system

Abstract

The system according to this embodiment aims to make intuitive decisions using the system and provide those decisions to employees. [Solution] The system according to the embodiment comprises a collection unit, a learning unit, a decision unit, and a provision unit. The collection unit collects data. The learning unit learns from the data collected by the collection unit. The decision unit makes an intuitive decision based on the model learned by the learning unit. The provision unit provides the decision made by the decision unit to the employee.

Description

Technical Field

[0004]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for a system to make intuitive judgments that cannot be quantitatively expressed.

[0005] The system according to the embodiment aims to make intuitive judgments by the system and provide them to employees.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, a learning unit, a judgment unit, and a provision unit. The collection unit collects data. The learning unit learns the data collected by the collection unit. The judgment unit makes intuitive judgments based on the model learned by the learning unit. The provision unit provides the judgments made by the judgment unit to employees. [Effects of the Invention]

[0007] The system according to this embodiment can make intuitive decisions and provide them to employees. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 intuitive decision generation system according to an embodiment of the present invention is a system that generates intuitive and instinctive judgments. By generating "instincts" that cannot be expressed quantitatively, this system can provide various perspectives. In particular, in a company, each employee can gain perspectives that they may not have obtained due to lack of experience, enabling the early detection of flaws in projects. First, the system learns from past data and cases to build a model for making intuitive judgments. This model learns not only quantitative data but also qualitative data. For example, it learns from past project success and failure cases and the judgment criteria of experienced employees. Next, the system makes intuitive judgments about new projects. At this time, the system predicts the risks and problems of the project based on past data and cases. For example, at the planning stage of a new project, the system can determine that "this plan has risks." Furthermore, the system provides intuitive judgments to employees. Employees can compensate for the lack of perspectives due to lack of experience by referring to the intuitive judgments provided by the system. For example, when a young employee is in charge of a new project, the system can provide advice such as "pay attention to this part," thereby increasing the success rate of the project. This system allows employees to compensate for a lack of perspective due to inexperience and to identify flaws in projects early. For example, if the system determines that "this plan has risks," employees can take measures to address those risks. This is expected to improve the success rate of projects and enhance the overall performance of the company. In this way, the intuitive decision-making system allows employees to compensate for a lack of perspective due to inexperience and to identify risks and problems in projects early.

[0029] The intuitive decision generation system according to this embodiment comprises a collection unit, a learning unit, a decision unit, and a provision unit. The collection unit collects data. The collection unit collects, for example, past project data, employee feedback, industry best practices, etc. For example, the collection unit can collect project progress data and deliverable data. The collection unit can also collect employee feedback as survey results or interview results. Furthermore, the collection unit can also collect industry standards and success stories. The learning unit learns from the data collected by the collection unit. The learning unit learns from, for example, neural networks or other machine learning methods. For example, the learning unit can learn from image data using a convolutional neural network (CNN). The learning unit can also learn from time series data using a recurrent neural network (RNN). Furthermore, the learning unit can learn from text data using a support vector machine (SVM) or a decision tree. The decision unit makes intuitive decisions based on the model learned by the learning unit. For example, the decision unit predicts risks and problems based on the learned model. For example, the decision-making unit can predict project delay risks and technical problems. It can also distinguish and predict project success factors and failure factors. Furthermore, the decision-making unit can combine different risk factors to perform a comprehensive risk assessment. The delivery unit provides the decisions made by the decision-making unit to employees. The delivery unit provides decisions to employees, for example, through dashboards and alert functions. For example, the delivery unit can display real-time progress and key indicators on the dashboard. The delivery unit can also provide alerts via email notifications and pop-up notifications. Furthermore, the delivery unit can estimate the user's emotions and adjust how information is displayed based on the estimated emotions. As a result, the intuitive decision-making system according to this embodiment allows employees to compensate for a lack of perspective due to inexperience and to detect project risks and problems early.

[0030] The data collection unit can collect past project data, employee feedback, and industry best practices. For example, the data collection unit can collect past project data, such as project progress data and deliverable data. It can also collect employee feedback, such as survey results and interview results. Furthermore, it can collect industry best practices, such as industry standards and success stories. By collecting diverse data, the accuracy of intuitive decision-making improves. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input past project data into AI and have AI perform the data collection.

[0031] The learning unit can train a model using a neural network or other machine learning techniques. For example, the learning unit can train a model using a neural network. For example, the learning unit can train image data using a convolutional neural network (CNN). The learning unit can also train time series data using a recurrent neural network (RNN). Furthermore, the learning unit can train text data using a support vector machine (SVM) or a decision tree. This improves the accuracy of intuitive judgments by using advanced machine learning techniques. Some or all of the above processes in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input neural network training data into an AI and have the AI ​​train the model.

[0032] The decision unit can predict risks and problems based on a trained model. For example, the decision unit can predict project delay risks and technical problems. The decision unit can also distinguish and predict project success factors and failure factors. Furthermore, the decision unit can combine different risk factors to perform a comprehensive risk assessment. By predicting risks and problems, the success rate of the project is improved. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input a trained model into AI and have the AI ​​perform the prediction of risks and problems.

[0033] The service provider can provide employees with decision-making capabilities through dashboards and alert functions. For example, the service provider can provide employees with decision-making capabilities through dashboards. For example, the service provider can display real-time progress and key metrics on the dashboard. The service provider can also provide employees with decision-making capabilities through alert functions. For example, the service provider can provide alerts via email notifications or pop-up notifications. This allows employees to quickly receive intuitive decision-making information. Some or all of the above processes in the service provider may be performed using AI, or not using AI. For example, the service provider can input the content to be displayed on the dashboard into the AI ​​and have the AI ​​display the information.

[0034] The data collection unit can distinguish between project success factors and failure factors when collecting past project data. For example, the data collection unit can collect good schedule management and team collaboration as project success factors. It can also collect budget overruns and poor communication as project failure factors. Furthermore, the data collection unit can tag success factors and failure factors to aid in later analysis. By distinguishing between success factors and failure factors, the data can be used to aid in later analysis. 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 project data into AI and have the AI ​​distinguish between success factors and failure factors.

[0035] The data collection unit can evaluate the reliability of employee feedback and prioritize the collection of highly reliable feedback. For example, the data collection unit can evaluate the reliability of employee feedback based on the years of experience and position of the feedback provider. It can also evaluate the reliability based on whether the content of the feedback is specific. Furthermore, it can evaluate the reliability based on the accuracy of past feedback. By prioritizing the collection of highly reliable feedback, more accurate data can be obtained. 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 feedback data into AI and have the AI ​​perform the reliability evaluation.

[0036] The data collection unit can collect best practices from different industries when gathering industry best practices and perform comparative analysis. For example, when collecting industry best practices, the data collection unit can collect best practices from different industries. For example, the data collection unit can compare best practices from the IT industry with those from the manufacturing industry. It can also compare best practices from the healthcare industry with those from the education industry. Furthermore, the data collection unit can integrate best practices from different industries and propose new best practices. This allows for the proposal of new best practices by comparing and analyzing best practices from different industries. 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 best practice data from different industries into an AI and have the AI ​​perform the comparative analysis.

[0037] The data collection unit can obtain candid opinions by ensuring anonymity when collecting employee feedback. For example, the data collection unit can ensure anonymity when collecting employee feedback. For example, the data collection unit can provide a dedicated form for collecting anonymous feedback. The data collection unit can also encrypt the information of the feedback submitter to ensure anonymity. Furthermore, the data collection unit can implement an algorithm to evaluate the reliability of anonymous feedback. This ensures anonymity and allows for candid opinions from employees. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input anonymous feedback data into AI and have the AI ​​perform a reliability evaluation.

[0038] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the most effective algorithm from past learning data. The learning unit can also adjust the algorithm parameters based on past learning data. Furthermore, the learning unit can analyze past learning data and identify areas for improvement in the algorithm. This improves the accuracy of 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 AI. For example, the learning unit can input past learning data into AI and have AI perform the algorithm optimization.

[0039] The learning unit can improve the accuracy of the model by combining different machine learning methods during training. For example, the learning unit can combine neural networks and decision trees for training. It can also combine support vector machines and random forests for training. Furthermore, the learning unit can perform ensemble learning by combining different machine learning methods. This improves the accuracy of the model by combining different machine learning methods. 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 data from different machine learning methods into AI and have the AI ​​perform the model accuracy improvement.

[0040] The learning unit can weight the training data based on the data collection timing during training. For example, the learning unit can weight the training data based on the data collection timing during training. For example, the learning unit can perform training by assigning a high weight to the most recent data. Alternatively, the learning unit can perform training by assigning a low weight to older data. Furthermore, the learning unit can optimize training by weighting data for a specific period. This improves the accuracy of training by weighting based on the data collection timing. 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 data collection timing into the AI ​​and have the AI ​​perform the weighting.

[0041] The learning unit can integrate data from different data sources during training. For example, the learning unit can integrate internal and external data during training. It can also integrate data from different departments. Furthermore, the learning unit can integrate data in different formats (text, images, audio, etc.). This improves the accuracy of training by integrating different data sources. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data from different data sources into an AI and have the AI ​​perform the data integration.

[0042] The decision-making unit can improve the accuracy of its decisions by referring to past decision results when making a decision. For example, the decision-making unit can improve the accuracy of its decisions by referring to past decision results when making a decision. For example, the decision-making unit can optimize the current decision based on past decision results. The decision-making unit can also analyze past decision results and identify areas for improvement in its decisions. Furthermore, the decision-making unit can predict risks and problems by referring to past decision results. As a result, the accuracy of decisions is improved by referring to past decision results. Some or all of the above processes in the decision-making unit may be performed using AI, for example, or without using AI. For example, the decision-making unit can input past decision result data into AI and have the AI ​​perform the improvement of decision accuracy.

[0043] The decision-making unit can perform a comprehensive risk assessment by combining different risk factors at the time of decision-making. For example, the decision-making unit can perform a comprehensive risk assessment by combining different risk factors at the time of decision-making. For example, the decision-making unit can assess project schedule risk and budget risk by combining them. It can also assess team skill risk and communication risk by combining them. Furthermore, the decision-making unit can perform a comprehensive risk assessment by combining external environmental risk and internal risk. This makes a comprehensive risk assessment possible by combining different risk factors. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without using AI. For example, the decision-making unit can input different risk factor data into AI and have the AI ​​perform a comprehensive risk assessment.

[0044] The decision-making unit can make decisions by comparing the risk factors of different projects at the time of decision-making. For example, the decision-making unit can make decisions by comparing the risk factors of different projects. For example, the decision-making unit can make decisions by comparing the risk factors of Project A and Project B. The decision-making unit can also make decisions by comparing the success factors and failure factors of different projects. Furthermore, the decision-making unit can integrate the risk factors of different projects to make a comprehensive decision. This makes it possible to make more accurate decisions by comparing the risk factors of different projects. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without using AI. For example, the decision-making unit can input risk factor data from different projects into AI and have the AI ​​perform a comparative analysis.

[0045] The decision-making unit can make decisions by referring to risk factors from different industries. For example, the decision-making unit can make decisions by referring to risk factors from different industries. For example, the decision-making unit can make decisions by comparing risk factors from the IT industry with those from the manufacturing industry. It can also make decisions by comparing risk factors from the healthcare industry with those from the education industry. Furthermore, the decision-making unit can integrate risk factors from different industries to make comprehensive decisions. This makes it possible to make comprehensive decisions by referring to risk factors from different industries. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input risk factor data from different industries into AI and have the AI ​​perform a comparative analysis.

[0046] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can prioritize providing the display method that the user has preferred to use in the past. The service provider can also suggest the optimal display method based on the user's past operation history. Furthermore, the service provider can analyze the user's past operation history and identify areas for improvement in the display method. This allows the service provider to provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's operation history data into AI and have the AI ​​perform the selection of the display method.

[0047] The information provider can improve the efficiency of information transmission by combining different information provision methods at the time of delivery. For example, the information provider can provide a combination of text information and image information. It can also provide a combination of audio information and video information. Furthermore, the information provider can improve the efficiency of information transmission by integrating different information provision methods. As a result, the efficiency of information transmission is improved by combining different information provision methods. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input data from different information provision methods into AI and have the AI ​​perform the information integration.

[0048] The service provider can select the optimal display method based on the user's device information at the time of delivery. For example, the service provider can select the optimal display method based on the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Also, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This improves user convenience by providing the optimal display method based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI and have AI select the display method.

[0049] The information delivery unit can provide customized information to different user groups at the time of delivery. For example, the information delivery unit can provide visually appealing information to younger generations. It can also provide simple and easily understandable information to older generations. Furthermore, it can provide detailed and technical information to experts. By providing customized information to different user groups, it becomes possible to provide information that meets the needs of each user group. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input user group data into AI and have the AI ​​perform the delivery of customized information.

[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 intuitive decision generation system further includes a feedback evaluation unit. The feedback evaluation unit assesses the reliability of feedback collected from employees and can prioritize providing highly reliable feedback to the learning unit. For example, the feedback evaluation unit can evaluate reliability based on the feedback provider's position and years of experience. It can also evaluate reliability based on the specificity of the feedback content. Furthermore, it can evaluate reliability based on the accuracy of past feedback. This allows for improved accuracy of intuitive decision-making by prioritizing the learning of highly reliable feedback.

[0052] The data collection unit can further gather and compare best practices from different industries. For example, it can compare best practices from the IT industry with those from the manufacturing industry. It can also compare best practices from the healthcare industry with those from the education industry. Furthermore, it can integrate best practices from different industries and propose new best practices. This allows for new perspectives to be gained by comparing and analyzing best practices from different industries.

[0053] The learning unit can further improve the model's accuracy by combining different machine learning techniques. For example, it can combine neural networks and decision trees for learning. It can also combine support vector machines and random forests for learning. Furthermore, it can perform ensemble learning by combining different machine learning techniques. This improves the model's accuracy by combining different machine learning methods.

[0054] The decision-making unit can further compare risk factors of different projects to make a judgment. For example, it can compare the risk factors of Project A and Project B to make a judgment. It can also compare the success factors and failure factors of different projects to make a judgment. Furthermore, it can integrate the risk factors of different projects to make a comprehensive judgment. This allows for more accurate judgments by comparing the risk factors of different projects.

[0055] The information provider can further improve the efficiency of information transmission by combining different information delivery methods. For example, text information and image information can be provided in combination. Audio information and video information can also be provided in combination. Furthermore, different information delivery methods can be integrated to improve the efficiency of information transmission. Thus, by combining different information delivery methods, the efficiency of information transmission is improved.

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

[0057] Step 1: The data collection department collects data. The data collection department collects data such as past project data, employee feedback, and industry best practices. Specifically, it can collect project progress data, deliverable data, employee feedback as survey results or interview results, and also collect industry standards and success stories. Step 2: The learning unit learns from the data collected by the collection unit. The learning unit learns a model using, for example, a neural network or other machine learning techniques. Specifically, it can learn image data using a convolutional neural network (CNN), learn time series data using a recurrent neural network (RNN), and learn text data using a support vector machine (SVM) or decision tree. Step 3: The decision unit makes intuitive decisions based on the model learned by the learning unit. For example, the decision unit predicts risks and problems based on the learned model. Specifically, it can predict project delay risks and technical problems, distinguish and predict project success factors and failure factors, and perform a comprehensive risk assessment by combining different risk factors. Step 4: The delivery unit provides employees with the decisions made by the decision-making unit. The delivery unit provides employees with decisions, for example, through dashboards and alert functions. Specifically, it can display real-time progress and key metrics on dashboards, provide alerts through email and pop-up notifications, estimate user sentiment, and adjust how information is displayed based on the estimated user sentiment.

[0058] (Example of form 2) The intuitive decision generation system according to an embodiment of the present invention is a system that generates intuitive and instinctive judgments. By generating "instincts" that cannot be expressed quantitatively, this system can provide various perspectives. In particular, in a company, each employee can gain perspectives that they may not have obtained due to lack of experience, enabling the early detection of flaws in projects. First, the system learns from past data and cases to build a model for making intuitive judgments. This model learns not only quantitative data but also qualitative data. For example, it learns from past project success and failure cases and the judgment criteria of experienced employees. Next, the system makes intuitive judgments about new projects. At this time, the system predicts the risks and problems of the project based on past data and cases. For example, at the planning stage of a new project, the system can determine that "this plan has risks." Furthermore, the system provides intuitive judgments to employees. Employees can compensate for the lack of perspectives due to lack of experience by referring to the intuitive judgments provided by the system. For example, when a young employee is in charge of a new project, the system can provide advice such as "pay attention to this part," thereby increasing the success rate of the project. This system allows employees to compensate for a lack of perspective due to inexperience and to identify flaws in projects early. For example, if the system determines that "this plan has risks," employees can take measures to address those risks. This is expected to improve the success rate of projects and enhance the overall performance of the company. In this way, the intuitive decision-making system allows employees to compensate for a lack of perspective due to inexperience and to identify risks and problems in projects early.

[0059] The intuitive decision generation system according to this embodiment comprises a collection unit, a learning unit, a decision unit, and a provision unit. The collection unit collects data. The collection unit collects, for example, past project data, employee feedback, industry best practices, etc. For example, the collection unit can collect project progress data and deliverable data. The collection unit can also collect employee feedback as survey results or interview results. Furthermore, the collection unit can also collect industry standards and success stories. The learning unit learns from the data collected by the collection unit. The learning unit learns from, for example, neural networks or other machine learning methods. For example, the learning unit can learn from image data using a convolutional neural network (CNN). The learning unit can also learn from time series data using a recurrent neural network (RNN). Furthermore, the learning unit can learn from text data using a support vector machine (SVM) or a decision tree. The decision unit makes intuitive decisions based on the model learned by the learning unit. For example, the decision unit predicts risks and problems based on the learned model. For example, the decision-making unit can predict project delay risks and technical problems. It can also distinguish and predict project success factors and failure factors. Furthermore, the decision-making unit can combine different risk factors to perform a comprehensive risk assessment. The delivery unit provides the decisions made by the decision-making unit to employees. The delivery unit provides decisions to employees, for example, through dashboards and alert functions. For example, the delivery unit can display real-time progress and key indicators on the dashboard. The delivery unit can also provide alerts via email notifications and pop-up notifications. Furthermore, the delivery unit can estimate the user's emotions and adjust how information is displayed based on the estimated emotions. As a result, the intuitive decision-making system according to this embodiment allows employees to compensate for a lack of perspective due to inexperience and to detect project risks and problems early.

[0060] The data collection unit can collect past project data, employee feedback, and industry best practices. For example, the data collection unit can collect past project data, such as project progress data and deliverable data. It can also collect employee feedback, such as survey results and interview results. Furthermore, it can collect industry best practices, such as industry standards and success stories. By collecting diverse data, the accuracy of intuitive decision-making improves. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input past project data into AI and have AI perform the data collection.

[0061] The learning unit can train a model using a neural network or other machine learning techniques. For example, the learning unit can train a model using a neural network. For example, the learning unit can train image data using a convolutional neural network (CNN). The learning unit can also train time series data using a recurrent neural network (RNN). Furthermore, the learning unit can train text data using a support vector machine (SVM) or a decision tree. This improves the accuracy of intuitive judgments by using advanced machine learning techniques. Some or all of the above processes in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input neural network training data into an AI and have the AI ​​train the model.

[0062] The decision unit can predict risks and problems based on a trained model. For example, the decision unit can predict project delay risks and technical problems. The decision unit can also distinguish and predict project success factors and failure factors. Furthermore, the decision unit can combine different risk factors to perform a comprehensive risk assessment. By predicting risks and problems, the success rate of the project is improved. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input a trained model into AI and have the AI ​​perform the prediction of risks and problems.

[0063] The service provider can provide employees with decision-making capabilities through dashboards and alert functions. For example, the service provider can provide employees with decision-making capabilities through dashboards. For example, the service provider can display real-time progress and key metrics on the dashboard. The service provider can also provide employees with decision-making capabilities through alert functions. For example, the service provider can provide alerts via email notifications or pop-up notifications. This allows employees to quickly receive intuitive decision-making information. Some or all of the above processes in the service provider may be performed using AI, or not using AI. For example, the service provider can input the content to be displayed on the dashboard into the AI ​​and have the AI ​​display the information.

[0064] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is focused, the data collection unit can prioritize the collection of important data. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into AI and have the AI ​​adjust the timing of data collection.

[0065] The data collection unit can distinguish between project success factors and failure factors when collecting past project data. For example, the data collection unit can collect good schedule management and team collaboration as project success factors. It can also collect budget overruns and poor communication as project failure factors. Furthermore, the data collection unit can tag success factors and failure factors to aid in later analysis. By distinguishing between success factors and failure factors, the data can be used to aid in later analysis. 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 project data into AI and have the AI ​​distinguish between success factors and failure factors.

[0066] The data collection unit can evaluate the reliability of employee feedback and prioritize the collection of highly reliable feedback. For example, the data collection unit can evaluate the reliability of employee feedback based on the years of experience and position of the feedback provider. It can also evaluate the reliability based on whether the content of the feedback is specific. Furthermore, it can evaluate the reliability based on the accuracy of past feedback. By prioritizing the collection of highly reliable feedback, more accurate data can be obtained. 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 feedback data into AI and have the AI ​​perform the reliability evaluation.

[0067] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can postpone the collection of less important data. If the user is relaxed, the data collection unit can prioritize the collection of detailed data. Furthermore, if the user is focused, the data collection unit can prioritize the collection of important data. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​determine the data priority.

[0068] The data collection unit can collect best practices from different industries when gathering industry best practices and perform comparative analysis. For example, when collecting industry best practices, the data collection unit can collect best practices from different industries. For example, the data collection unit can compare best practices from the IT industry with those from the manufacturing industry. It can also compare best practices from the healthcare industry with those from the education industry. Furthermore, the data collection unit can integrate best practices from different industries and propose new best practices. This allows for the proposal of new best practices by comparing and analyzing best practices from different industries. 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 best practice data from different industries into an AI and have the AI ​​perform the comparative analysis.

[0069] The data collection unit can obtain candid opinions by ensuring anonymity when collecting employee feedback. For example, the data collection unit can ensure anonymity when collecting employee feedback. For example, the data collection unit can provide a dedicated form for collecting anonymous feedback. The data collection unit can also encrypt the information of the feedback submitter to ensure anonymity. Furthermore, the data collection unit can implement an algorithm to evaluate the reliability of anonymous feedback. This ensures anonymity and allows for candid opinions from employees. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input anonymous feedback data into AI and have the AI ​​perform a reliability evaluation.

[0070] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, the learning unit can use detailed data for training when the user is relaxed. It can also use simplified data for training when the user is stressed. Furthermore, it can prioritize the use of important data for training when the user is focused. This improves the accuracy of training by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, 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 user emotion data into an AI and have the AI ​​perform the selection of training data.

[0071] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the most effective algorithm from past learning data. The learning unit can also adjust the algorithm parameters based on past learning data. Furthermore, the learning unit can analyze past learning data and identify areas for improvement in the algorithm. This improves the accuracy of 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 AI. For example, the learning unit can input past learning data into AI and have AI perform the algorithm optimization.

[0072] The learning unit can improve the accuracy of the model by combining different machine learning methods during training. For example, the learning unit can combine neural networks and decision trees for training. It can also combine support vector machines and random forests for training. Furthermore, the learning unit can perform ensemble learning by combining different machine learning methods. This improves the accuracy of the model by combining different machine learning methods. 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 data from different machine learning methods into AI and have the AI ​​perform the model accuracy improvement.

[0073] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can increase the learning frequency when the user is relaxed. It can also decrease the learning frequency when the user is stressed. Furthermore, the learning unit can optimize the learning frequency when the user is focused. This improves learning efficiency by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into AI and have the AI ​​adjust the learning frequency.

[0074] The learning unit can weight the training data based on the data collection timing during training. For example, the learning unit can weight the training data based on the data collection timing during training. For example, the learning unit can perform training by assigning a high weight to the most recent data. Alternatively, the learning unit can perform training by assigning a low weight to older data. Furthermore, the learning unit can optimize training by weighting data for a specific period. This improves the accuracy of training by weighting based on the data collection timing. 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 data collection timing into the AI ​​and have the AI ​​perform the weighting.

[0075] The learning unit can integrate data from different data sources during training. For example, the learning unit can integrate internal and external data during training. It can also integrate data from different departments. Furthermore, the learning unit can integrate data in different formats (text, images, audio, etc.). This improves the accuracy of training by integrating different data sources. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data from different data sources into an AI and have the AI ​​perform the data integration.

[0076] The decision unit can estimate the user's emotions and adjust the way the decision is expressed based on the estimated emotions. For example, if the user is nervous, the decision unit can provide a simple and easily understandable expression. If the user is relaxed, the decision unit can also provide an expression that includes detailed information. Furthermore, if the user is in a hurry, the decision unit can provide an expression that gets straight to the point. By adjusting the way the decision is expressed according to the user's emotions, information that is easy for the user to understand 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 decision unit may be performed using AI, for example, or not using AI. For example, the decision unit can input user emotion data into AI and have the AI ​​adjust the way the decision is expressed.

[0077] The decision-making unit can improve the accuracy of its decisions by referring to past decision results when making a decision. For example, the decision-making unit can improve the accuracy of its decisions by referring to past decision results when making a decision. For example, the decision-making unit can optimize the current decision based on past decision results. The decision-making unit can also analyze past decision results and identify areas for improvement in its decisions. Furthermore, the decision-making unit can predict risks and problems by referring to past decision results. As a result, the accuracy of decisions is improved by referring to past decision results. Some or all of the above processes in the decision-making unit may be performed using AI, for example, or without using AI. For example, the decision-making unit can input past decision result data into AI and have the AI ​​perform the improvement of decision accuracy.

[0078] The decision-making unit can perform a comprehensive risk assessment by combining different risk factors at the time of decision-making. For example, the decision-making unit can perform a comprehensive risk assessment by combining different risk factors at the time of decision-making. For example, the decision-making unit can assess project schedule risk and budget risk by combining them. It can also assess team skill risk and communication risk by combining them. Furthermore, the decision-making unit can perform a comprehensive risk assessment by combining external environmental risk and internal risk. This makes a comprehensive risk assessment possible by combining different risk factors. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without using AI. For example, the decision-making unit can input different risk factor data into AI and have the AI ​​perform a comprehensive risk assessment.

[0079] The decision-making unit can estimate the user's emotions and determine the priority of decisions based on the estimated emotions. For example, if the user is stressed, the decision-making unit can postpone decisions of lower importance. If the user is relaxed, the decision-making unit can prioritize detailed decisions. Furthermore, if the user is focused, the decision-making unit can prioritize important decisions. In this way, by determining the priority of decisions according to the user's emotions, important decisions can be prioritized. 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 decision-making unit may be performed using AI, for example, or not using AI. For example, the decision-making unit can input user emotion data into AI and have the AI ​​perform the determination of decision priorities.

[0080] The decision-making unit can make decisions by comparing the risk factors of different projects at the time of decision-making. For example, the decision-making unit can make decisions by comparing the risk factors of different projects. For example, the decision-making unit can make decisions by comparing the risk factors of Project A and Project B. The decision-making unit can also make decisions by comparing the success factors and failure factors of different projects. Furthermore, the decision-making unit can integrate the risk factors of different projects to make a comprehensive decision. This makes it possible to make more accurate decisions by comparing the risk factors of different projects. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without using AI. For example, the decision-making unit can input risk factor data from different projects into AI and have the AI ​​perform a comparative analysis.

[0081] The decision-making unit can make decisions by referring to risk factors from different industries. For example, the decision-making unit can make decisions by referring to risk factors from different industries. For example, the decision-making unit can make decisions by comparing risk factors from the IT industry with those from the manufacturing industry. It can also make decisions by comparing risk factors from the healthcare industry with those from the education industry. Furthermore, the decision-making unit can integrate risk factors from different industries to make comprehensive decisions. This makes it possible to make comprehensive decisions by referring to risk factors from different industries. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input risk factor data from different industries into AI and have the AI ​​perform a comparative analysis.

[0082] The service provider can estimate the user's emotions and adjust the way the information is displayed based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise display method. By adjusting the way information is displayed according to the user's emotions, the service provider can provide information that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into AI and have the AI ​​adjust the way information is displayed.

[0083] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can prioritize providing the display method that the user has preferred to use in the past. The service provider can also suggest the optimal display method based on the user's past operation history. Furthermore, the service provider can analyze the user's past operation history and identify areas for improvement in the display method. This allows the service provider to provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's operation history data into AI and have the AI ​​perform the selection of the display method.

[0084] The information provider can improve the efficiency of information transmission by combining different information provision methods at the time of delivery. For example, the information provider can provide a combination of text information and image information. It can also provide a combination of audio information and video information. Furthermore, the information provider can improve the efficiency of information transmission by integrating different information provision methods. As a result, the efficiency of information transmission is improved by combining different information provision methods. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input data from different information provision methods into AI and have the AI ​​perform the information integration.

[0085] The service provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is stressed, the service provider can postpone providing less important information. If the user is relaxed, the service provider can prioritize providing detailed information. Furthermore, if the user is focused, the service provider can prioritize providing important information. In this way, by prioritizing information according to the user's emotions, important information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into AI and have the AI ​​perform the determination of information prioritization.

[0086] The service provider can select the optimal display method based on the user's device information at the time of delivery. For example, the service provider can select the optimal display method based on the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Also, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This improves user convenience by providing the optimal display method based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI and have AI select the display method.

[0087] The information delivery unit can provide customized information to different user groups at the time of delivery. For example, the information delivery unit can provide visually appealing information to younger generations. It can also provide simple and easily understandable information to older generations. Furthermore, it can provide detailed and technical information to experts. By providing customized information to different user groups, it becomes possible to provide information that meets the needs of each user group. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input user group data into AI and have the AI ​​perform the delivery of customized information. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, learning unit, decision unit, and provision 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 using the camera 42 and microphone 38B of the smart device 14 and transmits the collected data to the data processing unit 12 by the control unit 46A. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and learns a model based on the collected data. The decision unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes intuitive decisions based on the learned model. The provision unit is implemented in the specific processing unit 46A of the smart device 14 and provides the decision results to employees through a dashboard or alert function. === Hard Collateral 1-2 === Each of the multiple elements described above, including the data collection unit, learning unit, decision unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected data to the data processing unit 12 by the control unit 46A. The learning unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and learns a model based on the collected data. The decision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and makes intuitive decisions based on the learned model. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the decision results to employees through a dashboard or alert function. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, learning unit, decision unit, and provision 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 using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected data to the data processing unit 12 by the control unit 46A. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and learns a model based on the collected data. The decision unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes intuitive decisions based on the learned model. The provision unit is implemented in the specific processing unit 46A of the headset terminal 314 and provides the decision results to employees through a dashboard or alert function. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, learning unit, decision unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the robot 414 and transmits the collected data to the data processing unit 12 by the control unit 46A. The learning unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and learns a model based on the collected data. The decision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and makes intuitive decisions based on the learned model. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the decision results to employees through a dashboard or alert function.

[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 intuitive decision generation system further includes a feedback evaluation unit. The feedback evaluation unit assesses the reliability of feedback collected from employees and can prioritize providing highly reliable feedback to the learning unit. For example, the feedback evaluation unit can evaluate reliability based on the feedback provider's position and years of experience. It can also evaluate reliability based on the specificity of the feedback content. Furthermore, it can evaluate reliability based on the accuracy of past feedback. This allows for improved accuracy of intuitive decision-making by prioritizing the learning of highly reliable feedback.

[0090] The data collection unit can further gather and compare best practices from different industries. For example, it can compare best practices from the IT industry with those from the manufacturing industry. It can also compare best practices from the healthcare industry with those from the education industry. Furthermore, it can integrate best practices from different industries and propose new best practices. This allows for new perspectives to be gained by comparing and analyzing best practices from different industries.

[0091] The learning unit can further improve the model's accuracy by combining different machine learning techniques. For example, it can combine neural networks and decision trees for learning. It can also combine support vector machines and random forests for learning. Furthermore, it can perform ensemble learning by combining different machine learning techniques. This improves the model's accuracy by combining different machine learning methods.

[0092] The decision-making unit can further compare risk factors of different projects to make a judgment. For example, it can compare the risk factors of Project A and Project B to make a judgment. It can also compare the success factors and failure factors of different projects to make a judgment. Furthermore, it can integrate the risk factors of different projects to make a comprehensive judgment. This allows for more accurate judgments by comparing the risk factors of different projects.

[0093] The information provider can further improve the efficiency of information transmission by combining different information delivery methods. For example, text information and image information can be provided in combination. Audio information and video information can also be provided in combination. Furthermore, different information delivery methods can be integrated to improve the efficiency of information transmission. Thus, by combining different information delivery methods, the efficiency of information transmission is improved.

[0094] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. Conversely, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is focused, important data can be prioritized for collection. In this way, by adjusting the timing of data collection according to the user's emotions, the user's burden can be reduced.

[0095] The learning unit can estimate the user's emotions and select training data based on those estimated emotions. For example, if the user is relaxed, detailed data can be used for training. If the user is stressed, simplified data can be used for training. Furthermore, if the user is focused, important data can be prioritized for training. This improves the accuracy of learning by selecting training data according to the user's emotions.

[0096] The decision-making unit can estimate the user's emotions and adjust the way the decision is presented based on those emotions. For example, if the user is nervous, it can provide a simple and easily visible presentation. If the user is relaxed, it can provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, it can provide a presentation that gets straight to the point. By adjusting the presentation of the decision according to the user's emotions, it is possible to provide information that is easy for the user to understand.

[0097] The information provider can estimate the user's emotions and adjust how the information is displayed based on those emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the essentials can be provided. By adjusting how information is displayed according to the user's emotions, the system can provide information that is easy for the user to understand.

[0098] The information delivery unit can estimate the user's emotions and determine the priority of the information to be delivered based on those estimated emotions. For example, if the user is stressed, the delivery of less important information can be postponed. Conversely, if the user is relaxed, detailed information can be prioritized. Furthermore, if the user is focused, important information can be prioritized. In this way, by determining the priority of information according to the user's emotions, important information can be delivered preferentially.

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

[0100] Step 1: The data collection department collects data. The data collection department collects data such as past project data, employee feedback, and industry best practices. Specifically, it can collect project progress data, deliverable data, employee feedback as survey results or interview results, and also collect industry standards and success stories. Step 2: The learning unit learns from the data collected by the collection unit. The learning unit learns a model using, for example, a neural network or other machine learning techniques. Specifically, it can learn image data using a convolutional neural network (CNN), learn time series data using a recurrent neural network (RNN), and learn text data using a support vector machine (SVM) or decision tree. Step 3: The decision unit makes intuitive decisions based on the model learned by the learning unit. For example, the decision unit predicts risks and problems based on the learned model. Specifically, it can predict project delay risks and technical problems, distinguish and predict project success factors and failure factors, and perform a comprehensive risk assessment by combining different risk factors. Step 4: The delivery unit provides employees with the decisions made by the decision-making unit. The delivery unit provides employees with decisions, for example, through dashboards and alert functions. Specifically, it can display real-time progress and key metrics on dashboards, provide alerts through email and pop-up notifications, estimate user sentiment, and adjust how information is displayed based on the estimated user sentiment.

[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 unit that collects data, A learning unit that learns from the data collected by the aforementioned collection unit, A decision unit that makes an intuitive decision based on the model learned by the learning unit, A provisioning unit that provides the judgment made by the aforementioned judgment unit to the employee, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Collect past project data, employee feedback, and industry best practices. The system according to feature 1.

3. The aforementioned learning unit, Train the model using a neural network or other machine learning techniques. The system according to feature 1.

4. The unit that makes the determination said, Predicting risks and problems based on a trained model. The system according to feature 1.

5. The aforementioned supply unit is, Providing employees with decision-making capabilities through dashboards and alert functions. The system according to feature 1.

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

7. The aforementioned collection unit is When collecting past project data, distinguish between project success factors and failure factors. The system according to feature 1.

8. The aforementioned collection unit is When collecting employee feedback, evaluate the reliability of the feedback and prioritize collecting reliable feedback. The system according to feature 1.

Images