System

Abstract

An object of a system according to an embodiment is to improve the motivation of employees in a remote meeting.SOLUTION: A system includes an acquisition unit, an analysis unit, a conversion unit, and a provision unit. The acquisition unit acquires video and audio in real time. The analysis unit analyzes the video and the sound acquired by the acquisition unit. The conversion unit replaces the face and the voice of the boss with a face and a voice of a specific suggestion on the basis of a result analyzed by the analysis unit. The providing unit provides the employee with the video and the voice converted by the conversion unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to maintain employee motivation during remote meetings.

[0005] The system according to the embodiment aims to improve employee motivation during remote meetings. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a conversion unit, and a provision unit. The acquisition unit acquires video and audio in real time. The analysis unit analyzes the video and audio acquired by the acquisition unit. The conversion unit replaces the boss's face and voice with the face and voice of a specific favorite person based on the results of the analysis by the analysis unit. The provision unit provides the employee with the video and audio converted by the conversion unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve employee motivation during remote meetings. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A system according to an embodiment of the present invention increases employee motivation by replacing the boss's face and voice with that of their favorite idol during ZOOM meetings. This system captures video and audio in real time, and a generation AI processes the process of replacing the boss's face and voice with that of their favorite idol. This allows employees to hold meetings using their favorite idol's face and voice, improving their motivation. For example, the system captures video and audio of a ZOOM meeting in real time. The generation AI then analyzes the captured video and audio and processes the process of replacing the boss's face and voice with that of their favorite idol. The generation AI converts the boss's facial features into those of their favorite idol, and converts the boss's vocal features into those of their favorite idol. For example, the generation AI converts the boss's facial contours, eye shape, vocal tone, and pitch into those of their favorite idol. The video and audio converted by the generation AI are provided to employees in real time. This allows employees to hold meetings using their favorite idol's face and voice, improving their motivation. This system improves employee motivation and improves work efficiency. For example, when employees have meetings with their favorite person's face and voice, they can concentrate more easily on the content of the meeting, which can improve work productivity. It can also be expected to reduce employee stress and improve the atmosphere in the workplace.

[0029] A motivation-boosting system according to an embodiment includes an acquisition unit, an analysis unit, a conversion unit, and a provision unit. The acquisition unit acquires video and audio in real time. For example, the acquisition unit acquires video and audio of a ZOOM meeting in real time. The acquisition unit can also simultaneously acquire data for identifying the boss's face and voice. For example, pre-registering the boss's facial features and voice features makes it easier to identify the boss in real time. The analysis unit analyzes the video and audio acquired by the acquisition unit. For example, the analysis unit analyzes the boss's facial features and voice features. The conversion unit replaces the boss's face and voice with the face and voice of the favorite idol based on the results of the analysis by the analysis unit. For example, the conversion unit converts the boss's facial contours, eye shape, voice tone, pitch, etc. into those of the favorite idol. The provision unit provides the video and audio converted by the conversion unit to the employee. For example, the provision unit provides the video and audio converted by the conversion unit to the employee in real time. This allows the motivation-boosting system according to an embodiment to improve employee motivation. As a result, the motivation-boosting system according to the embodiment can improve employee motivation and improve work efficiency. For example, by having a meeting with the face and voice of an employee's favorite person, employees can concentrate more easily on the content of the meeting, improving work productivity. In addition, it is expected that stress for employees will be reduced and the atmosphere in the workplace will improve.

[0030] The motivation-boosting system includes a registration unit that registers the facial and vocal features of a boss in advance. The registration unit registers the facial and vocal features of a boss in advance. For example, the registration unit registers the facial contours, eye shape, tone and pitch of the boss's voice, etc. In this way, by registering the facial and vocal features of a boss in advance, it becomes easier to identify the boss in real time. For example, the registration unit photographs the facial features of a boss with a camera and registers the facial contours and eye shape as data. The registration unit can also record the vocal features of a boss with a microphone and register the tone and pitch of the voice as data. In this way, by registering the facial and vocal features of a boss in advance, it becomes easier to identify the boss in real time. In this way, by registering the facial and vocal features of a boss in advance, it becomes easier to identify the boss in real time, thereby improving employee motivation.

[0031] The acquisition unit can analyze the boss's movements and speech content in real time when acquiring video and audio, and select an appropriate acquisition method. For example, the acquisition unit can prioritize acquisition of important speech parts depending on what the boss is saying. Furthermore, if the boss is actively moving, the acquisition unit can adjust the camera angle to capture that movement. Furthermore, if the boss's speech is interrupted, the acquisition unit can wait for the next speech before acquiring video and audio. This allows important data to be acquired preferentially by selecting the optimal acquisition method depending on the boss's movements and speech content. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the boss's speech content into a generation AI and have the generation AI identify important speech parts.

[0032] The acquisition unit can improve the quality of the acquired data by analyzing the boss's facial expression and tone of voice when acquiring video and audio. For example, the acquisition unit can capture the moment when the boss's facial expression changes to acquire more natural video. The acquisition unit can also improve the quality of the audio data by capturing changes in the boss's tone of voice. Furthermore, if the boss's facial expression is bright, the acquisition unit can acquire video by emphasizing that expression. In this way, by analyzing the boss's facial expression and tone of voice, more natural and high-quality data can be acquired. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the boss's facial expression data into the generation AI and have the generation AI identify changes in facial expression.

[0033] The acquisition unit can filter the user's environmental sound when acquiring video and audio to acquire clear audio. For example, the acquisition unit can filter noise around the user and clearly acquire only the boss's voice. Furthermore, if the user's environmental sound is loud, the acquisition unit can suppress the sound and emphasize the boss's voice. Furthermore, when the user's environmental sound changes, the acquisition unit can detect the change in real time and adjust the audio data. In this way, clear audio can be acquired by filtering the user's environmental sound. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input environmental sound data to a generation AI and cause the generation AI to filter the environmental sound.

[0034] When acquiring video and audio, the acquisition unit can prioritize acquiring highly relevant data by taking into account the user's geographical location information. For example, when the user is in the office, the acquisition unit can prioritize acquiring relevant data within the office. Furthermore, when the user is on a business trip, the acquisition unit can prioritize acquiring relevant data for the business trip destination. Furthermore, when the user is at home, the acquisition unit can prioritize acquiring relevant data within the home. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input geographical location information data to the generation AI and cause the generation AI to identify highly relevant data.

[0035] The acquisition unit can analyze the user's social media activities when acquiring the video and audio, and acquire related data. For example, the acquisition unit can acquire data on locations where the user has checked in on social media. The acquisition unit can also analyze the content of the user's social media posts and acquire related data. The acquisition unit can also acquire related data by referring to the activities of the user's friends on social media. In this way, related data can be acquired by analyzing the user's social media activities. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input social media data into a generation AI and cause the generation AI to identify related data.

[0036] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring video and audio. For example, the acquisition unit preferentially uses a video and audio acquisition method that the user has previously preferred. The acquisition unit can also adjust the acquisition method based on the user's past feedback. The acquisition unit can also avoid acquisition methods that the user has previously been dissatisfied with. In this way, the acquisition method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input past feedback data into a generation AI and cause the generation AI to customize the acquisition method.

[0037] During analysis, the analysis unit can improve conversion accuracy by analyzing the boss's facial features and voice features in detail. The analysis unit can improve conversion accuracy by analyzing, for example, the boss's facial contours and eye shape in detail. The analysis unit can also improve conversion accuracy by analyzing the boss's tone and pitch in detail. The analysis unit can also improve conversion accuracy by analyzing the boss's facial expressions in detail. In this way, conversion accuracy is improved by analyzing the boss's facial features and voice features in detail. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the boss's facial feature data into the generation AI and cause the generation AI to improve conversion accuracy.

[0038] During analysis, the analysis unit can convert the boss's speech into text and use it as analysis data. For example, the analysis unit can convert the boss's speech into text in real time and use it as analysis data. The analysis unit can also convert the boss's speech into text and save it for later reference. The analysis unit can also convert the boss's speech into text and compare it with other data to improve the accuracy of the analysis. In this way, by converting the boss's speech into text, it can be used as analysis data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the boss's speech audio data into a generation AI and have the generation AI perform the text conversion.

[0039] During analysis, the analysis unit analyzes the boss's movements and gestures, enabling a more natural conversion. For example, the analysis unit analyzes the boss's hand movements and converts them into the boss's hand movements. The analysis unit can also analyze the boss's facial expression changes and convert them into the boss's facial expressions. The analysis unit can also analyze the boss's body movements and convert them into the boss's body movements. In this way, by analyzing the boss's movements and gestures, a more natural conversion can be achieved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the boss's movement data into a generation AI and have the generation AI convert the movements.

[0040] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the boss's past speech history. For example, the analysis unit refers to the boss's past speech history and analyzes the content of the current speech. The analysis unit can also analyze speech patterns based on the boss's past speech history. The analysis unit can also compare the boss's past speech history and improve the accuracy of the analysis. In this way, by referring to the boss's past speech history, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the boss's past speech data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0041] During analysis, the analysis unit can customize the analysis method based on the supervisor's job description and position. The analysis unit adjusts the analysis method according to, for example, the supervisor's job description. The analysis unit can also adjust the level of analysis detail based on the supervisor's position. The analysis unit can also select the optimal analysis method taking into account the supervisor's job description and position. This enables more appropriate analysis by customizing the analysis method based on the supervisor's job description and position. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the supervisor's job description data into the generation AI and have the generation AI customize the analysis method.

[0042] During analysis, the analysis unit can compare the content of the superior's remarks with other data sources to improve the accuracy of the analysis. For example, the analysis unit analyzes the content of the superior's remarks by comparing them with records of other meetings. The analysis unit can also analyze the content of the superior's remarks by comparing them with related documents. The analysis unit can also improve the accuracy of the analysis by comparing the content of the superior's remarks with other data sources. In this way, the analysis accuracy is improved by comparing the content of the superior's remarks with other data sources. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input other data sources into the generation AI and have the generation AI compare the content of the remarks.

[0043] The conversion unit can adjust the sound quality when converting the characteristics of the boss's voice into the characteristics of the favorite idol's voice during conversion. For example, the conversion unit converts the tone of the boss's voice by optimizing it to the tone of the favorite idol's voice. The conversion unit can also convert the pitch of the boss's voice by optimizing it to the pitch of the favorite idol's voice. The conversion unit can also convert the sound quality of the boss's voice by optimizing it to the sound quality of the favorite idol's voice. In this way, by optimizing and converting the characteristics of the boss's voice to the characteristics of the favorite idol's voice, a more natural voice can be provided. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input data on the characteristics of the boss's voice into a generation AI and have the generation AI adjust the sound quality.

[0044] During conversion, the conversion unit converts the boss's movements and gestures into those of the favorite idol, thereby providing a more natural image. The conversion unit, for example, converts the boss's hand movements into those of the favorite idol. The conversion unit can also convert the boss's facial expressions into those of the favorite idol. The conversion unit can also convert the boss's body movements into those of the favorite idol. In this way, by converting the boss's movements and gestures into those of the favorite idol, a more natural image can be provided. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the boss's movement data into a generation AI and have the generation AI convert the movements.

[0045] During conversion, the conversion unit can select the optimal conversion method by referencing a database of the person's face and voice. For example, the conversion unit can select the optimal face conversion method by referencing a database of the person's voice. The conversion unit can also select the optimal voice conversion method by referencing a database of the person's voice. The conversion unit can also incorporate the most recent face and voice data of the person's voice to improve conversion accuracy. This allows the optimal conversion method to be selected by referencing the database of the person's face and voice. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the face and voice data of the person's voice into a generation AI and have the generation AI select the optimal conversion method.

[0046] The conversion unit can customize the conversion method by reflecting the user's past feedback during conversion. For example, the conversion unit preferentially uses a conversion method that the user has previously preferred. The conversion unit can also adjust the conversion method based on the user's past feedback. The conversion unit can also avoid conversion methods that the user has previously been dissatisfied with. In this way, the conversion method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the conversion unit may be performed using AI, for example, or may be performed without using AI. For example, the conversion unit can input past feedback data into a generation AI and have the generation AI customize the conversion method.

[0047] During conversion, the conversion unit can incorporate the latest video and audio data of the favorite character to improve conversion accuracy. For example, the conversion unit can incorporate the latest video data of the favorite character to improve facial conversion accuracy. The conversion unit can also incorporate the latest audio data of the favorite character to improve voice conversion accuracy. The conversion unit can also incorporate the latest movement data of the favorite character to improve movement conversion accuracy. In this way, by incorporating the latest video and audio data of the favorite character, conversion accuracy is improved. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the latest video and audio data of the favorite character into the generation AI and have the generation AI improve conversion accuracy.

[0048] The providing unit can provide optimal video and audio by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide video and audio that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can provide video and audio that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide video and audio that is concise and highly visible. This allows optimal video and audio to be provided by taking into account the user's device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input device information to a generation AI and cause the generation AI to provide optimal video and audio.

[0049] The providing unit can customize the content to be provided by referring to the user's past viewing history when providing the content. The providing unit selects the optimal content to be provided based on, for example, a history of video and audio viewed by the user in the past. The providing unit can also analyze the user's past viewing history and customize the content to suit the user's preferences. The providing unit can also provide related video and audio by referring to content viewed by the user in the past. This makes it possible to customize the content to be provided by referring to the user's past viewing history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input viewing history data to a generation AI and have the generation AI customize the content to be provided.

[0050] The providing unit can optimize the quality of video and audio by taking into account the user's network environment when providing the video and audio. For example, if the user's network speed is slow, the providing unit can provide video and audio at lower quality. Furthermore, if the user's network speed is fast, the providing unit can provide high-quality video and audio. Furthermore, when the user's network environment changes, the providing unit can detect the change in real time and adjust the quality of video and audio. This makes it possible to optimize the quality of video and audio by taking the user's network environment into account. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input network environment data to the generating AI and cause the generating AI to optimize the quality.

[0051] The providing unit can provide highly relevant video and audio by taking into account the user's geographical location information. For example, when the user is in the office, the providing unit can prioritize providing relevant data within the office. Furthermore, when the user is on a business trip, the providing unit can prioritize providing relevant data for the business trip destination. Furthermore, when the user is at home, the providing unit can prioritize providing relevant data within the home. In this way, highly relevant video and audio can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input geographical location information data to the generation AI and cause the generation AI to identify highly relevant video and audio.

[0052] At the time of providing, the providing unit can analyze the user's social media activity and provide related video and audio. For example, the providing unit provides data on locations where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide related video and audio. The providing unit can also provide related video and audio by referring to the activities of the user's friends on social media. In this way, related video and audio can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input social media data to a generation AI and cause the generation AI to identify related video and audio.

[0053] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the content. For example, the providing unit preferentially uses a delivery method that the user has previously preferred. The providing unit can also adjust the delivery method based on the user's past feedback. The providing unit can also avoid delivery methods that the user has previously been dissatisfied with. In this way, the delivery method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input past feedback data into the generation AI and cause the generation AI to customize the delivery method.

[0054] During registration, the registration unit can analyze the boss's facial features in detail and register highly accurate data. For example, the registration unit can analyze the boss's facial contours in detail and register highly accurate data. The registration unit can also analyze the boss's eye shape in detail and register highly accurate data. The registration unit can also analyze the boss's facial expression changes in detail and register highly accurate data. In this way, highly accurate data can be registered by analyzing the boss's facial features in detail. Some or all of the above-described processing in the registration unit may be performed using AI, for example, or may be performed without using AI. For example, the registration unit can input the boss's facial feature data into the generation AI and cause the generation AI to register highly accurate data.

[0055] During registration, the registration unit can analyze the characteristics of the boss's voice in detail and register highly accurate data. For example, the registration unit can analyze the tone of the boss's voice in detail and register highly accurate data. The registration unit can also analyze the pitch of the boss's voice in detail and register highly accurate data. The registration unit can also analyze the tone of the boss's voice in detail and register highly accurate data. In this way, by analyzing the characteristics of the boss's voice in detail, highly accurate data can be registered. Some or all of the above-mentioned processing in the registration unit may be performed using AI, for example, or may be performed without using AI. For example, the registration unit can input data on the characteristics of the boss's voice to the generation AI and cause the generation AI to register highly accurate data.

[0056] During registration, the registration unit can improve registration accuracy by referring to past video and audio data of the boss. For example, the registration unit refers to past video data of the boss and registers facial features in detail. The registration unit can also refer to past audio data of the boss and register voice features in detail. The registration unit can also improve registration accuracy by referring to past video and audio data of the boss. In this way, by referring to past video and audio data of the boss, registration accuracy is improved. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input past video and audio data of the boss to the generation AI and cause the generation AI to improve registration accuracy.

[0057] The registration unit can customize the registration method based on the supervisor's job description and position at the time of registration. The registration unit adjusts the registration method according to, for example, the supervisor's job description. The registration unit can also adjust the level of registration detail based on the supervisor's position. The registration unit can also select the optimal registration method taking into account the supervisor's job description and position. In this way, by customizing the registration method based on the supervisor's job description and position, more appropriate data can be registered. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the supervisor's job description data into the generation AI and cause the generation AI to customize the registration method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The motivation-boosting system can further include a feedback unit. The feedback unit can collect feedback from employees and use it to improve the system. For example, the feedback unit can collect changes in motivation felt by employees after a meeting and their satisfaction with the conversion of their favorite idol's face and voice. The feedback unit can also collect improvements that employees suggest for the conversion of their favorite idol's face and voice. Furthermore, the feedback unit can analyze the collected feedback and identify areas for improvement in the system. Thus, by including a feedback unit, it is possible to improve the system to reflect employee opinions, which is expected to result in more effective motivation-boosting.

[0060] The motivation enhancement system can further include a notification unit. The notification unit can notify employees of the start and end of meetings. For example, the notification unit can send a reminder to employees when the meeting is about to start. The notification unit can also send an end notification to employees when the meeting has ended. Furthermore, the notification unit can notify employees in real time when important comments or agenda items are raised during the meeting. Thus, by including a notification unit, employees can easily grasp the progress of the meeting, making meetings more efficient.

[0061] When capturing video and audio, the capture unit can analyze the boss's movements and what they say in real time and select the appropriate capture method. For example, it can prioritize capturing important parts of what the boss is saying depending on what they are saying. It can also adjust the camera angle to capture active movements if the boss is moving around a lot. Furthermore, if the boss stops speaking, it can wait for the next comment before capturing video and audio. This allows it to prioritize the capture of important data by selecting the optimal capture method depending on the boss's movements and what they say.

[0062] The acquisition unit can improve the quality of the acquired data by analyzing the boss's facial expression and tone of voice when acquiring video and audio. For example, it can capture the moment when the boss's facial expression changes to acquire more natural video. It can also improve the quality of the audio data by capturing changes in the tone of the boss's voice. Furthermore, if the boss has a bright expression, it can acquire video that emphasizes that expression. In this way, by analyzing the boss's facial expression and tone of voice, it is possible to acquire more natural, high-quality data.

[0063] During analysis, the analysis unit can convert the boss's comments into text and use it as analysis data. For example, the boss's comments can be converted into text in real time and used as analysis data. The boss's comments can also be converted into text and saved for later reference. Furthermore, the boss's comments can be converted into text and compared with other data to improve the accuracy of the analysis. In this way, the boss's comments can be converted into text and used as analysis data.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The acquisition unit acquires video and audio in real time. For example, the acquisition unit acquires video and audio of a ZOOM meeting in real time. The acquisition unit can also simultaneously acquire data for identifying the boss's face and voice. For example, by registering the boss's facial features and voice features in advance, it becomes easier to identify the boss in real time. Step 2: The analysis unit analyzes the video and audio acquired by the acquisition unit. For example, the analysis unit analyzes the facial features of the boss and the voice features of the boss. Step 3: The conversion unit replaces the boss's face and voice with the face and voice of the favorite person based on the results of the analysis by the analysis unit. For example, the conversion unit converts the boss's facial contours, eye shape, voice tone, and pitch to those of the favorite person. Step 4: The providing unit provides the employee with the video and audio converted by the converting unit. For example, the providing unit provides the employee with the video and audio in real time.

[0066] (Example 2) A system according to an embodiment of the present invention increases employee motivation by replacing the boss's face and voice with that of their favorite idol during ZOOM meetings. This system captures video and audio in real time, and a generation AI processes the process of replacing the boss's face and voice with that of their favorite idol. This allows employees to hold meetings using their favorite idol's face and voice, improving their motivation. For example, the system captures video and audio of a ZOOM meeting in real time. The generation AI then analyzes the captured video and audio and processes the process of replacing the boss's face and voice with that of their favorite idol. The generation AI converts the boss's facial features into those of their favorite idol, and converts the boss's vocal features into those of their favorite idol. For example, the generation AI converts the boss's facial contours, eye shape, vocal tone, and pitch into those of their favorite idol. The video and audio converted by the generation AI are provided to employees in real time. This allows employees to hold meetings using their favorite idol's face and voice, improving their motivation. This system improves employee motivation and improves work efficiency. For example, when employees have meetings with their favorite person's face and voice, they can concentrate more easily on the content of the meeting, which can improve work productivity. It can also be expected to reduce employee stress and improve the atmosphere in the workplace.

[0067] A motivation-boosting system according to an embodiment includes an acquisition unit, an analysis unit, a conversion unit, and a provision unit. The acquisition unit acquires video and audio in real time. For example, the acquisition unit acquires video and audio of a ZOOM meeting in real time. The acquisition unit can also simultaneously acquire data for identifying the boss's face and voice. For example, pre-registering the boss's facial features and voice features makes it easier to identify the boss in real time. The analysis unit analyzes the video and audio acquired by the acquisition unit. For example, the analysis unit analyzes the boss's facial features and voice features. The conversion unit replaces the boss's face and voice with the face and voice of the favorite idol based on the results of the analysis by the analysis unit. For example, the conversion unit converts the boss's facial contours, eye shape, voice tone, pitch, etc. into those of the favorite idol. The provision unit provides the video and audio converted by the conversion unit to the employee. For example, the provision unit provides the video and audio converted by the conversion unit to the employee in real time. This allows the motivation-boosting system according to an embodiment to improve employee motivation. As a result, the motivation-boosting system according to the embodiment can improve employee motivation and improve work efficiency. For example, by having a meeting with the face and voice of an employee's favorite person, employees can concentrate more easily on the content of the meeting, improving work productivity. In addition, it is expected that stress for employees will be reduced and the atmosphere in the workplace will improve.

[0068] The motivation-boosting system includes a registration unit that registers the facial and vocal features of a boss in advance. The registration unit registers the facial and vocal features of a boss in advance. For example, the registration unit registers the facial contours, eye shape, tone and pitch of the boss's voice, etc. In this way, by registering the facial and vocal features of a boss in advance, it becomes easier to identify the boss in real time. For example, the registration unit photographs the facial features of a boss with a camera and registers the facial contours and eye shape as data. The registration unit can also record the vocal features of a boss with a microphone and register the tone and pitch of the voice as data. In this way, by registering the facial and vocal features of a boss in advance, it becomes easier to identify the boss in real time. In this way, by registering the facial and vocal features of a boss in advance, it becomes easier to identify the boss in real time, thereby improving employee motivation.

[0069] The acquisition unit can estimate the user's emotions and adjust the timing of video and audio acquisition based on the estimated user emotions. For example, if the user is tense, the acquisition unit acquires video and audio at a timing that allows the user to relax. Furthermore, if the user is concentrating, the acquisition unit can adjust the timing of video and audio acquisition so as not to disrupt the user's concentration. Furthermore, if the user is tired, the acquisition unit can acquire video and audio after a break. This allows for more appropriate data to be acquired by adjusting the timing of video and audio acquisition according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0070] The acquisition unit can analyze the boss's movements and speech content in real time when acquiring video and audio, and select an appropriate acquisition method. For example, the acquisition unit can prioritize acquisition of important speech parts depending on what the boss is saying. Furthermore, if the boss is actively moving, the acquisition unit can adjust the camera angle to capture that movement. Furthermore, if the boss's speech is interrupted, the acquisition unit can wait for the next speech before acquiring video and audio. This allows important data to be acquired preferentially by selecting the optimal acquisition method depending on the boss's movements and speech content. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the boss's speech content into a generation AI and have the generation AI identify important speech parts.

[0071] The acquisition unit can improve the quality of the acquired data by analyzing the boss's facial expression and tone of voice when acquiring video and audio. For example, the acquisition unit can capture the moment when the boss's facial expression changes to acquire more natural video. The acquisition unit can also improve the quality of the audio data by capturing changes in the boss's tone of voice. Furthermore, if the boss's facial expression is bright, the acquisition unit can acquire video by emphasizing that expression. In this way, by analyzing the boss's facial expression and tone of voice, more natural and high-quality data can be acquired. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the boss's facial expression data into the generation AI and have the generation AI identify changes in facial expression.

[0072] The acquisition unit can filter the user's environmental sound when acquiring video and audio to acquire clear audio. For example, the acquisition unit can filter noise around the user and clearly acquire only the boss's voice. Furthermore, if the user's environmental sound is loud, the acquisition unit can suppress the sound and emphasize the boss's voice. Furthermore, when the user's environmental sound changes, the acquisition unit can detect the change in real time and adjust the audio data. In this way, clear audio can be acquired by filtering the user's environmental sound. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input environmental sound data to a generation AI and cause the generation AI to filter the environmental sound.

[0073] The acquisition unit can estimate the user's emotions and determine the priority of the video and audio to be acquired based on the estimated user emotions. For example, when the user is excited, the acquisition unit prioritizes acquisition of important utterances and actions. Furthermore, when the user is relaxed, the acquisition unit can also acquire overall video and audio in a balanced manner. Furthermore, when the user is concentrating, the acquisition unit can also prioritize acquisition of specific utterances and actions. Thus, by determining the priority of video and audio according to the user's emotions, important data can be acquired preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI. For example, the acquisition unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0074] When acquiring video and audio, the acquisition unit can prioritize acquiring highly relevant data by taking into account the user's geographical location information. For example, when the user is in the office, the acquisition unit can prioritize acquiring relevant data within the office. Furthermore, when the user is on a business trip, the acquisition unit can prioritize acquiring relevant data for the business trip destination. Furthermore, when the user is at home, the acquisition unit can prioritize acquiring relevant data within the home. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input geographical location information data to the generation AI and cause the generation AI to identify highly relevant data.

[0075] The acquisition unit can analyze the user's social media activities when acquiring the video and audio, and acquire related data. For example, the acquisition unit can acquire data on locations where the user has checked in on social media. The acquisition unit can also analyze the content of the user's social media posts and acquire related data. The acquisition unit can also acquire related data by referring to the activities of the user's friends on social media. In this way, related data can be acquired by analyzing the user's social media activities. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input social media data into a generation AI and cause the generation AI to identify related data.

[0076] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring video and audio. For example, the acquisition unit preferentially uses a video and audio acquisition method that the user has previously preferred. The acquisition unit can also adjust the acquisition method based on the user's past feedback. The acquisition unit can also avoid acquisition methods that the user has previously been dissatisfied with. In this way, the acquisition method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input past feedback data into a generation AI and cause the generation AI to customize the acquisition method.

[0077] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. If the user is excited, the analysis unit can also provide visually stimulating analysis results. By adjusting the way the analysis is presented based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0078] During analysis, the analysis unit can improve conversion accuracy by analyzing the boss's facial features and voice features in detail. The analysis unit can improve conversion accuracy by analyzing, for example, the boss's facial contours and eye shape in detail. The analysis unit can also improve conversion accuracy by analyzing the boss's tone and pitch in detail. The analysis unit can also improve conversion accuracy by analyzing the boss's facial expressions in detail. In this way, conversion accuracy is improved by analyzing the boss's facial features and voice features in detail. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the boss's facial feature data into the generation AI and cause the generation AI to improve conversion accuracy.

[0079] During analysis, the analysis unit can convert the boss's speech into text and use it as analysis data. For example, the analysis unit can convert the boss's speech into text in real time and use it as analysis data. The analysis unit can also convert the boss's speech into text and save it for later reference. The analysis unit can also convert the boss's speech into text and compare it with other data to improve the accuracy of the analysis. In this way, by converting the boss's speech into text, it can be used as analysis data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the boss's speech audio data into a generation AI and have the generation AI perform the text conversion.

[0080] During analysis, the analysis unit analyzes the boss's movements and gestures, enabling a more natural conversion. For example, the analysis unit analyzes the boss's hand movements and converts them into the boss's hand movements. The analysis unit can also analyze the boss's facial expression changes and convert them into the boss's facial expressions. The analysis unit can also analyze the boss's body movements and convert them into the boss's body movements. In this way, by analyzing the boss's movements and gestures, a more natural conversion can be achieved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the boss's movement data into a generation AI and have the generation AI convert the movements.

[0081] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, when the user is excited, the analysis unit prioritizes analysis of important statements and actions. Furthermore, when the user is relaxed, the analysis unit can also perform a balanced overall analysis. Furthermore, when the user is focused, the analysis unit can prioritize analysis of specific statements and actions. Thus, by determining the analysis priority according to the user's emotions, important data can be analyzed preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0082] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the boss's past speech history. For example, the analysis unit refers to the boss's past speech history and analyzes the content of the current speech. The analysis unit can also analyze speech patterns based on the boss's past speech history. The analysis unit can also compare the boss's past speech history and improve the accuracy of the analysis. In this way, by referring to the boss's past speech history, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the boss's past speech data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0083] During analysis, the analysis unit can customize the analysis method based on the supervisor's job description and position. The analysis unit adjusts the analysis method according to, for example, the supervisor's job description. The analysis unit can also adjust the level of analysis detail based on the supervisor's position. The analysis unit can also select the optimal analysis method taking into account the supervisor's job description and position. This enables more appropriate analysis by customizing the analysis method based on the supervisor's job description and position. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the supervisor's job description data into the generation AI and have the generation AI customize the analysis method.

[0084] During analysis, the analysis unit can compare the content of the superior's remarks with other data sources to improve the accuracy of the analysis. For example, the analysis unit analyzes the content of the superior's remarks by comparing them with records of other meetings. The analysis unit can also analyze the content of the superior's remarks by comparing them with related documents. The analysis unit can also improve the accuracy of the analysis by comparing the content of the superior's remarks with other data sources. In this way, the analysis accuracy is improved by comparing the content of the superior's remarks with other data sources. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input other data sources into the generation AI and have the generation AI compare the content of the remarks.

[0085] The conversion unit can estimate the user's emotion and adjust the conversion expression method based on the estimated user emotion. For example, if the user is relaxed, the conversion unit can perform the conversion using a natural expression method. Furthermore, if the user is in a hurry, the conversion unit can perform the conversion using a concise expression method. Furthermore, if the user is excited, the conversion unit can perform the conversion using a visually stimulating expression method. This enables more appropriate conversion by adjusting the conversion expression method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the conversion unit can be performed using, for example, an AI, or without an AI. For example, the conversion unit can input the user's facial expression data into the generation AI and have the generation AI adjust the conversion expression method.

[0086] The conversion unit can adjust the sound quality when converting the characteristics of the boss's voice into the characteristics of the favorite idol's voice during conversion. For example, the conversion unit converts the tone of the boss's voice by optimizing it to the tone of the favorite idol's voice. The conversion unit can also convert the pitch of the boss's voice by optimizing it to the pitch of the favorite idol's voice. The conversion unit can also convert the sound quality of the boss's voice by optimizing it to the sound quality of the favorite idol's voice. In this way, by optimizing and converting the characteristics of the boss's voice to the characteristics of the favorite idol's voice, a more natural voice can be provided. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input data on the characteristics of the boss's voice into a generation AI and have the generation AI adjust the sound quality.

[0087] During conversion, the conversion unit converts the boss's movements and gestures into those of the favorite idol, thereby providing a more natural image. The conversion unit, for example, converts the boss's hand movements into those of the favorite idol. The conversion unit can also convert the boss's facial expressions into those of the favorite idol. The conversion unit can also convert the boss's body movements into those of the favorite idol. In this way, by converting the boss's movements and gestures into those of the favorite idol, a more natural image can be provided. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the boss's movement data into a generation AI and have the generation AI convert the movements.

[0088] The conversion unit can estimate the user's emotions and determine the conversion priority based on the estimated user emotions. For example, when the user is excited, the conversion unit prioritizes conversion of important utterances and actions. Furthermore, when the user is relaxed, the conversion unit can also perform a balanced overall conversion. Furthermore, when the user is focused, the conversion unit can prioritize conversion of specific utterances and actions. Thus, by determining the conversion priority according to the user's emotions, important data can be converted preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the conversion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the conversion unit can input the user's facial expression data into the generation AI and have the generation AI determine the conversion priority.

[0089] During conversion, the conversion unit can select the optimal conversion method by referencing a database of the person's face and voice. For example, the conversion unit can select the optimal face conversion method by referencing a database of the person's voice. The conversion unit can also select the optimal voice conversion method by referencing a database of the person's voice. The conversion unit can also incorporate the most recent face and voice data of the person's voice to improve conversion accuracy. This allows the optimal conversion method to be selected by referencing the database of the person's face and voice. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the face and voice data of the person's voice into a generation AI and have the generation AI select the optimal conversion method.

[0090] The conversion unit can customize the conversion method by reflecting the user's past feedback during conversion. For example, the conversion unit preferentially uses a conversion method that the user has previously preferred. The conversion unit can also adjust the conversion method based on the user's past feedback. The conversion unit can also avoid conversion methods that the user has previously been dissatisfied with. In this way, the conversion method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the conversion unit may be performed using AI, for example, or may be performed without using AI. For example, the conversion unit can input past feedback data into a generation AI and have the generation AI customize the conversion method.

[0091] During conversion, the conversion unit can incorporate the latest video and audio data of the favorite character to improve conversion accuracy. For example, the conversion unit can incorporate the latest video data of the favorite character to improve facial conversion accuracy. The conversion unit can also incorporate the latest audio data of the favorite character to improve voice conversion accuracy. The conversion unit can also incorporate the latest movement data of the favorite character to improve movement conversion accuracy. In this way, by incorporating the latest video and audio data of the favorite character, conversion accuracy is improved. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the latest video and audio data of the favorite character into the generation AI and have the generation AI improve conversion accuracy.

[0092] The providing unit can estimate the user's emotions and adjust the presentation of the video and audio based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide the video and audio in a natural presentation. If the user is in a hurry, the providing unit can provide the video and audio in a concise presentation. If the user is excited, the providing unit can provide the video and audio in a visually stimulating presentation. This allows for more appropriate presentation by adjusting the presentation of the video and audio according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using AI, or without AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the presentation of the video and audio.

[0093] The providing unit can provide optimal video and audio by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide video and audio that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can provide video and audio that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide video and audio that is concise and highly visible. This allows optimal video and audio to be provided by taking into account the user's device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input device information to a generation AI and cause the generation AI to provide optimal video and audio.

[0094] The providing unit can customize the content to be provided by referring to the user's past viewing history when providing the content. The providing unit selects the optimal content to be provided based on, for example, a history of video and audio viewed by the user in the past. The providing unit can also analyze the user's past viewing history and customize the content to suit the user's preferences. The providing unit can also provide related video and audio by referring to content viewed by the user in the past. This makes it possible to customize the content to be provided by referring to the user's past viewing history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input viewing history data to a generation AI and have the generation AI customize the content to be provided.

[0095] The providing unit can optimize the quality of video and audio by taking into account the user's network environment when providing the video and audio. For example, if the user's network speed is slow, the providing unit can provide video and audio at lower quality. Furthermore, if the user's network speed is fast, the providing unit can provide high-quality video and audio. Furthermore, when the user's network environment changes, the providing unit can detect the change in real time and adjust the quality of video and audio. This makes it possible to optimize the quality of video and audio by taking the user's network environment into account. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input network environment data to the generating AI and cause the generating AI to optimize the quality.

[0096] The providing unit can estimate the user's emotions and determine the priority of the video and audio to be provided based on the estimated user emotions. For example, when the user is excited, the providing unit can prioritize providing important utterances and actions. Furthermore, when the user is relaxed, the providing unit can also provide a balanced overall video and audio. Furthermore, when the user is concentrating, the providing unit can prioritize providing specific utterances and actions. Thus, by determining the priority of the video and audio to be provided according to the user's emotions, important data can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to determine the priority of the video and audio to be provided.

[0097] The providing unit can provide highly relevant video and audio by taking into account the user's geographical location information. For example, when the user is in the office, the providing unit can prioritize providing relevant data within the office. Furthermore, when the user is on a business trip, the providing unit can prioritize providing relevant data for the business trip destination. Furthermore, when the user is at home, the providing unit can prioritize providing relevant data within the home. In this way, highly relevant video and audio can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input geographical location information data to the generation AI and cause the generation AI to identify highly relevant video and audio.

[0098] At the time of providing, the providing unit can analyze the user's social media activity and provide related video and audio. For example, the providing unit provides data on locations where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide related video and audio. The providing unit can also provide related video and audio by referring to the activities of the user's friends on social media. In this way, related video and audio can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input social media data to a generation AI and cause the generation AI to identify related video and audio.

[0099] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the content. For example, the providing unit preferentially uses a delivery method that the user has previously preferred. The providing unit can also adjust the delivery method based on the user's past feedback. The providing unit can also avoid delivery methods that the user has previously been dissatisfied with. In this way, the delivery method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input past feedback data into the generation AI and cause the generation AI to customize the delivery method.

[0100] The registration unit can estimate the user's emotions and register the boss's facial and vocal features based on the estimated user emotions. For example, if the user is relaxed, the registration unit can register detailed facial and vocal features. If the user is in a hurry, the registration unit can also register brief facial and vocal features. If the user is excited, the registration unit can also register visually stimulating facial and vocal features. This allows more appropriate data to be registered by registering the boss's facial and vocal features according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the registration unit can be performed using AI, for example, or without AI. For example, the registration unit can input the user's facial expression data into the generation AI and cause the generation AI to register the boss's facial and vocal features.

[0101] During registration, the registration unit can analyze the boss's facial features in detail and register highly accurate data. For example, the registration unit can analyze the boss's facial contours in detail and register highly accurate data. The registration unit can also analyze the boss's eye shape in detail and register highly accurate data. The registration unit can also analyze the boss's facial expression changes in detail and register highly accurate data. In this way, highly accurate data can be registered by analyzing the boss's facial features in detail. Some or all of the above-described processing in the registration unit may be performed using AI, for example, or may be performed without using AI. For example, the registration unit can input the boss's facial feature data into the generation AI and cause the generation AI to register highly accurate data.

[0102] During registration, the registration unit can analyze the characteristics of the boss's voice in detail and register highly accurate data. For example, the registration unit can analyze the tone of the boss's voice in detail and register highly accurate data. The registration unit can also analyze the pitch of the boss's voice in detail and register highly accurate data. The registration unit can also analyze the tone of the boss's voice in detail and register highly accurate data. In this way, by analyzing the characteristics of the boss's voice in detail, highly accurate data can be registered. Some or all of the above-mentioned processing in the registration unit may be performed using AI, for example, or may be performed without using AI. For example, the registration unit can input data on the characteristics of the boss's voice to the generation AI and cause the generation AI to register highly accurate data.

[0103] The registration unit can estimate the user's emotions and determine the priority of data to be registered based on the estimated user emotions. For example, when the user is excited, the registration unit prioritizes registering important facial and vocal features. Furthermore, when the user is relaxed, the registration unit can also register overall facial and vocal features in a balanced manner. Furthermore, when the user is focused, the registration unit can also prioritize registering specific facial and vocal features. Thus, by determining the priority of data to be registered according to the user's emotions, important data can be registered preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the registration unit can be performed using, for example, an AI. For example, the registration unit can input the user's facial expression data into the generation AI and have the generation AI determine the priority of the data to be registered.

[0104] During registration, the registration unit can improve registration accuracy by referring to past video and audio data of the boss. For example, the registration unit refers to past video data of the boss and registers facial features in detail. The registration unit can also refer to past audio data of the boss and register voice features in detail. The registration unit can also improve registration accuracy by referring to past video and audio data of the boss. In this way, by referring to past video and audio data of the boss, registration accuracy is improved. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input past video and audio data of the boss to the generation AI and cause the generation AI to improve registration accuracy.

[0105] The registration unit can customize the registration method based on the supervisor's job description and position at the time of registration. The registration unit adjusts the registration method according to, for example, the supervisor's job description. The registration unit can also adjust the level of registration detail based on the supervisor's position. The registration unit can also select the optimal registration method taking into account the supervisor's job description and position. In this way, by customizing the registration method based on the supervisor's job description and position, more appropriate data can be registered. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the supervisor's job description data into the generation AI and cause the generation AI to customize the registration method. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, analysis unit, conversion unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires video and audio of the ZOOM meeting in real time using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired video and audio. The conversion unit is realized by the specific processing unit 290 of the data processing device 12 and replaces the boss's face and voice with the face and voice of the favorite employee based on the analysis results. The provision unit is realized by the control unit 46A of the smart device 14 and provides the converted video and audio to the employee in real time. The registration unit is realized by the specific processing unit 290 of the data processing device 12 and registers the boss's facial and vocal characteristics in advance. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, analysis unit, conversion unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires video and audio of the ZOOM meeting in real time using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired video and audio. The conversion unit is realized by the specific processing unit 290 of the data processing device 12 and replaces the boss's face and voice with the face and voice of the favorite employee based on the analysis results. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the converted video and audio to the employee in real time. The registration unit is realized by the specific processing unit 290 of the data processing device 12 and registers the boss's facial and vocal characteristics in advance. === Hard Collateral 1-3 === Each of the multiple elements, including the acquisition unit, analysis unit, conversion unit, and provision unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the acquisition unit acquires video and audio of the ZOOM meeting in real time using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired video and audio. The conversion unit is realized by the specific processing unit 290 of the data processing device 12 and replaces the boss's face and voice with the face and voice of the favorite employee based on the analysis results. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and provides the converted video and audio to the employee in real time. The registration unit is realized by the specific processing unit 290 of the data processing device 12 and registers the boss's facial and vocal characteristics in advance. === Hard Collateral 1-4 === Each of the multiple elements, including the acquisition unit, analysis unit, conversion unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires video and audio of the ZOOM meeting in real time using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired video and audio. The conversion unit is realized by the specific processing unit 290 of the data processing device 12 and replaces the boss's face and voice with the face and voice of the favorite employee based on the analysis results. The provision unit is realized by the control unit 46A of the robot 414 and provides the converted video and audio to the employee in real time. The registration unit is realized by the specific processing unit 290 of the data processing device 12 and registers the boss's facial and voice characteristics in advance.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The motivation-boosting system can further include a feedback unit. The feedback unit can collect feedback from employees and use it to improve the system. For example, the feedback unit can collect changes in motivation felt by employees after a meeting and their satisfaction with the conversion of their favorite idol's face and voice. The feedback unit can also collect improvements that employees suggest for the conversion of their favorite idol's face and voice. Furthermore, the feedback unit can analyze the collected feedback and identify areas for improvement in the system. Thus, by including a feedback unit, it is possible to improve the system to reflect employee opinions, which is expected to result in more effective motivation-boosting.

[0108] The motivation enhancement system can further include a notification unit. The notification unit can notify employees of the start and end of meetings. For example, the notification unit can send a reminder to employees when the meeting is about to start. The notification unit can also send an end notification to employees when the meeting has ended. Furthermore, the notification unit can notify employees in real time when important comments or agenda items are raised during the meeting. Thus, by including a notification unit, employees can easily grasp the progress of the meeting, making meetings more efficient.

[0109] The acquisition unit can estimate the user's emotions and customize the method for acquiring video and audio based on the estimated user emotions. For example, if the user is tense, it can prioritize acquiring video and audio that will relax the user. Also, if the user is concentrating, it can adjust the method for acquiring video and audio so as not to disturb the user's concentration. Furthermore, if the user is tired, it can acquire video and audio that will refresh the user. In this way, by customizing the method for acquiring video and audio according to the user's emotions, it is possible to acquire more appropriate data.

[0110] When capturing video and audio, the capture unit can analyze the boss's movements and what they say in real time and select the appropriate capture method. For example, it can prioritize capturing important parts of what the boss is saying depending on what they are saying. It can also adjust the camera angle to capture active movements if the boss is moving around a lot. Furthermore, if the boss stops speaking, it can wait for the next comment before capturing video and audio. This allows it to prioritize the capture of important data by selecting the optimal capture method depending on the boss's movements and what they say.

[0111] The acquisition unit can improve the quality of the acquired data by analyzing the boss's facial expression and tone of voice when acquiring video and audio. For example, it can capture the moment when the boss's facial expression changes to acquire more natural video. It can also improve the quality of the audio data by capturing changes in the tone of the boss's voice. Furthermore, if the boss has a bright expression, it can acquire video that emphasizes that expression. In this way, by analyzing the boss's facial expression and tone of voice, it is possible to acquire more natural, high-quality data.

[0112] The acquisition unit can estimate the user's emotions and determine the priority of the video and audio to be acquired based on the estimated user emotions. For example, if the user is excited, important statements and actions can be acquired with priority. Also, if the user is relaxed, it is possible to acquire overall video and audio in a balanced manner. Furthermore, if the user is concentrating, it is possible to acquire specific statements and actions with priority. In this way, by determining the priority of video and audio according to the user's emotions, it is possible to acquire important data with priority.

[0113] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results that focus on the main points can be provided. Furthermore, if the user is excited, visually stimulating analysis results can be provided. In this way, by adjusting the way the analysis is presented according to the user's emotions, more appropriate analysis results can be provided.

[0114] During analysis, the analysis unit can convert the boss's comments into text and use it as analysis data. For example, the boss's comments can be converted into text in real time and used as analysis data. The boss's comments can also be converted into text and saved for later reference. Furthermore, the boss's comments can be converted into text and compared with other data to improve the accuracy of the analysis. In this way, the boss's comments can be converted into text and used as analysis data.

[0115] The conversion unit can estimate the user's emotion and adjust the conversion expression method based on the estimated user's emotion. For example, if the user is relaxed, the conversion can be performed using a natural expression method. If the user is in a hurry, the conversion can be performed using a concise expression method. Furthermore, if the user is excited, the conversion can be performed using a visually stimulating expression method. In this way, by adjusting the conversion expression method according to the user's emotion, more appropriate conversion can be achieved.

[0116] The providing unit can estimate the user's emotion and adjust the presentation method of the video and audio to be provided based on the estimated user's emotion. For example, if the user is relaxed, the video and audio can be provided in a natural presentation method. If the user is in a hurry, the video and audio can be provided in a concise presentation method. Furthermore, if the user is excited, the video and audio can be provided in a visually stimulating presentation method. In this way, by adjusting the presentation method of the video and audio to be provided according to the user's emotion, more appropriate presentation is possible.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The acquisition unit acquires video and audio in real time. For example, the acquisition unit acquires video and audio of a ZOOM meeting in real time. The acquisition unit can also simultaneously acquire data for identifying the boss's face and voice. For example, by registering the boss's facial features and voice features in advance, it becomes easier to identify the boss in real time. Step 2: The analysis unit analyzes the video and audio acquired by the acquisition unit. For example, the analysis unit analyzes the facial features of the boss and the voice features of the boss. Step 3: The conversion unit replaces the boss's face and voice with the face and voice of the favorite person based on the results of the analysis by the analysis unit. For example, the conversion unit converts the boss's facial contours, eye shape, voice tone, and pitch to those of the favorite person. Step 4: The providing unit provides the employee with the video and audio converted by the converting unit. For example, the providing unit provides the employee with the video and audio in real time.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0126] The 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.

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

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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. an acquisition unit that acquires video and audio in real time; an analysis unit that analyzes the video and audio acquired by the acquisition unit; a conversion unit that replaces the face and voice of the boss with the face and voice of a specific favorite person based on the analysis result by the analysis unit; a providing unit that provides the video and audio converted by the converting unit to employees; Equipped with A system characterized by:

2. Equipped with a registration section that allows users to register the facial and voice characteristics of their superiors in advance 2. The system of claim 1.

3. The acquisition unit Estimate the user's emotions and adjust the timing of video and audio capture based on the estimated user emotions.

2. The system of claim 1.

4. The acquisition unit When capturing video and audio, the supervisor's actions and comments are analyzed in real time to select the appropriate capture method.

2. The system of claim 1.

5. The acquisition unit Analyze the supervisor's facial expressions and tone of voice during video and audio capture to improve the quality of the captured data 2. The system of claim 1.

6. The acquisition unit When capturing video and audio, the system filters out the user's ambient sounds to obtain clear audio.

2. The system of claim 1.

7. The acquisition unit Estimate the user's emotions and determine the priority of the video and audio to be acquired based on the estimated user emotions.

2. The system of claim 1.

8. The acquisition unit When capturing video and audio, the system prioritizes the acquisition of relevant data by taking into account the user's geographic location.

2. The system of claim 1.

9. The acquisition unit When capturing video and audio, analyze the user's social media activity and obtain relevant data.

2. The system of claim 1.

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