System

Abstract

To provide means for predicting risks of projects and supporting early detection and solution of problems.SOLUTION: A system includes AI that analyzes past project data and risk factors, as means for predicting risks of projects and supporting early detect and resolution of problems.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] The present invention aims to provide a means for predicting project risks and supporting early detection and resolution of problems. [Means for solving the problem]

[0005] A system that includes AI that analyzes past project data and risk factors as a means of predicting project risks and helping to detect and resolve problems early. [Brief description of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2]1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0010] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0014] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0015] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0016] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0017] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0018] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicative of the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicative of the user input.

[0019] The output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice 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 voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0020] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0021] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0022] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0023] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0024] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0025] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, or a home appliance) owned by a user. Next, the identification process by the identification processing unit 290 of the data processing device 12 will be described.

[0026] (Example 1) An embodiment of the present invention is a system for predicting project risks and supporting early detection and resolution of problems. The system utilizes AI to analyze past project data and risk factors, and provides teams with alerts and suggestions regarding potential risks and issues. Specifically, the AI ​​monitors project progress and related data in real time, and evaluates the likelihood of risks based on a predictive model. The AI ​​can also learn data from similar past projects and identify similar risk factors. This allows development teams to identify risks early, take preventive measures, or respond quickly. (Example 2) An embodiment of the present invention is a system for predicting project risks and supporting early detection and resolution of problems. The system is provided as a project management tool equipped with AI. Specifically, a user inputs project data, and the AI ​​analyzes it to identify potential risks and issues. The AI ​​can also refer to a database of past projects to suggest similar risk factors and solutions. The AI ​​can also monitor the progress of the project in real time and detect abnormal patterns and delays. This allows the project team to take prompt and effective measures against risks. (Example 3) An embodiment of the present invention is a system for predicting project risks and supporting early detection and resolution of problems. This system is provided as a risk management platform that utilizes AI. Specifically, a user inputs data related to a project, and AI analyzes the data to identify potential risk factors. AI can also learn from past project data and industry best practices to build a risk prediction model. AI can also evaluate the importance and impact of risks and prioritize them. This allows project managers and stakeholders to take appropriate measures against risks.

[0027] The process flow of each embodiment will be described below.

[0028] (Example 1) Step 1: Enter your project data Users input data about their projects into the system, including project progress, task completion, and resource availability. Step 2: Analyze data and assess risks The AI ​​analyzes input project data and evaluates correlations with past project data and risk factors. It uses learning models to predict the likelihood of risks and evaluate their importance and impact. Step 3: Providing alerts and suggestions AI provides teams with alerts and suggestions about potential risks and challenges, including identifying risks, suggesting countermeasures and prioritizing risks, so teams can take preventative measures or respond quickly. (Example 2) Step 1: Enter your project data Users input data about their projects into the system, including task progress, resource utilization, and problem reports. Step 2: Analyze data and identify risks AI analyzes input project data and evaluates its relevance to past project data and industry best practices, then identifies risk factors and evaluates their likelihood and severity. Step 3: Providing alerts and suggestions AI provides teams with alerts and suggestions about potential risks and challenges, including identifying risks, proposing solutions and prioritizing risks, which teams can then use to address. (Example 3) Step 1: Enter your project data Users input data about their projects into the system, including task progress, budget usage, and identification of risk factors. Step 2: Data analysis and risk prediction AI analyzes input project data, learns from past project data and industry best practices, and builds risk prediction models to assess the likelihood and impact of risks. Step 3: Risk management and countermeasure proposals AI evaluates the importance and impact of risks, prioritizes them, and suggests countermeasures and solutions for risks, allowing project managers and stakeholders to take appropriate measures against risks.

[0029] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0030] (Example 1) An embodiment of the present invention is a system for predicting project risks and supporting early detection and resolution of problems. The system recognizes the emotional states of team members by combining an emotion engine and reflects them in project risk management. Specifically, team members input emotion data using an emotion recognition interface provided by the system. The emotion engine analyzes these data and evaluates how the team members' emotions are related to the progress of the project and risk factors. The emotion engine also analyzes the motivation and stress levels of the team to consider the human element in risk management. In this way, the team can receive risk assessments and countermeasure suggestions based on the emotion data, improving the probability of project success. (Example 2) An embodiment of the present invention is a system for predicting project risks and supporting early problem detection and resolution. The system utilizes an emotion engine to monitor the emotional state of a team and reflect it in project risk prediction. Specifically, team members collect emotion data using emotion recognition devices provided by the system. The emotion engine analyzes these data in real time and evaluates how the team members' emotions are related to the progress of the project and risk factors. The emotion engine also incorporates the emotion data into a project risk prediction model and utilizes it to evaluate risks and propose countermeasures. This allows the team to perform risk management based on the emotion data and maximize project results. (Example 3) An embodiment of the present invention is a system for predicting project risks and supporting early problem detection and resolution. The system realizes risk management that takes into account the emotional state of the team by combining an emotion engine. Specifically, team members input emotion data using an emotion recognition interface provided by the system. The emotion engine analyzes these data and evaluates how the team members' emotions are related to the progress of the project and risk factors. The emotion engine also adjusts and prioritizes the importance and impact of risks according to the emotional state of the team. This allows the team to implement risk countermeasures and communication improvement measures based on the emotion data, thereby increasing the probability of project success.

[0031] The process flow of each embodiment will be described below.

[0032] (Example 1) Step 1: Input emotion data Team members use an emotion recognition interface to input emotion data, which includes information such as what they say and their facial expressions. Step 2: Analyze sentiment data and assess risk The emotion engine analyzes the input emotion data and evaluates how team members' emotions relate to project progress and risk factors. The emotion engine uses learning models to predict the likelihood of risks and evaluate their importance and impact. Step 3: Providing sentiment-based alerts and suggestions The sentiment engine provides teams with alerts and suggestions about potential risks and issues, including identifying risks by taking into account the correlation between sentiment data and project progress, suggesting countermeasures based on sentiment, and prioritizing risks, so that teams can use this information to address risks. (Example 2) Step 1: Collect sentiment data Team members use emotion recognition devices to collect emotional data, which includes audio, visual and other information. Step 2: Analyze sentiment data in real time and identify risks The emotion engine analyzes the collected emotion data in real time and evaluates how team members' emotions relate to project progress and risk factors. The emotion engine identifies risk factors and evaluates their likelihood and severity. Step 3: Providing sentiment-based alerts and suggestions The sentiment engine provides teams with alerts and suggestions on potential risks and challenges, including identifying risks by taking into account the correlation between sentiment data and project progress, suggesting countermeasures according to sentiment, and prioritizing risks. Teams can use this information to manage risks and maximize project outcomes. (Example 3) Step 1: Input emotion data Team members use an emotion recognition interface to input emotion data, which includes information such as what they say and their facial expressions. Step 2: Analyzing sentiment data and predicting risks The emotion engine analyzes the input emotion data and evaluates how team members' emotions relate to project progress and risk factors. The emotion engine learns from past project data and industry best practices to build a risk prediction model. The emotion engine also adjusts and prioritizes the importance and impact of risks according to the team's emotional state. Step 3: Emotion-based risk management and countermeasure proposals The emotion engine provides teams with emotion-based risk response and communication improvement suggestions, including identifying risks by taking into account the correlation between emotion data and project progress, suggesting responses based on emotion, and prioritizing risks. Teams can use this information to address risks and increase the chances of project success.

[0033] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0034] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58.

[0035] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0036] [Second embodiment]

[0037] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0038] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0039] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0040] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0041] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0042] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0043] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0044] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0045] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0046] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0047] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0048] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, or a home appliance) owned by a user. Next, the identification process by the identification processing unit 290 of the data processing device 12 will be described.

[0049] (Example 1) An embodiment of the present invention is a system for predicting project risks and supporting early detection and resolution of problems. The system utilizes AI to analyze past project data and risk factors, and provides teams with alerts and suggestions regarding potential risks and issues. Specifically, the AI ​​monitors project progress and related data in real time, and evaluates the likelihood of risks based on a predictive model. The AI ​​can also learn data from similar past projects and identify similar risk factors. This allows development teams to identify risks early, take preventive measures, or respond quickly. (Example 2) An embodiment of the present invention is a system for predicting project risks and supporting early detection and resolution of problems. The system is provided as a project management tool equipped with AI. Specifically, a user inputs project data, and the AI ​​analyzes it to identify potential risks and issues. The AI ​​can also refer to a database of past projects to suggest similar risk factors and solutions. The AI ​​can also monitor the progress of the project in real time and detect abnormal patterns and delays. This allows the project team to take prompt and effective measures against risks. (Example 3) An embodiment of the present invention is a system for predicting project risks and supporting early detection and resolution of problems. This system is provided as a risk management platform that utilizes AI. Specifically, a user inputs data related to a project, and AI analyzes the data to identify potential risk factors. AI can also learn from past project data and industry best practices to build a risk prediction model. AI can also evaluate the importance and impact of risks and prioritize them. This allows project managers and stakeholders to take appropriate measures against risks.

[0050] The process flow of each embodiment will be described below.

[0051] (Example 1) Step 1: Enter your project data Users input data about their projects into the system, including project progress, task completion, and resource availability. Step 2: Analyze data and assess risks The AI ​​analyzes input project data and evaluates correlations with past project data and risk factors. It uses learning models to predict the likelihood of risks and evaluate their importance and impact. Step 3: Providing alerts and suggestions AI provides teams with alerts and suggestions about potential risks and challenges, including identifying risks, suggesting countermeasures and prioritizing risks, so teams can take preventative measures or respond quickly. (Example 2) Step 1: Enter your project data Users input data about their projects into the system, including task progress, resource utilization, and problem reports. Step 2: Analyze data and identify risks AI analyzes input project data and evaluates its relevance to past project data and industry best practices, then identifies risk factors and evaluates their likelihood and severity. Step 3: Providing alerts and suggestions AI provides teams with alerts and suggestions about potential risks and challenges, including identifying risks, proposing solutions and prioritizing risks, which teams can then use to address. (Example 3) Step 1: Enter your project data Users input data about their projects into the system, including task progress, budget usage, and identification of risk factors. Step 2: Data analysis and risk prediction AI analyzes input project data, learns from past project data and industry best practices, and builds risk prediction models to assess the likelihood and impact of risks. Step 3: Risk management and countermeasure proposals AI evaluates the importance and impact of risks, prioritizes them, and suggests countermeasures and solutions for risks, allowing project managers and stakeholders to take appropriate measures against risks.

[0052] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0053] (Example 1) An embodiment of the present invention is a system for predicting project risks and supporting early detection and resolution of problems. The system recognizes the emotional states of team members by combining an emotion engine and reflects them in project risk management. Specifically, team members input emotion data using an emotion recognition interface provided by the system. The emotion engine analyzes these data and evaluates how the team members' emotions are related to the progress of the project and risk factors. The emotion engine also analyzes the motivation and stress levels of the team to consider the human element in risk management. In this way, the team can receive risk assessments and countermeasure suggestions based on the emotion data, improving the probability of project success. (Example 2) An embodiment of the present invention is a system for predicting project risks and supporting early problem detection and resolution. The system utilizes an emotion engine to monitor the emotional state of a team and reflect it in project risk prediction. Specifically, team members collect emotion data using emotion recognition devices provided by the system. The emotion engine analyzes these data in real time and evaluates how the team members' emotions are related to the progress of the project and risk factors. The emotion engine also incorporates the emotion data into a project risk prediction model and utilizes it to evaluate risks and propose countermeasures. This allows the team to perform risk management based on the emotion data and maximize project results. (Example 3) An embodiment of the present invention is a system for predicting project risks and supporting early problem detection and resolution. The system realizes risk management that takes into account the emotional state of the team by combining an emotion engine. Specifically, team members input emotion data using an emotion recognition interface provided by the system. The emotion engine analyzes these data and evaluates how the team members' emotions are related to the progress of the project and risk factors. The emotion engine also adjusts and prioritizes the importance and impact of risks according to the emotional state of the team. This allows the team to implement risk countermeasures and communication improvement measures based on the emotion data, thereby increasing the probability of project success.

[0054] The process flow of each embodiment will be described below.

[0055] (Example 1) Step 1: Input emotion data Team members use an emotion recognition interface to input emotion data, which includes information such as what they say and their facial expressions. Step 2: Analyze sentiment data and assess risk The emotion engine analyzes the input emotion data and evaluates how team members' emotions relate to project progress and risk factors. The emotion engine uses learning models to predict the likelihood of risks and evaluate their importance and impact. Step 3: Providing sentiment-based alerts and suggestions The sentiment engine provides teams with alerts and suggestions about potential risks and issues, including identifying risks by taking into account the correlation between sentiment data and project progress, suggesting countermeasures based on sentiment, and prioritizing risks, so that teams can use this information to address risks. (Example 2) Step 1: Collect sentiment data Team members use emotion recognition devices to collect emotional data, which includes audio, visual and other information. Step 2: Analyze sentiment data in real time and identify risks The emotion engine analyzes the collected emotion data in real time and evaluates how team members' emotions relate to project progress and risk factors. The emotion engine identifies risk factors and evaluates their likelihood and severity. Step 3: Providing sentiment-based alerts and suggestions The sentiment engine provides teams with alerts and suggestions on potential risks and challenges, including identifying risks by taking into account the correlation between sentiment data and project progress, suggesting countermeasures according to sentiment, and prioritizing risks. Teams can use this information to manage risks and maximize project outcomes. (Example 3) Step 1: Input emotion data Team members use an emotion recognition interface to input emotion data, which includes information such as what they say and their facial expressions. Step 2: Analyzing sentiment data and predicting risks The emotion engine analyzes the input emotion data and evaluates how team members' emotions relate to project progress and risk factors. The emotion engine learns from past project data and industry best practices to build a risk prediction model. The emotion engine also adjusts and prioritizes the importance and impact of risks according to the team's emotional state. Step 3: Emotion-based risk management and countermeasure proposals The emotion engine provides teams with emotion-based risk response and communication improvement suggestions, including identifying risks by taking into account the correlation between emotion data and project progress, suggesting responses based on emotion, and prioritizing risks. Teams can use this information to address risks and increase the chances of project success.

[0056] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0057] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58.

[0058] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0059] [Third embodiment]

[0060] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0061] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0062] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0063] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0064] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0065] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0066] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0067] Fig. 6 shows an example of main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0068] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0069] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0070] In the headset type terminal 314, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0071] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, or a home appliance) owned by a user. Next, the identification process by the identification processing unit 290 of the data processing device 12 will be described.

[0072] (Example 1) An embodiment of the present invention is a system for predicting project risks and supporting early detection and resolution of problems. The system utilizes AI to analyze past project data and risk factors, and provides teams with alerts and suggestions regarding potential risks and issues. Specifically, the AI ​​monitors project progress and related data in real time, and evaluates the likelihood of risks based on a predictive model. The AI ​​can also learn data from similar past projects and identify similar risk factors. This allows development teams to identify risks early, take preventive measures, or respond quickly. (Example 2) An embodiment of the present invention is a system for predicting project risks and supporting early detection and resolution of problems. The system is provided as a project management tool equipped with AI. Specifically, a user inputs project data, and the AI ​​analyzes it to identify potential risks and issues. The AI ​​can also refer to a database of past projects to suggest similar risk factors and solutions. The AI ​​can also monitor the progress of the project in real time and detect abnormal patterns and delays. This allows the project team to take prompt and effective measures against risks. (Example 3) An embodiment of the present invention is a system for predicting project risks and supporting early detection and resolution of problems. This system is provided as a risk management platform that utilizes AI. Specifically, a user inputs data related to a project, and AI analyzes the data to identify potential risk factors. AI can also learn from past project data and industry best practices to build a risk prediction model. AI can also evaluate the importance and impact of risks and prioritize them. This allows project managers and stakeholders to take appropriate measures against risks.

[0073] The process flow of each embodiment will be described below.

[0074] (Example 1) Step 1: Enter your project data Users input data about their projects into the system, including project progress, task completion, and resource availability. Step 2: Analyze data and assess risks The AI ​​analyzes input project data and evaluates correlations with past project data and risk factors. It uses learning models to predict the likelihood of risks and evaluate their importance and impact. Step 3: Providing alerts and suggestions AI provides teams with alerts and suggestions about potential risks and challenges, including identifying risks, suggesting countermeasures and prioritizing risks, so teams can take preventative measures or respond quickly. (Example 2) Step 1: Enter your project data Users input data about their projects into the system, including task progress, resource utilization, and problem reports. Step 2: Analyze data and identify risks AI analyzes input project data and evaluates its relevance to past project data and industry best practices, then identifies risk factors and evaluates their likelihood and severity. Step 3: Providing alerts and suggestions AI provides teams with alerts and suggestions about potential risks and challenges, including identifying risks, proposing solutions and prioritizing risks, which teams can then use to address. (Example 3) Step 1: Enter your project data Users input data about their projects into the system, including task progress, budget usage, and identification of risk factors. Step 2: Data analysis and risk prediction AI analyzes input project data, learns from past project data and industry best practices, and builds risk prediction models to assess the likelihood and impact of risks. Step 3: Risk management and countermeasure proposals AI evaluates the importance and impact of risks, prioritizes them, and suggests countermeasures and solutions for risks, allowing project managers and stakeholders to take appropriate measures against risks.

[0075] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0076] (Example 1) An embodiment of the present invention is a system for predicting project risks and supporting early detection and resolution of problems. The system recognizes the emotional states of team members by combining an emotion engine and reflects them in project risk management. Specifically, team members input emotion data using an emotion recognition interface provided by the system. The emotion engine analyzes these data and evaluates how the team members' emotions are related to the progress of the project and risk factors. The emotion engine also analyzes the motivation and stress levels of the team to consider the human element in risk management. In this way, the team can receive risk assessments and countermeasure suggestions based on the emotion data, improving the probability of project success. (Example 2) An embodiment of the present invention is a system for predicting project risks and supporting early problem detection and resolution. The system utilizes an emotion engine to monitor the emotional state of a team and reflect it in project risk prediction. Specifically, team members collect emotion data using emotion recognition devices provided by the system. The emotion engine analyzes these data in real time and evaluates how the team members' emotions are related to the progress of the project and risk factors. The emotion engine also incorporates the emotion data into a project risk prediction model and utilizes it to evaluate risks and propose countermeasures. This allows the team to perform risk management based on the emotion data and maximize project results. (Example 3) An embodiment of the present invention is a system for predicting project risks and supporting early problem detection and resolution. The system realizes risk management that takes into account the emotional state of the team by combining an emotion engine. Specifically, team members input emotion data using an emotion recognition interface provided by the system. The emotion engine analyzes these data and evaluates how the team members' emotions are related to the progress of the project and risk factors. The emotion engine also adjusts and prioritizes the importance and impact of risks according to the emotional state of the team. This allows the team to implement risk countermeasures and communication improvement measures based on the emotion data, thereby increasing the probability of project success.

[0077] The process flow of each embodiment will be described below.

[0078] (Example 1) Step 1: Input emotion data Team members use an emotion recognition interface to input emotion data, which includes information such as what they say and their facial expressions. Step 2: Analyze sentiment data and assess risk The emotion engine analyzes the input emotion data and evaluates how team members' emotions relate to project progress and risk factors. The emotion engine uses learning models to predict the likelihood of risks and evaluate their importance and impact. Step 3: Providing sentiment-based alerts and suggestions The sentiment engine provides teams with alerts and suggestions about potential risks and issues, including identifying risks by taking into account the correlation between sentiment data and project progress, suggesting countermeasures based on sentiment, and prioritizing risks, so that teams can use this information to address risks. (Example 2) Step 1: Collect sentiment data Team members use emotion recognition devices to collect emotional data, which includes audio, visual and other information. Step 2: Analyze sentiment data in real time and identify risks The emotion engine analyzes the collected emotion data in real time and evaluates how team members' emotions relate to project progress and risk factors. The emotion engine identifies risk factors and evaluates their likelihood and severity. Step 3: Providing sentiment-based alerts and suggestions The sentiment engine provides teams with alerts and suggestions on potential risks and challenges, including identifying risks by taking into account the correlation between sentiment data and project progress, suggesting countermeasures according to sentiment, and prioritizing risks. Teams can use this information to manage risks and maximize project outcomes. (Example 3) Step 1: Input emotion data Team members use an emotion recognition interface to input emotion data, which includes information such as what they say and their facial expressions. Step 2: Analyzing sentiment data and predicting risks The emotion engine analyzes the input emotion data and evaluates how team members' emotions relate to project progress and risk factors. The emotion engine learns from past project data and industry best practices to build a risk prediction model. The emotion engine also adjusts and prioritizes the importance and impact of risks according to the team's emotional state. Step 3: Emotion-based risk management and countermeasure proposals The emotion engine provides teams with emotion-based risk response and communication improvement suggestions, including identifying risks by taking into account the correlation between emotion data and project progress, suggesting responses based on emotion, and prioritizing risks. Teams can use this information to address risks and increase the chances of project success.

[0079] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0080] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58.

[0081] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314. [Fourth embodiment]

[0082] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0083] 7, a 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.

[0084] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0085] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0086] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0087] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0088] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0089] The control target 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, legs, etc. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, legs, etc. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0090] Fig. 8 shows an example of main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0091] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0092] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0093] In the robot 414, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0094] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, or a home appliance) owned by a user. Next, the identification process by the identification processing unit 290 of the data processing device 12 will be described.

[0095] (Example 1) An embodiment of the present invention is a system for predicting project risks and supporting early detection and resolution of problems. The system utilizes AI to analyze past project data and risk factors, and provides teams with alerts and suggestions regarding potential risks and issues. Specifically, the AI ​​monitors project progress and related data in real time, and evaluates the likelihood of risks based on a predictive model. The AI ​​can also learn data from similar past projects and identify similar risk factors. This allows development teams to identify risks early, take preventive measures, or respond quickly. (Example 2) An embodiment of the present invention is a system for predicting project risks and supporting early detection and resolution of problems. The system is provided as a project management tool equipped with AI. Specifically, a user inputs project data, and the AI ​​analyzes it to identify potential risks and issues. The AI ​​can also refer to a database of past projects to suggest similar risk factors and solutions. The AI ​​can also monitor the progress of the project in real time and detect abnormal patterns and delays. This allows the project team to take prompt and effective measures against risks. (Example 3) An embodiment of the present invention is a system for predicting project risks and supporting early detection and resolution of problems. This system is provided as a risk management platform that utilizes AI. Specifically, a user inputs data related to a project, and AI analyzes the data to identify potential risk factors. AI can also learn from past project data and industry best practices to build a risk prediction model. AI can also evaluate the importance and impact of risks and prioritize them. This allows project managers and stakeholders to take appropriate measures against risks.

[0096] The process flow of each embodiment will be described below.

[0097] (Example 1) Step 1: Enter your project data Users input data about their projects into the system, including project progress, task completion, and resource availability. Step 2: Analyze data and assess risks The AI ​​analyzes input project data and evaluates correlations with past project data and risk factors. It uses learning models to predict the likelihood of risks and evaluate their importance and impact. Step 3: Providing alerts and suggestions AI provides teams with alerts and suggestions about potential risks and challenges, including identifying risks, suggesting countermeasures and prioritizing risks, so teams can take preventative measures or respond quickly. (Example 2) Step 1: Enter your project data Users input data about their projects into the system, including task progress, resource utilization, and problem reports. Step 2: Analyze data and identify risks AI analyzes input project data and evaluates its relevance to past project data and industry best practices, then identifies risk factors and evaluates their likelihood and severity. Step 3: Providing alerts and suggestions AI provides teams with alerts and suggestions about potential risks and challenges, including identifying risks, proposing solutions and prioritizing risks, which teams can then use to address. (Example 3) Step 1: Enter your project data Users input data about their projects into the system, including task progress, budget usage, and identification of risk factors. Step 2: Data analysis and risk prediction AI analyzes input project data, learns from past project data and industry best practices, and builds risk prediction models to assess the likelihood and impact of risks. Step 3: Risk management and countermeasure proposals AI evaluates the importance and impact of risks, prioritizes them, and suggests countermeasures and solutions for risks, allowing project managers and stakeholders to take appropriate measures against risks.

[0098] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0099] (Example 1) An embodiment of the present invention is a system for predicting project risks and supporting early detection and resolution of problems. The system recognizes the emotional states of team members by combining an emotion engine and reflects them in project risk management. Specifically, team members input emotion data using an emotion recognition interface provided by the system. The emotion engine analyzes these data and evaluates how the team members' emotions are related to the progress of the project and risk factors. The emotion engine also analyzes the motivation and stress levels of the team to consider the human element in risk management. In this way, the team can receive risk assessments and countermeasure suggestions based on the emotion data, improving the probability of project success. (Example 2) An embodiment of the present invention is a system for predicting project risks and supporting early problem detection and resolution. The system utilizes an emotion engine to monitor the emotional state of a team and reflect it in project risk prediction. Specifically, team members collect emotion data using emotion recognition devices provided by the system. The emotion engine analyzes these data in real time and evaluates how the team members' emotions are related to the progress of the project and risk factors. The emotion engine also incorporates the emotion data into a project risk prediction model and utilizes it to evaluate risks and propose countermeasures. This allows the team to perform risk management based on the emotion data and maximize project results. (Example 3) An embodiment of the present invention is a system for predicting project risks and supporting early problem detection and resolution. The system realizes risk management that takes into account the emotional state of the team by combining an emotion engine. Specifically, team members input emotion data using an emotion recognition interface provided by the system. The emotion engine analyzes these data and evaluates how the team members' emotions are related to the progress of the project and risk factors. The emotion engine also adjusts and prioritizes the importance and impact of risks according to the emotional state of the team. This allows the team to implement risk countermeasures and communication improvement measures based on the emotion data, thereby increasing the probability of project success.

[0100] The process flow of each embodiment will be described below.

[0101] (Example 1) Step 1: Input emotion data Team members use an emotion recognition interface to input emotion data, which includes information such as what they say and their facial expressions. Step 2: Analyze sentiment data and assess risk The emotion engine analyzes the input emotion data and evaluates how team members' emotions relate to project progress and risk factors. The emotion engine uses learning models to predict the likelihood of risks and evaluate their importance and impact. Step 3: Providing sentiment-based alerts and suggestions The sentiment engine provides teams with alerts and suggestions about potential risks and issues, including identifying risks by taking into account the correlation between sentiment data and project progress, suggesting countermeasures based on sentiment, and prioritizing risks, so that teams can use this information to address risks. (Example 2) Step 1: Collect sentiment data Team members use emotion recognition devices to collect emotional data, which includes audio, visual and other information. Step 2: Analyze sentiment data in real time and identify risks The emotion engine analyzes the collected emotion data in real time and evaluates how team members' emotions relate to project progress and risk factors. The emotion engine identifies risk factors and evaluates their likelihood and severity. Step 3: Providing sentiment-based alerts and suggestions The sentiment engine provides teams with alerts and suggestions on potential risks and challenges, including identifying risks by taking into account the correlation between sentiment data and project progress, suggesting countermeasures according to sentiment, and prioritizing risks. Teams can use this information to manage risks and maximize project outcomes. (Example 3) Step 1: Input emotion data Team members use an emotion recognition interface to input emotion data, which includes information such as what they say and their facial expressions. Step 2: Analyzing sentiment data and predicting risks The emotion engine analyzes the input emotion data and evaluates how team members' emotions relate to project progress and risk factors. The emotion engine learns from past project data and industry best practices to build a risk prediction model. The emotion engine also adjusts and prioritizes the importance and impact of risks according to the team's emotional state. Step 3: Emotion-based risk management and countermeasure proposals The emotion engine provides teams with emotion-based risk response and communication improvement suggestions, including identifying risks by taking into account the correlation between emotion data and project progress, suggesting responses based on emotion, and prioritizing risks. Teams can use this information to address risks and increase the chances of project success.

[0102] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58.

[0104] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the robot 414.

[0105] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0106] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0107] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0108] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0109] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0110] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0111] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0112] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0113] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0114] In addition, 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 upon request from the data processing device 12.

[0115] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0116] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing the specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0117] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0118] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0119] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0120] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above description and illustrations, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above description and illustrations omit explanations of technical common sense that do not require explanation in order to enable the implementation of the technology of the present disclosure.

[0121] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or standard was specifically and individually indicated to be incorporated by reference.

[0122] The following is further disclosed regarding the above embodiment.

[0123] (Appendix 1) A system that includes AI that analyzes past project data and risk factors as a means of predicting project risks and helping to detect and resolve problems early. (Appendix 2) The system described in Appendix 1 is characterized in that the AI ​​provides the team with alerts and suggestions regarding potential risks and challenges. (Appendix 3) The system described in Appendix 1 is characterized in that it allows the development team to take preventive measures and respond quickly.

[0124] (Appendix 1) A system that includes an emotion engine as a means of predicting project risks and supporting early detection and resolution of problems. The emotion engine recognizes emotions from team members' comments and facial expressions, and evaluates the relevance to project progress and risk factors. The emotion engine also analyzes team motivation and stress levels, taking into account the human element in risk management. (Appendix 2) The system described in Appendix 1 is characterized in that the emotion engine monitors the emotion data of team members in real time and reflects it in project risk prediction. The emotion engine analyzes how the emotions of team members are related to the progress of the project and risk factors, and uses it to evaluate risks and propose countermeasures. (Appendix 3) The system described in Appendix 1 is characterized in that the emotion engine considers the team's emotional state in project risk management. The emotion engine comprehensively analyzes the relationship between team members' emotion data, project progress, and risk factors, and proposes risk countermeasures and communication improvement measures according to the team's motivation and stress level. This contributes to improving team cooperation and performance, and increases the probability of project success. [Explanation of symbols]

[0125] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system that includes AI that analyzes past project data and risk factors as a means of predicting project risks and helping to detect and resolve problems early.

2. The system of claim 1, wherein the AI ​​provides the team with alerts and suggestions regarding potential risks and challenges.

3. 2. The system of claim 1, wherein the development team is able to take preventative measures and respond quickly.

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