system

Abstract

An object of the system according to the embodiment is to realize effective conference progress while reproducing the speech and behavior and the facial expression of the CEO and eliminating the stress of the employee.SOLUTION: A system according to an embodiment includes a user response unit, a facial expression reproduction unit, a voice reproduction unit, a conference participation unit, and a stress relief unit. The user response unit receives an input from a user. The expression reproduction unit reproduces a CEO-like expression on the basis of the input received by the user response unit. The voice reproduction unit generates a CEO-like voice based on the expression reproduced by the expression reproduction unit. The conference participation unit participates in the video conference by using the voice generated by the voice reproduction unit. The stress elimination part performs speech and behavior for eliminating the stress of the employee.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 was difficult to reproduce the words, actions, and facial expressions of a CEO, which made it difficult to help employees relieve stress and conduct meetings effectively.

[0005] The system of the embodiment aims to reproduce the words, actions, and facial expressions of the CEO, thereby relieving employees' stress and ensuring effective meeting conduct. [Means for solving the problem]

[0006] The system according to the embodiment includes a user response unit, a facial expression reproduction unit, a voice reproduction unit, a conference participation unit, and a stress relief unit. The user response unit accepts user input. The facial expression reproduction unit reproduces a CEO-like facial expression based on the input accepted by the user response unit. The voice reproduction unit generates a CEO-like voice based on the facial expression reproduced by the facial expression reproduction unit. The conference participation unit participates in a video conference using the voice generated by the voice reproduction unit. The stress relief unit performs speech and actions to relieve employees' stress. [Effects of the Invention]

[0007] The system according to the embodiment reproduces the words, actions, and facial expressions of the CEO, enabling meetings to proceed effectively while relieving employees' stress. [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) An AI system according to an embodiment of the present invention uses multimodal AI to reproduce the CEO's behavior, values, personality, appearance, and voice, allowing it to behave just like the CEO. When a user speaks to the system, it responds with a CEO-like tone and facial expression, and even listens carefully to trivial content and responds with a smile. It can also participate in video conferences, demonstrating its presence by yelling at important meetings to calm the atmosphere or telling jokes to help employees relieve stress. This allows the AI ​​system to respond like a CEO when spoken to by the user, and it can also participate in video conferences and demonstrate its presence. It is also expected to contribute to employee stress relief.

[0029] The AI ​​system according to the embodiment includes a user response unit, a facial expression reproduction unit, a voice reproduction unit, a conference participation unit, and a stress relief unit. The user response unit accepts user input. For example, the user input can be accepted in the form of text input, voice input, gesture input, or the like. The user response unit analyzes the CEO's past statements and behavioral data to generate a CEO-like response. The facial expression reproduction unit reproduces a CEO-like facial expression based on the input accepted by the user response unit. For example, it can reproduce features such as a smile, a serious expression, and a surprised expression. The facial expression reproduction unit analyzes photo and video data of the CEO to learn the CEO's appearance and facial expression. The voice reproduction unit generates a CEO-like voice based on the facial expression reproduced by the facial expression reproduction unit. For example, it can reproduce the tone of voice, speaking rhythm, and phrases used. The voice reproduction unit analyzes the CEO's voice data and learns the CEO's vocal characteristics and speaking style. The conference participation unit participates in a video conference using the voice generated by the voice reproduction unit. For example, it can express a CEO-like opinion on an important agenda item. The conference participation unit analyzes the content of comments made by participants during the video conference in real time and makes CEO-like comments at appropriate times. The stress relief unit performs speech and actions to relieve employees' stress. For example, it can provide words of encouragement, relaxation techniques, jokes, etc. The stress relief unit also periodically monitors employees' stress levels and provides words of encouragement and jokes at appropriate times. As a result, the AI ​​system according to the embodiment can respond like a CEO based on user input, participate in the video conference, and relieve employees' stress.

[0030] The user response unit analyzes the CEO's books and blog posts in addition to past statements and behavioral data, allowing for a deeper reproduction of the CEO's values. For example, the user response unit collects books and blog posts written by the CEO and analyzes their contents using natural language processing technology. This allows for a deep understanding of the CEO's values ​​and thoughts, and the AI ​​generates statements that reflect them. For example, the unit analyzes the CEO's autobiography, business books, online articles, etc. to learn the CEO's leadership style, management philosophy, and ethics. This allows for a deeper reproduction of the CEO's values.

[0031] The user response unit can analyze the CEO's behavioral patterns by time of day and day of the week, and reproduce appropriate words and actions according to the time of day. For example, the user response unit can analyze the CEO's past behavioral data by time of day and day of the week, and learn behavioral patterns during specific time periods. This allows the AI ​​to reproduce appropriate words and actions according to the time of day. For example, it can reproduce appropriate words and actions according to the time of day, such as making a cheerful greeting in a morning meeting and creating a relaxed atmosphere in an afternoon meeting.

[0032] The user response unit can also reproduce the behavior and values ​​of other positions, allowing AI for multiple positions to be operated simultaneously. For example, the user response unit collects data on past statements and actions of the CTO and CFO, and the AI ​​learns the behavior and values ​​of those positions. This allows AI for multiple positions to be operated simultaneously, allowing users to ask questions to each position. For example, it is possible to receive technical advice from the CTO and financial advice from the CFO at the same time.

[0033] The user response unit not only reproduces the CEO's words and actions, but also provides advice on specific projects and tasks. For example, the user response unit adds a function that analyzes data on the CEO's past projects and tasks, and the AI ​​provides advice based on that data. For example, in response to a question about a specific project, it generates advice that sounds like a CEO. This makes it possible to provide advice on specific projects and tasks.

[0034] The facial expression reproduction unit analyzes not only the CEO's appearance and facial expressions, but also his or her gestures and posture, allowing it to reproduce more natural movements. For example, the facial expression reproduction unit analyzes video data of the CEO and learns his or her gestures and posture. This allows the AI ​​to reproduce more natural movements and respond in a manner that is typical of the CEO when spoken to by the user. For example, it can reproduce hand movements, body movements, gestures, etc. This allows it to reproduce more natural movements.

[0035] The facial expression reproduction unit can reproduce the CEO's appearance as a 3D model, enabling interaction in a VR or AR environment. The facial expression reproduction unit, for example, reproduces the CEO's appearance as a 3D model, building a system that enables interaction in a VR or AR environment. For example, a user puts on a VR headset and interacts with the CEO. This makes it possible to interact in a VR or AR environment.

[0036] The facial expression reproduction unit can not only reproduce the CEO's appearance, but also simulate different clothes and hairstyles, allowing the user to select from them. For example, the facial expression reproduction unit adds a function to simulate different clothes and hairstyles when reproducing the CEO's appearance. This allows the user to select their preferred clothes and hairstyle. For example, it can simulate clothes such as business suits, casual wear, and formal wear, and hairstyles such as short hair, long hair, and permed hair. This allows different clothes and hairstyles to be simulated, allowing the user to select from them.

[0037] The facial expression reproduction unit can also reproduce the appearance and expressions of other job titles, making it possible to display AI for multiple job titles simultaneously. The facial expression reproduction unit can also reproduce the appearance and expressions of other job titles, such as CTO and CFO, creating a system that displays AI for multiple job titles simultaneously. This allows a user to interact with multiple job titles at the same time. For example, a user can receive technical advice from the CTO and financial advice from the CFO at the same time. This makes it possible to display AI for multiple job titles at the same time.

[0038] The voice reproduction unit can reproduce the CEO's voice in multiple languages, enabling international communication. The voice reproduction unit, for example, builds a system that reproduces the CEO's voice data in multiple languages. For example, it generates the CEO's voice in multiple languages, such as English, French, and Chinese. This makes international communication possible. For example, a system can be built that includes interpretation functions and cultural considerations to accommodate international conferences and business communications.

[0039] The voice reproduction unit can also reproduce the voices of other job titles, allowing AI with multiple job titles to converse simultaneously. The voice reproduction unit collects voice data from other job titles, such as CTO and CFO, and the AI ​​learns the voices of those positions. This allows AI with multiple job titles to converse simultaneously. For example, it is possible to receive technical advice from the CTO and financial advice from the CFO at the same time. This makes it possible for AI with multiple job titles to converse simultaneously.

[0040] The voice reproduction unit not only reproduces the CEO's voice, but can also provide voice advice on specific tasks or projects. For example, the voice reproduction unit adds a function that analyzes data on the CEO's past projects and tasks, and the AI ​​provides voice advice based on that data. For example, in response to a question about a specific project, it generates voice advice that sounds like the CEO. This makes it possible to provide voice advice on specific tasks or projects.

[0041] The conference participation unit analyzes the content of comments made by participants during a video conference in real time, allowing them to make CEO-like comments at the appropriate time. The conference participation unit, for example, builds a system that analyzes the content of comments made by participants during a video conference in real time, allowing them to make CEO-like comments at the appropriate time. For example, expressing a CEO-like opinion on an important agenda item. For example, the conference participation unit analyzes the content of comments using technologies such as keyword extraction, context analysis, and sentiment analysis, allowing them to make comments at the appropriate time. This allows the content of comments made during a video conference to be analyzed in real time, allowing the CEO-like comments to be made at the appropriate time.

[0042] The conference participation unit can automatically generate minutes of a conference and distribute them to participants after the conference. The conference participation unit, for example, builds a system that analyzes the content of comments made during a video conference in real time and automatically generates minutes. This makes it possible to distribute minutes to participants after the conference. For example, the minutes can be generated using technologies such as speech recognition, natural language processing, and summarization algorithms, and distributed by email, cloud sharing, printed distribution, or other methods. This makes it possible to automatically generate minutes of a conference and distribute them to participants after the conference.

[0043] The conference participation unit allows AI with other job titles to also participate in the video conference, realizing a conference in which multiple positions participate simultaneously. The conference participation unit builds a system that allows AI with other job titles, such as CTO and CFO, to also participate in the video conference, realizing a conference in which multiple positions participate simultaneously. This allows a user to simultaneously interact with multiple positions. For example, a user can simultaneously receive technical advice from the CTO and financial advice from the CFO. This makes it possible to realize a conference in which multiple positions participate simultaneously.

[0044] The conference participation unit can provide advice regarding a specific project or task during a video conference. The conference participation unit adds a function for providing advice regarding a specific project or task during a video conference, for example. This allows the user to obtain appropriate advice during the conference. For example, advice regarding a new product development project or a marketing campaign can be provided. This allows advice regarding a specific project or task to be provided during a video conference.

[0045] The stress relief unit can provide relaxation music and meditation guides to help employees relieve stress. The stress relief unit can add a function to provide relaxation music and meditation guides to help employees relieve stress, for example. This makes it possible to provide an environment in which employees can relax. For example, relaxation music such as classical music, nature sounds, and healing music, and meditation guides such as audio guides, video guides, and text guides can be provided. This makes it possible to provide relaxation music and meditation guides to help employees relieve stress.

[0046] The stress relief department can provide advice on specific tasks or projects to help employees relieve stress. For example, the stress relief department adds a function to provide advice on specific tasks or projects to help employees relieve stress. This allows employees to receive appropriate advice when they have a question about their work. For example, advice on new product development projects or marketing campaigns can be provided. This makes it possible to provide advice on specific tasks or projects to help employees relieve stress.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The user response section not only reproduces the CEO's words and actions, but can also provide advice on specific projects or tasks. For example, a function will be added that analyzes data on the CEO's past projects and tasks, and the AI ​​will provide advice based on that data. For example, in response to a question about a specific project, advice that sounds like it would come from a CEO can be generated. This makes it possible to provide advice on specific projects or tasks.

[0049] The user response unit can also reproduce the behavior and values ​​of other positions, allowing AI for multiple positions to be operated simultaneously. For example, data on the past statements and actions of the CTO and CFO can be collected, and the AI ​​can learn the behavior and values ​​of those positions. This allows AI for multiple positions to be operated simultaneously, allowing users to ask questions to each position. For example, it is possible to simultaneously receive technical advice from the CTO and financial advice from the CFO.

[0050] The facial expression reproduction unit analyzes not only the CEO's appearance and facial expressions, but also his gestures and posture, allowing it to reproduce more natural movements. For example, it analyzes video data of the CEO and learns his gestures and posture. This allows the AI ​​to reproduce more natural movements and respond in a manner that is typical of the CEO when spoken to by the user. For example, it can reproduce hand movements, body movements, gestures, etc. This allows it to reproduce more natural movements.

[0051] The facial expression reproduction unit can reproduce the CEO's appearance as a 3D model, enabling interaction in a VR or AR environment. For example, we will build a system that reproduces the CEO's appearance as a 3D model and enables interaction in a VR or AR environment. For example, a user can wear a VR headset and interact with the CEO. This makes it possible to interact in a VR or AR environment.

[0052] The voice reproduction unit can reproduce the CEO's voice in multiple languages, enabling international communication. For example, we can build a system that reproduces the CEO's voice data in multiple languages. For example, we can generate the CEO's voice in multiple languages, such as English, French, and Chinese. This makes international communication possible. For example, we can build a system that includes interpretation functions and cultural considerations, making it possible to handle international conferences and business communications.

[0053] The conference participation unit analyzes the content of comments made by participants during a video conference in real time, allowing them to make CEO-like comments at the appropriate time. For example, we will build a system that analyzes the content of comments made by participants during a video conference in real time, allowing them to make CEO-like comments at the appropriate time. For example, expressing a CEO-like opinion on an important agenda item. For example, the content of comments can be analyzed using technologies such as keyword extraction, context analysis, and sentiment analysis, allowing them to make comments at the appropriate time. This allows the content of comments made during a video conference to be analyzed in real time, allowing CEO-like comments to be made at the appropriate time.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The user response unit accepts user input. For example, user input can be accepted in the form of text input, voice input, gesture input, etc. The user response unit also analyzes the CEO's past statements and behavioral data to generate a response that sounds like the CEO. Step 2: The facial expression reproduction unit reproduces facial expressions that are typical of the CEO based on the input received by the user response unit. For example, it can reproduce features such as a smile, a serious expression, or a surprised expression. The facial expression reproduction unit also analyzes photos and video data of the CEO to learn his or her appearance and facial expressions. Step 3: The voice reproduction unit generates a voice that sounds like the CEO based on the facial expressions reproduced by the facial expression reproduction unit. For example, it can reproduce the tone of voice, the rhythm of speech, and the phrases used. The voice reproduction unit also analyzes the CEO's voice data and learns the characteristics of his voice and speaking style. Step 4: The conference participant uses the voice generated by the voice reproduction unit to participate in the video conference. For example, the participant can express their opinions on important topics in a manner that sounds like a CEO. The conference participant also analyzes the content of participants' comments in real time during the video conference and makes CEO-like comments at the appropriate time. Step 5: The stress relief department will provide words and actions to relieve employees' stress, such as encouraging words, relaxation techniques, jokes, etc. The stress relief department will also monitor employees' stress levels regularly and provide encouraging words and jokes at appropriate times.

[0056] (Example 2) An AI system according to an embodiment of the present invention uses multimodal AI to reproduce the CEO's behavior, values, personality, appearance, and voice, allowing it to behave just like the CEO. When a user speaks to the system, it responds with a CEO-like tone and facial expression, and even listens carefully to trivial content and responds with a smile. It can also participate in video conferences, demonstrating its presence by yelling at important meetings to calm the atmosphere or telling jokes to help employees relieve stress. This allows the AI ​​system to respond like a CEO when spoken to by the user, and it can also participate in video conferences and demonstrate its presence. It is also expected to contribute to employee stress relief.

[0057] The AI ​​system according to the embodiment includes a user response unit, a facial expression reproduction unit, a voice reproduction unit, a conference participation unit, and a stress relief unit. The user response unit accepts user input. For example, the user input can be accepted in the form of text input, voice input, gesture input, or the like. The user response unit analyzes the CEO's past statements and behavioral data to generate a CEO-like response. The facial expression reproduction unit reproduces a CEO-like facial expression based on the input accepted by the user response unit. For example, it can reproduce features such as a smile, a serious expression, and a surprised expression. The facial expression reproduction unit analyzes photo and video data of the CEO to learn the CEO's appearance and facial expression. The voice reproduction unit generates a CEO-like voice based on the facial expression reproduced by the facial expression reproduction unit. For example, it can reproduce the tone of voice, speaking rhythm, and phrases used. The voice reproduction unit analyzes the CEO's voice data and learns the CEO's vocal characteristics and speaking style. The conference participation unit participates in a video conference using the voice generated by the voice reproduction unit. For example, it can express a CEO-like opinion on an important agenda item. The conference participation unit analyzes the content of comments made by participants during the video conference in real time and makes CEO-like comments at appropriate times. The stress relief unit performs speech and actions to relieve employees' stress. For example, it can provide words of encouragement, relaxation techniques, jokes, etc. The stress relief unit also periodically monitors employees' stress levels and provides words of encouragement and jokes at appropriate times. As a result, the AI ​​system according to the embodiment can respond like a CEO based on user input, participate in the video conference, and relieve employees' stress.

[0058] The user response unit analyzes the CEO's books and blog posts in addition to past statements and behavioral data, allowing for a deeper reproduction of the CEO's values. For example, the user response unit collects books and blog posts written by the CEO and analyzes their contents using natural language processing technology. This allows for a deep understanding of the CEO's values ​​and thoughts, and the AI ​​generates statements that reflect them. For example, the unit analyzes the CEO's autobiography, business books, online articles, etc. to learn the CEO's leadership style, management philosophy, and ethics. This allows for a deeper reproduction of the CEO's values.

[0059] The user response unit can use the CEO's emotion estimation function to learn emotional reactions in specific situations and generate responses based on those emotions. For example, the user response unit analyzes the CEO's past statements and behavioral data to learn emotional reactions in specific situations. For example, by analyzing statements and behavior in stressful situations, the AI ​​reproduces appropriate emotional reactions in similar situations. For example, it learns emotions such as tension felt by the CEO during a presentation or anger during a meeting and generates responses based on those emotions. This makes it possible to learn emotional reactions in specific situations and generate responses based on those emotions.

[0060] The user response unit can analyze the CEO's behavioral patterns by time of day and day of the week, and reproduce appropriate words and actions according to the time of day. For example, the user response unit can analyze the CEO's past behavioral data by time of day and day of the week, and learn behavioral patterns during specific time periods. This allows the AI ​​to reproduce appropriate words and actions according to the time of day. For example, it can reproduce appropriate words and actions according to the time of day, such as making a cheerful greeting in a morning meeting and creating a relaxed atmosphere in an afternoon meeting.

[0061] The user response unit can also reproduce the behavior and values ​​of other positions, allowing AI for multiple positions to be operated simultaneously. For example, the user response unit collects data on past statements and actions of the CTO and CFO, and the AI ​​learns the behavior and values ​​of those positions. This allows AI for multiple positions to be operated simultaneously, allowing users to ask questions to each position. For example, it is possible to receive technical advice from the CTO and financial advice from the CFO at the same time.

[0062] The user response unit not only reproduces the CEO's words and actions, but also provides advice on specific projects and tasks. For example, the user response unit adds a function that analyzes data on the CEO's past projects and tasks, and the AI ​​provides advice based on that data. For example, in response to a question about a specific project, it generates advice that sounds like a CEO. This makes it possible to provide advice on specific projects and tasks.

[0063] The user response unit can use the emotion estimation function to select appropriate words and actions according to the user's emotions and provide a response that is in tune with the user's emotions. The user response unit adds a function, for example, to analyze the user's emotions in real time and select appropriate words and actions according to those emotions. For example, if the user is feeling stressed, the unit can offer words of encouragement. This allows a response that is in tune with the user's emotions.

[0064] The facial expression reproduction unit analyzes not only the CEO's appearance and facial expressions, but also his or her gestures and posture, allowing it to reproduce more natural movements. For example, the facial expression reproduction unit analyzes video data of the CEO and learns his or her gestures and posture. This allows the AI ​​to reproduce more natural movements and respond in a manner that is typical of the CEO when spoken to by the user. For example, it can reproduce hand movements, body movements, gestures, etc. This allows it to reproduce more natural movements.

[0065] The facial expression reproduction unit can use the CEO's emotion estimation function to reproduce subtle changes in facial expression corresponding to specific emotions. The facial expression reproduction unit builds a system that reproduces subtle changes in facial expression corresponding to specific emotions, for example, based on the CEO's emotion estimation data. For example, it reproduces facial expression changes corresponding to emotions such as joy and surprise. For example, it can reproduce eyebrow movements, the way the corners of the mouth turn up, eye movements, etc. This makes it possible to reproduce subtle changes in facial expression corresponding to specific emotions.

[0066] The facial expression reproduction unit can reproduce the CEO's appearance as a 3D model, enabling interaction in a VR or AR environment. The facial expression reproduction unit, for example, reproduces the CEO's appearance as a 3D model, building a system that enables interaction in a VR or AR environment. For example, a user puts on a VR headset and interacts with the CEO. This makes it possible to interact in a VR or AR environment.

[0067] The facial expression reproduction unit can not only reproduce the CEO's appearance, but also simulate different clothes and hairstyles, allowing the user to select from them. For example, the facial expression reproduction unit adds a function to simulate different clothes and hairstyles when reproducing the CEO's appearance. This allows the user to select their preferred clothes and hairstyle. For example, it can simulate clothes such as business suits, casual wear, and formal wear, and hairstyles such as short hair, long hair, and permed hair. This allows different clothes and hairstyles to be simulated, allowing the user to select from them.

[0068] The facial expression reproduction unit can also reproduce the appearance and expressions of other job titles, making it possible to display AI for multiple job titles simultaneously. The facial expression reproduction unit can also reproduce the appearance and expressions of other job titles, such as CTO and CFO, creating a system that displays AI for multiple job titles simultaneously. This allows a user to interact with multiple job titles at the same time. For example, a user can receive technical advice from the CTO and financial advice from the CFO at the same time. This makes it possible to display AI for multiple job titles at the same time.

[0069] The facial expression reproduction unit uses the emotion estimation function to change the facial expression in real time according to the user's emotion, allowing for an empathetic response. For example, the facial expression reproduction unit uses the emotion estimation function to build a system that changes the facial expression in real time according to the user's emotion. This makes it possible to respond with a facial expression that corresponds to the emotion when the user speaks to it. For example, if the user is sad, a comforting facial expression is shown. This makes it possible to change the facial expression in real time according to the user's emotion, allowing for an empathetic response.

[0070] The voice reproduction unit can use the CEO's emotion estimation function to reproduce voice tones and rhythms corresponding to specific emotions. The voice reproduction unit builds a system that reproduces voice tones and rhythms corresponding to specific emotions, for example, based on the CEO's emotion estimation data. For example, it reproduces voice changes corresponding to emotions such as joy and surprise. For example, it can reproduce tones such as high-pitched, low-pitched, and soft voices, as well as rhythms such as speaking speed and pauses. This makes it possible to reproduce voice tones and rhythms corresponding to specific emotions.

[0071] The voice reproduction unit can reproduce the CEO's voice in multiple languages, enabling international communication. The voice reproduction unit, for example, builds a system that reproduces the CEO's voice data in multiple languages. For example, it generates the CEO's voice in multiple languages, such as English, French, and Chinese. This makes international communication possible. For example, a system can be built that includes interpretation functions and cultural considerations to accommodate international conferences and business communications.

[0072] The voice reproduction unit can also reproduce the voices of other job titles, allowing AI with multiple job titles to converse simultaneously. The voice reproduction unit collects voice data from other job titles, such as CTO and CFO, and the AI ​​learns the voices of those positions. This allows AI with multiple job titles to converse simultaneously. For example, it is possible to receive technical advice from the CTO and financial advice from the CFO at the same time. This makes it possible for AI with multiple job titles to converse simultaneously.

[0073] The voice reproduction unit not only reproduces the CEO's voice, but can also provide voice advice on specific tasks or projects. For example, the voice reproduction unit adds a function that analyzes data on the CEO's past projects and tasks, and the AI ​​provides voice advice based on that data. For example, in response to a question about a specific project, it generates voice advice that sounds like the CEO. This makes it possible to provide voice advice on specific tasks or projects.

[0074] The voice reproduction unit uses the emotion estimation function to change the tone and rhythm of the voice in real time according to the user's emotion, thereby enabling an empathetic response. For example, the voice reproduction unit uses the emotion estimation function to build a system that changes the tone and rhythm of the voice in real time according to the user's emotion. This makes it possible to respond in a voice that corresponds to the emotion when the user speaks to it. For example, if the user is sad, the voice reproduction unit responds with a comforting tone of voice. This makes it possible to change the tone and rhythm of the voice in real time according to the user's emotion and enable an empathetic response.

[0075] The conference participation unit analyzes the content of comments made by participants during a video conference in real time, allowing them to make CEO-like comments at the appropriate time. The conference participation unit, for example, builds a system that analyzes the content of comments made by participants during a video conference in real time, allowing them to make CEO-like comments at the appropriate time. For example, expressing a CEO-like opinion on an important agenda item. For example, the conference participation unit analyzes the content of comments using technologies such as keyword extraction, context analysis, and sentiment analysis, allowing them to make comments at the appropriate time. This allows the content of comments made during a video conference to be analyzed in real time, allowing the CEO-like comments to be made at the appropriate time.

[0076] The meeting participation unit can use the CEO's emotion estimation function to select appropriate facial expressions and behaviors according to the atmosphere of the meeting. The meeting participation unit, for example, analyzes the atmosphere during a video conference in real time and builds a system that selects appropriate facial expressions and behaviors using the CEO's emotion estimation function. For example, it reproduces appropriate facial expressions in tense situations. For example, it analyzes the participants' facial expressions, tone of speech, and content of discussion to select appropriate facial expressions and behaviors. This makes it possible to select appropriate facial expressions and behaviors according to the atmosphere of the meeting.

[0077] The conference participation unit can automatically generate minutes of a conference and distribute them to participants after the conference. The conference participation unit, for example, builds a system that analyzes the content of comments made during a video conference in real time and automatically generates minutes. This makes it possible to distribute minutes to participants after the conference. For example, the minutes can be generated using technologies such as speech recognition, natural language processing, and summarization algorithms, and distributed by email, cloud sharing, printed distribution, or other methods. This makes it possible to automatically generate minutes of a conference and distribute them to participants after the conference.

[0078] The conference participation unit allows AI with other job titles to also participate in the video conference, realizing a conference in which multiple positions participate simultaneously. The conference participation unit builds a system that allows AI with other job titles, such as CTO and CFO, to also participate in the video conference, realizing a conference in which multiple positions participate simultaneously. This allows a user to simultaneously interact with multiple positions. For example, a user can simultaneously receive technical advice from the CTO and financial advice from the CFO. This makes it possible to realize a conference in which multiple positions participate simultaneously.

[0079] The conference participation unit can provide advice regarding a specific project or task during a video conference. The conference participation unit adds a function for providing advice regarding a specific project or task during a video conference, for example. This allows the user to obtain appropriate advice during the conference. For example, advice regarding a new product development project or a marketing campaign can be provided. This allows advice regarding a specific project or task to be provided during a video conference.

[0080] The conference participation unit can use the emotion estimation function to monitor the emotions of participants during a conference in real time and respond appropriately. The conference participation unit, for example, uses the emotion estimation function to build a system that monitors the emotions of participants during a conference in real time. This makes it possible to respond appropriately during a conference. For example, the conference participation unit can analyze the facial expressions, voice, text, etc. of participants and provide empathetic words, comforting words, encouraging words, etc. This makes it possible to monitor the emotions of participants during a conference in real time and respond appropriately.

[0081] The stress relief department can periodically monitor the stress levels of employees and provide words of encouragement or jokes at appropriate times. The stress relief department, for example, builds a system that periodically monitors the stress levels of employees and provides words of encouragement or jokes at appropriate times. This makes it possible to reduce employee stress. For example, stress levels can be measured using methods such as questionnaire surveys, physiological indicators, and behavioral analysis, and words of encouragement or jokes can be provided when stress levels are high or after specific events. This makes it possible to periodically monitor the stress levels of employees and provide words of encouragement or jokes at appropriate times.

[0082] The stress relief unit can use the CEO's emotion estimation function to select appropriate words and actions based on the employee's emotions, thereby helping to relieve stress. For example, the stress relief unit can use the CEO's emotion estimation function to build a system that selects appropriate words and actions based on the employee's emotions. This allows the AI ​​to take appropriate action when an employee is feeling stressed. For example, it can analyze the employee's emotions in real time and provide empathetic words, comforting words, encouraging words, etc. This allows the system to select appropriate words and actions based on the employee's emotions, helping to relieve stress.

[0083] The stress relief unit can provide relaxation music and meditation guides to help employees relieve stress. The stress relief unit can add a function to provide relaxation music and meditation guides to help employees relieve stress, for example. This makes it possible to provide an environment in which employees can relax. For example, relaxation music such as classical music, nature sounds, and healing music, and meditation guides such as audio guides, video guides, and text guides can be provided. This makes it possible to provide relaxation music and meditation guides to help employees relieve stress.

[0084] The stress relief department can provide advice on specific tasks or projects to help employees relieve stress. For example, the stress relief department adds a function to provide advice on specific tasks or projects to help employees relieve stress. This allows employees to receive appropriate advice when they have a question about their work. For example, advice on new product development projects or marketing campaigns can be provided. This makes it possible to provide advice on specific tasks or projects to help employees relieve stress.

[0085] The stress relief unit uses the emotion estimation function to select appropriate words and actions in real time according to the employee's emotions, thereby helping to relieve stress. For example, the stress relief unit uses the emotion estimation function to build a system that selects appropriate words and actions in real time according to the employee's emotions. This allows the AI ​​to take appropriate action when an employee is feeling stressed. For example, it analyzes the employee's emotions in real time and provides empathetic words, comforting words, encouraging words, etc. This allows appropriate words and actions in real time according to the employee's emotions to be selected to help relieve stress.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The user response section not only reproduces the CEO's words and actions, but can also provide advice on specific projects or tasks. For example, a function will be added that analyzes data on the CEO's past projects and tasks, and the AI ​​will provide advice based on that data. For example, in response to a question about a specific project, advice that sounds like it would come from a CEO can be generated. This makes it possible to provide advice on specific projects or tasks.

[0088] The user response unit can also reproduce the behavior and values ​​of other positions, allowing AI for multiple positions to be operated simultaneously. For example, data on the past statements and actions of the CTO and CFO can be collected, and the AI ​​can learn the behavior and values ​​of those positions. This allows AI for multiple positions to be operated simultaneously, allowing users to ask questions to each position. For example, it is possible to simultaneously receive technical advice from the CTO and financial advice from the CFO.

[0089] The user response unit can use the emotion estimation function to select appropriate words and actions according to the user's emotions and respond in a way that is in tune with the user's emotions. For example, a function can be added to analyze the user's emotions in real time and select appropriate words and actions according to those emotions. For example, if the user is feeling stressed, words of encouragement can be offered. This allows for a response that is in tune with the user's emotions.

[0090] The facial expression reproduction unit analyzes not only the CEO's appearance and facial expressions, but also his gestures and posture, allowing it to reproduce more natural movements. For example, it analyzes video data of the CEO and learns his gestures and posture. This allows the AI ​​to reproduce more natural movements and respond in a manner that is typical of the CEO when spoken to by the user. For example, it can reproduce hand movements, body movements, gestures, etc. This allows it to reproduce more natural movements.

[0091] The facial expression reproduction unit can use the emotion estimation function to reproduce subtle changes in facial expressions corresponding to specific emotions. For example, a system can be constructed that reproduces subtle changes in facial expressions corresponding to specific emotions based on emotion estimation data of a CEO. For example, it can reproduce facial changes corresponding to emotions such as joy and surprise. For example, it can reproduce eyebrow movements, the way the corners of the mouth turn up, and eye movements. This makes it possible to reproduce subtle changes in facial expressions corresponding to specific emotions.

[0092] The facial expression reproduction unit can reproduce the CEO's appearance as a 3D model, enabling interaction in a VR or AR environment. For example, we will build a system that reproduces the CEO's appearance as a 3D model and enables interaction in a VR or AR environment. For example, a user can wear a VR headset and interact with the CEO. This makes it possible to interact in a VR or AR environment.

[0093] The voice reproduction unit can reproduce the CEO's voice in multiple languages, enabling international communication. For example, we can build a system that reproduces the CEO's voice data in multiple languages. For example, we can generate the CEO's voice in multiple languages, such as English, French, and Chinese. This makes international communication possible. For example, we can build a system that includes interpretation functions and cultural considerations, making it possible to handle international conferences and business communications.

[0094] The voice reproduction unit can use the emotion estimation function to change the tone and rhythm of the voice in real time according to the user's emotion, thereby providing an empathetic response. For example, a system can be constructed that uses the emotion estimation function to change the tone and rhythm of the voice in real time according to the user's emotion. This makes it possible to respond in a voice that corresponds to the emotion when the user speaks to it. For example, if the user is sad, the voice reproduction unit can respond with a comforting tone of voice. This allows the tone and rhythm of the voice to be changed in real time according to the user's emotion, providing an empathetic response.

[0095] The conference participation unit analyzes the content of comments made by participants during a video conference in real time, allowing them to make CEO-like comments at the appropriate time. For example, we will build a system that analyzes the content of comments made by participants during a video conference in real time, allowing them to make CEO-like comments at the appropriate time. For example, expressing a CEO-like opinion on an important agenda item. For example, the content of comments can be analyzed using technologies such as keyword extraction, context analysis, and sentiment analysis, allowing them to make comments at the appropriate time. This allows the content of comments made during a video conference to be analyzed in real time, allowing CEO-like comments to be made at the appropriate time.

[0096] The conference participation unit can use the emotion estimation function to monitor the emotions of participants during a conference in real time and respond appropriately. For example, a system can be constructed that uses the emotion estimation function to monitor the emotions of participants during a conference in real time. This makes it possible to respond appropriately during a conference. For example, the system can analyze participants' facial expressions, voices, text, etc., and provide empathetic words, comforting words, encouraging words, etc. This makes it possible to monitor the emotions of participants during a conference in real time and respond appropriately.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The user response unit accepts user input. For example, user input can be accepted in the form of text input, voice input, gesture input, etc. The user response unit also analyzes the CEO's past statements and behavioral data to generate a response that sounds like the CEO. Step 2: The facial expression reproduction unit reproduces facial expressions that are typical of the CEO based on the input received by the user response unit. For example, it can reproduce features such as a smile, a serious expression, or a surprised expression. The facial expression reproduction unit also analyzes photos and video data of the CEO to learn his or her appearance and facial expressions. Step 3: The voice reproduction unit generates a voice that sounds like the CEO based on the facial expressions reproduced by the facial expression reproduction unit. For example, it can reproduce the tone of voice, the rhythm of speech, and the phrases used. The voice reproduction unit also analyzes the CEO's voice data and learns the characteristics of his voice and speaking style. Step 4: The conference participant uses the voice generated by the voice reproduction unit to participate in the video conference. For example, the participant can express their opinions on important topics in a manner that sounds like a CEO. The conference participant also analyzes the content of participants' comments in real time during the video conference and makes CEO-like comments at the appropriate time. Step 5: The stress relief department will provide words and actions to relieve employees' stress, such as encouraging words, relaxation techniques, jokes, etc. The stress relief department will also monitor employees' stress levels regularly and provide encouraging words and jokes at appropriate times.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

[0166] 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 user response unit that accepts user input; an expression reproduction unit that reproduces a CEO-like expression based on the input received by the user response unit; a voice reproducing unit that generates a CEO-like voice based on the facial expression reproduced by the facial expression reproducing unit; a conference participation unit that participates in a video conference using the voice generated by the voice reproduction unit; A stress relief department that takes actions and words to relieve employees' stress. A system characterized by:

2. The facial expression reproduction unit In addition to the CEO's appearance and facial expressions, the system also analyzes his gestures and posture to reproduce more natural movements.

2. The system of claim 1.

3. The sound reproduction unit In addition to the CEO's voice data, the system learns the specific phrases and expressions used by the CEO to reproduce more natural conversations.

2. The system of claim 1.

4. The conference participation unit Analyze the content of participants' comments during the video conference in real time and make comments that are appropriate for the CEO at the appropriate time.

2. The system of claim 1.

5. The stress relief unit is Regularly monitor the employee's stress level and provide encouraging words or jokes at appropriate times 2. The system of claim 1.

6. The user response unit Using the CEO's emotion estimation function, it learns the emotional reactions in specific situations and generates emotion-based responses.

2. The system of claim 1.

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