System

Abstract

An object of a system according to an embodiment is to enable intuitive use of generated AI.SOLUTION: A system according to an embodiment includes a screen sharing unit, a voice input unit, and a voice output unit. The screen sharing unit shares a screen of a user. The sound input unit issues an instruction to the generation AI by sound. The sound outputting unit outputs the generated AI by sound.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback that the operation of the generation AI is complicated, making it difficult for users to use it intuitively.

[0005] The system according to the embodiment aims to make generative AI intuitive to use. [Means for solving the problem]

[0006] The system according to the embodiment includes a screen sharing unit, an audio input unit, and an audio output unit. The screen sharing unit shares the user's screen. The audio input unit issues audio instructions to the generation AI. The audio output unit conveys the output result of the generation AI by audio. [Effects of the Invention]

[0007] The system according to the embodiment can make generative AI intuitive to use. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The generation AI operation support system according to an embodiment of the present invention is a system that allows a user to intuitively operate the generation AI and visually and audibly confirm the output results of the generation AI. As a result, the generation AI operation support system allows a user to intuitively operate the generation AI and visually and audibly confirm the output results of the generation AI.

[0029] A generation AI operation support system according to an embodiment includes a screen sharing unit, an audio input unit, and an audio output unit. The screen sharing unit shares a user's screen. For example, the screen sharing unit shares the user's desktop screen with other users in real time. The screen sharing unit can also select and share a specific application screen. The screen sharing unit also provides a function that allows other users to add comments and annotations during screen sharing. For example, participants can add comments in real time during a presentation. The audio input unit issues audio instructions to the generation AI. For example, the audio input unit allows a user to issue an audio instruction such as "display the next slide," and the generation AI operates according to the instruction. The audio input unit also automatically translates audio instructions in different languages, providing a multilingual function for the generation AI. For example, it translates instructions in English into Japanese and executes them. The audio output unit communicates the output results of the generation AI via audio. For example, the audio output unit reads out the text generated by the generation AI. The audio output unit also provides a function that outputs the content generated by the generation AI in combination with music and sound effects. For example, adding background music during a presentation allows the generation AI operation support system according to the embodiment to intuitively operate the generation AI and visually and audibly confirm the output results.

[0030] The screen sharing unit can analyze user operations in real time and suggest optimal operating procedures. For example, the generation AI in the screen sharing unit monitors user operations in real time and analyzes the flow of operations. For example, if a user is working with multiple windows open, the generation AI will suggest the optimal window switching procedure. The screen sharing unit also records the user's operation history and automatically generates an operating guide that can be played back later. For example, the generation AI records the user's operations in real time and automatically generates an operating guide based on that operation history. This makes it possible to optimize user operations and improve efficiency.

[0031] The screen sharing unit can record the user's operation history and automatically generate an operation guide that can be played back later. For example, the screen sharing unit uses a generation AI to record the user's operations in real time and automatically generate an operation guide based on the operation history. For example, it can create a step-by-step guide for completing a specific task. The screen sharing unit can also highlight important operations and points for later reference. For example, the generation AI can provide a function to automatically detect and highlight important operations and points during screen sharing. This makes it possible to provide a guide based on the operation history and support the user's learning.

[0032] The screen sharing unit can integrate a function that allows other users to add comments and annotations in real time. The screen sharing unit provides, for example, a function that allows other users to add comments in real time while the screen is being shared. For example, during a presentation, participants can add questions or opinions as comments. The screen sharing unit also provides a function that allows other users to add annotations in real time. For example, they can write directly on the screen. This makes it possible to provide a function that facilitates collaborative work.

[0033] The screen sharing unit can highlight important operations and points so that they can be referenced later. For example, the screen sharing unit provides a function in which the generation AI automatically detects and highlights important operations and points during screen sharing. For example, the operation of clicking a specific button is highlighted. The screen sharing unit also provides a function in which the highlighted operations and points can be referenced later. For example, the highlighted operations can be recorded and played back later. This makes it possible to emphasize important operations and points so that they can be referenced later.

[0034] The voice input unit can analyze the content of a user's utterance and automatically generate the optimal prompt. For example, the generation AI in the voice input unit analyzes the content of a user's utterance in real time and automatically generates the optimal prompt. For example, if a user says, "Show the next slide," an appropriate prompt is generated. The voice input unit also provides a function that analyzes the content of a user's utterance and allows the generation AI to suggest the optimal operating procedure. For example, if a user says, "What should I do?", the generation AI suggests the operating procedure. This allows the optimal prompt to be generated based on the content of the user's utterance, making operations more efficient.

[0035] The audio output unit can adjust the tone and speed of the audio to suit the user's hearing characteristics. For example, the audio output unit provides a function in which the generation AI analyzes the user's hearing characteristics and adjusts the tone and speed of the audio. For example, if the user has difficulty hearing high-pitched sounds, the audio tone is lowered. The audio output unit also provides a function to adjust the speed of the audio based on the user's hearing characteristics. For example, if the user has difficulty understanding fast speech, the audio speed is slowed down. This makes it possible to optimize the audio output to suit the user's hearing characteristics.

[0036] The voice input unit can add a function that automatically translates voice instructions in different languages, enabling the generation AI to support multiple languages. The voice input unit, for example, allows the generation AI to automatically translate voice instructions in different languages ​​and provide the corresponding function. For example, it translates instructions in English into Japanese and executes them. The voice input unit also provides a function that analyzes the content of the user's speech and automatically generates the optimal prompt. For example, if the user says, "Display the next slide," it generates an appropriate prompt. This makes it possible to automatically translate voice instructions in different languages ​​and achieve multilingual support.

[0037] The audio output unit can integrate a function to output the content generated by the generation AI in combination with music and sound effects. The audio output unit provides, for example, a function to output the content generated by the generation AI in combination with music and sound effects. For example, adding background music during a presentation. The audio output unit also improves the user experience by outputting the content generated by the generation AI in combination with music and sound effects. For example, reading out text generated by the generation AI along with music. This allows the output content of the generation AI to be combined with music and sound effects to improve the user experience.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The generative AI operation assistance system can further include a prediction unit that predicts user operations and suggests the next operation. For example, if a user frequently performs a specific operation, that operation can be automatically suggested. The prediction unit can also predict and suggest the next operation that the user is likely to perform based on the user's past operation history. For example, it can predict the operation that the user will always perform after opening a specific application and suggest that operation. This can make user operations more efficient and improve work speed.

[0040] The generative AI operation assistance system can further include a recording unit that records user operations and automatically generates operation guides that can be played back later. For example, the system can record the operations a user performs to complete a specific task and automatically generate a step-by-step guide based on those operations. The recording unit can also provide a function that highlights important operations and points for later reference. For example, the system can highlight the operation of clicking a specific button. This allows the system to record user operations and provide a guide that can be referenced later.

[0041] The generative AI operation assistance system can further include an analysis unit that analyzes user operations in real time and proposes optimal operation procedures. For example, if a user is working with multiple windows open, the analysis unit proposes the optimal window switching procedure. The analysis unit can also propose efficient operation procedures based on the user's operation history. For example, it can propose the optimal procedure for the user to complete a specific task. This can optimize user operations and improve efficiency.

[0042] The generative AI operation support system can further integrate a function that allows other users to add comments and annotations in real time. For example, it can provide a function that allows other users to add comments in real time while sharing a screen. For example, participants can add questions or opinions as comments during a presentation. It can also provide a function that allows other users to add annotations in real time. For example, they can write directly on the screen. This can provide a function that facilitates collaborative work.

[0043] The generative AI operation assistance system can further include a prediction unit that predicts user operations and suggests the next operation. For example, if a user frequently performs a specific operation, that operation can be automatically suggested. The prediction unit can also predict and suggest the next operation that the user is likely to perform based on the user's past operation history. For example, it can predict the operation that the user will always perform after opening a specific application and suggest that operation. This can make user operations more efficient and improve work speed.

[0044] The generative AI operation support system can further analyze the content of user utterances and automatically generate the optimal prompt. For example, if a user says, "Show the next slide," an appropriate prompt is generated. It can also provide a function that analyzes the content of user utterances and allows the generative AI to suggest the optimal operating procedure. For example, if a user says, "What should I do?", the generative AI will suggest the operating procedure. This allows the system to generate the optimal prompt based on the content of user utterances, making operations more efficient.

[0045] The generative AI operation assistance system can further adjust the tone and speed of the voice to suit the user's hearing characteristics. For example, if the user has difficulty hearing high-pitched sounds, the voice tone can be lowered. The system can also adjust the voice speed based on the user's hearing characteristics. For example, if the user has difficulty understanding fast speech, the voice speed can be slowed down. This allows the voice output to be optimized to suit the user's hearing characteristics.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The screen sharing section shares the user's screen. For example, the user's desktop screen can be shared with other users in real time. It is also possible to select and share a specific application screen. Furthermore, it provides a function that allows other users to add comments and annotations while the screen is being shared. For example, participants can add comments in real time during a presentation. Step 2: The voice input unit issues voice instructions to the generation AI. For example, the user can issue a voice instruction such as "Display the next slide," and the generation AI will act in accordance with that instruction. The voice input unit also automatically translates voice instructions in different languages, providing the generation AI with the ability to support multiple languages. For example, it can translate instructions in English into Japanese and execute them. Step 3: The audio output unit communicates the output results of the generation AI by voice. For example, the text generated by the generation AI is read aloud. The audio output unit also provides a function to output the content generated by the generation AI in combination with music and sound effects. For example, background music can be added during a presentation.

[0048] (Example 2) The generation AI operation support system according to an embodiment of the present invention is a system that allows a user to intuitively operate the generation AI and visually and audibly confirm the output results of the generation AI. As a result, the generation AI operation support system allows a user to intuitively operate the generation AI and visually and audibly confirm the output results of the generation AI.

[0049] A generation AI operation support system according to an embodiment includes a screen sharing unit, an audio input unit, and an audio output unit. The screen sharing unit shares a user's screen. For example, the screen sharing unit shares the user's desktop screen with other users in real time. The screen sharing unit can also select and share a specific application screen. The screen sharing unit also provides a function that allows other users to add comments and annotations during screen sharing. For example, participants can add comments in real time during a presentation. The audio input unit issues audio instructions to the generation AI. For example, the audio input unit allows a user to issue an audio instruction such as "display the next slide," and the generation AI operates according to the instruction. The audio input unit also automatically translates audio instructions in different languages, providing a multilingual function for the generation AI. For example, it translates instructions in English into Japanese and executes them. The audio output unit communicates the output results of the generation AI via audio. For example, the audio output unit reads out the text generated by the generation AI. The audio output unit also provides a function that outputs the content generated by the generation AI in combination with music and sound effects. For example, adding background music during a presentation allows the generation AI operation support system according to the embodiment to intuitively operate the generation AI and visually and audibly confirm the output results.

[0050] The screen sharing unit can analyze user operations in real time and suggest optimal operating procedures. For example, the generation AI in the screen sharing unit monitors user operations in real time and analyzes the flow of operations. For example, if a user is working with multiple windows open, the generation AI will suggest the optimal window switching procedure. The screen sharing unit also records the user's operation history and automatically generates an operating guide that can be played back later. For example, the generation AI records the user's operations in real time and automatically generates an operating guide based on that operation history. This makes it possible to optimize user operations and improve efficiency.

[0051] The screen sharing unit can record the user's operation history and automatically generate an operation guide that can be played back later. For example, the screen sharing unit uses a generation AI to record the user's operations in real time and automatically generate an operation guide based on the operation history. For example, it can create a step-by-step guide for completing a specific task. The screen sharing unit can also highlight important operations and points for later reference. For example, the generation AI can provide a function to automatically detect and highlight important operations and points during screen sharing. This makes it possible to provide a guide based on the operation history and support the user's learning.

[0052] The screen sharing unit uses the emotion estimation function to analyze the user's emotions and can make suggestions to simplify operations if the user is feeling stressed. For example, the generation AI in the screen sharing unit analyzes the user's facial expressions and voice to determine whether the user is feeling stressed. For example, if the user looks confused, the screen sharing unit makes suggestions to simplify operations. The screen sharing unit also uses the emotion estimation function to analyze the user's emotions and provide interactive feedback to elicit positive emotions. For example, the generation AI analyzes the user's facial expressions and voice to provide feedback to elicit positive emotions. This can reduce the user's stress and simplify operations.

[0053] The screen sharing unit can integrate a function that allows other users to add comments and annotations in real time. The screen sharing unit provides, for example, a function that allows other users to add comments in real time while the screen is being shared. For example, during a presentation, participants can add questions or opinions as comments. The screen sharing unit also provides a function that allows other users to add annotations in real time. For example, they can write directly on the screen. This makes it possible to provide a function that facilitates collaborative work.

[0054] The screen sharing unit can highlight important operations and points so that they can be referenced later. For example, the screen sharing unit provides a function in which the generation AI automatically detects and highlights important operations and points during screen sharing. For example, the operation of clicking a specific button is highlighted. The screen sharing unit also provides a function in which the highlighted operations and points can be referenced later. For example, the highlighted operations can be recorded and played back later. This makes it possible to emphasize important operations and points so that they can be referenced later.

[0055] The screen sharing unit can use an emotion estimation function to analyze the user's emotions and provide interactive feedback to elicit positive emotions. For example, the screen sharing unit uses a generation AI to analyze the user's facial expressions and voice and provide feedback to elicit positive emotions. For example, if the user smiles, a compliment is displayed. The screen sharing unit also provides a function to monitor the user's emotions in real time and provide feedback according to the emotions. For example, an encouraging message is displayed if the user is confused. This can elicit positive emotions from the user and improve the operating experience.

[0056] The voice input unit can analyze the content of a user's utterance and automatically generate the optimal prompt. For example, the generation AI in the voice input unit analyzes the content of a user's utterance in real time and automatically generates the optimal prompt. For example, if a user says, "Show the next slide," an appropriate prompt is generated. The voice input unit also provides a function that analyzes the content of a user's utterance and allows the generation AI to suggest the optimal operating procedure. For example, if a user says, "What should I do?", the generation AI suggests the operating procedure. This allows the optimal prompt to be generated based on the content of the user's utterance, making operations more efficient.

[0057] The audio output unit can adjust the tone and speed of the audio to suit the user's hearing characteristics. For example, the audio output unit provides a function in which the generation AI analyzes the user's hearing characteristics and adjusts the tone and speed of the audio. For example, if the user has difficulty hearing high-pitched sounds, the audio tone is lowered. The audio output unit also provides a function to adjust the speed of the audio based on the user's hearing characteristics. For example, if the user has difficulty understanding fast speech, the audio speed is slowed down. This makes it possible to optimize the audio output to suit the user's hearing characteristics.

[0058] The voice input unit uses an emotion estimation function to analyze the user's emotions and can output an encouraging message if the user is feeling negative. For example, the voice input unit outputs an encouraging message if the generation AI analyzes the user's voice and detects negative emotions. For example, if the user sounds tired, the voice input unit can provide words of encouragement. The voice input unit also provides a function to monitor the user's emotions in real time and provide feedback according to the emotion. For example, if the user seems confused, an encouraging message can be displayed. This can reduce the user's negative emotions and improve the operating experience.

[0059] The voice input unit can add a function that automatically translates voice instructions in different languages, enabling the generation AI to support multiple languages. The voice input unit, for example, allows the generation AI to automatically translate voice instructions in different languages ​​and provide the corresponding function. For example, it translates instructions in English into Japanese and executes them. The voice input unit also provides a function that analyzes the content of the user's speech and automatically generates the optimal prompt. For example, if the user says, "Display the next slide," it generates an appropriate prompt. This makes it possible to automatically translate voice instructions in different languages ​​and achieve multilingual support.

[0060] The audio output unit can integrate a function to output the content generated by the generation AI in combination with music and sound effects. The audio output unit provides, for example, a function to output the content generated by the generation AI in combination with music and sound effects. For example, adding background music during a presentation. The audio output unit also improves the user experience by outputting the content generated by the generation AI in combination with music and sound effects. For example, reading out text generated by the generation AI along with music. This allows the output content of the generation AI to be combined with music and sound effects to improve the user experience.

[0061] The voice output unit can use an emotion estimation function to analyze the user's emotions and provide voice feedback to elicit positive emotions. For example, the voice output unit uses a generative AI to analyze the user's voice and provide voice feedback to elicit positive emotions. For example, it outputs a voice message that makes the user smile. The voice output unit also provides a function to monitor the user's emotions in real time and provide feedback according to the emotions. For example, if the user is confused, it displays an encouraging message. This can elicit positive emotions from the user and improve the operating experience.

[0062] The voice output unit can use an emotion estimation function to analyze the user's emotions and provide voice feedback to elicit positive emotions. For example, the voice output unit uses a generative AI to analyze the user's voice and provide voice feedback to elicit positive emotions. For example, it outputs a voice message that makes the user smile. The voice output unit also provides a function to monitor the user's emotions in real time and provide feedback according to the emotions. For example, if the user is confused, it displays an encouraging message. This can elicit positive emotions from the user and improve the operating experience.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The generative AI operation assistance system can further include a prediction unit that predicts user operations and suggests the next operation. For example, if a user frequently performs a specific operation, that operation can be automatically suggested. The prediction unit can also predict and suggest the next operation that the user is likely to perform based on the user's past operation history. For example, it can predict the operation that the user will always perform after opening a specific application and suggest that operation. This can make user operations more efficient and improve work speed.

[0065] The generative AI operation assistance system can further include a recording unit that records user operations and automatically generates operation guides that can be played back later. For example, the system can record the operations a user performs to complete a specific task and automatically generate a step-by-step guide based on those operations. The recording unit can also provide a function that highlights important operations and points for later reference. For example, the system can highlight the operation of clicking a specific button. This allows the system to record user operations and provide a guide that can be referenced later.

[0066] The generative AI operation assistance system can further include an analysis unit that analyzes user operations in real time and proposes optimal operation procedures. For example, if a user is working with multiple windows open, the analysis unit proposes the optimal window switching procedure. The analysis unit can also propose efficient operation procedures based on the user's operation history. For example, it can propose the optimal procedure for the user to complete a specific task. This can optimize user operations and improve efficiency.

[0067] The generative AI operation assistance system can also analyze the user's emotions and make suggestions to simplify operations if the user is feeling stressed. For example, if the user has a confused expression, the system will make suggestions to simplify operations. It can also use its emotion estimation function to analyze the user's emotions and provide interactive feedback to elicit positive emotions. For example, it can display compliments when the user smiles. This can reduce the user's stress and simplify operations.

[0068] The generative AI operation support system can further integrate a function that allows other users to add comments and annotations in real time. For example, it can provide a function that allows other users to add comments in real time while sharing a screen. For example, participants can add questions or opinions as comments during a presentation. It can also provide a function that allows other users to add annotations in real time. For example, they can write directly on the screen. This can provide a function that facilitates collaborative work.

[0069] The generative AI operation assistance system can further analyze the user's emotions and provide interactive feedback to elicit positive emotions. For example, it can display a compliment when the user smiles. It can also provide a function to monitor the user's emotions in real time and provide feedback according to the emotion. For example, it can display an encouraging message when the user is confused. This can elicit positive emotions from the user and improve the operation experience.

[0070] The generative AI operation assistance system can further include a prediction unit that predicts user operations and suggests the next operation. For example, if a user frequently performs a specific operation, that operation can be automatically suggested. The prediction unit can also predict and suggest the next operation that the user is likely to perform based on the user's past operation history. For example, it can predict the operation that the user will always perform after opening a specific application and suggest that operation. This can make user operations more efficient and improve work speed.

[0071] The generative AI operation support system can further analyze the content of user utterances and automatically generate the optimal prompt. For example, if a user says, "Show the next slide," an appropriate prompt is generated. It can also provide a function that analyzes the content of user utterances and allows the generative AI to suggest the optimal operating procedure. For example, if a user says, "What should I do?", the generative AI will suggest the operating procedure. This allows the system to generate the optimal prompt based on the content of user utterances, making operations more efficient.

[0072] The generative AI operation assistance system can further adjust the tone and speed of the voice to suit the user's hearing characteristics. For example, if the user has difficulty hearing high-pitched sounds, the voice tone can be lowered. The system can also adjust the voice speed based on the user's hearing characteristics. For example, if the user has difficulty understanding fast speech, the voice speed can be slowed down. This allows the voice output to be optimized to suit the user's hearing characteristics.

[0073] The generative AI operation assistance system can further analyze the user's emotions and output encouraging messages if the user has negative emotions. For example, if the user sounds tired, it can provide encouraging words. It can also provide a function to monitor the user's emotions in real time and provide feedback according to their emotions. For example, if the user seems confused, it can display an encouraging message. This can reduce the user's negative emotions and improve the operation experience.

[0074] The processing flow of the second embodiment will be briefly explained below.

[0075] Step 1: The screen sharing section shares the user's screen. For example, the user's desktop screen can be shared with other users in real time. It is also possible to select and share a specific application screen. Furthermore, it provides a function that allows other users to add comments and annotations while the screen is being shared. For example, participants can add comments in real time during a presentation. Step 2: The voice input unit issues voice instructions to the generation AI. For example, the user can issue a voice instruction such as "Display the next slide," and the generation AI will act in accordance with that instruction. The voice input unit also automatically translates voice instructions in different languages, providing the generation AI with the ability to support multiple languages. For example, it can translate instructions in English into Japanese and execute them. Step 3: The audio output unit communicates the output results of the generation AI by voice. For example, the text generated by the generation AI is read aloud. The audio output unit also provides a function to output the content generated by the generation AI in combination with music and sound effects. For example, background music can be added during a presentation.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0080] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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).

[0085] 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.

[0086] 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.

[0087] 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.

[0088] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0089] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0095] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0110] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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."

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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]

[0143] 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 screen sharing section for sharing the user's screen; A voice input unit that gives voice instructions to the generation AI; and an audio output unit that outputs the output result of the generation AI by voice. A system characterized by:

2. The screen sharing unit Analyzing the user's emotions and making suggestions to simplify operations if the user is feeling stressed 2. The system of claim 1.

3. The screen sharing unit Integrate the ability for other users to add comments and annotations in real time 2. The system of claim 1.

4. The voice input unit Analyze the user's speech and automatically generate the optimal prompt 2. The system of claim 1.

5. The audio output unit Adjusting the tone and speed of the speech to suit the user's hearing characteristics 2. The system of claim 1.

6. The voice input unit Analyzing the user's emotions and outputting an encouraging message if the user has negative emotions 2. The system of claim 1.

7. The voice input unit Add a function to automatically translate voice commands in different languages ​​and enable the generation AI to support multiple languages.

2. The system of claim 1.

8. The audio output unit Analyzing the user's emotions and providing audio feedback to elicit positive emotions 2. The system of claim 1.

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