system

Abstract

An object of a system according to an embodiment is to appropriately convert an instruction through a communication tool in order to maximize the level of understanding and morale of a staff member.SOLUTION: A system includes a face color conversion unit, a voice color conversion unit, and a content conversion unit. The face color conversion unit converts a face color when issuing an instruction through a communication tool such as ZOOM or e-mail. The timbre conversion unit converts timbre. The content conversion unit converts the content.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately translate instructions given through communication tools to maximize staff understanding and morale, leaving room for improvement.

[0005] The system according to the embodiment aims to appropriately translate instructions given through communication tools in order to maximize staff understanding and morale. [Means for solving the problem]

[0006] The system according to the embodiment includes a facial color conversion unit, a voice tone conversion unit, and a content conversion unit. The facial color conversion unit converts the facial color when issuing instructions via a communication tool such as ZOOM or email. The voice tone conversion unit converts the voice tone. The content conversion unit converts the content. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately translate instructions given through communication tools to maximize staff understanding and morale. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The communication support system according to an embodiment of the present invention utilizes generative AI to automatically change facial expressions, tone of voice, and content when issuing instructions using communication tools such as ZOOM or email. This maximizes staff understanding and morale, enabling optimal communication tailored to each individual staff member. It also reduces communication errors with staff and streamlines team management.

[0029] A communication support system according to an embodiment includes a generation AI, a facial color conversion unit, a voice tone conversion unit, and a content conversion unit. The generation AI automatically changes the facial color, voice tone, and content when issuing instructions via a communication tool such as ZOOM or email. For example, the generation AI adjusts the content of instructions using a text generation AI (e.g., LLM). The generation AI can also convert the facial color and voice tone using a multimodal generation AI. The generation AI can also analyze past data to select the optimal instruction method. For example, the facial color conversion unit brightens the speaker's facial color, making the staff more likely to understand the instructions. For example, the facial color conversion unit softens the speaker's facial color, making the staff more likely to accept the instructions. For example, the voice tone conversion unit softens the speaker's voice, making the staff more likely to understand the instructions. For example, the voice tone conversion unit brightens the speaker's voice, making the staff more likely to accept the instructions. The content conversion unit, for example, improves staff understanding and morale by having the generation AI adjust the content of instructions. The content conversion unit also improves staff understanding by having the generation AI simplify the content of instructions. This allows the communication support system according to the embodiment to maximize staff understanding and morale and reduce communication errors. For example, the generation AI analyzes the content of instructions and automatically corrects ambiguous expressions or parts that may lead to misunderstandings. The generation AI also analyzes feedback from staff in real time and readjusts the content of instructions as necessary. This facilitates communication within the team and improves work efficiency.

[0030] The voice conversion unit can analyze the speaker's past communication history and learn and apply the most effective voice tone patterns. For example, the generation AI of the voice conversion unit analyzes the speaker's past meeting and email data to learn effective voice tone patterns. For example, it reproduces a similar voice tone based on data from past successful presentations. The voice conversion unit also improves the effectiveness of communication by analyzing the speaker's past communication history and learning and applying the most effective voice tone patterns. For example, a similar effect can be achieved by reproducing a voice tone that the speaker has received high praise for in the past. This improves the effectiveness of communication by applying the optimal voice tone based on past data.

[0031] The content conversion unit can monitor the speaker's health condition and adjust the content based on that. For example, the content conversion unit collects data from a wearable device so that the generation AI can monitor the speaker's health condition. For example, it can analyze heart rate and stress level and make the face appear healthy if the level of fatigue is high. The content conversion unit also improves the quality of communication by having the generation AI monitor the speaker's health condition and adjust the content based on that. For example, if the speaker is tired, the instructions can be simplified to make them easier to understand. This improves the quality of communication by adjusting the content according to the speaker's health condition.

[0032] Generative AI can adapt to different cultures and languages, changing facial expressions and tone of voice to suit the cultural background. For example, generative AI can learn communication styles from different cultures and change facial expressions and tone of voice to suit the cultural background. For example, if a more subdued tone of voice is preferred in Asia, that style will be applied. Generative AI also uses multilingual translation technology to adapt to different languages. For example, it can automatically translate instructions from English to Spanish. This allows it to adapt to different cultures and languages, facilitating smoother global communication.

[0033] Generative AI can automatically adjust the speaker's background and clothing during a video conference to give a more professional impression. Generative AI can automatically adjust the speaker's background during a video conference to give a more professional impression. For example, it can automatically set an office-style background. Generative AI can also automatically adjust the speaker's clothing to give a more professional impression. For example, it can change casual clothing to formal clothing. This gives a professional impression during a video conference, improving credibility.

[0034] Generative AI can analyze the learning style of staff and select the optimal instruction method based on that. Generative AI can, for example, analyze the learning style of staff and select the optimal instruction method based on that. For example, it can provide instructions using diagrams and charts to visual learners. Generative AI can also provide instructions using voice to auditory learners. Furthermore, generative AI can provide instructions using tasks that require hands-on experience to tactile learners. This improves comprehension by selecting an instruction method that suits the staff's learning style.

[0035] The generation AI can analyze the staff's past performance data and customize the instructions based on their individual growth curves. The generation AI can, for example, analyze the staff's past performance data and customize the instructions based on their individual growth curves. For example, the generation AI can assign challenging tasks to staff who grow quickly. The generation AI can also improve comprehension by giving basic tasks to staff who grow slowly. In this way, customizing the instructions based on the staff's growth curves improves comprehension and morale.

[0036] Generative AI can take into account the personal interests and hobbies of staff members and provide instructions using examples and metaphors related to those interests. For example, generative AI can analyze the personal interests and hobbies of staff members and provide instructions using examples and metaphors related to those interests. For example, for staff members who like sports, it can use sports-related examples. Generative AI can also improve comprehension and morale by customizing instructions based on staff members' hobbies. For example, for staff members who like music, it can use music-related metaphors. This improves comprehension and morale by providing instructions based on staff members' interests and hobbies.

[0037] Generative AI can optimize staff working hours and break times and issue instructions at the most effective timing. For example, generative AI can analyze staff working hours and break times and issue instructions at the most effective timing. For example, it can give important instructions when staff are refreshed after a break. Generative AI can also improve comprehension and morale by optimizing staff working hours and break times. For example, it can make instructions easier to understand by giving concise instructions after working long hours. In this way, optimizing working hours and break times improves comprehension and morale.

[0038] Generative AI can analyze data on past communication errors, learn error patterns, and take preventative measures. Generative AI, for example, analyzes data on past communication errors and learns error patterns. For example, if a particular expression is likely to lead to misunderstanding, it can adjust the instructions to avoid that expression. Generative AI can also reduce communication errors by learning error patterns and taking preventative measures. For example, based on past error patterns, it can adjust the instructions to prevent similar errors from occurring. In this way, communication errors can be reduced by learning past error patterns and taking preventative measures.

[0039] The generative AI can evaluate the language skills and comprehension of staff members and adjust the instructions based on that. For example, the generative AI can evaluate the language skills of staff members and adjust the instructions based on that. For example, it can give instructions using simple language to staff members with low language skills. The generative AI can also evaluate the staff members' comprehension and adjust the instructions based on that, thereby reducing communication errors. For example, it can provide detailed explanations to staff members with low comprehension. This reduces communication errors by adjusting the instructions based on the staff members' language skills and comprehension.

[0040] The generative AI can accommodate different languages ​​and dialects and translate instructions to match the staff's native language. For example, the generative AI uses multilingual translation technology to accommodate different languages. For example, it can automatically translate instructions from English to Spanish. The generative AI also reduces communication errors by using dialect-compatible translation technology to accommodate different dialects. For example, by supporting the dialect of a specific region, it can make instructions easier for staff to understand. This reduces communication errors by supporting different languages ​​and dialects.

[0041] Generative AI can automatically generate visual aids to visually complement instructions. For example, generative AI analyzes instructions and automatically generates visual aids. For example, it creates a flowchart to explain a complex process. Generative AI can also reduce communication errors by visually complementing instructions. For example, using diagrams and charts can make instructions easier for staff to understand. In this way, the automatic generation of visual aids visually complements instructions and reduces communication errors.

[0042] The generation AI can analyze each staff member's schedule and task progress, and issue instructions at the optimal time. The generation AI can, for example, analyze each staff member's schedule and issue instructions at the optimal time. For example, sending a reminder before an important task. The generation AI can also analyze task progress and issue instructions at the optimal time, smoothing team management. For example, if a task is behind schedule, issuing instructions early can prevent delays. This allows for smooth team management by timing instructions based on schedules and task progress.

[0043] Generative AI can analyze communication patterns within a team and suggest efficient methods of sharing information. For example, generative AI can analyze communication patterns within a team and suggest efficient methods of sharing information. For example, it can suggest regular meetings for members who frequently lose communication. Generative AI can also smoothen team management by suggesting efficient methods of sharing information. For example, it can suggest dedicated tools for information sharing. This makes team management smoother by suggesting efficient methods of sharing information.

[0044] Generative AI can work with different project management tools to centrally manage task progress. For example, generative AI can work with different project management tools to build a system that centrally manages task progress. For example, it can integrate tools such as Trello and Asana. Generative AI can also smoothen team management by centrally managing task progress. For example, it can integrate data from each tool to make progress clear at a glance. This centralized management of task progress smoothes team management.

[0045] The generation AI can suggest virtual team building activities to promote communication between staff members. The generation AI can suggest virtual team building activities to promote communication between staff members, for example, by suggesting online games or quizzes. The generation AI can also smoothen team management by suggesting virtual team building activities. For example, it can suggest regular online events. In this way, the suggestion of virtual team building activities can smoothen team management.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] Generative AI can analyze each staff member's individual learning style and select the most appropriate instruction method based on that. For example, visual learners can be given instructions using diagrams and charts. Auditory learners can be given instructions using voice. Furthermore, tactile learners can be given instructions using tasks that require hands-on experience. This improves comprehension by selecting an instruction method that suits each staff member's learning style.

[0048] Generative AI can adapt to different cultures and languages, changing facial expressions and tone of voice to suit cultural backgrounds. For example, if a more subdued tone of voice is preferred in Asia, that style can be applied. Generative AI can also use multilingual translation technology to adapt to different languages. For example, it can automatically translate instructions from English to Spanish. This allows it to adapt to different cultures and languages, facilitating global communication.

[0049] Generative AI can automatically adjust the speaker's background and clothing during a video conference to give a more professional impression. For example, it can automatically set an office-style background. Generative AI can also automatically adjust the speaker's clothing to give a more professional impression. For example, it can change casual clothing to formal clothing. This gives a professional impression during a video conference, improving credibility.

[0050] Generative AI can take into account staff members' personal interests and hobbies and provide instructions using examples and metaphors related to those interests. For example, it can analyze staff members' personal interests and hobbies and provide instructions using examples and metaphors related to those interests. For example, sports-related examples can be used for staff members who love sports. Generative AI can also improve comprehension and morale by customizing instructions based on staff members' hobbies. For example, music-related metaphors can be used for staff members who love music. This improves comprehension and morale by providing instructions based on staff members' interests and hobbies.

[0051] Generative AI can optimize staff working hours and break times and issue instructions at the most effective timing. For example, it can analyze staff working hours and break times and issue instructions at the most effective timing. For example, it can issue important instructions when staff are refreshed after a break. Generative AI can also improve comprehension and morale by optimizing staff working hours and break times. For example, it can make instructions easier to understand by giving concise instructions after long shifts. In this way, optimizing working hours and break times improves comprehension and morale.

[0052] Generative AI can analyze data on past communication errors, learn error patterns, and take preventive measures. For example, it can analyze data on past communication errors and learn error patterns. For example, if a particular expression is likely to lead to misunderstanding, it can adjust the instructions to avoid that expression. Generative AI can also reduce communication errors by learning error patterns and taking preventive measures. For example, it can adjust the instructions based on past error patterns to prevent similar errors from occurring. In this way, communication errors can be reduced by learning past error patterns and taking preventive measures.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: In the facial color conversion section, the generation AI changes the speaker's facial color. For example, by brightening the speaker's facial color, the instructions given to staff members are easier to understand. Also, by softening the speaker's facial color, the instructions given to staff members are easier to accept. Step 2: In the voice conversion section, the generation AI changes the speaker's voice tone. For example, softening the speaker's voice tone makes it easier for staff to understand instructions. Brightening the speaker's voice tone also makes it easier for staff to accept instructions. Step 3: In the content conversion section, the generation AI converts the instructions. For example, adjusting the instructions can improve staff understanding and morale. Simplifying the instructions can also improve staff understanding. Furthermore, it automatically corrects ambiguous expressions and parts that could be misunderstood, and analyzes feedback from staff in real time to readjust the instructions as needed.

[0055] (Example 2) The communication support system according to an embodiment of the present invention utilizes generative AI to automatically change facial expressions, tone of voice, and content when issuing instructions using communication tools such as ZOOM or email. This maximizes staff understanding and morale, enabling optimal communication tailored to each individual staff member. It also reduces communication errors with staff and streamlines team management.

[0056] A communication support system according to an embodiment includes a generation AI, a facial color conversion unit, a voice tone conversion unit, and a content conversion unit. The generation AI automatically changes the facial color, voice tone, and content when issuing instructions via a communication tool such as ZOOM or email. For example, the generation AI adjusts the content of instructions using a text generation AI (e.g., LLM). The generation AI can also convert the facial color and voice tone using a multimodal generation AI. The generation AI can also analyze past data to select the optimal instruction method. For example, the facial color conversion unit brightens the speaker's facial color, making the staff more likely to understand the instructions. For example, the facial color conversion unit softens the speaker's facial color, making the staff more likely to accept the instructions. For example, the voice tone conversion unit softens the speaker's voice, making the staff more likely to understand the instructions. For example, the voice tone conversion unit brightens the speaker's voice, making the staff more likely to accept the instructions. The content conversion unit, for example, improves staff understanding and morale by having the generation AI adjust the content of instructions. The content conversion unit also improves staff understanding by having the generation AI simplify the content of instructions. This allows the communication support system according to the embodiment to maximize staff understanding and morale and reduce communication errors. For example, the generation AI analyzes the content of instructions and automatically corrects ambiguous expressions or parts that may lead to misunderstandings. The generation AI also analyzes feedback from staff in real time and readjusts the content of instructions as necessary. This facilitates communication within the team and improves work efficiency.

[0057] The facial color conversion unit can dynamically change the facial color according to the speaker's emotions. For example, the generation AI analyzes the speaker's facial expressions and vocal tone to estimate their emotions in real time. For example, if the speaker is nervous, the generation AI can brighten their facial color and soften their voice to make them feel relaxed. The facial color conversion unit also improves the quality of communication by dynamically changing the facial color according to the speaker's emotions. For example, if the speaker is happy, the generation AI can further brighten their facial color to create a positive atmosphere. This improves the quality of communication by changing the facial color according to the speaker's emotions.

[0058] The voice conversion unit can analyze the speaker's past communication history and learn and apply the most effective voice tone patterns. For example, the generation AI of the voice conversion unit analyzes the speaker's past meeting and email data to learn effective voice tone patterns. For example, it reproduces a similar voice tone based on data from past successful presentations. The voice conversion unit also improves the effectiveness of communication by analyzing the speaker's past communication history and learning and applying the most effective voice tone patterns. For example, a similar effect can be achieved by reproducing a voice tone that the speaker has received high praise for in the past. This improves the effectiveness of communication by applying the optimal voice tone based on past data.

[0059] The content conversion unit can monitor the speaker's health condition and adjust the content based on that. For example, the content conversion unit collects data from a wearable device so that the generation AI can monitor the speaker's health condition. For example, it can analyze heart rate and stress level and make the face appear healthy if the level of fatigue is high. The content conversion unit also improves the quality of communication by having the generation AI monitor the speaker's health condition and adjust the content based on that. For example, if the speaker is tired, the instructions can be simplified to make them easier to understand. This improves the quality of communication by adjusting the content according to the speaker's health condition.

[0060] Generative AI can adapt to different cultures and languages, changing facial expressions and tone of voice to suit the cultural background. For example, generative AI can learn communication styles from different cultures and change facial expressions and tone of voice to suit the cultural background. For example, if a more subdued tone of voice is preferred in Asia, that style will be applied. Generative AI also uses multilingual translation technology to adapt to different languages. For example, it can automatically translate instructions from English to Spanish. This allows it to adapt to different cultures and languages, facilitating smoother global communication.

[0061] Generative AI can automatically adjust the speaker's background and clothing during a video conference to give a more professional impression. Generative AI can automatically adjust the speaker's background during a video conference to give a more professional impression. For example, it can automatically set an office-style background. Generative AI can also automatically adjust the speaker's clothing to give a more professional impression. For example, it can change casual clothing to formal clothing. This gives a professional impression during a video conference, improving credibility.

[0062] Using its emotion estimation function, the generative AI can automatically add background music and sound effects that correspond to the speaker's emotions, improving the quality of communication. For example, the generative AI can estimate the speaker's emotions and automatically add background music that corresponds to those emotions. For example, it can play calm music to create a relaxing atmosphere. The generative AI can also improve the quality of communication by automatically adding sound effects that correspond to the speaker's emotions. For example, if the speaker is excited, it can add energetic sound effects. This improves the quality of communication by adding background music and sound effects that correspond to emotions.

[0063] The generative AI can estimate the real-time emotions of staff and dynamically adjust instructions according to those emotions. For example, the generative AI analyzes the facial expressions and tone of voice of staff to estimate emotions in real time. For example, if a staff member is feeling anxious, the instructions can be made more specific. The generative AI can also dynamically adjust instructions according to the staff member's emotions, improving comprehension and morale. For example, if a staff member is relaxed, the instructions can be made simpler. This improves comprehension and morale by adjusting instructions according to the staff member's emotions.

[0064] Generative AI can analyze the learning style of staff and select the optimal instruction method based on that. Generative AI can, for example, analyze the learning style of staff and select the optimal instruction method based on that. For example, it can provide instructions using diagrams and charts to visual learners. Generative AI can also provide instructions using voice to auditory learners. Furthermore, generative AI can provide instructions using tasks that require hands-on experience to tactile learners. This improves comprehension by selecting an instruction method that suits the staff's learning style.

[0065] The generation AI can analyze the staff's past performance data and customize the instructions based on their individual growth curves. The generation AI can, for example, analyze the staff's past performance data and customize the instructions based on their individual growth curves. For example, the generation AI can assign challenging tasks to staff who grow quickly. The generation AI can also improve comprehension by giving basic tasks to staff who grow slowly. In this way, customizing the instructions based on the staff's growth curves improves comprehension and morale.

[0066] Generative AI can take into account the personal interests and hobbies of staff members and provide instructions using examples and metaphors related to those interests. For example, generative AI can analyze the personal interests and hobbies of staff members and provide instructions using examples and metaphors related to those interests. For example, for staff members who like sports, it can use sports-related examples. Generative AI can also improve comprehension and morale by customizing instructions based on staff members' hobbies. For example, for staff members who like music, it can use music-related metaphors. This improves comprehension and morale by providing instructions based on staff members' interests and hobbies.

[0067] Generative AI can optimize staff working hours and break times and issue instructions at the most effective timing. For example, generative AI can analyze staff working hours and break times and issue instructions at the most effective timing. For example, it can give important instructions when staff are refreshed after a break. Generative AI can also improve comprehension and morale by optimizing staff working hours and break times. For example, it can make instructions easier to understand by giving concise instructions after working long hours. In this way, optimizing working hours and break times improves comprehension and morale.

[0068] Using its emotion estimation function, the generation AI can automatically provide motivational messages and rewards according to the emotions of staff members. For example, the generation AI can estimate the emotions of staff members and automatically provide motivational messages according to those emotions. For example, if a staff member is feeling down, it can send an encouraging message. The generation AI can also improve motivation by automatically providing rewards according to the emotions of staff members. For example, if a staff member demonstrates high performance, it can offer a bonus or extra vacation. This improves understanding and morale by providing motivational messages and rewards according to emotions.

[0069] Generative AI can estimate staff emotions in real time and correct any parts that could lead to misunderstandings in advance. For example, generative AI analyzes staff facial expressions and tone of voice to estimate emotions in real time. For example, if a staff member appears confused, it will correct its instructions to be more specific. Generative AI can also reduce communication errors by correcting parts that could lead to misunderstandings in advance. For example, it can avoid ambiguous expressions and give clear instructions. This reduces communication errors by correcting parts that could lead to misunderstandings in advance.

[0070] Generative AI can analyze data on past communication errors, learn error patterns, and take preventative measures. Generative AI, for example, analyzes data on past communication errors and learns error patterns. For example, if a particular expression is likely to lead to misunderstanding, it can adjust the instructions to avoid that expression. Generative AI can also reduce communication errors by learning error patterns and taking preventative measures. For example, based on past error patterns, it can adjust the instructions to prevent similar errors from occurring. In this way, communication errors can be reduced by learning past error patterns and taking preventative measures.

[0071] The generative AI can evaluate the language skills and comprehension of staff members and adjust the instructions based on that. For example, the generative AI can evaluate the language skills of staff members and adjust the instructions based on that. For example, it can give instructions using simple language to staff members with low language skills. The generative AI can also evaluate the staff members' comprehension and adjust the instructions based on that, thereby reducing communication errors. For example, it can provide detailed explanations to staff members with low comprehension. This reduces communication errors by adjusting the instructions based on the staff members' language skills and comprehension.

[0072] The generative AI can accommodate different languages ​​and dialects and translate instructions to match the staff's native language. For example, the generative AI uses multilingual translation technology to accommodate different languages. For example, it can automatically translate instructions from English to Spanish. The generative AI also reduces communication errors by using dialect-compatible translation technology to accommodate different dialects. For example, by supporting the dialect of a specific region, it can make instructions easier for staff to understand. This reduces communication errors by supporting different languages ​​and dialects.

[0073] Generative AI can automatically generate visual aids to visually complement instructions. For example, generative AI analyzes instructions and automatically generates visual aids. For example, it creates a flowchart to explain a complex process. Generative AI can also reduce communication errors by visually complementing instructions. For example, using diagrams and charts can make instructions easier for staff to understand. In this way, the automatic generation of visual aids visually complements instructions and reduces communication errors.

[0074] Using its emotion estimation function, the generative AI can provide real-time feedback according to the staff's emotions, instantly correcting communication errors. For example, the generative AI can estimate the staff's emotions and provide real-time feedback according to those emotions. For example, if the staff member is confused, it can provide additional explanation. The generative AI can also improve the quality of communication by instantly correcting communication errors. For example, if the staff member misunderstands something, it can revise the instructions and explain them again. In this way, communication errors are instantly corrected by providing feedback according to emotions.

[0075] Generative AI can estimate staff emotions in real time and suggest actions to boost the morale of the entire team. For example, generative AI analyzes staff facial expressions and voice tones to estimate emotions in real time. For example, if the entire team is tired, it can suggest a break to refresh. Generative AI can also smoothen team management by suggesting actions to boost the morale of the entire team. For example, if the entire team is losing motivation, it can suggest activities to improve motivation. This makes team management smoother by suggesting actions to boost the morale of the entire team.

[0076] The generation AI can analyze each staff member's schedule and task progress, and issue instructions at the optimal time. The generation AI can, for example, analyze each staff member's schedule and issue instructions at the optimal time. For example, sending a reminder before an important task. The generation AI can also analyze task progress and issue instructions at the optimal time, smoothing team management. For example, if a task is behind schedule, issuing instructions early can prevent delays. This allows for smooth team management by timing instructions based on schedules and task progress.

[0077] Generative AI can analyze communication patterns within a team and suggest efficient methods of sharing information. For example, generative AI can analyze communication patterns within a team and suggest efficient methods of sharing information. For example, it can suggest regular meetings for members who frequently lose communication. Generative AI can also smoothen team management by suggesting efficient methods of sharing information. For example, it can suggest dedicated tools for information sharing. This makes team management smoother by suggesting efficient methods of sharing information.

[0078] Generative AI can work with different project management tools to centrally manage task progress. For example, generative AI can work with different project management tools to build a system that centrally manages task progress. For example, it can integrate tools such as Trello and Asana. Generative AI can also smoothen team management by centrally managing task progress. For example, it can integrate data from each tool to make progress clear at a glance. This centralized management of task progress smoothes team management.

[0079] The generation AI can suggest virtual team building activities to promote communication between staff members. The generation AI can suggest virtual team building activities to promote communication between staff members, for example, by suggesting online games or quizzes. The generation AI can also smoothen team management by suggesting virtual team building activities. For example, it can suggest regular online events. In this way, the suggestion of virtual team building activities can smoothen team management.

[0080] The generative AI can use its emotion estimation function to monitor the emotional state of the entire team and automatically take actions to boost morale as needed. For example, the generative AI can monitor the emotional state of the entire team and automatically take actions to boost morale. For example, if the entire team is tired, it can suggest a break to refresh. The generative AI can also monitor the emotional state of the entire team and automatically take actions to boost morale as needed, thereby smoothing team management. For example, if the entire team is losing motivation, it can suggest activities to improve motivation. This allows the generative AI to monitor the emotional state of the entire team and automatically take actions to boost morale, thereby smoothing team management.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] Generative AI can analyze each staff member's individual learning style and select the most appropriate instruction method based on that. For example, visual learners can be given instructions using diagrams and charts. Auditory learners can be given instructions using voice. Furthermore, tactile learners can be given instructions using tasks that require hands-on experience. This improves comprehension by selecting an instruction method that suits each staff member's learning style.

[0083] Generative AI can adapt to different cultures and languages, changing facial expressions and tone of voice to suit cultural backgrounds. For example, if a more subdued tone of voice is preferred in Asia, that style can be applied. Generative AI can also use multilingual translation technology to adapt to different languages. For example, it can automatically translate instructions from English to Spanish. This allows it to adapt to different cultures and languages, facilitating global communication.

[0084] Generative AI can automatically adjust the speaker's background and clothing during a video conference to give a more professional impression. For example, it can automatically set an office-style background. Generative AI can also automatically adjust the speaker's clothing to give a more professional impression. For example, it can change casual clothing to formal clothing. This gives a professional impression during a video conference, improving credibility.

[0085] The generative AI can estimate the real-time emotions of staff and dynamically adjust instructions according to those emotions. For example, it can analyze the facial expressions and tone of voice of staff to estimate emotions in real time. For example, if a staff member is feeling anxious, it can make the instructions more specific. The generative AI can also improve comprehension and morale by dynamically adjusting instructions according to the staff's emotions. For example, if a staff member is relaxed, it can make the instructions simpler. This improves comprehension and morale by adjusting instructions according to the staff's emotions.

[0086] Generative AI can take into account staff members' personal interests and hobbies and provide instructions using examples and metaphors related to those interests. For example, it can analyze staff members' personal interests and hobbies and provide instructions using examples and metaphors related to those interests. For example, sports-related examples can be used for staff members who love sports. Generative AI can also improve comprehension and morale by customizing instructions based on staff members' hobbies. For example, music-related metaphors can be used for staff members who love music. This improves comprehension and morale by providing instructions based on staff members' interests and hobbies.

[0087] Using its emotion estimation function, the generative AI can improve the quality of communication by automatically adding background music and sound effects that correspond to the speaker's emotions. For example, it can estimate the speaker's emotions and automatically add background music that corresponds to those emotions. For example, it can play calm music to create a relaxing atmosphere. The generative AI can also improve the quality of communication by automatically adding sound effects that correspond to the speaker's emotions. For example, if the speaker is excited, it can add energetic sound effects. This improves the quality of communication by adding background music and sound effects that correspond to emotions.

[0088] Generative AI can optimize staff working hours and break times and issue instructions at the most effective timing. For example, it can analyze staff working hours and break times and issue instructions at the most effective timing. For example, it can issue important instructions when staff are refreshed after a break. Generative AI can also improve comprehension and morale by optimizing staff working hours and break times. For example, it can make instructions easier to understand by giving concise instructions after long shifts. In this way, optimizing working hours and break times improves comprehension and morale.

[0089] Using its emotion estimation function, the generative AI can automatically provide motivational messages and rewards according to staff emotions. For example, it can estimate staff emotions and automatically provide motivational messages according to those emotions. For example, if a staff member is feeling down, it can send an encouraging message. The generative AI can also improve motivation by automatically providing rewards according to staff emotions. For example, if a staff member demonstrates high performance, it can offer a bonus or extra vacation. This improves understanding and morale by providing motivational messages and rewards according to emotions.

[0090] Generative AI can analyze data on past communication errors, learn error patterns, and take preventive measures. For example, it can analyze data on past communication errors and learn error patterns. For example, if a particular expression is likely to lead to misunderstanding, it can adjust the instructions to avoid that expression. Generative AI can also reduce communication errors by learning error patterns and taking preventive measures. For example, it can adjust the instructions based on past error patterns to prevent similar errors from occurring. In this way, communication errors can be reduced by learning past error patterns and taking preventive measures.

[0091] Using its emotion estimation function, the generative AI can provide real-time feedback based on the staff's emotions, instantly correcting communication errors. For example, it can estimate the staff's emotions and provide real-time feedback based on those emotions. For example, if the staff member is confused, it can provide additional explanations. The generative AI can also improve the quality of communication by instantly correcting communication errors. For example, if the staff member misunderstands something, it can revise the instructions and explain them again. In this way, communication errors are instantly corrected by providing feedback based on emotions.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: In the facial color conversion section, the generation AI changes the speaker's facial color. For example, by brightening the speaker's facial color, the instructions given to staff members are easier to understand. Also, by softening the speaker's facial color, the instructions given to staff members are easier to accept. Step 2: In the voice conversion section, the generation AI changes the speaker's voice tone. For example, softening the speaker's voice tone makes it easier for staff to understand instructions. Brightening the speaker's voice tone also makes it easier for staff to accept instructions. Step 3: In the content conversion section, the generation AI converts the instructions. For example, adjusting the instructions can improve staff understanding and morale. Simplifying the instructions can also improve staff understanding. Furthermore, it automatically corrects ambiguous expressions and parts that could be misunderstood, and analyzes feedback from staff in real time to readjust the instructions as needed.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 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. Equipped with generative AI, The generated AI is A facial color conversion unit that changes the facial color when giving instructions via communication tools such as ZOOM or email, a voice conversion unit that converts voice tone; A content conversion unit that converts the content A system characterized by:

2. The complexion conversion unit Dynamically change facial color according to the speaker's emotions 2. The system of claim 1.

3. The voice conversion unit Analyzes the speaker's past communication history and learns and applies the most effective tone of voice patterns 2. The system of claim 1.

4. The content conversion unit Monitor the speaker's health and adjust the content accordingly 2. The system of claim 1.

5. The generated AI is Improve the quality of communication by automatically adding background music and sound effects according to the speaker's emotions 2. The system of claim 1.

6. The generated AI is Estimate real-time emotions of staff and dynamically adjust instructions accordingly 2. The system of claim 1.

7. The generated AI is Estimate staff sentiment in real time and proactively correct any misleading content 2. The system of claim 1.

8. The generated AI is Estimate staff sentiment in real time and suggest actions to boost morale across the team 2. The system of claim 1.

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