System

Abstract

An object of a system according to an embodiment is to automatically execute work of a user.SOLUTION: A system includes a moving image analysis unit, a learning unit, and an automatic operation unit. The video analysis unit analyzes the video of the screenshot. The learning unit learns the work of the user from the moving image of the screen shot analyzed by the moving image analysis unit. The automatic operation unit automatically executes a mouse operation or a keyboard operation based on the work learned by the learning unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques require users to create programs to automate their work, which is time-consuming and labor-intensive.

[0005] The system according to the embodiment aims to automatically execute a user's work. [Means for solving the problem]

[0006] The system according to the embodiment includes a video analysis unit, a learning unit, and an automatic operation unit. The video analysis unit analyzes the video of the screenshots. The learning unit learns the user's operations from the video of the screenshots analyzed by the video analysis unit. The automatic operation unit automatically performs mouse operations or keyboard operations based on the operations learned by the learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically perform the user's tasks. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) In the automatic operation system according to the embodiment of the present invention, a generation AI analyzes screenshot videos, learns user operations, and automatically reproduces those operations as mouse operations and keyboard operations. This allows the automatic operation system to automatically reproduce the user's operations and improve work efficiency.

[0029] An automatic operation system according to an embodiment includes a video analysis unit, a learning unit, and an automatic operation unit. The video analysis unit analyzes a screenshot video. For example, the video analysis unit analyzes each frame to identify the operation the user is performing. The video analysis unit can also recognize objects in the video using image recognition technology. For example, the video analysis unit recognizes objects such as buttons and text fields and tracks their changes. The learning unit learns the user's operations from the screenshot video analyzed by the video analysis unit. For example, the learning unit memorizes the user's operation patterns and procedures and builds a model for reproducing similar operations. The learning unit can also learn the user's operation patterns using a machine learning algorithm. For example, the learning unit learns the user's operations of moving the mouse in a specific order and pressing specific keys. The automatic operation unit automatically performs mouse operations and keyboard operations based on the operations learned by the learning unit. For example, the automatic operation unit moves the mouse and presses keys according to the learned operation procedures. The automatic operation unit can also monitor the success rate and error rate of operations in real time and correct operations as necessary. For example, the automatic operation unit determines whether the operation was successful and corrects any errors that occur. This allows the automatic operation system according to the embodiment to automatically reproduce the user's work, thereby improving work efficiency. For example, the user can leave the work to the generation AI without the intervention of a program. Furthermore, the generation AI can automatically upload an Excel image link to a web page. For example, the generation AI opens an Excel file, copies the image link, opens a web browser, accesses the specified web page, clicks a button to upload the image link, pastes the image link, and clicks a button to complete the upload.

[0030] The video analysis unit can recognize specific objects within a video and track changes in those objects. For example, the generative AI can recognize specific buttons or text fields within a video and track changes in those objects. For example, it can perform detailed analysis of the position of the button the user clicked or the content of the text they entered. This allows for more accurate analysis of user operations.

[0031] The video analysis unit can improve the accuracy of analysis by simultaneously using the user's gaze tracking data when analyzing the video to identify the part the user is focusing on. For example, the video analysis unit collects gaze tracking data at the same time as analyzing the video to identify the part the user is focusing on. For example, it analyzes which part of the screen the user is looking at and focuses on analyzing the operation of that part. This identifies the part the user is focusing on and improves the accuracy of analysis.

[0032] The video analysis unit enables video analysis of screenshots on different devices, achieving multi-device compatibility. For example, the video analysis unit enables video analysis of screenshots on smartphones and tablets. For example, it analyzes smartphone screen operations and identifies user operations. This allows video analysis of screenshots on different devices.

[0033] The video analysis unit can add an interactive guide function that provides feedback on the results of the video analysis to the user in real time and helps optimize operations. The video analysis unit, for example, provides feedback on the results of the video analysis to the user in real time and helps optimize operations. For example, it evaluates the accuracy and efficiency of operations performed by the user in real time. This makes it possible to provide an interactive guide function that helps optimize operations.

[0034] When learning a user's operation patterns, the learning unit can refer to past operation history and analyze the consistency and changes in operations. For example, the generation AI of the learning unit refers to the user's past operation history and analyzes the consistency and changes in operation patterns. For example, it analyzes whether the user is repeating the same operation or performing different operations. This makes it possible to refer to past operation history and analyze the consistency and changes in operations.

[0035] The learning unit can evaluate the user's operation speed and accuracy during the learning process and propose optimal operation procedures. For example, the learning unit uses a generation AI to evaluate the user's operation speed and accuracy and propose optimal operation procedures. For example, it prioritizes learning procedures that allow the user to operate quickly and accurately. This makes it possible to evaluate the user's operation speed and accuracy and propose optimal operation procedures.

[0036] The learning unit can compare operation patterns of different users, extract common operation procedures, and build a general-purpose operation model. The learning unit, for example, compares operation patterns of different users and extracts common operation procedures. For example, when multiple users perform the same operation, the learning unit builds the procedure as a general-purpose operation model. This makes it possible to compare operation patterns of different users, extract common operation procedures, and build a general-purpose operation model.

[0037] The learning unit can generalize the operation model so that the learned operation procedures can be applied to other applications and systems. For example, the learning unit generalizes the operation model so that the learned operation procedures can be applied to other applications and systems. For example, the learning unit applies the operation procedures learned in Excel to other spreadsheet applications. This allows the operation model to be generalized so that the learned operation procedures can be applied to other applications and systems.

[0038] The automatic operation unit monitors the success rate and error rate of the operation in real time when the generation AI executes an automatic operation, and can correct the operation as necessary.The automatic operation unit monitors the success rate and error rate of the operation in real time when the generation AI executes an automatic operation, for example, by determining whether the operation was successful and correcting any errors that occur.This makes it possible to monitor the success rate and error rate of the operation in real time and correct the operation as necessary.

[0039] The automatic operation unit can collect user feedback while the automatic operation is being performed, and continuously improve the accuracy and efficiency of the operation. For example, the automatic operation unit can collect user feedback while the automatic operation is being performed, and continuously improve the accuracy and efficiency of the operation. For example, the automatic operation unit can evaluate whether the user is satisfied with the result of the operation. This allows the automatic operation unit to collect user feedback and continuously improve the accuracy and efficiency of the operation.

[0040] The automatic operation unit can extend the operation model so that the execution of automatic operations can be applied to different applications and systems. The automatic operation unit extends the operation model so that the execution of automatic operations can be applied to different applications and systems, for example. For example, the operation procedures learned in Excel can be applied to other spreadsheet applications. This allows the operation model to be extended so that the execution of automatic operations can be applied to different applications and systems.

[0041] The automatic operation unit can visualize the results of the automatic operation, allowing the user to intuitively understand the progress of the operation. The automatic operation unit, for example, visualizes the results of the automatic operation, allowing the user to intuitively understand the progress of the operation. For example, the progress of the operation is displayed in a graph or chart. This visualizes the results of the automatic operation, allowing the user to intuitively understand the progress of the operation.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The automatic operation system may further include a voice recognition unit. The voice recognition unit analyzes a user's voice commands and executes a specific operation. For example, if a user says "open a file," the voice recognition unit analyzes the command and executes the corresponding operation. The voice recognition unit may also learn the user's voice commands to achieve more accurate voice recognition. This allows the user to operate the system more intuitively using voice commands.

[0044] The automatic operation system may further include a gesture recognition unit. The gesture recognition unit analyzes the user's hand movements and executes a specific operation. For example, when the user waves their hand, the gesture recognition unit analyzes the movement and executes the corresponding operation. The gesture recognition unit may also learn the user's gestures and achieve more accurate gesture recognition. This allows the user to operate the system more intuitively using hand movements.

[0045] The automatic operation system may further include an environment recognition unit. The environment recognition unit analyzes the user's surrounding environment and executes specific operations. For example, if the user is in a dark room, the environment recognition unit analyzes the environment and automatically adjusts the screen brightness. The environment recognition unit may also analyze the sounds around the user and provide a noise cancellation function. This allows the user to operate the system in a comfortable environment.

[0046] The automatic operation system may further include an efficiency suggestion unit that analyzes the user's operation history and makes suggestions to improve operation efficiency. The efficiency suggestion unit analyzes the user's operation history and suggests efficient operation procedures. For example, it may suggest automating operations that the user frequently performs. The efficiency suggestion unit may also learn the user's operation patterns and make more appropriate efficiency suggestions. This allows the user to receive suggestions to improve operation efficiency.

[0047] The automatic operation system may further include an error prevention suggestion unit that detects user operation errors and makes suggestions to prevent the errors. The error prevention suggestion unit analyzes the user's operation history and identifies patterns of operation errors. For example, if the user frequently makes errors in a specific operation, it makes suggestions to simplify that operation. The error prevention suggestion unit can also learn the user's operation patterns and make more appropriate error prevention suggestions. This allows the user to receive suggestions to prevent operation errors.

[0048] The automatic operation system can further include a customization suggestion unit that analyzes the user's operation patterns and suggests customization of the operation. The customization suggestion unit analyzes the user's operation patterns and suggests the optimal operation procedure for each individual user. For example, it may preferentially suggest the user's preferred operation method. The customization suggestion unit can also learn the user's operation patterns and make more appropriate customization suggestions. This allows the user to receive suggestions for the optimal operation procedure for themselves.

[0049] The automatic operation system can further include an environment optimization unit that analyzes the user's operation environment and optimizes operation according to the environment. The environment optimization unit analyzes the user's operation environment and proposes appropriate operation procedures. For example, if the user is in a noisy environment, it proposes to avoid voice input. The environment optimization unit can also learn changes in the user's operation environment and propose more appropriate environment optimization. This allows the user to receive proposals for optimal operation procedures according to the operation environment.

[0050] The automatic operation system can further include a prediction unit that predicts operations based on the user's operation history. The prediction unit analyzes the user's operation history and predicts the next operation. For example, it predicts operations that the user will frequently perform after performing a specific operation and prepares those operations in advance. The prediction unit can also learn the user's operation patterns and predict operations with higher accuracy. This allows the user's next operation to be predicted, allowing them to proceed with operations smoothly.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The video analyzer analyzes the video of the screenshot. For example, the video analyzer analyzes each frame to identify the operation the user is performing. The video analyzer can also use image recognition technology to recognize objects in the video. For example, the video analyzer recognizes objects such as buttons and text fields and tracks their changes. Step 2: The learning unit learns the user's operations from the screenshot video analyzed by the video analysis unit. For example, the learning unit memorizes the user's operation patterns and procedures and builds a model to reproduce similar operations. The learning unit can also use machine learning algorithms to learn the user's operation patterns. For example, the learning unit learns the user's operations of moving the mouse in a specific order and pressing specific keys. Step 3: The automatic operation unit automatically performs mouse and keyboard operations based on the tasks learned by the learning unit. For example, the automatic operation unit moves the mouse and presses keys according to the learned operating procedure. The automatic operation unit can also monitor the success rate and error rate of operations in real time and correct operations as necessary. For example, the automatic operation unit determines whether an operation was successful and corrects any errors that occur.

[0053] (Example 2) In the automatic operation system according to the embodiment of the present invention, a generation AI analyzes screenshot videos, learns user operations, and automatically reproduces those operations as mouse operations and keyboard operations. This allows the automatic operation system to automatically reproduce the user's operations and improve work efficiency.

[0054] An automatic operation system according to an embodiment includes a video analysis unit, a learning unit, and an automatic operation unit. The video analysis unit analyzes a screenshot video. For example, the video analysis unit analyzes each frame to identify the operation the user is performing. The video analysis unit can also recognize objects in the video using image recognition technology. For example, the video analysis unit recognizes objects such as buttons and text fields and tracks their changes. The learning unit learns the user's operations from the screenshot video analyzed by the video analysis unit. For example, the learning unit memorizes the user's operation patterns and procedures and builds a model for reproducing similar operations. The learning unit can also learn the user's operation patterns using a machine learning algorithm. For example, the learning unit learns the user's operations of moving the mouse in a specific order and pressing specific keys. The automatic operation unit automatically performs mouse operations and keyboard operations based on the operations learned by the learning unit. For example, the automatic operation unit moves the mouse and presses keys according to the learned operation procedures. The automatic operation unit can also monitor the success rate and error rate of operations in real time and correct operations as necessary. For example, the automatic operation unit determines whether the operation was successful and corrects any errors that occur. This allows the automatic operation system according to the embodiment to automatically reproduce the user's work, thereby improving work efficiency. For example, the user can leave the work to the generation AI without the intervention of a program. Furthermore, the generation AI can automatically upload an Excel image link to a web page. For example, the generation AI opens an Excel file, copies the image link, opens a web browser, accesses the specified web page, clicks a button to upload the image link, pastes the image link, and clicks a button to complete the upload.

[0055] The video analysis unit can recognize specific objects within a video and track changes in those objects. For example, the generative AI can recognize specific buttons or text fields within a video and track changes in those objects. For example, it can perform detailed analysis of the position of the button the user clicked or the content of the text they entered. This allows for more accurate analysis of user operations.

[0056] The video analysis unit can improve the accuracy of analysis by simultaneously using the user's gaze tracking data when analyzing the video to identify the part the user is focusing on. For example, the video analysis unit collects gaze tracking data at the same time as analyzing the video to identify the part the user is focusing on. For example, it analyzes which part of the screen the user is looking at and focuses on analyzing the operation of that part. This identifies the part the user is focusing on and improves the accuracy of analysis.

[0057] The video analysis unit uses the emotion estimation function to analyze the stress and satisfaction felt by the user during operation, and can make suggestions for improving the operation based on the emotions. The video analysis unit, for example, uses the emotion estimation function to analyze the stress and satisfaction felt by the user during operation. For example, it analyzes the user's facial expressions and voice and quantifies the stress level and satisfaction level. This makes it possible to make suggestions for improving the operation based on the user's emotions.

[0058] The video analysis unit enables video analysis of screenshots on different devices, achieving multi-device compatibility. For example, the video analysis unit enables video analysis of screenshots on smartphones and tablets. For example, it analyzes smartphone screen operations and identifies user operations. This allows video analysis of screenshots on different devices.

[0059] The video analysis unit can add an interactive guide function that provides feedback on the results of the video analysis to the user in real time and helps optimize operations. The video analysis unit, for example, provides feedback on the results of the video analysis to the user in real time and helps optimize operations. For example, it evaluates the accuracy and efficiency of operations performed by the user in real time. This makes it possible to provide an interactive guide function that helps optimize operations.

[0060] The video analysis unit can use the emotion estimation function to propose an interface design that reinforces the positive emotions felt by the user during operation. The video analysis unit, for example, uses the emotion estimation function to propose an interface design that reinforces the positive emotions felt by the user during operation. For example, the video analysis unit incorporates design elements that give the user a sense of satisfaction. This makes it possible to propose an interface design that reinforces the positive emotions felt by the user during operation.

[0061] When learning a user's operation patterns, the learning unit can refer to past operation history and analyze the consistency and changes in operations. For example, the generation AI of the learning unit refers to the user's past operation history and analyzes the consistency and changes in operation patterns. For example, it analyzes whether the user is repeating the same operation or performing different operations. This makes it possible to refer to past operation history and analyze the consistency and changes in operations.

[0062] The learning unit can evaluate the user's operation speed and accuracy during the learning process and propose optimal operation procedures. For example, the learning unit uses a generation AI to evaluate the user's operation speed and accuracy and propose optimal operation procedures. For example, it prioritizes learning procedures that allow the user to operate quickly and accurately. This makes it possible to evaluate the user's operation speed and accuracy and propose optimal operation procedures.

[0063] The learning unit uses the emotion estimation function to learn emotions felt by the user during operation and can optimize the operation based on the emotions. The learning unit, for example, uses the emotion estimation function to learn emotions felt by the user during operation and can optimize the operation based on the emotions. For example, the learning unit simplifies an operation that causes stress to the user. This makes it possible to optimize the operation based on the user's emotions.

[0064] The learning unit can compare operation patterns of different users, extract common operation procedures, and build a general-purpose operation model. The learning unit, for example, compares operation patterns of different users and extracts common operation procedures. For example, when multiple users perform the same operation, the learning unit builds the procedure as a general-purpose operation model. This makes it possible to compare operation patterns of different users, extract common operation procedures, and build a general-purpose operation model.

[0065] The learning unit can generalize the operation model so that the learned operation procedures can be applied to other applications and systems. For example, the learning unit generalizes the operation model so that the learned operation procedures can be applied to other applications and systems. For example, the learning unit applies the operation procedures learned in Excel to other spreadsheet applications. This allows the operation model to be generalized so that the learned operation procedures can be applied to other applications and systems.

[0066] The learning unit uses the emotion estimation function to identify the operation procedure that the user feels most comfortable with and can suggest that procedure to other users. The learning unit, for example, uses the emotion estimation function to identify the operation procedure that the user feels most comfortable with. For example, the learning unit preferentially learns operation procedures that the user feels satisfied with. This allows the learning unit to identify the operation procedure that the user feels most comfortable with and suggest that procedure to other users.

[0067] The automatic operation unit monitors the success rate and error rate of the operation in real time when the generation AI executes an automatic operation, and can correct the operation as necessary.The automatic operation unit monitors the success rate and error rate of the operation in real time when the generation AI executes an automatic operation, for example, by determining whether the operation was successful and correcting any errors that occur.This makes it possible to monitor the success rate and error rate of the operation in real time and correct the operation as necessary.

[0068] The automatic operation unit can collect user feedback while the automatic operation is being performed, and continuously improve the accuracy and efficiency of the operation. For example, the automatic operation unit can collect user feedback while the automatic operation is being performed, and continuously improve the accuracy and efficiency of the operation. For example, the automatic operation unit can evaluate whether the user is satisfied with the result of the operation. This allows the automatic operation unit to collect user feedback and continuously improve the accuracy and efficiency of the operation.

[0069] The automatic operation unit can use the emotion estimation function to evaluate the sense of security and trust that the user feels toward the automatic operation, and optimize the operation based on the emotion. The automatic operation unit, for example, uses the emotion estimation function to evaluate the sense of security and trust that the user feels toward the automatic operation. For example, it analyzes the user's facial expression and voice and calculates a score for the sense of security and trust. This allows the sense of security and trust that the user feels toward the automatic operation to be evaluated, and the operation to be optimized based on the emotion.

[0070] The automatic operation unit can extend the operation model so that the execution of automatic operations can be applied to different applications and systems. The automatic operation unit extends the operation model so that the execution of automatic operations can be applied to different applications and systems, for example. For example, the operation procedures learned in Excel can be applied to other spreadsheet applications. This allows the operation model to be extended so that the execution of automatic operations can be applied to different applications and systems.

[0071] The automatic operation unit can visualize the results of the automatic operation, allowing the user to intuitively understand the progress of the operation. The automatic operation unit, for example, visualizes the results of the automatic operation, allowing the user to intuitively understand the progress of the operation. For example, the progress of the operation is displayed in a graph or chart. This visualizes the results of the automatic operation, allowing the user to intuitively understand the progress of the operation.

[0072] The automatic operation unit can add a feedback function using the emotion estimation function to reinforce the positive emotion the user feels toward the automatic operation. The automatic operation unit, for example, uses the emotion estimation function to add a feedback function to reinforce the positive emotion the user feels toward the automatic operation. For example, the automatic operation unit emphasizes an operation result that the user feels satisfied with. This makes it possible to add a feedback function to reinforce the positive emotion the user feels toward the automatic operation.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The automatic operation system may further include a voice recognition unit. The voice recognition unit analyzes a user's voice commands and executes a specific operation. For example, if a user says "open a file," the voice recognition unit analyzes the command and executes the corresponding operation. The voice recognition unit may also learn the user's voice commands to achieve more accurate voice recognition. This allows the user to operate the system more intuitively using voice commands.

[0075] The automatic operation system may further include a gesture recognition unit. The gesture recognition unit analyzes the user's hand movements and executes a specific operation. For example, when the user waves their hand, the gesture recognition unit analyzes the movement and executes the corresponding operation. The gesture recognition unit may also learn the user's gestures and achieve more accurate gesture recognition. This allows the user to operate the system more intuitively using hand movements.

[0076] The automatic operation system may further include an environment recognition unit. The environment recognition unit analyzes the user's surrounding environment and executes specific operations. For example, if the user is in a dark room, the environment recognition unit analyzes the environment and automatically adjusts the screen brightness. The environment recognition unit may also analyze the sounds around the user and provide a noise cancellation function. This allows the user to operate the system in a comfortable environment.

[0077] The automatic operation system can further include a health management unit that monitors the user's health condition. The health management unit analyzes the user's heart rate and stress level and suggests appropriate operations. For example, if the user is feeling high stress, the health management unit will suggest operations to relax them. The health management unit can also learn the user's health condition and suggest more appropriate operations. This allows the user to operate the system while maintaining their health.

[0078] The automatic operation system may further include a music providing unit that estimates the user's emotions and plays music based on the emotions. The music providing unit analyzes the user's emotions and selects and plays appropriate music. For example, if the user is feeling stressed, it plays relaxing music. The music providing unit can also learn the user's musical preferences and provide more appropriate music. This allows the user to enjoy music that suits their emotions.

[0079] The automatic operation system may further include a notification management unit that estimates the user's emotions and prioritizes notifications based on the emotions. The notification management unit analyzes the user's emotions and prioritizes the display of important notifications. For example, when the user is concentrating, only important notifications are displayed, and other notifications are postponed. The notification management unit can also learn changes in the user's emotions and manage notifications more appropriately. This allows the user to receive notification management that is tailored to their emotions.

[0080] The automatic operation system may further include a break suggestion unit that estimates the user's emotions and suggests breaks based on the emotions. The break suggestion unit analyzes the user's emotions and suggests breaks at appropriate times. For example, if the user is tired, it suggests taking a break. The break suggestion unit can also learn changes in the user's emotions and make more appropriate break suggestions. This allows the user to take a break according to their emotions.

[0081] The automatic operation system may further include a difficulty adjustment unit that estimates the user's emotions and adjusts the difficulty of the operation based on the emotions. The difficulty adjustment unit analyzes the user's emotions and adjusts the difficulty of the operation. For example, if the user is feeling stressed, the difficulty of the operation may be lowered. The difficulty adjustment unit may also learn changes in the user's emotions and adjust the difficulty more appropriately. This allows the user to adjust the difficulty of the operation according to their emotions.

[0082] The automatic operation system may further include a feedback unit that estimates the user's emotions and provides operation feedback based on the emotions. The feedback unit analyzes the user's emotions and provides appropriate feedback. For example, if the user is satisfied, it provides positive feedback. The feedback unit may also learn changes in the user's emotions and provide more appropriate feedback. This allows the user to receive feedback according to their emotions.

[0083] The automatic operation system may further include an efficiency suggestion unit that analyzes the user's operation history and makes suggestions to improve operation efficiency. The efficiency suggestion unit analyzes the user's operation history and suggests efficient operation procedures. For example, it may suggest automating operations that the user frequently performs. The efficiency suggestion unit may also learn the user's operation patterns and make more appropriate efficiency suggestions. This allows the user to receive suggestions to improve operation efficiency.

[0084] The automatic operation system may further include an error prevention suggestion unit that detects user operation errors and makes suggestions to prevent the errors. The error prevention suggestion unit analyzes the user's operation history and identifies patterns of operation errors. For example, if the user frequently makes errors in a specific operation, it makes suggestions to simplify that operation. The error prevention suggestion unit can also learn the user's operation patterns and make more appropriate error prevention suggestions. This allows the user to receive suggestions to prevent operation errors.

[0085] The automatic operation system can further include a customization suggestion unit that analyzes the user's operation patterns and suggests customization of the operation. The customization suggestion unit analyzes the user's operation patterns and suggests the optimal operation procedure for each individual user. For example, it may preferentially suggest the user's preferred operation method. The customization suggestion unit can also learn the user's operation patterns and make more appropriate customization suggestions. This allows the user to receive suggestions for the optimal operation procedure for themselves.

[0086] The automatic operation system can further include an environment optimization unit that analyzes the user's operation environment and optimizes operation according to the environment. The environment optimization unit analyzes the user's operation environment and proposes appropriate operation procedures. For example, if the user is in a noisy environment, it proposes to avoid voice input. The environment optimization unit can also learn changes in the user's operation environment and propose more appropriate environment optimization. This allows the user to receive proposals for optimal operation procedures according to the operation environment.

[0087] The automatic operation system can further include a prediction unit that predicts operations based on the user's operation history. The prediction unit analyzes the user's operation history and predicts the next operation. For example, it predicts operations that the user will frequently perform after performing a specific operation and prepares those operations in advance. The prediction unit can also learn the user's operation patterns and predict operations with higher accuracy. This allows the user's next operation to be predicted, allowing them to proceed with operations smoothly.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The video analyzer analyzes the video of the screenshot. For example, the video analyzer analyzes each frame to identify the operation the user is performing. The video analyzer can also use image recognition technology to recognize objects in the video. For example, the video analyzer recognizes objects such as buttons and text fields and tracks their changes. Step 2: The learning unit learns the user's operations from the screenshot video analyzed by the video analysis unit. For example, the learning unit memorizes the user's operation patterns and procedures and builds a model to reproduce similar operations. The learning unit can also use machine learning algorithms to learn the user's operation patterns. For example, the learning unit learns the user's operations of moving the mouse in a specific order and pressing specific keys. Step 3: The automatic operation unit automatically performs mouse and keyboard operations based on the tasks learned by the learning unit. For example, the automatic operation unit moves the mouse and presses keys according to the learned operating procedure. The automatic operation unit can also monitor the success rate and error rate of operations in real time and correct operations as necessary. For example, the automatic operation unit determines whether an operation was successful and corrects any errors that occur.

[0090] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0096] 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.

[0097] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0098] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0099] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0100] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0101] 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.

[0102] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0104] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 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.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] 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.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0131] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0134] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0140] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0141] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0142] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0143] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0144] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0145] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0146] 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.

[0147] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0148] 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.

[0149] 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.

[0150] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0151] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0152] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0153] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0154] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0155] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0156] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0157] 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. The device includes a video analysis unit that analyzes the video of the screenshot, a learning unit that learns the user's operations from the video of the screenshot analyzed by the video analysis unit, and an automatic operation unit that automatically performs mouse operations or keyboard operations based on the operations learned by the learning unit. A system characterized by:

2. The video analysis unit When analyzing a video, the user's gaze tracking data is also used to identify the part the user is paying attention to, thereby improving the accuracy of the analysis. The system of claim 1 .

3. The video analysis unit To enable video analysis of the screenshots on different devices, achieving multi-device compatibility. The system of claim 1 .

4. The learning unit When learning the user's operation pattern, the past operation history is referenced and the consistency or change of the operation is analyzed.

2. The system of claim 1.

5. The automatic operation unit is When the generative AI performs automated operations, it monitors the success rate and error rate of the operations in real time and corrects the operations as necessary. The system of claim 1 .

6. The video analysis unit Analyzing the stress and satisfaction felt by the user during operation and making suggestions for improving the operation based on emotions. The system of claim 1 .

7. The learning unit Learning the emotions felt by the user during operation and optimizing the operation based on the emotions. The system of claim 1 .

8. The automatic operation unit is Evaluating the sense of security and trust felt by the user regarding automated operation and optimizing the operation based on emotions The system of claim 1 .

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