system

Abstract

The system according to the embodiment aims to create and output cue cards online, thereby achieving a paperless system. [Solution] A system according to an embodiment includes a collection unit, a generation unit, an output unit, and an adjustment unit. The collection unit collects conversational flow from the performers' microphones. The generation unit generates cue cards based on the conversational flow collected by the collection unit. The output unit outputs the cue cards generated by the generation unit to a monitor or in-ear monitor. The adjustment unit allows the director to fine-tune the cue cards generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the large amount of paper used and wasted due to handwritten cue cards has become a social issue, and there is room for improvement.

[0005] The system according to the embodiment aims to create and output cue cards online, thereby achieving a paperless system. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, an output unit, and an adjustment unit. The collection unit collects the flow of conversation from the performers' microphones. The generation unit generates cue cards based on the flow of conversation collected by the collection unit. The output unit outputs the cue cards generated by the generation unit to a monitor or in-ear monitor. The adjustment unit allows the director to fine-tune the cue cards generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows for the creation and output of cue cards online, making it possible to go paperless. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A cue card generation system according to an embodiment of the present invention is a paperless system that provides online cue cards to performers during recording. This cue card generation system collects the flow of conversation from the performers' microphones in real time, and a generation AI generates cue cards that are then output to a monitor or the performers' personal in-ear monitors. The director (assistant) only needs to make minor adjustments, reducing manpower and manpower required for smooth and speedy variety show recording. For example, a cue card generation system collects the flow of conversation from the performers' microphones in real time. It then analyzes the content, tone, and context of the conversation in detail to identify the next lines and instructions required. For example, the generation AI automatically generates the next lines the performers should speak and instructions for the progression. The cue cards generated by the generation AI are then output to a monitor or the performers' personal in-ear monitors. This allows the performers to check the next lines and instructions in real time. For example, the performers' next lines are displayed on a monitor or conveyed to their ears via in-ear monitors. Furthermore, the director (assistant) only needs to make minor adjustments to the cue cards generated by the AI ​​generation system, reducing the number of manpower and manpower required. For example, the director no longer has to manually create cue cards; instead, they simply need to review and revise the cue cards automatically generated by the AI ​​generation system. This system enables smooth and speedy variety show recording. Performers can check their next lines and instructions in real time, which makes recording progress more smoothly and is expected to shorten recording times. Furthermore, going paperless reduces the amount of paper used and discarded, contributing to a lighter environmental impact. This allows the cue card generation system to go paperless by providing online cue cards to convey lines to performers during recording.

[0029] A cue card generation system according to an embodiment includes a collection unit, a generation unit, an output unit, and an adjustment unit. The collection unit collects a conversation flow from a performer's microphone. The conversation flow obtained from the performer's microphone includes, for example, the content, tone, and context of the performer's speech, but is not limited to these examples. The collection unit, for example, collects the conversation flow from the performer's microphone in real time. The collection unit can also analyze the content of the conversation in detail and identify the next lines and instructions. For example, the collection unit analyzes the tone and context of the conversation and identifies the next lines to be spoken. The generation unit generates a cue card based on the conversation flow collected by the collection unit. The generation unit, for example, automatically generates the next lines and instructions using a generation AI. The generation unit generates a cue card in response to a prompt from the generation AI saying, "Please generate the next lines to be spoken." For example, the generation unit analyzes the conversation flow and generates the next lines to be spoken using the generation AI. The output unit outputs the cue card generated by the generation unit to a monitor or in-ear monitor. The output unit, for example, displays the generated cue card on a monitor. The output unit can also output the generated cue card to the performer's personal ear monitor. For example, the output unit transmits the generated cue card to the performer's ear through the ear monitor. The adjustment unit allows the director to fine-tune the cue card generated by the generation unit. For example, the adjustment unit allows the director to check the generated cue card and correct it as necessary. The adjustment unit only needs to fine-tune the cue card generated by the generation AI, thereby reducing labor and manpower. For example, the adjustment unit eliminates the director's need to manually create cue cards; instead, the director only needs to check and correct the cue card automatically generated by the generation AI. This allows the cue card generation system according to the embodiment to achieve a paperless system by providing online cue cards that convey lines to performers during recording. Furthermore, this is expected to smooth the progress of recording and shorten recording times. Furthermore, a paperless system reduces the mass use and waste of paper, contributing to a reduction in environmental impact.

[0030] The collection unit can analyze the performer's past conversation history and select an appropriate collection method. For example, the collection unit can analyze the conversation patterns used by the performer in the past and select the optimal collection method. The collection unit can also preferentially collect specific phrases and tones from the performer's past conversation history. Furthermore, the collection unit can customize and optimize the collection method based on the performer's past conversation history. In this way, the optimal collection method can be selected by analyzing the past conversation history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the performer's past conversation data into the generation AI and have the generation AI select the optimal collection method.

[0031] When collecting conversations, the collection unit can filter the conversations based on the actor's current scene or role. For example, if the actor is in a specific scene, the collection unit collects only conversations related to that scene. The collection unit can also filter and collect necessary conversations based on the actor's role. Furthermore, the collection unit can also exclude and collect unnecessary conversations according to the actor's current scene or role. In this way, by filtering the conversations based on the current scene or role, only necessary conversations can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input actor's scene and role data to a generation AI and have the generation AI perform conversation filtering.

[0032] When collecting conversations, the collection unit can prioritize collection of highly relevant conversations based on the geographical location information of the performer. For example, if the performer is in a specific location, the collection unit prioritizes collection of conversations related to that location. The collection unit can also filter and collect highly relevant conversations based on the geographical location information of the performer. Furthermore, if the performer is moving, the collection unit can also collect the most appropriate conversations based on the performer's current location. In this way, highly relevant conversations can be collected preferentially by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the performer's geographical location data to the generation AI and cause the generation AI to collect highly relevant conversations.

[0033] The collection unit can analyze the social media activity of the performer and collect related conversations when collecting conversations. For example, the collection unit can analyze the social media activity of the performer and collect conversations on related topics. The collection unit can also collect related conversations based on content mentioned by the performer on social media. Furthermore, the collection unit can monitor the social media activity of the performer in real time and collect related conversations. In this way, related conversations can be collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the performer's social media data into a generation AI and cause the generation AI to collect related conversations.

[0034] When generating a cue card, the generation unit can adjust the level of detail of the cue card based on the importance of the conversation. For example, in the case of an important conversation, the generation unit generates a detailed cue card. In addition, in the case of a conversation with low importance, the generation unit can also generate a concise cue card. Furthermore, the generation unit can dynamically adjust the level of detail of the cue card according to the importance of the conversation. In this way, by adjusting the level of detail of the cue card according to the importance of the conversation, a cue card with an appropriate amount of information can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input conversation importance data into the generation AI and cause the generation AI to adjust the level of detail of the cue card.

[0035] When generating cue cards, the generation unit can apply different generation algorithms depending on the category of the conversation. For example, in the case of a comedy scene, the generation unit applies an algorithm that generates a humorous cue card. In addition, in the case of a drama scene, the generation unit can also apply an algorithm that generates an emotional cue card. Furthermore, in the case of an action scene, the generation unit can also apply an algorithm that generates a tense cue card. In this way, by applying a generation algorithm depending on the category of the conversation, a cue card appropriate for the scene can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input conversation category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0036] When generating cue cards, the generation unit can determine the priority of the cue cards based on the time of submission of the conversations. For example, in the case of urgent conversations, the generation unit generates the cue cards with priority. In addition, the generation unit can also generate cue cards with lower priority for conversations submitted early. Furthermore, the generation unit can dynamically adjust the order in which cue cards are generated depending on the time of submission of the conversations. In this way, by determining the priority of cue cards depending on the time of submission of the conversations, conversations with high urgency can be generated as cue cards with priority. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the time of submission of conversations into the generation AI and have the generation AI determine the priority of the cue cards.

[0037] When generating cue cards, the generation unit can adjust the order of the cue cards based on the relevance of the conversations. For example, the generation unit prioritizes highly relevant conversations when generating cue cards. The generation unit can also generate cue cards by placing less relevant conversations on hold when generating cue cards. Furthermore, the generation unit can dynamically adjust the order in which the cue cards are generated according to the relevance of the conversations. This allows highly relevant conversations to be generated as cue cards with priority by adjusting the order of the cue cards according to the relevance of the conversations. Some or all of the above-described processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input conversation relevance data into the generation AI and cause the generation AI to adjust the order of the cue cards.

[0038] When outputting cue cards, the output unit can select an appropriate output method by referring to the performer's past cue card usage history. For example, the output unit selects the optimal output method based on the performer's past cue card usage history. The output unit can also select an output method with high visibility from the performer's past cue card usage history. Furthermore, the output unit can analyze the performer's past cue card usage history and select the most efficient output method. In this way, the optimal output method can be selected by referring to the past cue card usage history. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the performer's past cue card usage data into the generation AI and have the generation AI select the optimal output method.

[0039] When outputting a cue card, the output unit can customize the output content of the cue card based on the actor's current scene and role. For example, if an actor is in a specific scene, the output unit outputs a cue card related to that scene. The output unit can also customize and output necessary cue cards based on the actor's role. Furthermore, the output unit can also exclude and output unnecessary cue cards according to the actor's current scene and role. In this way, by customizing the output content based on the current scene and role, only necessary cue cards can be output. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input actor scene and role data into a generation AI and have the generation AI customize the output content of the cue card.

[0040] When outputting cue cards, the output unit can select the optimal output method by taking into account the performer's geographical location information. For example, if the performer is in a specific location, the output unit prioritizes output of cue cards related to that location. The output unit can also filter and output highly relevant cue cards based on the performer's geographical location information. Furthermore, if the performer is traveling, the output unit can also output the optimal cue card based on the performer's current location. In this way, by taking into account the geographical location information, highly relevant cue cards can be output preferentially. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the performer's geographical location data to the generation AI and cause the generation AI to select the optimal output method.

[0041] When outputting a cue card, the output unit can analyze the performer's social media activity and adjust the output content. For example, the output unit analyzes the performer's social media activity and outputs cue cards on related topics. The output unit can also output related cue cards based on content mentioned by the performer on social media. Furthermore, the output unit can monitor the performer's social media activity in real time and output related cue cards. In this way, related cue cards can be output by analyzing social media activity. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the performer's social media data into a generation AI and have the generation AI adjust the output content.

[0042] When adjusting the cue cards, the adjustment unit can select an appropriate adjustment method by referring to the director's past adjustment history. The adjustment unit, for example, selects the optimal adjustment method based on adjustments made by the director in the past. The adjustment unit can also select an adjustment method appropriate for a specific scene from the director's past adjustment history. Furthermore, the adjustment unit can analyze the director's past adjustment history and select the most efficient adjustment method. In this way, the optimal adjustment method can be selected by referring to the past adjustment history. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the director's past adjustment data into the generation AI and have the generation AI select the optimal adjustment method.

[0043] When adjusting the cue card, the adjustment unit can customize the adjustment content of the cue card based on the progress of the recording. For example, if the recording is progressing smoothly, the adjustment unit can provide a detailed cue card. Also, if the recording is delayed, the adjustment unit can provide a concise cue card. Furthermore, the adjustment unit can dynamically adjust the content of the cue card according to the progress of the recording. This improves the efficiency of the recording by customizing the content of the cue card according to the progress of the recording. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input recording progress data into the generation AI and cause the generation AI to customize the adjustment content of the cue card.

[0044] When adjusting the cue card, the adjustment unit can select an appropriate adjustment method by taking into account the director's geographical location information. For example, if the director is in a specific location, the adjustment unit selects an adjustment method related to that location. The adjustment unit can also filter and select the optimal adjustment method based on the director's geographical location information. Furthermore, if the director is moving, the adjustment unit can also select the optimal adjustment method based on the director's current location. In this way, the optimal adjustment method can be selected by taking the geographical location information into consideration. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the director's geographical location data into the generation AI and cause the generation AI to select the optimal adjustment method.

[0045] When adjusting a cue card, the adjustment unit can analyze the director's social media activity and suggest adjustments. For example, the adjustment unit can analyze the director's social media activity and suggest adjustments for related topics. The adjustment unit can also suggest related adjustments based on content mentioned by the director on social media. Furthermore, the adjustment unit can monitor the director's social media activity in real time and suggest related adjustments. In this way, related adjustments can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the director's social media data into a generation AI and have the generation AI execute the suggested adjustments.

[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] The generation unit can analyze the performer's past performance data and select the optimal cue card generation method. For example, it can generate cue cards to be used in similar situations based on data from the performer's past successful performances. It can also analyze data from the performer's past unsuccessful performances and generate cue cards to help the performer avoid similar failures. Furthermore, it can customize and optimize the content and format of cue cards based on the performer's past performance data. This makes it possible to generate more effective cue cards by utilizing past performance data.

[0048] The output unit can track the performer's line of sight and adjust the display position of the cue card according to the direction of the gaze. For example, if the performer is looking to the left, the cue card can be displayed on the left monitor. If the performer is looking to the right, the cue card can also be displayed on the right monitor. Furthermore, if the performer is looking to the center, the cue card can also be displayed on the center monitor. In this way, the display position of the cue card can be adjusted according to the performer's line of sight, making it easier for the performer to see the cue card.

[0049] The adjustment unit can dynamically change the content of the cue card based on the progress of the recording. For example, if the recording is progressing faster than scheduled, a detailed cue card can be provided. Alternatively, if the recording is behind schedule, a brief cue card can be provided. Furthermore, the content of the cue card can be adjusted in real time according to the progress of the recording. This allows the efficiency of the recording to be improved by dynamically changing the content of the cue card according to the progress of the recording.

[0050] The collection unit can adjust the conversation collection method based on the geographic location information of the speaker. For example, if the speaker is in a specific location, conversations related to that location can be collected preferentially. Also, if the speaker is moving, the optimal conversations can be collected based on the speaker's current location. Furthermore, the collection unit can filter and collect highly relevant conversations based on the speaker's geographic location information. In this way, highly relevant conversations can be collected preferentially by taking geographic location information into consideration.

[0051] When generating cue cards, the generation unit can apply different generation algorithms depending on the category of the conversation. For example, for a comedy scene, an algorithm that generates a humorous cue card can be applied. For a drama scene, an algorithm that generates an emotional cue card can be applied. Furthermore, for an action scene, an algorithm that generates a tense cue card can be applied. In this way, by applying a generation algorithm depending on the category of the conversation, cue cards appropriate for the scene can be generated.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The collection unit collects the conversation flow from the performer's microphone. The conversation flow obtained from the performer's microphone includes, for example, the performer's speech content, tone, and context. The collection unit collects the conversation flow in real time and analyzes the conversation content in detail to identify the next lines and instructions that are needed. Step 2: The generator generates a cue card based on the conversation flow collected by the collector. The generator uses a generation AI to automatically generate the next necessary lines and instructions, and generates the cue card based on the prompts. Step 3: The output unit outputs the cue cards generated by the generation unit to a monitor or in-ear monitors. The output unit displays the generated cue cards on a monitor or outputs them to the performers' personal in-ear monitors. Step 4: In the Adjustment Department, the director fine-tunes the cue cards generated by the Generation Department. In the Adjustment Department, the director checks the generated cue cards and makes corrections as necessary, thereby reducing the number of manpower and manpower required.

[0054] (Example 2) A cue card generation system according to an embodiment of the present invention is a paperless system that provides online cue cards to performers during recording. This cue card generation system collects the flow of conversation from the performers' microphones in real time, and a generation AI generates cue cards that are then output to a monitor or the performers' personal in-ear monitors. The director (assistant) only needs to make minor adjustments, reducing manpower and manpower required for smooth and speedy variety show recording. For example, a cue card generation system collects the flow of conversation from the performers' microphones in real time. It then analyzes the content, tone, and context of the conversation in detail to identify the next lines and instructions required. For example, the generation AI automatically generates the next lines the performers should speak and instructions for the progression. The cue cards generated by the generation AI are then output to a monitor or the performers' personal in-ear monitors. This allows the performers to check the next lines and instructions in real time. For example, the performers' next lines are displayed on a monitor or conveyed to their ears via in-ear monitors. Furthermore, the director (assistant) only needs to make minor adjustments to the cue cards generated by the AI ​​generation system, reducing the number of manpower and manpower required. For example, the director no longer has to manually create cue cards; instead, they simply need to review and revise the cue cards automatically generated by the AI ​​generation system. This system enables smooth and speedy variety show recording. Performers can check their next lines and instructions in real time, which makes recording progress more smoothly and is expected to shorten recording times. Furthermore, going paperless reduces the amount of paper used and discarded, contributing to a lighter environmental impact. This allows the cue card generation system to go paperless by providing online cue cards to convey lines to performers during recording.

[0055] A cue card generation system according to an embodiment includes a collection unit, a generation unit, an output unit, and an adjustment unit. The collection unit collects a conversation flow from a performer's microphone. The conversation flow obtained from the performer's microphone includes, for example, the content, tone, and context of the performer's speech, but is not limited to these examples. The collection unit, for example, collects the conversation flow from the performer's microphone in real time. The collection unit can also analyze the content of the conversation in detail and identify the next lines and instructions. For example, the collection unit analyzes the tone and context of the conversation and identifies the next lines to be spoken. The generation unit generates a cue card based on the conversation flow collected by the collection unit. The generation unit, for example, automatically generates the next lines and instructions using a generation AI. The generation unit generates a cue card in response to a prompt from the generation AI saying, "Please generate the next lines to be spoken." For example, the generation unit analyzes the conversation flow and generates the next lines to be spoken using the generation AI. The output unit outputs the cue card generated by the generation unit to a monitor or in-ear monitor. The output unit, for example, displays the generated cue card on a monitor. The output unit can also output the generated cue card to the performer's personal ear monitor. For example, the output unit transmits the generated cue card to the performer's ear through the ear monitor. The adjustment unit allows the director to fine-tune the cue card generated by the generation unit. For example, the adjustment unit allows the director to check the generated cue card and correct it as necessary. The adjustment unit only needs to fine-tune the cue card generated by the generation AI, thereby reducing labor and manpower. For example, the adjustment unit eliminates the director's need to manually create cue cards; instead, the director only needs to check and correct the cue card automatically generated by the generation AI. This allows the cue card generation system according to the embodiment to achieve a paperless system by providing online cue cards that convey lines to performers during recording. Furthermore, this is expected to smooth the progress of recording and shorten recording times. Furthermore, a paperless system reduces the mass use and waste of paper, contributing to a reduction in environmental impact.

[0056] The collection unit can estimate the emotion of the performer and adjust the timing of conversation collection based on the estimated emotion of the performer. For example, if the performer is nervous, the collection unit can delay the timing of conversation collection until the performer relaxes. Furthermore, if the performer is relaxed, the collection unit can also advance the timing of conversation collection to facilitate smooth recording. Furthermore, if the performer is excited, the collection unit can adjust the timing of conversation collection to collect at an appropriate timing. By adjusting the timing of conversation collection according to the emotion of the performer, conversation can be collected at a more appropriate timing. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI. For example, the collection unit can input the performer's voice data into the generation AI and have the generation AI estimate the actor's emotion.

[0057] The collection unit can analyze the performer's past conversation history and select an appropriate collection method. For example, the collection unit can analyze the conversation patterns used by the performer in the past and select the optimal collection method. The collection unit can also preferentially collect specific phrases and tones from the performer's past conversation history. Furthermore, the collection unit can customize and optimize the collection method based on the performer's past conversation history. In this way, the optimal collection method can be selected by analyzing the past conversation history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the performer's past conversation data into the generation AI and have the generation AI select the optimal collection method.

[0058] When collecting conversations, the collection unit can filter the conversations based on the actor's current scene or role. For example, if the actor is in a specific scene, the collection unit collects only conversations related to that scene. The collection unit can also filter and collect necessary conversations based on the actor's role. Furthermore, the collection unit can also exclude and collect unnecessary conversations according to the actor's current scene or role. In this way, by filtering the conversations based on the current scene or role, only necessary conversations can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input actor's scene and role data to a generation AI and have the generation AI perform conversation filtering.

[0059] The collection unit can estimate the emotion of the actor and determine the priority of conversations to be collected based on the estimated emotion of the actor. For example, if the actor is nervous, the collection unit can prioritize collecting important conversations. Furthermore, if the actor is relaxed, the collection unit can also collect the overall conversation in a balanced manner. Furthermore, if the actor is excited, the collection unit can prioritize collecting emotional conversations. In this way, by determining the priority of conversations according to the actor's emotion, important conversations can be preferentially collected. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the collection unit can input the actor's emotion data into the generation AI and have the generation AI determine the priority of conversations.

[0060] When collecting conversations, the collection unit can prioritize collection of highly relevant conversations based on the geographical location information of the performer. For example, if the performer is in a specific location, the collection unit prioritizes collection of conversations related to that location. The collection unit can also filter and collect highly relevant conversations based on the geographical location information of the performer. Furthermore, if the performer is moving, the collection unit can also collect the most appropriate conversations based on the performer's current location. In this way, highly relevant conversations can be collected preferentially by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the performer's geographical location data to the generation AI and cause the generation AI to collect highly relevant conversations.

[0061] The collection unit can analyze the social media activity of the performer and collect related conversations when collecting conversations. For example, the collection unit can analyze the social media activity of the performer and collect conversations on related topics. The collection unit can also collect related conversations based on content mentioned by the performer on social media. Furthermore, the collection unit can monitor the social media activity of the performer in real time and collect related conversations. In this way, related conversations can be collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the performer's social media data into a generation AI and cause the generation AI to collect related conversations.

[0062] The generation unit can estimate the performer's emotions and adjust the expression method of the cue card based on the estimated performer's emotions. For example, if the performer is nervous, the generation unit can generate a simple and easy-to-understand cue card. Furthermore, if the performer is relaxed, the generation unit can generate a cue card that includes detailed information. Furthermore, if the performer is excited, the generation unit can generate a visually stimulating cue card. This allows for the generation of more appropriate cue cards by adjusting the expression method of the cue card according to the performer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the performer's emotion data into the generation AI and have the generation AI adjust the expression method of the cue card.

[0063] When generating a cue card, the generation unit can adjust the level of detail of the cue card based on the importance of the conversation. For example, in the case of an important conversation, the generation unit generates a detailed cue card. In addition, in the case of a conversation with low importance, the generation unit can also generate a concise cue card. Furthermore, the generation unit can dynamically adjust the level of detail of the cue card according to the importance of the conversation. In this way, by adjusting the level of detail of the cue card according to the importance of the conversation, a cue card with an appropriate amount of information can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input conversation importance data into the generation AI and cause the generation AI to adjust the level of detail of the cue card.

[0064] When generating cue cards, the generation unit can apply different generation algorithms depending on the category of the conversation. For example, in the case of a comedy scene, the generation unit applies an algorithm that generates a humorous cue card. In addition, in the case of a drama scene, the generation unit can also apply an algorithm that generates an emotional cue card. Furthermore, in the case of an action scene, the generation unit can also apply an algorithm that generates a tense cue card. In this way, by applying a generation algorithm depending on the category of the conversation, a cue card appropriate for the scene can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input conversation category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0065] The generation unit can estimate the performer's emotions and adjust the length of the cue card based on the estimated performer's emotions. For example, if the performer is nervous, the generation unit can generate a short, concise cue card. If the performer is relaxed, the generation unit can also generate a longer cue card with detailed explanations. Furthermore, if the performer is excited, the generation unit can generate a cue card with visually stimulating effects. By adjusting the length of the cue card according to the performer's emotions, a cue card of an appropriate length can be generated. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the performer's emotion data into the generation AI and have the generation AI adjust the length of the cue card.

[0066] When generating cue cards, the generation unit can determine the priority of the cue cards based on the time of submission of the conversations. For example, in the case of urgent conversations, the generation unit generates the cue cards with priority. In addition, the generation unit can also generate cue cards with lower priority for conversations submitted early. Furthermore, the generation unit can dynamically adjust the order in which cue cards are generated depending on the time of submission of the conversations. In this way, by determining the priority of cue cards depending on the time of submission of the conversations, conversations with high urgency can be generated as cue cards with priority. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the time of submission of conversations into the generation AI and have the generation AI determine the priority of the cue cards.

[0067] When generating cue cards, the generation unit can adjust the order of the cue cards based on the relevance of the conversations. For example, the generation unit prioritizes highly relevant conversations when generating cue cards. The generation unit can also generate cue cards by placing less relevant conversations on hold when generating cue cards. Furthermore, the generation unit can dynamically adjust the order in which the cue cards are generated according to the relevance of the conversations. This allows highly relevant conversations to be generated as cue cards with priority by adjusting the order of the cue cards according to the relevance of the conversations. Some or all of the above-described processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input conversation relevance data into the generation AI and cause the generation AI to adjust the order of the cue cards.

[0068] The output unit can estimate the performer's emotions and adjust the cue card output method based on the estimated performer's emotions. For example, if the performer is nervous, the output unit can provide a simple, highly visible output method. Furthermore, if the performer is relaxed, the output unit can provide an output method that includes detailed information. Furthermore, if the performer is in a hurry, the output unit can provide an output method that focuses on the main points. This allows the cue card output method to be adjusted according to the performer's emotions, resulting in a more appropriate output method. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the output unit can be performed using, for example, AI, or without AI. For example, the output unit can input the performer's emotion data into the generation AI and have the generation AI adjust the cue card output method.

[0069] When outputting cue cards, the output unit can select an appropriate output method by referring to the performer's past cue card usage history. For example, the output unit selects the optimal output method based on the performer's past cue card usage history. The output unit can also select an output method with high visibility from the performer's past cue card usage history. Furthermore, the output unit can analyze the performer's past cue card usage history and select the most efficient output method. In this way, the optimal output method can be selected by referring to the past cue card usage history. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the performer's past cue card usage data into the generation AI and have the generation AI select the optimal output method.

[0070] When outputting a cue card, the output unit can customize the output content of the cue card based on the actor's current scene and role. For example, if an actor is in a specific scene, the output unit outputs a cue card related to that scene. The output unit can also customize and output necessary cue cards based on the actor's role. Furthermore, the output unit can also exclude and output unnecessary cue cards according to the actor's current scene and role. In this way, by customizing the output content based on the current scene and role, only necessary cue cards can be output. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input actor scene and role data into a generation AI and have the generation AI customize the output content of the cue card.

[0071] The output unit can estimate the performer's emotions and adjust the output order of the cue cards based on the estimated performer's emotions. For example, if the performer is nervous, the output unit prioritizes output of important cue cards. Furthermore, if the performer is relaxed, the output unit can also output the entire cue card in a balanced manner. Furthermore, if the performer is excited, the output unit can prioritize output of emotional cue cards. Thus, by adjusting the output order of the cue cards according to the performer's emotions, important cue cards can be output preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the output unit can be performed using, for example, an AI, or without an AI. For example, the output unit can input the performer's emotion data to the generation AI and have the generation AI adjust the output order of the cue cards.

[0072] When outputting cue cards, the output unit can select the optimal output method by taking into account the performer's geographical location information. For example, if the performer is in a specific location, the output unit prioritizes output of cue cards related to that location. The output unit can also filter and output highly relevant cue cards based on the performer's geographical location information. Furthermore, if the performer is traveling, the output unit can also output the optimal cue card based on the performer's current location. In this way, by taking into account the geographical location information, highly relevant cue cards can be output preferentially. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the performer's geographical location data to the generation AI and cause the generation AI to select the optimal output method.

[0073] When outputting a cue card, the output unit can analyze the performer's social media activity and adjust the output content. For example, the output unit analyzes the performer's social media activity and outputs cue cards on related topics. The output unit can also output related cue cards based on content mentioned by the performer on social media. Furthermore, the output unit can monitor the performer's social media activity in real time and output related cue cards. In this way, related cue cards can be output by analyzing social media activity. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the performer's social media data into a generation AI and have the generation AI adjust the output content.

[0074] The adjustment unit can estimate the performer's emotions and determine how to adjust the cue card based on the estimated performer's emotions. For example, if the performer is nervous, the adjustment unit adjusts the cue card to be simple and easy to understand. Furthermore, if the performer is relaxed, the adjustment unit can adjust the cue card to include more detailed information. Furthermore, if the performer is excited, the adjustment unit can adjust the cue card to be more visually stimulating. This allows for determining how to adjust the cue card based on the performer's emotions, thereby providing a more appropriate cue card. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the adjustment unit can be performed using, for example, an AI, or without an AI. For example, the adjustment unit can input the performer's emotion data into the generation AI and have the generation AI determine how to adjust the cue card.

[0075] When adjusting the cue cards, the adjustment unit can select an appropriate adjustment method by referring to the director's past adjustment history. The adjustment unit, for example, selects the optimal adjustment method based on adjustments made by the director in the past. The adjustment unit can also select an adjustment method appropriate for a specific scene from the director's past adjustment history. Furthermore, the adjustment unit can analyze the director's past adjustment history and select the most efficient adjustment method. In this way, the optimal adjustment method can be selected by referring to the past adjustment history. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the director's past adjustment data into the generation AI and have the generation AI select the optimal adjustment method.

[0076] When adjusting the cue card, the adjustment unit can customize the adjustment content of the cue card based on the progress of the recording. For example, if the recording is progressing smoothly, the adjustment unit can provide a detailed cue card. Also, if the recording is delayed, the adjustment unit can provide a concise cue card. Furthermore, the adjustment unit can dynamically adjust the content of the cue card according to the progress of the recording. This improves the efficiency of the recording by customizing the content of the cue card according to the progress of the recording. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input recording progress data into the generation AI and cause the generation AI to customize the adjustment content of the cue card.

[0077] The adjustment unit can estimate the performer's emotions and determine the adjustment priority of the cue cards based on the estimated performer's emotions. For example, if the performer is nervous, the adjustment unit prioritizes adjusting important cue cards. Furthermore, if the performer is relaxed, the adjustment unit can also adjust the entire cue card in a balanced manner. Furthermore, if the performer is excited, the adjustment unit can prioritize adjusting emotional cue cards. In this way, by determining the adjustment priority of the cue cards according to the performer's emotions, important cue cards can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the performer's emotion data into the generation AI and have the generation AI determine the adjustment priority of the cue cards.

[0078] When adjusting the cue card, the adjustment unit can select an appropriate adjustment method by taking into account the director's geographical location information. For example, if the director is in a specific location, the adjustment unit selects an adjustment method related to that location. The adjustment unit can also filter and select the optimal adjustment method based on the director's geographical location information. Furthermore, if the director is moving, the adjustment unit can also select the optimal adjustment method based on the director's current location. In this way, the optimal adjustment method can be selected by taking the geographical location information into consideration. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the director's geographical location data into the generation AI and cause the generation AI to select the optimal adjustment method.

[0079] When adjusting a cue card, the adjustment unit can analyze the director's social media activity and suggest adjustments. For example, the adjustment unit can analyze the director's social media activity and suggest adjustments for related topics. The adjustment unit can also suggest related adjustments based on content mentioned by the director on social media. Furthermore, the adjustment unit can monitor the director's social media activity in real time and suggest related adjustments. In this way, related adjustments can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the director's social media data into a generation AI and have the generation AI execute the suggested adjustments. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, generation unit, output unit, and adjustment unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the flow of the performers' conversation in real time using the microphone 38B of the smart device 14, and analyzes the content, tone, and context of the conversation using the control unit 46A. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, automatically generates the next required lines and instructions using a generation AI. The output unit, for example, provides the generated cue card to the performers via the display 40A or in-ear monitor of the smart device 14. The adjustment unit, realized, for example, by the specific processing unit 290 of the data processing device 12, allows the director to check and correct the generated cue card. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, generation unit, output unit, and adjustment unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the flow of the performers' conversation in real time using the microphone 238 of the smart glasses 214, and analyzes the content, tone, and context of the conversation using the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates the next required lines and instructions using a generation AI. The output unit provides the generated cue card to the performers, for example, via the display or in-ear monitor of the smart glasses 214. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and allows the director to check and correct the generated cue card. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, generation unit, output unit, and adjustment unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the flow of the performers' conversation in real time using the microphone 238 of the headset-type terminal 314, and analyzes the content, tone, and context of the conversation using the control unit 46A. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, automatically generates the next required lines and instructions using a generation AI. The output unit, for example, provides the generated cue cards to the performers via the display 343 or in-ear monitor of the headset-type terminal 314. The adjustment unit, realized, for example, by the specific processing unit 290 of the data processing device 12, allows the director to check and correct the generated cue cards. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, generation unit, output unit, and adjustment unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the flow of the performer's conversation in real time using the microphone 238 of the robot 414, and analyzes the content, tone, and context of the conversation using the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates the next required lines and instructions using a generation AI. The output unit provides the generated cue card to the performer, for example, via the display or in-ear monitor of the robot 414. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and allows the director to check and correct the generated cue card.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The collection unit can analyze the tone and speed of the performer's voice to estimate the performer's level of fatigue. For example, if the performer's voice is lower and slower than usual, the collection unit can estimate that the performer is tired and suggest that they take a break. Alternatively, if the performer's voice is higher and faster, the collection unit can estimate that the performer is excited and provide advice on how to relax. Furthermore, the collection unit can analyze the performer's voice fluctuations to estimate their stress level and suggest appropriate responses. In this way, by analyzing the characteristics of the performer's voice, the performer's condition can be understood and appropriate responses can be taken.

[0082] The generation unit can analyze the performer's past performance data and select the optimal cue card generation method. For example, it can generate cue cards to be used in similar situations based on data from the performer's past successful performances. It can also analyze data from the performer's past unsuccessful performances and generate cue cards to help the performer avoid similar failures. Furthermore, it can customize and optimize the content and format of cue cards based on the performer's past performance data. This makes it possible to generate more effective cue cards by utilizing past performance data.

[0083] The output unit can track the performer's line of sight and adjust the display position of the cue card according to the direction of the gaze. For example, if the performer is looking to the left, the cue card can be displayed on the left monitor. If the performer is looking to the right, the cue card can also be displayed on the right monitor. Furthermore, if the performer is looking to the center, the cue card can also be displayed on the center monitor. In this way, the display position of the cue card can be adjusted according to the performer's line of sight, making it easier for the performer to see the cue card.

[0084] The adjustment unit can dynamically change the content of the cue card based on the progress of the recording. For example, if the recording is progressing faster than scheduled, a detailed cue card can be provided. Alternatively, if the recording is behind schedule, a brief cue card can be provided. Furthermore, the content of the cue card can be adjusted in real time according to the progress of the recording. This allows the efficiency of the recording to be improved by dynamically changing the content of the cue card according to the progress of the recording.

[0085] The collection unit can estimate the emotion of the performer and determine the priority of conversations to be collected based on the estimated emotion of the performer. For example, if the performer is nervous, important conversations can be collected with priority. Also, if the performer is relaxed, it is possible to collect the entire conversation in a balanced manner. Furthermore, if the performer is excited, it is possible to collect emotional conversations with priority. In this way, by determining the priority of conversations according to the emotion of the performer, it is possible to collect important conversations with priority.

[0086] The generation unit can estimate the emotion of the performer and adjust the expression method of the cue card based on the estimated emotion of the performer. For example, if the performer is nervous, a simple and easy-to-understand cue card can be generated. If the performer is relaxed, a cue card containing detailed information can be generated. Furthermore, if the performer is excited, a visually stimulating cue card can be generated. In this way, by adjusting the expression method of the cue card according to the emotion of the performer, more appropriate cue cards can be generated.

[0087] The output unit can estimate the performer's emotions and adjust the cue card output method based on the estimated performer's emotions. For example, if the performer is nervous, a simple, highly visible output method can be provided. If the performer is relaxed, an output method including detailed information can be provided. Furthermore, if the performer is in a hurry, an output method that focuses on the main points can be provided. In this way, by adjusting the cue card output method according to the performer's emotions, the cue card can be output in a more appropriate manner.

[0088] The adjustment unit can estimate the emotions of the performer and determine how to adjust the cue cards based on the estimated emotions of the performer. For example, if the performer is nervous, the cue cards can be adjusted to be simple and easy to understand. If the performer is relaxed, the cue cards can be adjusted to include more detailed information. Furthermore, if the performer is excited, the cue cards can be adjusted to be more visually stimulating. In this way, by determining how to adjust the cue cards according to the performer's emotions, more appropriate cue cards can be provided.

[0089] The collection unit can adjust the conversation collection method based on the geographic location information of the speaker. For example, if the speaker is in a specific location, conversations related to that location can be collected preferentially. Also, if the speaker is moving, the optimal conversations can be collected based on the speaker's current location. Furthermore, the collection unit can filter and collect highly relevant conversations based on the speaker's geographic location information. In this way, highly relevant conversations can be collected preferentially by taking geographic location information into consideration.

[0090] When generating cue cards, the generation unit can apply different generation algorithms depending on the category of the conversation. For example, for a comedy scene, an algorithm that generates a humorous cue card can be applied. For a drama scene, an algorithm that generates an emotional cue card can be applied. Furthermore, for an action scene, an algorithm that generates a tense cue card can be applied. In this way, by applying a generation algorithm depending on the category of the conversation, cue cards appropriate for the scene can be generated.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The collection unit collects the conversation flow from the performer's microphone. The conversation flow obtained from the performer's microphone includes, for example, the performer's speech content, tone, and context. The collection unit collects the conversation flow in real time and analyzes the conversation content in detail to identify the next lines and instructions that are needed. Step 2: The generator generates a cue card based on the conversation flow collected by the collector. The generator uses a generation AI to automatically generate the next necessary lines and instructions, and generates the cue card based on the prompts. Step 3: The output unit outputs the cue cards generated by the generation unit to a monitor or in-ear monitors. The output unit displays the generated cue cards on a monitor or outputs them to the performers' personal in-ear monitors. Step 4: In the Adjustment Department, the director fine-tunes the cue cards generated by the Generation Department. In the Adjustment Department, the director checks the generated cue cards and makes corrections as necessary, thereby reducing the number of manpower and manpower required.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] 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, etc., and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device, etc.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0141] 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.

[0142] 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.

[0143] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects the flow of conversation from the performer's microphone; a generation unit that generates a cue card based on the conversation flow collected by the collection unit; an output unit that outputs the cue card generated by the generation unit to a monitor or an in-ear monitor; an adjustment unit in which a director fine-tunes the cue cards generated by the generation unit; A system characterized by:

2. The collecting unit Estimate the emotions of the speaker and adjust the timing of conversation collection based on the estimated emotions of the speaker.

2. The system of claim 1.

3. The collecting unit Analyze the speaker's past conversation history and select the appropriate collection method 2. The system of claim 1.

4. The collecting unit As dialogue is collected, filter it based on the actor's current scene or role 2. The system of claim 1.

5. The collecting unit Estimate the emotions of the speaker and prioritize conversations to collect based on the estimated emotions of the speaker.

2. The system of claim 1.

6. The collecting unit When collecting conversations, prioritize collection of relevant conversations based on the speaker's geographic location.

2. The system of claim 1.

7. The collecting unit When collecting conversations, analyze the speaker's social media activity and collect related conversations.

2. The system of claim 1.

8. The generation unit Estimate the emotions of the performer and adjust the way the cue cards are presented based on the estimated emotions of the performer 2. The system of claim 1.

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