system

Abstract

An object of a system according to an embodiment is to accurately and efficiently convert work contents of a person in charge into a manual.SOLUTION: A system according to an embodiment includes a PC operation monitoring unit, a voice recognition unit, an analysis unit, and a manual generation unit. The PC operation monitoring unit monitors the PC operation of the person in charge. The speech recognition unit performs speech recognition on the oral explanation of the person in charge. The analysis unit analyzes the information acquired by the PC operation monitoring unit and the voice recognition unit. The manual generation unit generates a manual based on the information analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to accurately and efficiently compile the work content of personnel into a manual, and there is room for improvement.

[0005] The system according to the embodiment aims to accurately and efficiently compile the work content of a person in charge into a manual. [Means for solving the problem]

[0006] The system according to the embodiment includes a PC operation monitoring unit, a voice recognition unit, an analysis unit, and a manual generation unit. The PC operation monitoring unit monitors the PC operations of the person in charge. The voice recognition unit voice-recognizes the oral explanations of the person in charge. The analysis unit analyzes the information acquired by the PC operation monitoring unit and the voice recognition unit. The manual generation unit generates a manual based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately and efficiently compile the work content of each person in charge into a manual. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The automatic manual creation system according to an embodiment of the present invention is a system in which AI monitors the operational status of a person in charge when the person in charge provides supplementary explanations of the work content through PC operation and verbal explanation, and automatically creates and updates a manual that includes images and explanatory captions. As a result, the automatic manual creation system can automatically create and update a manual that includes images and explanatory captions based on the person in charge's PC operation and verbal explanation.

[0029] An automatic manual creation system according to an embodiment includes a PC operation monitoring unit, a voice recognition unit, an analysis unit, and a manual generation unit. The PC operation monitoring unit monitors the PC operations of the person in charge. For example, when the person in charge changes software settings, the PC operation monitoring unit captures the operation screen and records details such as which buttons were clicked and which menu items were selected. The PC operation monitoring unit can also record the operation procedures as captured images in real time. The voice recognition unit recognizes the person in charge's verbal instructions. For example, when the person in charge explains, "Next, click this button to save the settings," the voice recognition unit converts the speech into text and adds it as an explanatory caption to the corresponding captured image. The voice recognition unit can also convert the person in charge's verbal instructions into text in real time using voice recognition technology. The analysis unit analyzes the information acquired by the PC operation monitoring unit and the voice recognition unit. For example, the analysis unit analyzes the person in charge's operation procedures and verbal instructions and automatically incorporates information such as the task overview, purpose, and subsequent tasks into the manual. The analysis unit can also evaluate the efficiency and accuracy of the operating procedures and suggest areas for improvement. The manual generation unit generates a manual based on the information analyzed by the analysis unit. For example, if a new operating procedure is added, the manual generation unit adds the procedure to the manual along with captured images and explanatory captions. In addition, if an existing procedure is changed, the manual generation unit can automatically update that part. As a result, the automatic manual creation system according to the embodiment can automatically create and update a manual including images and explanatory captions based on the PC operations and verbal explanations of the person in charge.

[0030] The PC operation monitoring unit records the speed and frequency of operations when capturing operation procedures, and is able to suggest efficient operation procedures. For example, when capturing operation procedures, the PC operation monitoring unit records the speed and frequency of operations and suggests efficient operation procedures. For example, it analyzes frequently used shortcut keys and mouse operations and suggests optimal operation procedures. The PC operation monitoring unit also suggests efficient operation procedures based on the speed and frequency of operations when capturing operation procedures. For example, it suggests a faster method when the same operation is repeated. The PC operation monitoring unit also records the speed and frequency of operations when capturing operation procedures and suggests efficient operation procedures. For example, it identifies operation delays and unnecessary steps and suggests improvement suggestions. This makes it possible to suggest efficient operation procedures based on the speed and frequency of operations.

[0031] The PC operation monitoring unit can automatically add background information of an operation to a captured image of an operation procedure. The PC operation monitoring unit, for example, automatically adds background information of an operation to a captured image of an operation procedure. For example, the purpose of the operation and related settings are displayed in the captured image. The PC operation monitoring unit also helps understand the operation procedure by adding background information of the operation to the captured image. For example, text explaining the purpose of the operation and related settings is automatically inserted. The PC operation monitoring unit also automatically adds background information of an operation to a captured image of an operation procedure. For example, the purpose of the operation and related settings are displayed in the captured image to deepen understanding of the operation procedure. In this way, adding background information to the captured image of an operation procedure helps understand the operation procedure.

[0032] The PC operation monitoring unit can simultaneously capture the operation procedures of different software and systems and create an integrated manual. The PC operation monitoring unit, for example, can simultaneously capture the operation procedures of different software and systems and create an integrated manual. For example, it can combine the operation procedures of multiple applications into a single manual. The PC operation monitoring unit can also simultaneously capture the operation procedures of different software and systems and create an integrated manual. For example, it can combine the operation procedures between different systems to provide a consistent manual. The PC operation monitoring unit can also simultaneously capture the operation procedures of different software and systems and create an integrated manual. For example, it can combine the operation procedures of multiple platforms into a single manual. This makes it possible to simultaneously capture the operation procedures of different software and systems and create an integrated manual.

[0033] The PC operation monitoring unit can display captured images of operation procedures as 3D models or animations to make them visually easier to understand. The PC operation monitoring unit, for example, displays captured images of operation procedures as 3D models or animations to make them visually easier to understand. For example, the operation procedures may be reproduced in a 3D model so that the user can experience the operation. The PC operation monitoring unit can also display captured images of operation procedures as animations to make them visually easier to understand. For example, the operation procedures may be shown in animation so that the user can easily understand the flow of operations. The PC operation monitoring unit can also display captured images of operation procedures as 3D models or animations to make them visually easier to understand. For example, the operation procedures may be reproduced in a 3D model so that the user can experience the operation. In this way, displaying captured images of operation procedures as 3D models or animations makes them visually easier to understand.

[0034] The speech recognition unit can use speech recognition technology to reflect the nuances and tone of the spoken explanation by the person in charge in the text. The speech recognition unit, for example, uses speech recognition technology to reflect the nuances and tone of the spoken explanation by the person in charge in the text. For example, emphasized parts are displayed in bold or in color. The speech recognition unit also uses speech recognition technology to reflect the nuances and tone of the spoken explanation by the person in charge in the text. For example, emotional parts are identified and reflected in the text. The speech recognition unit also uses speech recognition technology to reflect the nuances and tone of the spoken explanation by the person in charge in the text. For example, the tone and rhythm of the voice are analyzed and reflected in the text. In this way, by reflecting the nuances and tone of the spoken explanation by the person in charge in the text, the intention of the explanation can be conveyed more accurately.

[0035] The speech recognition unit can display the speech recognition results in real time, allowing the person in charge to make corrections or additions on the spot. The speech recognition unit, for example, displays the speech recognition results in real time, allowing the person in charge to make corrections or additions on the spot. For example, the speech recognition results may be displayed on a screen, providing an interface through which the person in charge can make corrections. The speech recognition unit can also display the speech recognition results in real time, allowing the person in charge to make corrections or additions on the spot. For example, the speech recognition results may be displayed in a text editor, allowing the person in charge to make corrections. The speech recognition unit can also display the speech recognition results in real time, allowing the person in charge to make corrections or additions on the spot. For example, the speech recognition results may be displayed on a screen, providing an interface through which the person in charge can make corrections. In this way, by displaying the speech recognition results in real time, allowing the person in charge to make corrections or additions on the spot, an accurate manual can be created.

[0036] The speech recognition unit can simultaneously recognize oral explanations in different languages ​​and create a multilingual manual. The speech recognition unit, for example, simultaneously recognizes oral explanations in different languages ​​and creates a multilingual manual. For example, explanations in multiple languages ​​such as English, Japanese, and Chinese are recognized simultaneously and reflected in the manual. The speech recognition unit can also simultaneously recognize oral explanations in different languages ​​and create a multilingual manual. For example, explanations in multiple languages ​​such as English, Japanese, and Chinese are recognized simultaneously and reflected in the manual. The speech recognition unit can also simultaneously recognize oral explanations in different languages ​​and create a multilingual manual. For example, explanations in multiple languages ​​such as English, Japanese, and Chinese are recognized simultaneously and reflected in the manual. This allows oral explanations in different languages ​​to be simultaneously recognized and a multilingual manual to be created.

[0037] The speech recognition unit can display the speech recognition results as visual notes or infographics to make them easier to understand visually. The speech recognition unit, for example, displays the speech recognition results as visual notes or infographics to make them easier to understand visually. For example, the speech recognition results are shown using diagrams or icons. The speech recognition unit can also display the speech recognition results as visual notes or infographics to make them easier to understand visually. For example, the speech recognition results are shown using diagrams or icons. The speech recognition unit can also display the speech recognition results as visual notes or infographics to make them easier to understand visually. For example, the speech recognition results are shown using diagrams or icons. In this way, displaying the speech recognition results as visual notes or infographics makes them easier to understand visually.

[0038] The analysis unit can reflect the latest information by referring to related industry news and trend information when automatically generating the work outline and purpose. For example, the analysis unit reflects the latest information by referring to related industry news and trend information when automatically generating the work outline and purpose. For example, the latest technological trends and market needs are reflected. Furthermore, the analysis unit reflects the latest information by referring to related industry news and trend information when automatically generating the work outline and purpose. For example, the latest technological trends and market needs are reflected. Furthermore, the analysis unit reflects the latest information by referring to related industry news and trend information when automatically generating the work outline and purpose. For example, the latest technological trends and market needs are reflected. In this way, by reflecting the latest information by referring to related industry news and trend information when automatically generating the work outline and purpose, it is possible to provide a more accurate and up-to-date manual.

[0039] When automatically generating procedures for subsequent tasks, the analysis unit can propose an optimal procedure by referring to past work history and success cases. For example, when automatically generating procedures for subsequent tasks, the analysis unit proposes an optimal procedure by referring to past work history and success cases. For example, the optimal procedure is proposed based on past success cases. When automatically generating procedures for subsequent tasks, the analysis unit also proposes an optimal procedure by referring to past work history and success cases. For example, the optimal procedure is proposed based on past success cases. When automatically generating procedures for subsequent tasks, the analysis unit also proposes an optimal procedure by referring to past work history and success cases. For example, the optimal procedure is proposed based on past success cases. In this way, when automatically generating procedures for subsequent tasks, the analysis unit proposes an optimal procedure by referring to past work history and success cases, thereby achieving efficient work.

[0040] The analysis unit can compare work procedures between different industries and applications and identify similarities and differences. For example, the analysis unit compares work procedures between different industries and applications and identifies similarities and differences. For example, the analysis unit compares work procedures between the manufacturing industry and the service industry and identifies similarities and differences. The analysis unit can also compare work procedures between different industries and applications and identify similarities and differences. For example, the analysis unit can compare work procedures between the manufacturing industry and the service industry and identify similarities and differences. The analysis unit can also compare work procedures between different industries and applications and identify similarities and differences. For example, the analysis unit can compare work procedures between the manufacturing industry and the service industry and identify similarities and differences. This makes it possible to compare work procedures between different industries and applications and identify similarities and differences, thereby creating a more versatile manual.

[0041] The analysis unit can display the outline and purpose of the work as a visual note or a mind map to make it easier to understand visually. The analysis unit, for example, displays the outline and purpose of the work as a visual note or a mind map to make it easier to understand visually. For example, the outline and purpose of the work are shown using diagrams and icons. The analysis unit can also display the outline and purpose of the work as a visual note or a mind map to make it easier to understand visually. For example, the outline and purpose of the work are shown using diagrams and icons. The analysis unit can also display the outline and purpose of the work as a visual note or a mind map to make it easier to understand visually. For example, the outline and purpose of the work are shown using diagrams and icons. In this way, the outline and purpose of the work are displayed as a visual note or a mind map to make it easier to understand visually.

[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 PC operation monitoring unit can automatically add background information of an operation to a captured image of an operation procedure. For example, the purpose of the operation and related settings are displayed in the captured image. The PC operation monitoring unit also helps understand the operation procedure by adding background information of the operation to the captured image. For example, text explaining the purpose of the operation and related settings is automatically inserted. The PC operation monitoring unit also automatically adds background information of an operation to a captured image of an operation procedure. For example, the purpose of the operation and related settings are displayed in the captured image to deepen understanding of the operation procedure. In this way, adding background information to the captured image of an operation procedure helps understand the operation procedure.

[0044] The PC Operation Monitoring Unit can simultaneously capture the operation procedures of different software and systems and create an integrated manual. For example, it can combine the operation procedures of multiple applications into a single manual. The PC Operation Monitoring Unit can also simultaneously capture the operation procedures of different software and systems and create an integrated manual. For example, it can combine the operation procedures between different systems to provide a consistent manual. The PC Operation Monitoring Unit can also simultaneously capture the operation procedures of different software and systems and create an integrated manual. For example, it can combine the operation procedures of multiple platforms into a single manual. This makes it possible to simultaneously capture the operation procedures of different software and systems and create an integrated manual.

[0045] The PC operation monitoring unit can display captured images of operation procedures as 3D models or animations to make them visually easier to understand. For example, the operation procedures can be reproduced in a 3D model so that the user can experience the operation. The PC operation monitoring unit can also display captured images of operation procedures as animations to make them visually easier to understand. For example, the operation procedures can be shown in animation so that the user can easily understand the flow of operations. The PC operation monitoring unit can also display captured images of operation procedures as 3D models or animations to make them visually easier to understand. For example, the operation procedures can be reproduced in a 3D model so that the user can experience the operation. In this way, displaying captured images of operation procedures as 3D models or animations makes them visually easier to understand.

[0046] The speech recognition unit can use speech recognition technology to reflect the nuances and tone of the spoken explanation by the person in charge in the text. For example, emphasized parts can be displayed in bold or in color. The speech recognition unit also uses speech recognition technology to reflect the nuances and tone of the spoken explanation by the person in charge in the text. For example, emotional parts can be identified and reflected in the text. The speech recognition unit also uses speech recognition technology to reflect the nuances and tone of the spoken explanation by the person in charge in the text. For example, the tone and rhythm of the voice can be analyzed and reflected in the text. In this way, the nuances and tone of the spoken explanation by the person in charge can be reflected in the text, making it possible to more accurately convey the intention of the explanation.

[0047] The speech recognition unit can display the speech recognition results in real time, allowing the person in charge to make corrections or additions on the spot. For example, the speech recognition results can be displayed on a screen, providing an interface that allows the person in charge to make corrections. The speech recognition unit can also display the speech recognition results in real time, allowing the person in charge to make corrections or additions on the spot. For example, the speech recognition results can be displayed in a text editor, allowing the person in charge to make corrections. The speech recognition unit can also display the speech recognition results in real time, allowing the person in charge to make corrections or additions on the spot. For example, the speech recognition results can be displayed on a screen, providing an interface that allows the person in charge to make corrections. In this way, by displaying the speech recognition results in real time, allowing the person in charge to make corrections or additions on the spot, accurate manuals can be created.

[0048] The speech recognition unit can simultaneously recognize oral explanations in different languages ​​and create a multilingual manual. For example, explanations in multiple languages ​​such as English, Japanese, and Chinese can be recognized simultaneously and reflected in the manual. The speech recognition unit can simultaneously recognize oral explanations in different languages ​​and create a multilingual manual. For example, explanations in multiple languages ​​such as English, Japanese, and Chinese can be recognized simultaneously and reflected in the manual. The speech recognition unit can simultaneously recognize oral explanations in different languages ​​and create a multilingual manual. For example, explanations in multiple languages ​​such as English, Japanese, and Chinese can be recognized simultaneously and reflected in the manual. This allows oral explanations in different languages ​​to be simultaneously recognized and a multilingual manual to be created.

[0049] When automatically generating the work outline and objectives, the analysis unit can refer to related industry news and trend information to reflect the latest information. For example, the latest technological trends and market needs are reflected. When automatically generating the work outline and objectives, the analysis unit can refer to related industry news and trend information to reflect the latest information. For example, the latest technological trends and market needs are reflected. When automatically generating the work outline and objectives, the analysis unit can refer to related industry news and trend information to reflect the latest information. For example, the latest technological trends and market needs are reflected. In this way, when automatically generating the work outline and objectives, the analysis unit can refer to related industry news and trend information to reflect the latest information, making it possible to provide a more accurate and up-to-date manual.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The PC operation monitoring unit monitors the PC operations of the person in charge. For example, if the person in charge performs an operation to change software settings, the operation screen is captured and details such as which buttons were clicked and which menu items were selected are recorded. The PC operation monitoring unit can also record the operation procedures as captured images in real time. Step 2: The speech recognition unit recognizes the agent's verbal instructions. For example, if the agent says, "Next, click this button to save the settings," the speech is converted into text and added as an explanatory caption to the corresponding captured image. The speech recognition unit can also use speech recognition technology to convert the agent's verbal instructions into text in real time. Step 3: The analysis unit analyzes the information acquired by the PC operation monitoring unit and the voice recognition unit. For example, it analyzes the operating procedures and verbal instructions of the person in charge, and automatically incorporates information such as the task overview, purpose, and subsequent tasks into the manual. It can also evaluate the efficiency and accuracy of the operating procedures and suggest areas for improvement. Step 4: The manual generation unit generates a manual based on the information analyzed by the analysis unit. For example, if a new operating procedure is added, it adds the procedure to the manual along with a captured image and explanatory caption. Also, if an existing procedure is changed, the changed part can be automatically updated.

[0052] (Example 2) The automatic manual creation system according to an embodiment of the present invention is a system in which AI monitors the operational status of a person in charge when the person in charge provides supplementary explanations of the work content through PC operation and verbal explanation, and automatically creates and updates a manual that includes images and explanatory captions. As a result, the automatic manual creation system can automatically create and update a manual that includes images and explanatory captions based on the person in charge's PC operation and verbal explanation.

[0053] An automatic manual creation system according to an embodiment includes a PC operation monitoring unit, a voice recognition unit, an analysis unit, and a manual generation unit. The PC operation monitoring unit monitors the PC operations of the person in charge. For example, when the person in charge changes software settings, the PC operation monitoring unit captures the operation screen and records details such as which buttons were clicked and which menu items were selected. The PC operation monitoring unit can also record the operation procedures as captured images in real time. The voice recognition unit recognizes the person in charge's verbal instructions. For example, when the person in charge explains, "Next, click this button to save the settings," the voice recognition unit converts the speech into text and adds it as an explanatory caption to the corresponding captured image. The voice recognition unit can also convert the person in charge's verbal instructions into text in real time using voice recognition technology. The analysis unit analyzes the information acquired by the PC operation monitoring unit and the voice recognition unit. For example, the analysis unit analyzes the person in charge's operation procedures and verbal instructions and automatically incorporates information such as the task overview, purpose, and subsequent tasks into the manual. The analysis unit can also evaluate the efficiency and accuracy of the operating procedures and suggest areas for improvement. The manual generation unit generates a manual based on the information analyzed by the analysis unit. For example, if a new operating procedure is added, the manual generation unit adds the procedure to the manual along with captured images and explanatory captions. In addition, if an existing procedure is changed, the manual generation unit can automatically update that part. As a result, the automatic manual creation system according to the embodiment can automatically create and update a manual including images and explanatory captions based on the PC operations and verbal explanations of the person in charge.

[0054] The PC operation monitoring unit records the speed and frequency of operations when capturing operation procedures, and is able to suggest efficient operation procedures. For example, when capturing operation procedures, the PC operation monitoring unit records the speed and frequency of operations and suggests efficient operation procedures. For example, it analyzes frequently used shortcut keys and mouse operations and suggests optimal operation procedures. The PC operation monitoring unit also suggests efficient operation procedures based on the speed and frequency of operations when capturing operation procedures. For example, it suggests a faster method when the same operation is repeated. The PC operation monitoring unit also records the speed and frequency of operations when capturing operation procedures and suggests efficient operation procedures. For example, it identifies operation delays and unnecessary steps and suggests improvement suggestions. This makes it possible to suggest efficient operation procedures based on the speed and frequency of operations.

[0055] The PC operation monitoring unit can automatically add background information of an operation to a captured image of an operation procedure. The PC operation monitoring unit, for example, automatically adds background information of an operation to a captured image of an operation procedure. For example, the purpose of the operation and related settings are displayed in the captured image. The PC operation monitoring unit also helps understand the operation procedure by adding background information of the operation to the captured image. For example, text explaining the purpose of the operation and related settings is automatically inserted. The PC operation monitoring unit also automatically adds background information of an operation to a captured image of an operation procedure. For example, the purpose of the operation and related settings are displayed in the captured image to deepen understanding of the operation procedure. In this way, adding background information to the captured image of an operation procedure helps understand the operation procedure.

[0056] The PC operation monitoring unit uses the emotion estimation function to analyze the emotions of the person in charge during operation, identify operations that cause high stress, and make suggestions for improvement. The PC operation monitoring unit, for example, uses the emotion estimation function to analyze the emotions of the person in charge during operation and identify operations that cause high stress. For example, it analyzes facial expressions and voices during operation to measure stress levels. The PC operation monitoring unit also uses the emotion estimation function to analyze the emotions of the person in charge during operation and identify operations that cause high stress. For example, it measures heart rate and galvanic skin response during operation to evaluate stress levels. The PC operation monitoring unit also uses the emotion estimation function to analyze the emotions of the person in charge during operation and identify operations that cause high stress. For example, it analyzes behavioral patterns during operation to identify the cause of stress. In this way, the efficiency of operations can be improved by analyzing the emotions of the person in charge during operation, identifying operations that cause high stress, and making suggestions for improvement.

[0057] The PC operation monitoring unit can simultaneously capture the operation procedures of different software and systems and create an integrated manual. The PC operation monitoring unit, for example, can simultaneously capture the operation procedures of different software and systems and create an integrated manual. For example, it can combine the operation procedures of multiple applications into a single manual. The PC operation monitoring unit can also simultaneously capture the operation procedures of different software and systems and create an integrated manual. For example, it can combine the operation procedures between different systems to provide a consistent manual. The PC operation monitoring unit can also simultaneously capture the operation procedures of different software and systems and create an integrated manual. For example, it can combine the operation procedures of multiple platforms into a single manual. This makes it possible to simultaneously capture the operation procedures of different software and systems and create an integrated manual.

[0058] The PC operation monitoring unit can display captured images of operation procedures as 3D models or animations to make them visually easier to understand. The PC operation monitoring unit, for example, displays captured images of operation procedures as 3D models or animations to make them visually easier to understand. For example, the operation procedures may be reproduced in a 3D model so that the user can experience the operation. The PC operation monitoring unit can also display captured images of operation procedures as animations to make them visually easier to understand. For example, the operation procedures may be shown in animation so that the user can easily understand the flow of operations. The PC operation monitoring unit can also display captured images of operation procedures as 3D models or animations to make them visually easier to understand. For example, the operation procedures may be reproduced in a 3D model so that the user can experience the operation. In this way, displaying captured images of operation procedures as 3D models or animations makes them visually easier to understand.

[0059] The PC operation monitoring unit uses the emotion estimation function to display the emotions felt by the person in charge in real time while capturing operation procedures and can make suggestions to elicit positive emotions. The PC operation monitoring unit, for example, uses the emotion estimation function to display the emotions felt by the person in charge in real time while capturing operation procedures. For example, it analyzes facial expressions and voice during operation and displays an emotion score. The PC operation monitoring unit also uses the emotion estimation function to display the emotions felt by the person in charge in real time while capturing operation procedures. For example, it measures the heart rate and electrodermal response during operation and displays an emotion score. The PC operation monitoring unit also uses the emotion estimation function to display the emotions felt by the person in charge in real time while capturing operation procedures. For example, it analyzes behavioral patterns during operation and displays an emotion score. In this way, the emotions felt by the person in charge in real time while capturing operation procedures can be displayed and suggestions to elicit positive emotions can be made, thereby improving operation efficiency.

[0060] The speech recognition unit can use speech recognition technology to reflect the nuances and tone of the spoken explanation by the person in charge in the text. The speech recognition unit, for example, uses speech recognition technology to reflect the nuances and tone of the spoken explanation by the person in charge in the text. For example, emphasized parts are displayed in bold or in color. The speech recognition unit also uses speech recognition technology to reflect the nuances and tone of the spoken explanation by the person in charge in the text. For example, emotional parts are identified and reflected in the text. The speech recognition unit also uses speech recognition technology to reflect the nuances and tone of the spoken explanation by the person in charge in the text. For example, the tone and rhythm of the voice are analyzed and reflected in the text. In this way, by reflecting the nuances and tone of the spoken explanation by the person in charge in the text, the intention of the explanation can be conveyed more accurately.

[0061] The speech recognition unit can display the speech recognition results in real time, allowing the person in charge to make corrections or additions on the spot. The speech recognition unit, for example, displays the speech recognition results in real time, allowing the person in charge to make corrections or additions on the spot. For example, the speech recognition results may be displayed on a screen, providing an interface through which the person in charge can make corrections. The speech recognition unit can also display the speech recognition results in real time, allowing the person in charge to make corrections or additions on the spot. For example, the speech recognition results may be displayed in a text editor, allowing the person in charge to make corrections. The speech recognition unit can also display the speech recognition results in real time, allowing the person in charge to make corrections or additions on the spot. For example, the speech recognition results may be displayed on a screen, providing an interface through which the person in charge can make corrections. In this way, by displaying the speech recognition results in real time, allowing the person in charge to make corrections or additions on the spot, an accurate manual can be created.

[0062] The voice recognition unit can use the emotion estimation function to analyze the emotion of the person in charge during the oral explanation and convert the text to emphasize positive emotions. The voice recognition unit, for example, uses the emotion estimation function to analyze the emotion of the person in charge during the oral explanation and convert the text to emphasize positive emotions. For example, parts with strong positive emotions are displayed in bold or color. The voice recognition unit also uses the emotion estimation function to analyze the emotion of the person in charge during the oral explanation and convert the text to emphasize positive emotions. For example, emotional parts are identified and reflected in the text. The voice recognition unit also uses the emotion estimation function to analyze the emotion of the person in charge during the oral explanation and convert the text to emphasize positive emotions. For example, the tone and rhythm of the voice are analyzed and reflected in the text. In this way, by analyzing the emotion of the person in charge during the oral explanation and emphasizing positive emotions, a more effective manual can be created.

[0063] The speech recognition unit can simultaneously recognize oral explanations in different languages ​​and create a multilingual manual. The speech recognition unit, for example, simultaneously recognizes oral explanations in different languages ​​and creates a multilingual manual. For example, explanations in multiple languages ​​such as English, Japanese, and Chinese are recognized simultaneously and reflected in the manual. The speech recognition unit can also simultaneously recognize oral explanations in different languages ​​and create a multilingual manual. For example, explanations in multiple languages ​​such as English, Japanese, and Chinese are recognized simultaneously and reflected in the manual. The speech recognition unit can also simultaneously recognize oral explanations in different languages ​​and create a multilingual manual. For example, explanations in multiple languages ​​such as English, Japanese, and Chinese are recognized simultaneously and reflected in the manual. This allows oral explanations in different languages ​​to be simultaneously recognized and a multilingual manual to be created.

[0064] The speech recognition unit can display the speech recognition results as visual notes or infographics to make them easier to understand visually. The speech recognition unit, for example, displays the speech recognition results as visual notes or infographics to make them easier to understand visually. For example, the speech recognition results are shown using diagrams or icons. The speech recognition unit can also display the speech recognition results as visual notes or infographics to make them easier to understand visually. For example, the speech recognition results are shown using diagrams or icons. The speech recognition unit can also display the speech recognition results as visual notes or infographics to make them easier to understand visually. For example, the speech recognition results are shown using diagrams or icons. In this way, displaying the speech recognition results as visual notes or infographics makes them easier to understand visually.

[0065] The voice recognition unit can use the emotion estimation function to display emotions during oral explanation in real time and make suggestions to elicit positive emotions. The voice recognition unit, for example, uses the emotion estimation function to display emotions during oral explanation in real time. For example, it analyzes facial expressions and voice during explanation and displays an emotion score. The voice recognition unit also uses the emotion estimation function to display emotions during oral explanation in real time. For example, it measures heart rate and electrodermal response during explanation and displays an emotion score. The voice recognition unit also uses the emotion estimation function to display emotions during oral explanation in real time. For example, it analyzes behavioral patterns during explanation and displays an emotion score. In this way, emotions during oral explanation can be displayed in real time and suggestions to elicit positive emotions can be made, thereby improving the effectiveness of the explanation.

[0066] The analysis unit can reflect the latest information by referring to related industry news and trend information when automatically generating the work outline and purpose. For example, the analysis unit reflects the latest information by referring to related industry news and trend information when automatically generating the work outline and purpose. For example, the latest technological trends and market needs are reflected. Furthermore, the analysis unit reflects the latest information by referring to related industry news and trend information when automatically generating the work outline and purpose. For example, the latest technological trends and market needs are reflected. Furthermore, the analysis unit reflects the latest information by referring to related industry news and trend information when automatically generating the work outline and purpose. For example, the latest technological trends and market needs are reflected. In this way, by reflecting the latest information by referring to related industry news and trend information when automatically generating the work outline and purpose, it is possible to provide a more accurate and up-to-date manual.

[0067] When automatically generating procedures for subsequent tasks, the analysis unit can propose an optimal procedure by referring to past work history and success cases. For example, when automatically generating procedures for subsequent tasks, the analysis unit proposes an optimal procedure by referring to past work history and success cases. For example, the optimal procedure is proposed based on past success cases. When automatically generating procedures for subsequent tasks, the analysis unit also proposes an optimal procedure by referring to past work history and success cases. For example, the optimal procedure is proposed based on past success cases. When automatically generating procedures for subsequent tasks, the analysis unit also proposes an optimal procedure by referring to past work history and success cases. For example, the optimal procedure is proposed based on past success cases. In this way, when automatically generating procedures for subsequent tasks, the analysis unit proposes an optimal procedure by referring to past work history and success cases, thereby achieving efficient work.

[0068] The analysis unit can use the emotion estimation function to analyze the user's emotional response to the summary and purpose of the work and emphasize content that elicits positive emotions. For example, the analysis unit uses the emotion estimation function to analyze the user's emotional response to the summary and purpose of the work and emphasize content that elicits positive emotions. For example, parts that show strong positive emotions are displayed in bold or color. The analysis unit also uses the emotion estimation function to analyze the user's emotional response to the summary and purpose of the work and emphasize content that elicits positive emotions. For example, emotional parts are identified and reflected in the text. The analysis unit also uses the emotion estimation function to analyze the user's emotional response to the summary and purpose of the work and emphasize content that elicits positive emotions. For example, the tone and rhythm of the voice are analyzed and reflected in the text. In this way, the user's emotional response to the summary and purpose of the work is analyzed and content that elicits positive emotions is emphasized, thereby improving the user's motivation.

[0069] The analysis unit can compare work procedures between different industries and applications and identify similarities and differences. For example, the analysis unit compares work procedures between different industries and applications and identifies similarities and differences. For example, the analysis unit compares work procedures between the manufacturing industry and the service industry and identifies similarities and differences. The analysis unit can also compare work procedures between different industries and applications and identify similarities and differences. For example, the analysis unit can compare work procedures between the manufacturing industry and the service industry and identify similarities and differences. The analysis unit can also compare work procedures between different industries and applications and identify similarities and differences. For example, the analysis unit can compare work procedures between the manufacturing industry and the service industry and identify similarities and differences. This makes it possible to compare work procedures between different industries and applications and identify similarities and differences, thereby creating a more versatile manual.

[0070] The analysis unit can display the outline and purpose of the work as a visual note or a mind map to make it easier to understand visually. The analysis unit, for example, displays the outline and purpose of the work as a visual note or a mind map to make it easier to understand visually. For example, the outline and purpose of the work are shown using diagrams and icons. The analysis unit can also display the outline and purpose of the work as a visual note or a mind map to make it easier to understand visually. For example, the outline and purpose of the work are shown using diagrams and icons. The analysis unit can also display the outline and purpose of the work as a visual note or a mind map to make it easier to understand visually. For example, the outline and purpose of the work are shown using diagrams and icons. In this way, the outline and purpose of the work are displayed as a visual note or a mind map to make it easier to understand visually.

[0071] The analysis unit uses the emotion estimation function to display the user's emotional response to the task summary and goal in real time and make suggestions to elicit positive emotions. The analysis unit, for example, uses the emotion estimation function to display the user's emotional response to the task summary and goal in real time. For example, it analyzes facial expressions and voices while working and displays an emotion score. The analysis unit also uses the emotion estimation function to display the user's emotional response to the task summary and goal in real time. For example, it measures the heart rate and galvanic skin response while working and displays an emotion score. The analysis unit also uses the emotion estimation function to display the user's emotional response to the task summary and goal in real time. For example, it analyzes behavioral patterns while working and displays an emotion score. In this way, the user's emotional response to the task summary and goal can be displayed in real time and suggestions to elicit positive emotions can be made, thereby improving the user's motivation.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The PC operation monitoring unit can automatically add background information of an operation to a captured image of an operation procedure. For example, the purpose of the operation and related settings are displayed in the captured image. The PC operation monitoring unit also helps understand the operation procedure by adding background information of the operation to the captured image. For example, text explaining the purpose of the operation and related settings is automatically inserted. The PC operation monitoring unit also automatically adds background information of an operation to a captured image of an operation procedure. For example, the purpose of the operation and related settings are displayed in the captured image to deepen understanding of the operation procedure. In this way, adding background information to the captured image of an operation procedure helps understand the operation procedure.

[0074] The PC operation monitoring unit uses the emotion estimation function to analyze the emotions of the person in charge while they are operating the system, identify operations that cause high stress, and make suggestions for improvement. For example, it analyzes facial expressions and voices during operation to measure stress levels. The PC operation monitoring unit also uses the emotion estimation function to analyze the emotions of the person in charge while they are operating the system, and identify operations that cause high stress. For example, it measures heart rate and galvanic skin response during operation to evaluate stress levels. The PC operation monitoring unit also uses the emotion estimation function to analyze the emotions of the person in charge while they are operating the system, and identify operations that cause high stress. For example, it analyzes behavioral patterns during operation to identify the cause of stress. In this way, the efficiency of operations can be improved by analyzing the emotions of the person in charge while they are operating the system, identifying operations that cause high stress, and making suggestions for improvement.

[0075] The PC Operation Monitoring Unit can simultaneously capture the operation procedures of different software and systems and create an integrated manual. For example, it can combine the operation procedures of multiple applications into a single manual. The PC Operation Monitoring Unit can also simultaneously capture the operation procedures of different software and systems and create an integrated manual. For example, it can combine the operation procedures between different systems to provide a consistent manual. The PC Operation Monitoring Unit can also simultaneously capture the operation procedures of different software and systems and create an integrated manual. For example, it can combine the operation procedures of multiple platforms into a single manual. This makes it possible to simultaneously capture the operation procedures of different software and systems and create an integrated manual.

[0076] The PC operation monitoring unit can display captured images of operation procedures as 3D models or animations to make them visually easier to understand. For example, the operation procedures can be reproduced in a 3D model so that the user can experience the operation. The PC operation monitoring unit can also display captured images of operation procedures as animations to make them visually easier to understand. For example, the operation procedures can be shown in animation so that the user can easily understand the flow of operations. The PC operation monitoring unit can also display captured images of operation procedures as 3D models or animations to make them visually easier to understand. For example, the operation procedures can be reproduced in a 3D model so that the user can experience the operation. In this way, displaying captured images of operation procedures as 3D models or animations makes them visually easier to understand.

[0077] The PC operation monitoring unit uses the emotion estimation function to display the emotions felt by the person in charge in real time while capturing operation procedures and can make suggestions to elicit positive emotions. For example, it analyzes facial expressions and voice during operation and displays an emotion score. The PC operation monitoring unit also uses the emotion estimation function to display the emotions felt by the person in charge in real time while capturing operation procedures. For example, it measures the heart rate and electrodermal response during operation and displays an emotion score. The PC operation monitoring unit also uses the emotion estimation function to display the emotions felt by the person in charge in real time while capturing operation procedures. For example, it analyzes behavioral patterns during operation and displays an emotion score. In this way, the emotions felt by the person in charge in real time while capturing operation procedures and can make suggestions to elicit positive emotions, thereby improving operation efficiency.

[0078] The speech recognition unit can use speech recognition technology to reflect the nuances and tone of the spoken explanation by the person in charge in the text. For example, emphasized parts can be displayed in bold or in color. The speech recognition unit also uses speech recognition technology to reflect the nuances and tone of the spoken explanation by the person in charge in the text. For example, emotional parts can be identified and reflected in the text. The speech recognition unit also uses speech recognition technology to reflect the nuances and tone of the spoken explanation by the person in charge in the text. For example, the tone and rhythm of the voice can be analyzed and reflected in the text. In this way, the nuances and tone of the spoken explanation by the person in charge can be reflected in the text, making it possible to more accurately convey the intention of the explanation.

[0079] The speech recognition unit can display the speech recognition results in real time, allowing the person in charge to make corrections or additions on the spot. For example, the speech recognition results can be displayed on a screen, providing an interface that allows the person in charge to make corrections. The speech recognition unit can also display the speech recognition results in real time, allowing the person in charge to make corrections or additions on the spot. For example, the speech recognition results can be displayed in a text editor, allowing the person in charge to make corrections. The speech recognition unit can also display the speech recognition results in real time, allowing the person in charge to make corrections or additions on the spot. For example, the speech recognition results can be displayed on a screen, providing an interface that allows the person in charge to make corrections. In this way, by displaying the speech recognition results in real time, allowing the person in charge to make corrections or additions on the spot, accurate manuals can be created.

[0080] The speech recognition unit can use the emotion estimation function to analyze the emotions expressed by the person in charge during the oral explanation and convert the text to emphasize positive emotions. For example, parts with strong positive emotions are displayed in bold or color. The speech recognition unit also uses the emotion estimation function to analyze the emotions expressed by the person in charge during the oral explanation and convert the text to emphasize positive emotions. For example, emotional parts are identified and reflected in the text. The speech recognition unit also uses the emotion estimation function to analyze the emotions expressed by the person in charge during the oral explanation and convert the text to emphasize positive emotions. For example, the tone and rhythm of the voice are analyzed and reflected in the text. In this way, by analyzing the emotions expressed by the person in charge during the oral explanation and emphasizing positive emotions, a more effective manual can be created.

[0081] The speech recognition unit can simultaneously recognize oral explanations in different languages ​​and create a multilingual manual. For example, explanations in multiple languages ​​such as English, Japanese, and Chinese can be recognized simultaneously and reflected in the manual. The speech recognition unit can simultaneously recognize oral explanations in different languages ​​and create a multilingual manual. For example, explanations in multiple languages ​​such as English, Japanese, and Chinese can be recognized simultaneously and reflected in the manual. The speech recognition unit can simultaneously recognize oral explanations in different languages ​​and create a multilingual manual. For example, explanations in multiple languages ​​such as English, Japanese, and Chinese can be recognized simultaneously and reflected in the manual. This allows oral explanations in different languages ​​to be simultaneously recognized and a multilingual manual to be created.

[0082] When automatically generating the work outline and objectives, the analysis unit can refer to related industry news and trend information to reflect the latest information. For example, the latest technological trends and market needs are reflected. When automatically generating the work outline and objectives, the analysis unit can refer to related industry news and trend information to reflect the latest information. For example, the latest technological trends and market needs are reflected. When automatically generating the work outline and objectives, the analysis unit can refer to related industry news and trend information to reflect the latest information. For example, the latest technological trends and market needs are reflected. In this way, when automatically generating the work outline and objectives, the analysis unit can refer to related industry news and trend information to reflect the latest information, making it possible to provide a more accurate and up-to-date manual.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The PC operation monitoring unit monitors the PC operations of the person in charge. For example, if the person in charge performs an operation to change software settings, the operation screen is captured and details such as which buttons were clicked and which menu items were selected are recorded. The PC operation monitoring unit can also record the operation procedures as captured images in real time. Step 2: The speech recognition unit recognizes the agent's verbal instructions. For example, if the agent says, "Next, click this button to save the settings," the speech is converted into text and added as an explanatory caption to the corresponding captured image. The speech recognition unit can also use speech recognition technology to convert the agent's verbal instructions into text in real time. Step 3: The analysis unit analyzes the information acquired by the PC operation monitoring unit and the voice recognition unit. For example, it analyzes the operating procedures and verbal instructions of the person in charge, and automatically incorporates information such as the task overview, purpose, and subsequent tasks into the manual. It can also evaluate the efficiency and accuracy of the operating procedures and suggest areas for improvement. Step 4: The manual generation unit generates a manual based on the information analyzed by the analysis unit. For example, if a new operating procedure is added, it adds the procedure to the manual along with a captured image and explanatory caption. Also, if an existing procedure is changed, the changed part can be automatically updated.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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 PC operation monitoring department that monitors the PC operations of personnel; a voice recognition unit that recognizes the oral explanation of the person in charge; an analysis unit that analyzes information acquired by the PC operation monitoring unit and the voice recognition unit; a manual generation unit that generates a manual based on the information analyzed by the analysis unit. A system characterized by:

2. The PC operation monitoring unit When capturing operation procedures, the speed and frequency of operations are recorded and efficient operation procedures are suggested.

2. The system of claim 1.

3. The PC operation monitoring unit Automatically add background information to captured images of operation procedures 2. The system of claim 1.

4. The PC operation monitoring unit Analyze the emotions of the person in charge while they are operating the device, identify operations that cause stress, and make suggestions for improvement.

2. The system of claim 1.

5. The PC operation monitoring unit Simultaneously capture operating procedures for different software and systems to create integrated manuals 2. The system of claim 1.

6. The PC operation monitoring unit Display captured images of operation procedures as 3D models and animations to make them easier to understand visually.

2. The system of claim 1.

7. The PC operation monitoring unit The system displays the emotions felt by the operator in real time while capturing the operating procedures, and makes suggestions to elicit positive emotions.

2. The system of claim 1.

8. The voice recognition unit Using voice recognition technology, the nuances and tone of the spoken explanation by the person in charge are reflected in the text.

2. The system of claim 1.

Images