System

Abstract

An object of a system according to an embodiment is to generate and transmit an efficient and unique canvas without using paper.SOLUTION: A system includes an input unit, a generation unit, and a transmission unit. The input unit inputs information such as a situation of a site, a character of a performer, and a conversation or a reaction so far. The generation unit generates a unique canvas based on the information input by the input unit. The transmission unit transmits the canvas generated by the generation unit to the performer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the large amount of paper used and wasted is a problem, and there is room for improvement in the efficient creation and transmission of cue cards.

[0005] The system according to the embodiment aims to generate and transmit efficient and unique cheat sheets without using paper. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a generation unit, and a transmission unit. The input unit inputs information such as the situation on-site, the performer's character, and previous conversations and reactions. The generation unit generates a unique cue card based on the information input by the input unit. The transmission unit transmits the cue card generated by the generation unit to the performer. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate and transmit unique cheat sheets without using paper. [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) The cue card generation system according to an embodiment of the present invention is a system in which information such as the situation on-site, the performer's character, previous conversations and reactions, etc. is input, and a generation AI generates a unique cue card based on that information and transmits it to the performer. This reduces the effort and time required for handwriting, and provides an environmentally friendly system that does not use any paper.

[0029] A cue card generation system according to an embodiment includes an input unit, a generation unit, and a transmission unit. The input unit inputs information such as the on-site situation, the performer's character, and previous conversations and reactions. For example, the input unit inputs information such as the location, time, weather, and number of participants as the on-site situation. Furthermore, the input unit inputs information such as the performer's personality, past roles, and performance style as the performer's character. Furthermore, the input unit inputs audio data, text data, video data, and the like as the previous conversations and reactions. The generation unit generates a unique cue card based on the information input by the input unit. For example, if the performer is a comedian, the generation AI generates a cue card with a humorous tone, and if the scene is serious, the generation AI generates a cue card with an appropriate tone. The generation AI also takes into account the previous conversations and reactions to provide the cue card in a natural flow. The transmission unit transmits the cue card generated by the generation unit to the performer. For example, the cue card is transmitted to the performer via ear monitors. Alternatively, the cue card can be visually displayed using smart glasses or a teleprompter. This allows the cue card generation system to reduce the time and effort required for handwriting and provide an environmentally friendly system that does not use any paper.

[0030] The input unit inputs the performer's past appearance history and performance data, enabling more accurate character analysis. For example, the input unit retrieves the performer's past appearance history from a database and inputs it into the generation AI. For example, it analyzes the content of the performer's acting and talk in past programs to understand the performer's character in detail. The input unit also collects the performer's performance data and inputs it into the generation AI. For example, it analyzes the performer's tone of voice and changes in facial expressions to extract character characteristics. The input unit also integrates the performer's past appearance history and performance data and inputs it into the generation AI. For example, it analyzes reactions and audience reactions in past programs to create a detailed character profile. This improves the accuracy of the performer's character analysis.

[0031] The input unit can analyze on-site audio data and video data in real time and generate cue cards according to changes in the situation. The input unit, for example, analyzes on-site audio data in real time and inputs it into the generation AI. For example, it analyzes the noise level and content of conversations on-site and generates cue cards according to the situation. The input unit also analyzes on-site video data in real time and inputs it into the generation AI. For example, it analyzes the movements and facial expressions of actors and generates cue cards according to the situation. The input unit also integrates on-site audio and video data and analyzes it in real time. For example, it synchronizes the audio and video and generates cue cards according to changes in the situation. This makes it possible to generate cue cards according to the situation on-site.

[0032] The input unit is designed for use in different media and can generate cue cards based on audio-only input information. For example, the input unit inputs audio data from a radio program, and the generation AI generates cue cards. For example, the content of the radio personality's talk is analyzed and an appropriate cue card is provided. The input unit also inputs audio data from a podcast, and the generation AI generates cue cards. For example, a cue card based on the theme or topic of the podcast is provided. The input unit also generates cue cards based on audio-only input information. For example, the speaker's emotions and tone are analyzed from the audio data and an appropriate cue card is provided. This makes it possible to use the system in different media.

[0033] The input unit can analyze the performer's social media posts and fan comments to generate cue cards that meet fan expectations. For example, the input unit analyzes the performer's social media posts, and the generation AI generates cue cards. For example, a cue card that meets fan expectations is provided based on the performer's recent posts and fan reactions. The input unit also analyzes fan comments, and the generation AI generates cue cards. For example, popular topics and requests are extracted from fan comments, and cue cards are provided accordingly. The input unit also integrates the performer's social media posts and fan comments, and the generation AI generates cue cards. For example, trends and topics on social media are analyzed, and cue cards that meet fan expectations are provided. This makes it possible to generate cue cards that meet fan expectations.

[0034] The generation unit can learn different scenarios and story patterns and generate a variety of cue cards according to the scene. For example, the generation unit learns different scenarios, and the generation AI generates cue cards according to the scene. For example, it learns scenarios such as comedy, drama, and talk show scenarios and provides appropriate cue cards. The generation unit also learns different story patterns, and the generation AI generates cue cards according to the scene. For example, it learns patterns such as introduction, development, twist, and conclusion and flashbacks and provides appropriate cue cards. The generation unit also integrates the scenario and story pattern, and the generation AI generates cue cards according to the scene. For example, it analyzes the flow of the scenario and story pattern and provides appropriate cue cards. This makes it possible to generate a variety of cue cards according to the scene.

[0035] The generation unit can analyze the performer's vocal tone and speaking style characteristics, and generate cue cards that match them. For example, the generation unit analyzes the performer's vocal tone, and the generation AI generates cue cards that match it. For example, it analyzes the pitch and speed of the voice, and provides cue cards with an appropriate tone. The generation unit also analyzes the performer's speaking style characteristics, and the generation AI generates cue cards that match it. For example, it analyzes the rhythm and accent of the speech, and provides cue cards with an appropriate tone. The generation unit also integrates the vocal tone and speaking style characteristics, and the generation AI generates cue cards that match it. For example, it analyzes the vocal tone and speaking rhythm, and provides cue cards with an appropriate tone. This makes it possible to generate cue cards that match the performer's vocal tone and speaking style.

[0036] The generation unit enables the generation of cue cards in different languages, making it possible to support international program production. For example, the generation unit trains the generation AI with multilingual data to enable the generation of cue cards in different languages. For example, it trains data in English, French, Chinese, etc. The generation unit also incorporates a translation function into the generation AI to enable the generation of cue cards in different languages. For example, it translates cue cards entered in Japanese into English and provides them. The generation unit also builds a multilingual cue card generation system to support international program production. For example, it generates and provides cue cards in different languages ​​in real time. This makes it possible to support international program production.

[0037] The generation unit can introduce AR technology to visually display the contents of the cue card, allowing the performer to visually confirm it. The generation unit, for example, uses AR technology to build a system that visually displays the contents of the cue card. For example, the performer wears smart glasses to visually confirm the cue card. The generation unit also introduces a teleprompter that uses AR technology to visually display the contents of the cue card. For example, the cue card is displayed in the performer's field of vision, allowing the performer to visually confirm it. The generation unit also uses AR technology to develop an application that visually displays the contents of the cue card. For example, the cue card can be visually confirmed using a smartphone or tablet. This allows the performer to visually confirm the cue card.

[0038] The transmission unit can display the cue card not only using ear monitors, but also using smart glasses or a teleprompter. The transmission unit, for example, uses smart glasses to build a system that visually displays the cue card. For example, a performer wears smart glasses to visually check the cue card. The transmission unit also builds a system that visually displays the cue card using a teleprompter. For example, the cue card is displayed in the performer's field of vision to allow the performer to visually check it. The transmission unit also integrates smart glasses and a teleprompter to build a system that visually displays the cue card. For example, a performer wears smart glasses and uses a teleprompter in combination to allow the performer to check the cue card. This allows cue cards to be displayed on a variety of devices.

[0039] The transmission unit can track the movements and positions of the performers and transmit cue cards at the optimal timing. For example, the transmission unit builds a system that tracks the movements of the performers and transmits cue cards at the optimal timing. For example, the cue cards are displayed when the performers reach a specific position. The transmission unit also develops a system that tracks the positions of the performers in real time and transmits cue cards at the optimal timing. For example, the cue cards are displayed the moment the performers stand in front of the camera. The transmission unit also builds a system that integrates and tracks the movements and positions of the performers and transmits cue cards at the optimal timing. For example, the cue cards are displayed when the performers perform a specific action. This makes it possible to transmit cue cards according to the movements and positions of the performers.

[0040] The transmission unit can enable the display of cue cards on different devices. The transmission unit, for example, builds a system for displaying cue cards on a smartphone. For example, the performer can check the cue cards using a smartphone. The transmission unit also builds a system for displaying cue cards on a tablet. For example, the performer can check the cue cards using a tablet. The transmission unit also builds a system for displaying cue cards by integrating a smartphone and a tablet. For example, the performer can check the cue cards using a smartphone or a tablet. This allows the cue cards to be displayed on a variety of devices.

[0041] The transmission unit can introduce a method of transmitting the contents of the cue card not only by sound but also by vibration or light. For example, the transmission unit builds a system that transmits the contents of the cue card by vibration. For example, a device worn by a performer vibrates to transmit the cue card. The transmission unit also builds a system that transmits the contents of the cue card by light. For example, a device worn by a performer lights up to transmit the cue card. The transmission unit also builds a system that combines vibration and light to transmit the cue card. For example, a device worn by a performer vibrates and lights up at the same time to transmit the cue card. This allows the cue card to be transmitted in a variety of ways.

[0042] The generation department can fully automate the cue card generation process, eliminating all manual work. For example, the generation department will build a system that fully automates the cue card generation process. For example, the generation AI will automatically generate cue cards, eliminating all manual work. In addition, the generation department will develop a system that automatically collects the data necessary for the generation AI to automate the cue card generation process. For example, the on-site situation and the performer's character will be automatically collected and input into the generation AI. In addition, the generation department will build a system that fully automates the cue card generation process, eliminating all manual work. For example, the generation AI will automatically generate cue cards and automatically send them to in-ear monitors or smart glasses. This will enable the complete automation of cue card generation.

[0043] The generation unit can optimize the energy efficiency of the generation AI and minimize the environmental load. For example, the generation unit improves the algorithm to optimize the energy efficiency of the generation AI. For example, the amount of calculation is reduced to suppress energy consumption. The generation unit also improves the hardware to optimize the energy efficiency of the generation AI. For example, a low-power processor is used to suppress energy consumption. The generation unit also builds a system that optimizes the energy efficiency of the generation AI and minimizes the environmental load. For example, the generation unit operates the generation AI using renewable energy. This makes it possible to optimize the energy efficiency of the generation AI and minimize the environmental load.

[0044] The generation unit can consider applications of the cue card generation system in different industries. The generation unit considers applications of the cue card generation system in the education industry, for example. For example, a system is built to generate cue cards for teachers to use during classes. The generation unit also considers applications of the cue card generation system in the medical industry. For example, a system is built to generate cue cards for doctors to use during examinations. The generation unit also considers applications of the cue card generation system in different industries. For example, a system is built to generate cue cards to be used in business presentations and conferences. This makes it possible to apply the cue card generation system in different industries.

[0045] The generation unit makes the cue card generation system cloud-based, and can also accommodate remote program production. The generation unit, for example, makes the cue card generation system cloud-based and builds a system that accommodates remote program production. For example, cue cards are generated via the Internet and provided to performers remotely. The generation unit also develops a cloud-based cue card generation system that accommodates remote program production. For example, cue cards are generated on the cloud and provided to performers remotely. The generation unit also makes the cue card generation system cloud-based and builds a system that accommodates remote program production. For example, cue cards are generated on the cloud and provided to performers remotely. This makes it possible to accommodate remote program production.

[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 cue card generation system can further include a health management unit that monitors the user's health condition. For example, the system can measure the performer's heart rate and blood pressure in real time and generate cue cards according to the performer's health condition. If the performer's heart rate is high, a cue card with relaxing content is provided, and if the blood pressure is low, a cue card with energy-boosting content is provided. The health management unit can also analyze the performer's past health data and generate cue cards that support long-term health management. This makes it possible to generate cue cards according to the performer's health condition.

[0048] The cue card generation system can further include a schedule management unit that manages the user's schedule. For example, it can analyze the performer's schedule and generate cue cards that match the next event. If the next event is an important meeting, it can provide a cue card that encourages preparation, and if it is a time to relax, it can provide a cue card that encourages relaxation. The schedule management unit can also analyze the performer's past schedule data and generate cue cards that support efficient schedule management. This makes it possible to generate cue cards that match the performer's schedule.

[0049] The cue card generation system can further include an interest analysis unit that analyzes the user's preferences and hobbies. For example, the performer's hobbies and preferences can be analyzed and cue cards can be generated based on the analysis. If the performer likes music, cue cards with content related to music can be provided, and if the performer likes sports, cue cards with content related to sports can be provided. The interest analysis unit can also analyze the performer's past hobby data and generate cue cards that reflect long-term changes in hobbies. This makes it possible to generate cue cards that match the performer's hobbies and preferences.

[0050] The cue card generation system can further include a learning analysis unit that analyzes the user's learning history. For example, the system analyzes the performer's past learning history and generates cue cards based on that. If the performer is knowledgeable in a particular field, it provides cue cards with content related to that field, and if the performer is currently studying a field, it provides cue cards with content that will support their learning. The learning analysis unit can also analyze the performer's learning history over the long term and generate cue cards according to their learning progress. This makes it possible to generate cue cards according to the performer's learning history.

[0051] The cue card generation system can further include a diet analysis unit that analyzes the user's diet history. For example, the performer's past diet history can be analyzed and a cue card can be generated based on that. If the performer has a preference for a particular food ingredient, a cue card related to that food ingredient can be provided, and a cue card that encourages healthy eating can also be provided. The diet analysis unit can also analyze the performer's diet history over the long term and generate cue cards that support health management. This makes it possible to generate cue cards based on the performer's diet history.

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

[0053] Step 1: The input unit inputs information such as the on-site situation, the performer's character, previous conversations and reactions, etc. For example, information such as location, time, weather, and number of participants is input as the on-site situation. Also, information such as the performer's personality, past roles, and performance style is input as the performer's character. Furthermore, audio data, text data, video data, etc. are input as previous conversations and reactions. Step 2: The generator generates a unique cue card based on the information input by the input unit. For example, if the actor is a comedian, the generator AI will generate a humorous cue card, and if the scene is serious, it will generate a cue card with an appropriate tone. The generator AI also takes into account previous conversations and reactions, providing the cue card in a natural flow. Step 3: The transmission unit transmits the cue card generated by the generation unit to the performer. For example, the cue card can be transmitted to the performer via ear monitors. The cue card can also be visually displayed using smart glasses or a teleprompter.

[0054] (Example 2) The cue card generation system according to an embodiment of the present invention is a system in which information such as the situation on-site, the performer's character, previous conversations and reactions, etc. is input, and a generation AI generates a unique cue card based on that information and transmits it to the performer. This reduces the effort and time required for handwriting, and provides an environmentally friendly system that does not use any paper.

[0055] A cue card generation system according to an embodiment includes an input unit, a generation unit, and a transmission unit. The input unit inputs information such as the on-site situation, the performer's character, and previous conversations and reactions. For example, the input unit inputs information such as the location, time, weather, and number of participants as the on-site situation. Furthermore, the input unit inputs information such as the performer's personality, past roles, and performance style as the performer's character. Furthermore, the input unit inputs audio data, text data, video data, and the like as the previous conversations and reactions. The generation unit generates a unique cue card based on the information input by the input unit. For example, if the performer is a comedian, the generation AI generates a cue card with a humorous tone, and if the scene is serious, the generation AI generates a cue card with an appropriate tone. The generation AI also takes into account the previous conversations and reactions to provide the cue card in a natural flow. The transmission unit transmits the cue card generated by the generation unit to the performer. For example, the cue card is transmitted to the performer via ear monitors. Alternatively, the cue card can be visually displayed using smart glasses or a teleprompter. This allows the cue card generation system to reduce the time and effort required for handwriting and provide an environmentally friendly system that does not use any paper.

[0056] The input unit inputs the performer's past appearance history and performance data, enabling more accurate character analysis. For example, the input unit retrieves the performer's past appearance history from a database and inputs it into the generation AI. For example, it analyzes the content of the performer's acting and talk in past programs to understand the performer's character in detail. The input unit also collects the performer's performance data and inputs it into the generation AI. For example, it analyzes the performer's tone of voice and changes in facial expressions to extract character characteristics. The input unit also integrates the performer's past appearance history and performance data and inputs it into the generation AI. For example, it analyzes reactions and audience reactions in past programs to create a detailed character profile. This improves the accuracy of the performer's character analysis.

[0057] The input unit can analyze on-site audio data and video data in real time and generate cue cards according to changes in the situation. The input unit, for example, analyzes on-site audio data in real time and inputs it into the generation AI. For example, it analyzes the noise level and content of conversations on-site and generates cue cards according to the situation. The input unit also analyzes on-site video data in real time and inputs it into the generation AI. For example, it analyzes the movements and facial expressions of actors and generates cue cards according to the situation. The input unit also integrates on-site audio and video data and analyzes it in real time. For example, it synchronizes the audio and video and generates cue cards according to changes in the situation. This makes it possible to generate cue cards according to the situation on-site.

[0058] The input unit can use the emotion estimation function to estimate the performer's current emotional state and adjust the cue card based on that. The input unit, for example, analyzes the performer's facial expression and estimates the performer's current emotional state using the emotion estimation function. For example, it analyzes facial expressions such as smiling and furrowed brows to calculate an emotion score. The input unit also analyzes the performer's tone of voice and estimates the performer's current emotional state using the emotion estimation function. For example, it analyzes the pitch and speed of the voice to calculate an emotion score. The input unit also analyzes the performer's movements and posture and estimates the performer's current emotional state using the emotion estimation function. For example, it analyzes hand movements and body tilt to calculate an emotion score. This makes it possible to generate cue cards that correspond to the performer's emotional state.

[0059] The input unit is designed for use in different media and can generate cue cards based on audio-only input information. For example, the input unit inputs audio data from a radio program, and the generation AI generates cue cards. For example, the content of the radio personality's talk is analyzed and an appropriate cue card is provided. The input unit also inputs audio data from a podcast, and the generation AI generates cue cards. For example, a cue card based on the theme or topic of the podcast is provided. The input unit also generates cue cards based on audio-only input information. For example, the speaker's emotions and tone are analyzed from the audio data and an appropriate cue card is provided. This makes it possible to use the system in different media.

[0060] The input unit can analyze the performer's social media posts and fan comments to generate cue cards that meet fan expectations. For example, the input unit analyzes the performer's social media posts, and the generation AI generates cue cards. For example, a cue card that meets fan expectations is provided based on the performer's recent posts and fan reactions. The input unit also analyzes fan comments, and the generation AI generates cue cards. For example, popular topics and requests are extracted from fan comments, and cue cards are provided accordingly. The input unit also integrates the performer's social media posts and fan comments, and the generation AI generates cue cards. For example, trends and topics on social media are analyzed, and cue cards that meet fan expectations are provided. This makes it possible to generate cue cards that meet fan expectations.

[0061] The input unit can use the emotion estimation function to analyze the emotional reactions of the audience in real time and adjust the cue cards based on the results. The input unit, for example, analyzes the facial expressions of the audience and uses the emotion estimation function to analyze the emotional reactions in real time. For example, it analyzes smiling and surprised expressions and calculates an emotion score. The input unit also analyzes the tone of the audience's voice and uses the emotion estimation function to analyze the emotional reactions in real time. For example, it analyzes cheers and laughter and calculates an emotion score. The input unit also analyzes the movements and postures of the audience and uses the emotion estimation function to analyze the emotional reactions in real time. For example, it analyzes movements such as clapping and standing up and calculates an emotion score. This makes it possible to generate cue cards based on the emotional reactions of the audience.

[0062] The generation unit can learn different scenarios and story patterns and generate a variety of cue cards according to the scene. For example, the generation unit learns different scenarios, and the generation AI generates cue cards according to the scene. For example, it learns scenarios such as comedy, drama, and talk show scenarios and provides appropriate cue cards. The generation unit also learns different story patterns, and the generation AI generates cue cards according to the scene. For example, it learns patterns such as introduction, development, twist, and conclusion and flashbacks and provides appropriate cue cards. The generation unit also integrates the scenario and story pattern, and the generation AI generates cue cards according to the scene. For example, it analyzes the flow of the scenario and story pattern and provides appropriate cue cards. This makes it possible to generate a variety of cue cards according to the scene.

[0063] The generation unit can analyze the performer's vocal tone and speaking style characteristics, and generate cue cards that match them. For example, the generation unit analyzes the performer's vocal tone, and the generation AI generates cue cards that match it. For example, it analyzes the pitch and speed of the voice, and provides cue cards with an appropriate tone. The generation unit also analyzes the performer's speaking style characteristics, and the generation AI generates cue cards that match it. For example, it analyzes the rhythm and accent of the speech, and provides cue cards with an appropriate tone. The generation unit also integrates the vocal tone and speaking style characteristics, and the generation AI generates cue cards that match it. For example, it analyzes the vocal tone and speaking rhythm, and provides cue cards with an appropriate tone. This makes it possible to generate cue cards that match the performer's vocal tone and speaking style.

[0064] The generation unit uses the emotion estimation function to generate cue cards that are optimal for the performer's emotions, thereby maintaining a natural flow of emotions. The generation unit, for example, analyzes the performer's emotional state and uses the emotion estimation function to generate optimal cue cards. For example, if the performer is nervous, it provides cue cards that help the performer relax. The generation unit also analyzes the performer's emotional flow and uses the emotion estimation function to generate cue cards that maintain a natural flow. For example, it adjusts the tone of the cue cards according to the heightened emotions. The generation unit also uses the emotion estimation function to generate cue cards that are optimal for the performer's emotions. For example, it dynamically adjusts the content of the cue cards according to changes in emotions. This makes it possible to generate cue cards that are optimal for the performer's emotions.

[0065] The generation unit enables the generation of cue cards in different languages, making it possible to support international program production. For example, the generation unit trains the generation AI with multilingual data to enable the generation of cue cards in different languages. For example, it trains data in English, French, Chinese, etc. The generation unit also incorporates a translation function into the generation AI to enable the generation of cue cards in different languages. For example, it translates cue cards entered in Japanese into English and provides them. The generation unit also builds a multilingual cue card generation system to support international program production. For example, it generates and provides cue cards in different languages ​​in real time. This makes it possible to support international program production.

[0066] The generation unit can introduce AR technology to visually display the contents of the cue card, allowing the performer to visually confirm it. The generation unit, for example, uses AR technology to build a system that visually displays the contents of the cue card. For example, the performer wears smart glasses to visually confirm the cue card. The generation unit also introduces a teleprompter that uses AR technology to visually display the contents of the cue card. For example, the cue card is displayed in the performer's field of vision, allowing the performer to visually confirm it. The generation unit also uses AR technology to develop an application that visually displays the contents of the cue card. For example, the cue card can be visually confirmed using a smartphone or tablet. This allows the performer to visually confirm the cue card.

[0067] The generation unit can use the emotion estimation function to predict the viewer's emotional response and adjust the cue card based on that. The generation unit uses the emotion estimation function, for example, to predict the viewer's emotional response. For example, the generation unit analyzes the viewer's facial expression and tone of voice and calculates an emotion score. The generation unit also builds a system that predicts the viewer's emotional response and adjusts the cue card based on that. For example, it adjusts the content of the cue card to match scenes that heighten the viewer's emotions. The generation unit also uses the emotion estimation function to predict the viewer's emotional response in real time and adjusts the cue card based on that. For example, it adjusts the tone of the cue card according to changes in the viewer's emotions. This makes it possible to generate cue cards that match the viewer's emotional response.

[0068] The transmission unit can display the cue card not only using ear monitors, but also using smart glasses or a teleprompter. The transmission unit, for example, uses smart glasses to build a system that visually displays the cue card. For example, a performer wears smart glasses to visually check the cue card. The transmission unit also builds a system that visually displays the cue card using a teleprompter. For example, the cue card is displayed in the performer's field of vision to allow the performer to visually check it. The transmission unit also integrates smart glasses and a teleprompter to build a system that visually displays the cue card. For example, a performer wears smart glasses and uses a teleprompter in combination to allow the performer to check the cue card. This allows cue cards to be displayed on a variety of devices.

[0069] The transmission unit can track the movements and positions of the performers and transmit cue cards at the optimal timing. For example, the transmission unit builds a system that tracks the movements of the performers and transmits cue cards at the optimal timing. For example, the cue cards are displayed when the performers reach a specific position. The transmission unit also develops a system that tracks the positions of the performers in real time and transmits cue cards at the optimal timing. For example, the cue cards are displayed the moment the performers stand in front of the camera. The transmission unit also builds a system that integrates and tracks the movements and positions of the performers and transmits cue cards at the optimal timing. For example, the cue cards are displayed when the performers perform a specific action. This makes it possible to transmit cue cards according to the movements and positions of the performers.

[0070] The transmission unit can use the emotion estimation function to transmit cue cards at a timing that corresponds to the emotional state of the performer. The transmission unit, for example, analyzes the emotional state of the performer and uses the emotion estimation function to transmit cue cards at the optimal timing. For example, it displays cue cards when the performer is relaxed. The transmission unit also analyzes changes in the performer's emotions in real time and uses the emotion estimation function to transmit cue cards at the optimal timing. For example, it displays cue cards that help the performer relax when the performer is nervous. The transmission unit also uses the emotion estimation function to build a system that transmits cue cards at a timing that corresponds to the emotional state of the performer. For example, it displays cue cards at the moment when the performer's emotions are at their highest. This makes it possible to transmit cue cards that correspond to the emotional state of the performer.

[0071] The transmission unit can enable the display of cue cards on different devices. The transmission unit, for example, builds a system for displaying cue cards on a smartphone. For example, the performer can check the cue cards using a smartphone. The transmission unit also builds a system for displaying cue cards on a tablet. For example, the performer can check the cue cards using a tablet. The transmission unit also builds a system for displaying cue cards by integrating a smartphone and a tablet. For example, the performer can check the cue cards using a smartphone or a tablet. This allows the cue cards to be displayed on a variety of devices.

[0072] The transmission unit can introduce a method of transmitting the contents of the cue card not only by sound but also by vibration or light. For example, the transmission unit builds a system that transmits the contents of the cue card by vibration. For example, a device worn by a performer vibrates to transmit the cue card. The transmission unit also builds a system that transmits the contents of the cue card by light. For example, a device worn by a performer lights up to transmit the cue card. The transmission unit also builds a system that combines vibration and light to transmit the cue card. For example, a device worn by a performer vibrates and lights up at the same time to transmit the cue card. This allows the cue card to be transmitted in a variety of ways.

[0073] The transmission unit can use the emotion estimation function to analyze the emotional reactions of the audience in real time and adjust the method of transmitting the cue card based on the analysis. For example, the transmission unit analyzes the emotional reactions of the audience in real time and uses the emotion estimation function to adjust the method of transmitting the cue card. For example, when the audience is surprised, the cue card is transmitted by vibration. The transmission unit also builds a system that analyzes the emotional reactions of the audience and uses the emotion estimation function to adjust the method of transmitting the cue card. For example, when the audience laughs, the cue card is transmitted by light. The transmission unit also uses the emotion estimation function to adjust the method of transmitting the cue card based on the emotional reactions of the audience. For example, the cue card is transmitted by vibration and light at the moment when the audience's emotions are heightened. This makes it possible to transmit the cue card according to the emotional reactions of the audience.

[0074] The generation department can fully automate the cue card generation process, eliminating all manual work. For example, the generation department will build a system that fully automates the cue card generation process. For example, the generation AI will automatically generate cue cards, eliminating all manual work. In addition, the generation department will develop a system that automatically collects the data necessary for the generation AI to automate the cue card generation process. For example, the on-site situation and the performer's character will be automatically collected and input into the generation AI. In addition, the generation department will build a system that fully automates the cue card generation process, eliminating all manual work. For example, the generation AI will automatically generate cue cards and automatically send them to in-ear monitors or smart glasses. This will enable the complete automation of cue card generation.

[0075] The generation unit can optimize the energy efficiency of the generation AI and minimize the environmental load. For example, the generation unit improves the algorithm to optimize the energy efficiency of the generation AI. For example, the amount of calculation is reduced to suppress energy consumption. The generation unit also improves the hardware to optimize the energy efficiency of the generation AI. For example, a low-power processor is used to suppress energy consumption. The generation unit also builds a system that optimizes the energy efficiency of the generation AI and minimizes the environmental load. For example, the generation unit operates the generation AI using renewable energy. This makes it possible to optimize the energy efficiency of the generation AI and minimize the environmental load.

[0076] The generation unit can use the emotion estimation function to generate cue cards to reduce the stress level of a performer. The generation unit, for example, analyzes the stress level of a performer and uses the emotion estimation function to generate cue cards that reduce stress. For example, it provides cue cards with relaxing content. The generation unit also builds a system that analyzes the stress level of a performer in real time and uses the emotion estimation function to generate cue cards that reduce stress. For example, it provides cue cards that help the performer relax when they are nervous. The generation unit also uses the emotion estimation function to generate cue cards to reduce the stress level of a performer. For example, it provides cue cards that help the performer relax at moments when their stress is rising. This makes it possible to generate cue cards that reduce the performer's stress level.

[0077] The generation unit can consider applications of the cue card generation system in different industries. The generation unit considers applications of the cue card generation system in the education industry, for example. For example, a system is built to generate cue cards for teachers to use during classes. The generation unit also considers applications of the cue card generation system in the medical industry. For example, a system is built to generate cue cards for doctors to use during examinations. The generation unit also considers applications of the cue card generation system in different industries. For example, a system is built to generate cue cards to be used in business presentations and conferences. This makes it possible to apply the cue card generation system in different industries.

[0078] The generation unit makes the cue card generation system cloud-based, and can also accommodate remote program production. The generation unit, for example, makes the cue card generation system cloud-based and builds a system that accommodates remote program production. For example, cue cards are generated via the Internet and provided to performers remotely. The generation unit also develops a cloud-based cue card generation system that accommodates remote program production. For example, cue cards are generated on the cloud and provided to performers remotely. The generation unit also makes the cue card generation system cloud-based and builds a system that accommodates remote program production. For example, cue cards are generated on the cloud and provided to performers remotely. This makes it possible to accommodate remote program production.

[0079] The generation unit can use the emotion estimation function to analyze the emotional responses of viewers and promote environmentally conscious program production. The generation unit, for example, analyzes the emotional responses of viewers and uses the emotion estimation function to promote environmentally conscious program production. For example, an environmental message is inserted in conjunction with a scene where the viewer's emotions are heightened. The generation unit also analyzes the emotional responses of viewers in real time and uses the emotion estimation function to build a system that promotes environmentally conscious program production. For example, an environmental message is inserted in a moment when the viewer's emotions are heightened. The generation unit also uses the emotion estimation function to analyze the emotional responses of viewers and promote environmentally conscious program production. For example, an environmental message is inserted in conjunction with a scene where the viewer's emotions are heightened. This can promote environmentally conscious program production.

[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 cue card generation system can further include a health management unit that monitors the user's health condition. For example, the system can measure the performer's heart rate and blood pressure in real time and generate cue cards according to the performer's health condition. If the performer's heart rate is high, a cue card with relaxing content is provided, and if the blood pressure is low, a cue card with energy-boosting content is provided. The health management unit can also analyze the performer's past health data and generate cue cards that support long-term health management. This makes it possible to generate cue cards according to the performer's health condition.

[0082] The cue card generation system can further include a schedule management unit that manages the user's schedule. For example, it can analyze the performer's schedule and generate cue cards that match the next event. If the next event is an important meeting, it can provide a cue card that encourages preparation, and if it is a time to relax, it can provide a cue card that encourages relaxation. The schedule management unit can also analyze the performer's past schedule data and generate cue cards that support efficient schedule management. This makes it possible to generate cue cards that match the performer's schedule.

[0083] The cue card generation system can further include an interest analysis unit that analyzes the user's preferences and hobbies. For example, the performer's hobbies and preferences can be analyzed and cue cards can be generated based on the analysis. If the performer likes music, cue cards with content related to music can be provided, and if the performer likes sports, cue cards with content related to sports can be provided. The interest analysis unit can also analyze the performer's past hobby data and generate cue cards that reflect long-term changes in hobbies. This makes it possible to generate cue cards that match the performer's hobbies and preferences.

[0084] The cue card generation system can further include a learning analysis unit that analyzes the user's learning history. For example, the system analyzes the performer's past learning history and generates cue cards based on that. If the performer is knowledgeable in a particular field, it provides cue cards with content related to that field, and if the performer is currently studying a field, it provides cue cards with content that will support their learning. The learning analysis unit can also analyze the performer's learning history over the long term and generate cue cards according to their learning progress. This makes it possible to generate cue cards according to the performer's learning history.

[0085] The cue card generation system can further include a diet analysis unit that analyzes the user's diet history. For example, the performer's past diet history can be analyzed and a cue card can be generated based on that. If the performer has a preference for a particular food ingredient, a cue card related to that food ingredient can be provided, and a cue card that encourages healthy eating can also be provided. The diet analysis unit can also analyze the performer's diet history over the long term and generate cue cards that support health management. This makes it possible to generate cue cards based on the performer's diet history.

[0086] The cue card generation system can further use the user's emotion estimation function to generate cue cards based on the performer's emotions. For example, if the performer is nervous, it can provide cue cards with content that will relax the performer, and if the performer is relaxed, it can provide cue cards with content that will help the performer to concentrate. In addition, the emotion estimation function can be used to generate cue cards in real time according to the performer's changes in emotion. This makes it possible to generate cue cards that are based on the performer's emotions.

[0087] The cue card generation system can further use the user's emotion estimation function to generate cue cards based on the audience's emotions. For example, if the audience is laughing, it can provide humorous cue cards, and if the audience is listening seriously, it can provide serious cue cards. In addition, the emotion estimation function can be used to generate cue cards in real time according to changes in the audience's emotions. This makes it possible to generate cue cards according to the audience's emotions.

[0088] The cue card generation system can further use a user emotion estimation function to generate cue cards based on the viewer's emotions. For example, if the viewer is moved, it can provide cue cards with content that will deepen the emotion, and if the viewer is excited, it can provide cue cards with content that will maintain the excitement. In addition, the emotion estimation function can be used to generate cue cards in real time according to changes in the viewer's emotions. This makes it possible to generate cue cards according to the viewer's emotions.

[0089] The cue card generation system can further use the user's emotion estimation function to generate cue cards to reduce the performer's stress level. For example, if the performer is nervous, it can provide a cue card with content to relax them, and if the performer is tired, it can provide a cue card with content to boost their energy. The emotion estimation function can also be used to generate cue cards in real time according to changes in the performer's stress level. This makes it possible to generate cue cards that reduce the performer's stress level.

[0090] The cue card generation system can further use the user's emotion estimation function to generate cue cards that maintain the natural flow of the performer's emotions. For example, if the performer is feeling emotionally heightened, the system provides cue cards with content that maintains that emotion, and if the performer is feeling emotionally lower, the system provides cue cards with content that enhances the emotion. The emotion estimation function can also be used to generate cue cards in real time according to the performer's changes in emotion. This makes it possible to generate cue cards that maintain the natural flow of the performer's emotions.

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

[0092] Step 1: The input unit inputs information such as the on-site situation, the performer's character, previous conversations and reactions, etc. For example, information such as location, time, weather, and number of participants is input as the on-site situation. Also, information such as the performer's personality, past roles, and performance style is input as the performer's character. Furthermore, audio data, text data, video data, etc. are input as previous conversations and reactions. Step 2: The generator generates a unique cue card based on the information input by the input unit. For example, if the actor is a comedian, the generator AI will generate a humorous cue card, and if the scene is serious, it will generate a cue card with an appropriate tone. The generator AI also takes into account previous conversations and reactions, providing the cue card in a natural flow. Step 3: The transmission unit transmits the cue card generated by the generation unit to the performer. For example, the cue card can be transmitted to the performer via ear monitors. The cue card can also be visually displayed using smart glasses or a teleprompter.

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

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

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] In the robot 414, 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 robot 414 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.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An input section for inputting information such as the situation on-site, the performer's character, previous conversations and reactions, etc. a generation unit that generates a unique cue card based on the information input by the input unit; a transmission unit that transmits the cue card generated by the generation unit to a performer. A system characterized by:

2. The input unit Input the performer's past performance history and performance data to perform a more accurate character analysis.

2. The system of claim 1.

3. The input unit Analyzes on-site audio and video data in real time and generates cue cards according to changes in the situation.

2. The system of claim 1.

4. The input unit Estimating the performer's current emotional state and adjusting the cue card accordingly 2. The system of claim 1.

5. The input unit The cue card is generated based on voice-only input information, assuming use in different media.

2. The system of claim 1.

6. The input unit Analyze the performers' social media posts and fan comments to generate the cue card that meets the fans' expectations.

2. The system of claim 1.

7. The input unit Analyze the audience's emotional response in real time and adjust the cue card accordingly 2. The system of claim 1.

8. The generation unit It learns different scenarios and story patterns and generates various cue cards according to the scene.

2. The system of claim 1.

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