal learning data.

[0041] The learning unit can analyze the supervisor's social media activity during training and use relevant data for learning. For example, the learning unit can use data related to topics mentioned by the supervisor on social media. It can also use relevant data based on the activity of accounts the supervisor follows. Furthermore, the learning unit can use relevant data based on information shared by the supervisor on social media. For example, the learning unit can use relevant data based on the supervisor's social media activity. This enables efficient learning by using relevant data based on the supervisor's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the supervisor's social media data into a generating AI and use the relevant data for learning.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the case during the analysis. For example, the analysis unit will perform a detailed analysis for cases with high importance. Conversely, the analysis unit can also perform a simplified analysis for cases with low importance. Furthermore, the analysis unit can set the optimal level of detail of the analysis based on the importance of the case. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the case. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the case. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input case importance data into a generating AI and adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the case during analysis. For example, the analysis unit can apply a technical analysis algorithm to a technical case. It can also apply a marketing-specific analysis algorithm to a marketing case. Furthermore, it can apply a financial-specific analysis algorithm to a financial case. For example, the analysis unit applies the most suitable analysis algorithm based on the case category. This enables efficient analysis by applying the most suitable analysis algorithm according to the case category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input case category data into an AI that generates data and apply an analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the submission timing of each case during the analysis process. For example, the analysis unit will prioritize analyzing cases with approaching submission deadlines. Conversely, it can also postpone analyzing cases with distant submission deadlines. Furthermore, the analysis unit can set the optimal analysis priority based on the submission timing of each case. For example, the analysis unit will determine the analysis priority based on the submission timing of each case. This enables efficient analysis by determining the analysis priority based on the submission timing of each case. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input case submission timing data into a generating AI to determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the cases during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant cases. It can also postpone the analysis of less relevant cases. Furthermore, the analysis unit can set the optimal order of analysis based on the relevance of the cases. For example, the analysis unit adjusts the order of analysis based on the relevance of the cases. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the cases. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input case relevance data into a generating AI and adjust the order of analysis.

[0046] The presentation unit can adjust the level of detail in the presentation based on the importance of the case when presenting priorities. For example, the presentation unit will provide detailed priority information for high-importance cases. Conversely, it can also provide concise priority information for low-importance cases. Furthermore, the presentation unit can set the optimal level of detail for the priority presentation based on the importance of the case. For example, the presentation unit adjusts the level of detail based on the importance of the case. This allows for efficient presentation by adjusting the level of detail based on the importance of the case. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input case importance data into a generating AI and adjust the level of detail of the presentation.

[0047] The presentation unit can apply different presentation algorithms depending on the category of the project when presenting priorities. For example, for technical projects, the presentation unit can apply a technical priority presentation algorithm. Furthermore, for marketing projects, it can apply a marketing-specific priority presentation algorithm. In addition, for financial projects, it can apply a finance-specific priority presentation algorithm. For example, the presentation unit applies the optimal presentation algorithm based on the project category. This enables efficient presentation by applying the optimal presentation algorithm according to the project category. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input project category data into a generating AI and apply a presentation algorithm.

[0048] The presentation unit can determine the priority of presentations based on the submission timing of each case when presenting priorities. For example, the presentation unit will prioritize presentations for cases with approaching submission deadlines. Conversely, the presentation unit can also postpone presentations for cases with distant submission deadlines. Furthermore, the presentation unit can set the optimal priority order for presentations based on the submission timing of each case. For example, the presentation unit can determine the priority order based on the submission timing of each case. This enables efficient presentations by determining the priority order based on the submission timing of each case. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input case submission timing data into a generating AI to determine the priority order for presentations.

[0049] The presentation unit can adjust the order of presentations based on the relevance of the cases when presenting priorities. For example, the presentation unit will present priorities to cases that are highly relevant. Conversely, the presentation unit can also postpone presenting priorities to cases that are less relevant. Furthermore, the presentation unit can set the optimal order of priority presentations based on the relevance of the cases. For example, the presentation unit adjusts the order of presentations based on the relevance of the cases. This allows for efficient presentations by adjusting the order of presentations based on the relevance of the cases. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can input case relevance data into a generating AI and adjust the order of presentations.

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

[0051] The data collection unit can collect biometric information from supervisors and monitor their health status. For example, it can collect biometric information such as the supervisor's heart rate, blood pressure, and stress level. It can also monitor the supervisor's sleep patterns and assess their fatigue level. Furthermore, it can track the supervisor's exercise level to help maintain their health. This allows for real-time monitoring of the supervisor's health status and improves work efficiency.

[0052] The learning department can analyze past success and failure cases of supervisors and construct optimal decision-making models. For example, the learning department can analyze data from past successful projects of supervisors and extract the factors for success. It can also analyze data from unsuccessful projects and identify the factors for failure. Furthermore, the learning department can compare and analyze success and failure cases to construct optimal decision-making models. This can improve the accuracy of supervisors' decision-making.

[0053] The analysis unit can analyze the skill sets of subordinates and propose optimal task assignments. For example, it can analyze subordinates' technical skills and experience to assign appropriate tasks. It can also analyze subordinates' past performance data to determine optimal task assignments. Furthermore, it can consider subordinates' interests and preferences to propose task assignments that enhance motivation. This maximizes subordinate performance.

[0054] The data collection unit can select the optimal data collection method by considering the supervisor's geographical location. For example, if the supervisor is on a business trip, it can prioritize collecting data related to the destination. It can also prioritize collecting data related to a specific region if the supervisor is in that region. Furthermore, if the supervisor is in the office, it can prioritize collecting data related to the office. This allows the system to select the optimal data collection method based on the supervisor's geographical location.

[0055] The learning department can analyze the supervisor's work patterns and suggest the optimal learning timing. For example, it can reduce the frequency of learning during busy periods and increase it during less busy periods. Furthermore, it can suggest the optimal learning timing based on the supervisor's work schedule. This enables efficient learning tailored to the supervisor's work patterns.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection team collects data on the supervisor's daily work or meetings. For example, the data collection team can collect data on tasks the supervisor performs daily, such as checking emails, preparing for meetings, and writing reports. The data collection team can also collect meeting data, such as meeting minutes, audio recordings, and video recordings. For example, the data collection team can record audio from meetings the supervisor attends and collect that data. The data collection team can also collect reports created by the supervisor in digital format. Furthermore, the data collection team can collect data from scheduling tools the supervisor uses on a daily basis. Step 2: The learning unit analyzes the data collected by the collection unit to learn about the supervisor's thinking, judgment, and values. For example, the learning unit analyzes the content of collected emails to learn about the supervisor's decision-making process. It can also analyze meeting minutes to learn about the supervisor's problem-solving methods. Furthermore, it can analyze the supervisor's reports to learn about the supervisor's risk assessment and prioritization methods. For example, the learning unit learns about the supervisor's thinking patterns based on data from past decision-making. Step 3: The analysis department analyzes the project information inputted by the subordinate and extracts issues or points of concern. For example, the analysis department analyzes the project details submitted by the subordinate and extracts technical problems or resource shortages. The analysis department can also analyze the progress reported by the subordinate and extract schedule delays or risk factors. Furthermore, the analysis department can analyze the problems submitted by the subordinate and extract potential problems or external impacts. For example, the analysis department uses natural language processing technology to analyze the text data submitted by the subordinate and extract keywords. Step 4: The presentation unit presents priorities for the issues or concerns extracted by the analysis unit, based on the manager's personalized model. For example, the presentation unit ranks the extracted issues and concerns based on their importance and urgency. It can also evaluate the scope of impact of the issues and concerns based on the manager's values. Furthermore, the presentation unit can determine the priority of issues and concerns based on the manager's decision-making criteria. For example, the presentation unit presents priorities for issues and concerns based on the manager's past decision-making data.

[0058] (Example of form 2) The human-digital twin system according to an embodiment of the present invention is a system that enables communication even when the person is not physically present. This system is particularly intended to streamline the reporting lines between superiors and subordinates within a company. Specifically, first, a human-digital twin, which is a surrogate of the superior, is created. This human-digital twin learns the superior's thinking, judgment, and values ​​using a generating AI to build a personalized model. Next, the subordinate inputs information about a matter they want to discuss with their superior into the human-digital twin. This information is analyzed by the generating AI, and issues and points of concern are extracted. Furthermore, the human-digital twin presents priorities for the extracted issues and points of concern based on the superior's personalized thinking, judgment, and values. As a result, the subordinate can refine the matter in advance and significantly reduce the time spent on meetings and consultations with their superior. This mechanism dramatically improves the time and workload involved in the reporting lines between superiors and subordinates, and accelerates the progress of each project and matter within the company. For example, data on the superior's daily work and meetings can be collected and input into the generating AI. The generating AI analyzes this data and learns the manager's thinking, judgment, and values. Next, the generating AI analyzes the case information input by the subordinate and extracts issues and concerns using natural language processing techniques. For example, text analysis and keyword extraction methods could be used. Finally, the human-digital twin, based on the manager's personalized model, presents priorities for the extracted issues and concerns. For example, prioritization could be based on importance or urgency. In this way, the human-digital twin system can streamline the reporting line between managers and subordinates and remove communication barriers.

[0059] The human-digital twin system according to this embodiment comprises a collection unit, a learning unit, an analysis unit, and a presentation unit. The collection unit collects data on the supervisor's daily work or meetings. For example, the collection unit can collect data on tasks the supervisor performs daily, such as checking emails, preparing for meetings, and writing reports. The collection unit can also collect meeting data, such as meeting minutes, audio recordings, and video recordings. For example, the collection unit can record audio from meetings the supervisor attends and collect that data. The collection unit can also collect reports created by the supervisor in digital format. Furthermore, the collection unit can collect data from schedule management tools that the supervisor uses daily. The learning unit analyzes the data collected by the collection unit and learns the supervisor's thinking, judgment, and values. For example, the learning unit analyzes the content of collected emails and learns the supervisor's decision-making process. The learning unit can also analyze meeting minutes and learn the supervisor's problem-solving methods. Furthermore, the learning unit can analyze the supervisor's reports and learn how the supervisor assesses risks and prioritizes tasks. For example, the learning unit learns the manager's thought patterns based on data from past decision-making. The analysis unit analyzes project information input by subordinates and extracts issues or concerns. For example, the analysis unit analyzes project details submitted by subordinates and extracts technical problems or resource shortages. It can also analyze progress reports submitted by subordinates and extract schedule delays or risk factors. Furthermore, the analysis unit can analyze problems submitted by subordinates and extract potential problems or external influences. For example, the analysis unit uses natural language processing technology to analyze text data submitted by subordinates and extract keywords. The presentation unit presents priorities for the issues or concerns extracted by the analysis unit based on the manager's personalized model. For example, the presentation unit ranks the extracted issues and concerns based on their importance and urgency. It can also evaluate the scope of impact of the issues and concerns based on the manager's values. Furthermore, the presentation unit can determine the priority of issues and concerns based on the manager's judgment criteria.For example, the presentation unit presents the priority of issues and concerns based on the supervisor's past decision-making data. This allows the human-digital twin system according to the embodiment to streamline the reporting line between supervisors and subordinates and remove communication barriers.

[0060] The data collection unit can collect data on the supervisor's daily work and meetings. For example, it can collect data on tasks such as checking emails, preparing for meetings, and writing reports. The data collection unit can also collect meeting data such as meeting minutes, audio recordings, and video recordings. For example, the data collection unit can record audio from meetings attended by the supervisor and collect that data. The data collection unit can also collect reports created by the supervisor in digital format. Furthermore, the data collection unit can collect data from schedule management tools that the supervisor uses on a daily basis. This allows for the efficient collection of data on the supervisor's daily work and meetings. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the supervisor's email data into a generating AI and analyze the content of the emails.

[0061] The learning unit can analyze collected data and learn the supervisor's thinking, judgment, and values. For example, the learning unit can analyze the content of collected emails to learn the supervisor's decision-making process. It can also analyze meeting minutes to learn the supervisor's problem-solving methods. Furthermore, it can analyze the supervisor's reports to learn how the supervisor assesses risks and prioritizes tasks. For example, the learning unit can learn the supervisor's thinking patterns based on data from past decision-making. This allows for efficient learning of the supervisor's thinking, judgment, and values. Some or all of the above processes in the learning unit may be performed using AI, for example, or not. For example, the learning unit can input collected data into a generating AI to learn the supervisor's thinking patterns.

[0062] The analysis unit can analyze project information input by subordinates and extract issues or points of concern using natural language processing technology. For example, the analysis unit can analyze detailed project information submitted by subordinates and extract technical problems or resource shortages. It can also analyze progress reports submitted by subordinates and extract schedule delays or risk factors. Furthermore, the analysis unit can analyze problems submitted by subordinates and extract potential problems or external influences. For example, the analysis unit can use natural language processing technology to analyze text data submitted by subordinates and extract keywords. This allows for efficient analysis of project information input by subordinates and the extraction of issues and points of concern. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input project information input by subordinates into a generating AI and extract issues and points of concern.

[0063] The presentation unit can present priorities to the extracted issues and concerns based on the supervisor's personalized model. For example, the presentation unit ranks the extracted issues and concerns based on their importance and urgency. It can also evaluate the scope of impact of the issues and concerns based on the supervisor's values. Furthermore, the presentation unit can determine the priority of the issues and concerns based on the supervisor's judgment criteria. For example, the presentation unit presents priorities to the issues and concerns based on the supervisor's past decision-making data. This allows the presentation unit to present priorities to the extracted issues and concerns based on the supervisor's personalized model. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can input the extracted issues and concerns into a generating AI and present priorities.

[0064] The data collection unit can estimate the supervisor's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the supervisor is stressed, the data collection unit can temporarily delay data collection and resume it when the supervisor is relaxed. Alternatively, if the supervisor is focused, the data collection unit can collect data at that time to minimize disruption to work. Furthermore, if the supervisor is in a meeting, the data collection unit can wait until the meeting ends before beginning collection. This allows for adjustment of data collection timing according to the supervisor's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the supervisor's emotion data into a generative AI to adjust the timing of data collection.

[0065] The data collection unit can analyze the supervisor's past work history and select the optimal data collection method. For example, the data collection unit can prioritize data collection methods that the supervisor has frequently used in the past. The data collection unit can also select the optimal data collection method for a specific time period based on the supervisor's work history. Furthermore, the data collection unit can analyze the supervisor's work history and select the most efficient data collection method. For example, the data collection unit can select the optimal data collection method based on data from projects the supervisor has worked on in the past. This allows the optimal data collection method to be selected based on the supervisor's past work history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the supervisor's past work history data into a generating AI and select the optimal data collection method.

[0066] The data collection unit can filter data based on the supervisor's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to projects the supervisor is currently working on. The data collection unit can also filter and collect highly relevant data based on the supervisor's areas of interest. Furthermore, if the supervisor is focused on a specific project, the data collection unit can collect only data related to that project. For example, the data collection unit can filter highly relevant data based on data from projects the supervisor is currently working on. This allows the data to be filtered based on the supervisor's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the supervisor's project data into a generating AI and perform filtering.

[0067] The data collection unit can estimate the supervisor's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the supervisor is stressed, the data collection unit will postpone the collection of less important data. Conversely, if the supervisor is relaxed, the data collection unit can prioritize the collection of highly important data. Furthermore, if the supervisor is focused, the data collection unit can prioritize the collection of important data related to the work. For example, the data collection unit determines the priority of data to collect based on the supervisor's emotional data. This allows the priority of data to be collected to be determined according to the supervisor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the supervisor's emotional data into a generative AI to determine the priority of the data.

[0068] The data collection unit can prioritize the collection of highly relevant data by considering the supervisor's geographical location information during data collection. For example, if the supervisor is on a business trip, the data collection unit will prioritize the collection of data related to the business trip destination. Furthermore, if the supervisor is in a specific region, the data collection unit can prioritize the collection of data related to that region. Additionally, if the supervisor is in the office, the data collection unit can prioritize the collection of data related to the office. For example, the data collection unit can prioritize the collection of highly relevant data based on the supervisor's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the supervisor's geographical location information into a generating AI and collect highly relevant data.

[0069] The data collection unit can analyze the supervisor's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to topics mentioned by the supervisor on social media. The data collection unit can also collect relevant data based on the activity of accounts that the supervisor follows. Furthermore, the data collection unit can collect relevant data based on information shared by the supervisor on social media. For example, the data collection unit can collect relevant data based on the supervisor's social media activity. This allows for the collection of relevant data based on the supervisor's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the supervisor's social media data into a generating AI and collect relevant data.

[0070] The learning unit can estimate the supervisor's emotions and select training data based on the estimated emotions. For example, if the supervisor is relaxed, the learning unit will select detailed training data. If the supervisor is stressed, the learning unit can also select concise training data. Furthermore, if the supervisor is focused, the learning unit can select training data relevant to the work. For example, the learning unit selects training data based on the supervisor's emotional data. This enables efficient learning by selecting training data according to the supervisor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the supervisor's emotional data into a generative AI and select training data.

[0071] The learning unit can adjust the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also adjust the parameters of the learning algorithm based on past learning data. Furthermore, the learning unit can analyze past learning data to improve the accuracy of the learning algorithm. For example, the learning unit optimizes the learning algorithm based on past learning data. This makes it possible to optimize the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and adjust the learning algorithm.

[0072] The learning unit can adjust the frequency of learning based on the supervisor's work patterns. For example, the learning unit can reduce the frequency of learning when the supervisor is busy, and increase it when the supervisor has more free time. Furthermore, the learning unit can set the optimal learning frequency based on the supervisor's work patterns. For example, the learning unit can adjust the learning frequency based on the supervisor's work schedule. This allows for more efficient learning by adjusting the learning frequency based on the supervisor's work patterns. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input supervisor work pattern data into a generating AI and adjust the learning frequency.

[0073] The learning unit can estimate the supervisor's emotions and determine learning priorities based on the estimated emotions. For example, if the supervisor is relaxed, the learning unit will prioritize learning important learning data. If the supervisor is stressed, the learning unit can also prioritize learning concise learning data. Furthermore, if the supervisor is focused, the learning unit can prioritize learning data relevant to the work. For example, the learning unit determines learning priorities based on the supervisor's emotional data. This enables efficient learning by prioritizing learning according to the supervisor's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not. For example, the learning unit can input the supervisor's emotional data into a generative AI to determine learning priorities.

[0074] The learning unit can select optimal learning data during training, taking into account the supervisor's geographical location information. For example, if the supervisor is on a business trip, the learning unit will select learning data related to the business trip destination. Furthermore, if the supervisor is in a specific region, the learning unit can select learning data related to that region. Additionally, if the supervisor is in the office, the learning unit can select learning data related to the office. For example, the learning unit can select optimal learning data based on the supervisor's geographical location information. This enables efficient learning by selecting optimal learning data based on the supervisor's geographical location information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the supervisor's geographical location information into a generating AI to select optimal learning data.

[0075] The learning unit can analyze the supervisor's social media activity during training and use relevant data for learning. For example, the learning unit can use data related to topics mentioned by the supervisor on social media. It can also use relevant data based on the activity of accounts the supervisor follows. Furthermore, the learning unit can use relevant data based on information shared by the supervisor on social media. For example, the learning unit can use relevant data based on the supervisor's social media activity. This enables efficient learning by using relevant data based on the supervisor's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the supervisor's social media data into a generating AI and use the relevant data for learning.

[0076] The analysis unit can estimate the emotions of its subordinates and adjust the presentation of the analysis based on the estimated emotions. For example, if a subordinate is tense, the analysis unit can provide a simple and easily understandable presentation. If a subordinate is relaxed, the analysis unit can also provide a presentation that includes detailed information. Furthermore, if a subordinate is in a hurry, the analysis unit can provide a concise presentation. For example, the analysis unit adjusts the presentation of the analysis based on the subordinate's emotional data. By adjusting the presentation of the analysis according to the subordinate's emotions, it is possible to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subordinate's emotional data into the generative AI and adjust the presentation of the analysis.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the case during the analysis. For example, the analysis unit will perform a detailed analysis for cases with high importance. Conversely, the analysis unit can also perform a simplified analysis for cases with low importance. Furthermore, the analysis unit can set the optimal level of detail of the analysis based on the importance of the case. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the case. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the case. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input case importance data into a generating AI and adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the category of the case during analysis. For example, the analysis unit can apply a technical analysis algorithm to a technical case. It can also apply a marketing-specific analysis algorithm to a marketing case. Furthermore, it can apply a financial-specific analysis algorithm to a financial case. For example, the analysis unit applies the most suitable analysis algorithm based on the case category. This enables efficient analysis by applying the most suitable analysis algorithm according to the case category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input case category data into an AI that generates data and apply an analysis algorithm.

[0079] The analysis unit can estimate the subordinate's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the subordinate is tense, the analysis unit will provide a short, concise analysis. If the subordinate is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the subordinate is in a hurry, the analysis unit can provide a brief analysis. For example, the analysis unit adjusts the length of the analysis based on the subordinate's emotion data. By adjusting the length of the analysis according to the subordinate's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subordinate's emotion data into the generative AI and adjust the length of the analysis.

[0080] The analysis unit can determine the priority of analysis based on the submission timing of each case during the analysis process. For example, the analysis unit will prioritize analyzing cases with approaching submission deadlines. Conversely, it can also postpone analyzing cases with distant submission deadlines. Furthermore, the analysis unit can set the optimal analysis priority based on the submission timing of each case. For example, the analysis unit will determine the analysis priority based on the submission timing of each case. This enables efficient analysis by determining the analysis priority based on the submission timing of each case. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input case submission timing data into a generating AI to determine the analysis priority.

[0081] The analysis unit can adjust the order of analysis based on the relevance of the cases during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant cases. It can also postpone the analysis of less relevant cases. Furthermore, the analysis unit can set the optimal order of analysis based on the relevance of the cases. For example, the analysis unit adjusts the order of analysis based on the relevance of the cases. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the cases. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input case relevance data into a generating AI and adjust the order of analysis.

[0082] The presentation unit can estimate the emotions of its subordinates and adjust the way priorities are presented based on those estimated emotions. For example, if a subordinate is tense, the presentation unit can provide a simple and highly visible presentation. If a subordinate is relaxed, the presentation unit can also provide a presentation that includes detailed information. Furthermore, if a subordinate is in a hurry, the presentation unit can provide a concise presentation. For example, the presentation unit adjusts the way priorities are presented based on the subordinate's emotional data. This allows for more appropriate presentations by adjusting the way priorities are presented according to the subordinate's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can input the subordinate's emotional data into a generative AI and adjust the way priorities are presented.

[0083] The presentation unit can adjust the level of detail in the presentation based on the importance of the case when presenting priorities. For example, the presentation unit will provide detailed priority information for high-importance cases. Conversely, it can also provide concise priority information for low-importance cases. Furthermore, the presentation unit can set the optimal level of detail for the priority presentation based on the importance of the case. For example, the presentation unit adjusts the level of detail based on the importance of the case. This allows for efficient presentation by adjusting the level of detail based on the importance of the case. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input case importance data into a generating AI and adjust the level of detail of the presentation.

[0084] The presentation unit can apply different presentation algorithms depending on the category of the project when presenting priorities. For example, for technical projects, the presentation unit can apply a technical priority presentation algorithm. Furthermore, for marketing projects, it can apply a marketing-specific priority presentation algorithm. In addition, for financial projects, it can apply a finance-specific priority presentation algorithm. For example, the presentation unit applies the optimal presentation algorithm based on the project category. This enables efficient presentation by applying the optimal presentation algorithm according to the project category. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input project category data into a generating AI and apply a presentation algorithm.

[0085] The presentation unit can estimate the subordinate's emotions and adjust the display order of priorities based on the estimated emotions. For example, if the subordinate is tense, the presentation unit will display important items first. If the subordinate is relaxed, the presentation unit can also display items in an order that includes detailed information. Furthermore, if the subordinate is in a hurry, the presentation unit can display items in an order that gets straight to the point. For example, the presentation unit adjusts the display order of priorities based on the subordinate's emotion data. This allows for a more appropriate display by adjusting the display order of priorities according to the subordinate's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can input the subordinate's emotion data into the generative AI and adjust the display order of priorities.

[0086] The presentation unit can determine the priority of presentations based on the submission timing of each case when presenting priorities. For example, the presentation unit will prioritize presentations for cases with approaching submission deadlines. Conversely, the presentation unit can also postpone presentations for cases with distant submission deadlines. Furthermore, the presentation unit can set the optimal priority order for presentations based on the submission timing of each case. For example, the presentation unit can determine the priority order based on the submission timing of each case. This enables efficient presentations by determining the priority order based on the submission timing of each case. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input case submission timing data into a generating AI to determine the priority order for presentations.

[0087] The presentation unit can adjust the order of presentations based on the relevance of the cases when presenting priorities. For example, the presentation unit will present priorities to cases that are highly relevant. Conversely, the presentation unit can also postpone presenting priorities to cases that are less relevant. Furthermore, the presentation unit can set the optimal order of priority presentations based on the relevance of the cases. For example, the presentation unit adjusts the order of presentations based on the relevance of the cases. This allows for efficient presentations by adjusting the order of presentations based on the relevance of the cases. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can input case relevance data into a generating AI and adjust the order of presentations. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, learning unit, analysis unit, and presentation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data on the supervisor's daily work and meetings using the camera 42 and microphone 38B of the smart device 14. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to learn the supervisor's thinking, judgment, and values. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the case information input by the subordinate to extract issues and points of concern. The presentation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and presents priorities based on the supervisor's personalized model. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, learning unit, analysis unit, and presentation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect data on the supervisor's daily work and meetings. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, to analyze the collected data and learn the supervisor's thinking, judgment, and values. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, to analyze the case information input by the subordinate and extract issues and points of concern. The presentation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, to present priorities based on the supervisor's personalized model. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, learning unit, analysis unit, and presentation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data on the supervisor's daily work and meetings using the camera 42 and microphone 238 of the headset terminal 314. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to learn the supervisor's thinking, judgment, and values. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the case information input by the subordinate to extract issues and points of concern. The presentation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and presents priorities based on the supervisor's personalized model. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, learning unit, analysis unit, and presentation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect data on the supervisor's daily work and meetings. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and learns the supervisor's thinking, judgment, and values. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the case information input by the subordinate and extracts issues and points of concern. The presentation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which presents priorities based on a personalized model of the supervisor.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The data collection unit can collect biometric information from supervisors and monitor their health status. For example, it can collect biometric information such as the supervisor's heart rate, blood pressure, and stress level. It can also monitor the supervisor's sleep patterns and assess their fatigue level. Furthermore, it can track the supervisor's exercise level to help maintain their health. This allows for real-time monitoring of the supervisor's health status and improves work efficiency.

[0090] The learning department can analyze past success and failure cases of supervisors and construct optimal decision-making models. For example, the learning department can analyze data from past successful projects of supervisors and extract the factors for success. It can also analyze data from unsuccessful projects and identify the factors for failure. Furthermore, the learning department can compare and analyze success and failure cases to construct optimal decision-making models. This can improve the accuracy of supervisors' decision-making.

[0091] The analysis unit can analyze the skill sets of subordinates and propose optimal task assignments. For example, it can analyze subordinates' technical skills and experience to assign appropriate tasks. It can also analyze subordinates' past performance data to determine optimal task assignments. Furthermore, it can consider subordinates' interests and preferences to propose task assignments that enhance motivation. This maximizes subordinate performance.

[0092] The presentation unit can estimate the supervisor's emotions and adjust the content of the feedback based on those emotions. For example, if the supervisor is stressed, positive feedback will be prioritized. If the supervisor is relaxed, detailed feedback can be provided. Furthermore, if the supervisor is focused, feedback including specific areas for improvement can be provided. This allows for the provision of appropriate feedback tailored to the supervisor's emotions.

[0093] The data collection unit can estimate the supervisor's emotions and adjust the frequency of data collection based on that estimate. For example, if the supervisor is stressed, the frequency of data collection can be reduced. Conversely, if the supervisor is relaxed, the frequency of data collection can be increased. Furthermore, if the supervisor is focused, important data can be prioritized for collection. This allows the frequency of data collection to be adjusted according to the supervisor's emotions.

[0094] The learning unit can estimate the supervisor's emotions and prioritize training data based on that estimation. For example, if the supervisor is relaxed, it can prioritize learning detailed data. If the supervisor is stressed, it can prioritize learning concise data. Furthermore, if the supervisor is focused, it can prioritize learning work-related data. This allows the system to prioritize training data according to the supervisor's emotions.

[0095] The analysis unit can estimate the emotions of subordinates and adjust the way the analysis results are presented based on the estimated emotions. For example, if a subordinate is tense, it can provide a simple and easily understandable presentation. If a subordinate is relaxed, it can provide a presentation that includes detailed information. Furthermore, if a subordinate is in a hurry, it can provide a presentation that gets straight to the point. In this way, the presentation of the analysis results can be adjusted according to the emotions of the subordinates.

[0096] The presentation unit can estimate the subordinate's emotions and adjust the way priorities are presented based on those emotions. For example, if the subordinate is nervous, a simple and highly visible presentation method can be provided. If the subordinate is relaxed, a presentation method including detailed information can be provided. Furthermore, if the subordinate is in a hurry, a presentation method that gets straight to the point can be provided. This allows the presentation method of priorities to be adjusted according to the subordinate's emotions.

[0097] The data collection unit can select the optimal data collection method by considering the supervisor's geographical location. For example, if the supervisor is on a business trip, it can prioritize collecting data related to the destination. It can also prioritize collecting data related to a specific region if the supervisor is in that region. Furthermore, if the supervisor is in the office, it can prioritize collecting data related to the office. This allows the system to select the optimal data collection method based on the supervisor's geographical location.

[0098] The learning department can analyze the supervisor's work patterns and suggest the optimal learning timing. For example, it can reduce the frequency of learning during busy periods and increase it during less busy periods. Furthermore, it can suggest the optimal learning timing based on the supervisor's work schedule. This enables efficient learning tailored to the supervisor's work patterns.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The data collection team collects data on the supervisor's daily work or meetings. For example, the data collection team can collect data on tasks the supervisor performs daily, such as checking emails, preparing for meetings, and writing reports. The data collection team can also collect meeting data, such as meeting minutes, audio recordings, and video recordings. For example, the data collection team can record audio from meetings the supervisor attends and collect that data. The data collection team can also collect reports created by the supervisor in digital format. Furthermore, the data collection team can collect data from scheduling tools the supervisor uses on a daily basis. Step 2: The learning unit analyzes the data collected by the collection unit to learn about the supervisor's thinking, judgment, and values. For example, the learning unit analyzes the content of collected emails to learn about the supervisor's decision-making process. It can also analyze meeting minutes to learn about the supervisor's problem-solving methods. Furthermore, it can analyze the supervisor's reports to learn about the supervisor's risk assessment and prioritization methods. For example, the learning unit learns about the supervisor's thinking patterns based on data from past decision-making. Step 3: The analysis department analyzes the project information inputted by the subordinate and extracts issues or points of concern. For example, the analysis department analyzes the project details submitted by the subordinate and extracts technical problems or resource shortages. The analysis department can also analyze the progress reported by the subordinate and extract schedule delays or risk factors. Furthermore, the analysis department can analyze the problems submitted by the subordinate and extract potential problems or external impacts. For example, the analysis department uses natural language processing technology to analyze the text data submitted by the subordinate and extract keywords. Step 4: The presentation unit presents priorities for the issues or concerns extracted by the analysis unit, based on the manager's personalized model. For example, the presentation unit ranks the extracted issues and concerns based on their importance and urgency. It can also evaluate the scope of impact of the issues and concerns based on the manager's values. Furthermore, the presentation unit can determine the priority of issues and concerns based on the manager's decision-making criteria. For example, the presentation unit presents priorities for issues and concerns based on the manager's past decision-making data.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0138] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0154] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection department that collects data on the supervisor's daily work or meetings, The data collected by the aforementioned collection unit is analyzed by the learning unit, which learns the supervisor's thinking, judgment, and values. The analysis department analyzes the project information input by subordinates and extracts issues or points of concern. A presentation unit that presents priorities for the issues or points of concern extracted by the analysis unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Collect data on your supervisor's daily work and meetings. The system according to feature 1.

3. The aforementioned learning unit, The collected data is analyzed to learn the supervisor's thinking, judgment, and values. The system according to feature 1.

4. The aforementioned analysis unit, The system analyzes project information inputted by subordinates and uses natural language processing technology to extract issues or points of concern. The system according to feature 1.

5. The aforementioned display unit is, For the identified issues and concerns, prioritize them based on the manager's personalized model. The system according to feature 1.

6. The aforementioned collection unit is We estimate the supervisor's emotions and adjust the timing of data collection based on the estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze your supervisor's past work history and select the appropriate data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filter it based on the supervisor's current projects and areas of interest. The system according to feature 1.

Images