system

Abstract

The system according to this embodiment aims to support the idea generation process and provide ideas and objective evaluations. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a proposal unit, an evaluation unit, and a rehearsal unit. The reception unit receives input of themes and keywords for creating material. The generation unit generates material based on the themes and keywords received by the reception unit. The proposal unit proposes various ideas for the material generated by the generation unit. The evaluation unit provides objective evaluation and feedback based on the ideas proposed by the proposal unit. The rehearsal unit conducts a rehearsal in front of a virtual audience based on the feedback provided by the evaluation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, problems such as depletion of ideas and lack of objective evaluation in idea creation exist, and there is room for improvement.

[0005] The system according to the embodiment aims to assist the process of idea creation and provide ideas and objective evaluations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a proposal unit, an evaluation unit, and a rehearsal unit. The reception unit receives input of themes and keywords for creating material. The generation unit generates material based on the themes and keywords received by the reception unit. The proposal unit proposes various ideas for the material generated by the generation unit. The evaluation unit provides objective evaluation and feedback based on the ideas proposed by the proposal unit. The rehearsal unit conducts a rehearsal in front of a virtual audience based on the feedback provided by the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can support the idea generation process and provide ideas and objective evaluations. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The comedy AI writer system according to an embodiment of the present invention is an innovative AI-assisted tool targeting professional comedians, students of comedy training schools, and ordinary people who enjoy comedy as a hobby. This comedy AI writer system reduces the time and effort required for joke creation, eliminates idea depletion, provides objective evaluation, and enables immediate response to current events. The comedy AI writer system utilizes generative AI technology to provide customization tailored to the user's personality and style, real-time feedback using speech recognition technology, high-quality joke generation that learns from past masterpiece skits and comedian styles, and a rehearsal function in front of a virtual audience through integration with VR technology. For example, the comedy AI writer system accepts the user inputting themes and keywords for joke creation. The generative AI quickly generates jokes based on the input themes and keywords. Next, the generative AI proposes diverse ideas using a vast database. Furthermore, the generative AI provides objective evaluation and feedback on the jokes created by the user. The generative AI also analyzes real-time news and proposes current events. Finally, through integration with VR technology, the user can rehearse in front of a virtual audience. This innovative AI-assisted tool reduces the time and effort required for creating jokes, eliminates idea depletion, provides objective evaluations, and enables immediate responses to current events. As a result, the AI ​​comedy writer system can streamline the user's joke creation process and deliver high-quality material.

[0029] The comedy AI writer system according to this embodiment comprises a reception unit, a generation unit, a proposal unit, an evaluation unit, and a rehearsal unit. The reception unit receives themes and keywords for joke creation from the user. The reception unit accepts themes and keywords entered by the user, for example. The generation unit generates jokes based on the themes and keywords received by the reception unit using a generation AI. The generation unit can generate jokes using a generation AI that receives themes and keywords as input. The generation AI can generate jokes using a text generation AI (e.g., LLM). The proposal unit proposes various ideas for the jokes generated by the generation unit. The proposal unit can propose various ideas for the generated jokes, for example, by utilizing a vast database. Based on the database, the proposal unit can propose ideas from different perspectives and unique ideas. The evaluation unit provides objective evaluation and feedback based on the ideas proposed by the proposal unit. The evaluation unit can analyze the voice of the user when performing the jokes using speech recognition technology and provide feedback in real time. The evaluation unit can also analyze real-time news and propose current events jokes. The rehearsal unit conducts rehearsals in front of a virtual audience based on feedback provided by the evaluation unit. The rehearsal unit can, for example, integrate with VR technology to allow users to rehearse in front of a virtual audience. This enables the comedy AI writer system according to this embodiment to streamline the user's joke creation process and provide high-quality jokes.

[0030] The reception desk receives themes and keywords for generating content. Specifically, users can input themes and keywords of interest through the system's interface. For example, if a user inputs themes such as "cats" or "space travel," the reception desk receives this information and passes it on to the next process. The reception desk not only accepts themes and keywords entered by users, but can also verify and correct the input. For example, it provides an interface for correcting keywords that the user has entered incorrectly, supporting users in entering accurate information. The reception desk also has a function to save the history of themes and keywords that the user has entered in the past, making them reusable. This allows users to create new content while referring to their past content creation history. Furthermore, the reception desk has a function to automatically suggest related themes and keywords based on the user's input. For example, if a user inputs "cats," it will suggest related keywords such as "pets" and "animals" to support the user's content creation. In this way, the reception desk can provide support to users in efficiently inputting themes and keywords and moving on to the next step.

[0031] The generation unit uses a generation AI to generate jokes based on themes and keywords received by the reception unit. Specifically, the generation AI receives themes and keywords entered by the user as prompts and generates comedy jokes based on them. The generation AI can generate jokes using a text generation AI (e.g., LLM). LLM has learned from a large amount of text data and has the ability to generate humor and jokes related to themes and keywords entered by the user. For example, if the user enters the keywords "cat" and "space travel," the generation AI can combine these keywords to generate a humorous joke such as "A cat goes on an adventure in a spaceship." The generation unit provides the jokes generated by the generation AI to the user and provides an interface that allows the user to review, modify, or add to the jokes. Furthermore, the generation unit also has the function to evaluate the quality of the jokes generated by the generation AI and regenerate them as needed. For example, if the generated joke does not meet the user's expectations, the generation unit will send a prompt to the generation AI again to generate new jokes. In this way, the generation unit can provide high-quality comedy jokes that satisfy the user.

[0032] The suggestion department proposes diverse ideas for the material generated by the generation department. Specifically, the suggestion department utilizes a vast database to suggest ideas from different perspectives and unique concepts for the generated material. For example, if the generated material is "A cat goes on an adventure in a spaceship," the suggestion department can propose additional ideas such as "An alien the cat encounters on the spaceship" or "The cat's troubles inside the spaceship." Based on the database, the suggestion department analyzes past successes and patterns of popular material, and generates new ideas based on that. Furthermore, the suggestion department can provide ideas from a wider range of perspectives by incorporating elements of humor from different cultures and regions. For example, incorporating jokes and satire from different countries can broaden the range of material. In addition, the suggestion department can propose individually customized ideas, taking into account the user's preferences and past material creation history. In this way, the suggestion department can support users in creating more diverse and unique material.

[0033] The evaluation department provides objective evaluations and feedback based on ideas proposed by the proposal department. Specifically, the evaluation department uses speech recognition technology to analyze the voice of the user performing the material and provides real-time feedback. For example, when a user actually performs a generated material, the evaluation department analyzes the voice and evaluates the clarity of pronunciation, tempo, and expression of emotion. The evaluation department can also analyze real-time news and suggest current events. This allows users to create material that incorporates the latest topics. The evaluation department provides specific areas for improvement and advice on the material performed by the user. For example, it may provide feedback such as, "It would be more effective if the tempo of this part were a little faster," or "Adjusting the timing of this joke would make it funnier." Furthermore, the evaluation department can analyze the user's past performance data and provide feedback to support long-term growth. This allows the evaluation department to support users in achieving higher quality performance.

[0034] The rehearsal department conducts rehearsals in front of a virtual audience based on feedback provided by the evaluation department. Specifically, the rehearsal department, in conjunction with VR technology, allows users to rehearse in front of a virtual audience. Users wear a VR headset, stand on a virtual stage, and perform their act while checking the audience's reactions in real time. The virtual audience provides realistic reactions to the user's performance, offering feedback such as laughter, applause, and boos. This allows users to refine their performance through rehearsals in a virtual environment before going on the actual stage. Furthermore, the rehearsal department has a function that records the user's performance data and allows them to review it later. This allows users to objectively review their performance and find areas for improvement. The rehearsal department can also simulate multiple scenarios. For example, by setting different audience groups and different stage environments and rehearsing in each scenario, users can prepare to handle various situations. In this way, the rehearsal department can provide support to help users prepare thoroughly for the actual performance and achieve a high-quality performance.

[0035] The generation unit can quickly generate content using a generation AI. For example, the generation unit can have the generation AI receive a theme or keywords as input and quickly generate content. The generation AI can generate content using a text generation AI (e.g., LLM). The generation unit uses the generation AI to generate content based on themes and keywords entered by the user. This allows for rapid content generation by using a generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit inputs a theme or keywords into the generation AI, and the generation AI generates content.

[0036] The proposal department can propose diverse ideas by utilizing a vast database. For example, the proposal department can propose diverse ideas for generated material by utilizing a vast database. Based on the database, the proposal department can propose ideas from different perspectives and unique ideas. The proposal department can propose diverse ideas by referring to past material and related topics contained in the database. In this way, by utilizing a vast database, diverse ideas can be proposed. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department inputs the database into AI, and the AI ​​proposes diverse ideas.

[0037] The evaluation unit can analyze the voice of the user performing a skit using speech recognition technology and provide real-time feedback. For example, the evaluation unit uses speech recognition technology to analyze the voice of the user performing a skit. The evaluation unit uses speech recognition technology to analyze the accuracy of the user's speech content and pronunciation and provides real-time feedback. The evaluation unit can also use speech recognition technology to analyze the tone and speed of the user's voice and provide real-time feedback. In this way, real-time feedback can be provided by using speech recognition technology. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit inputs the audio data acquired using speech recognition technology into the AI, the AI ​​analyzes the audio data and provides feedback.

[0038] The evaluation unit can analyze real-time news and suggest current events. For example, the evaluation unit analyzes real-time news and suggests current events. The evaluation unit acquires news feeds and extracts relevant current events using topic modeling techniques. The evaluation unit can also analyze the content of news and suggest current events that may be interesting to the user. In this way, current events can be suggested by analyzing real-time news. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit inputs a news feed into an AI, the AI ​​analyzes the news and suggests current events.

[0039] The rehearsal unit can conduct rehearsals in front of a virtual audience by integrating with VR technology. For example, the rehearsal unit uses VR technology to allow users to rehearse in front of a virtual audience. The rehearsal unit allows users to immerse themselves in a virtual environment and rehearse using a head-mounted display. The rehearsal unit can also simulate the reactions of the virtual audience and allow users to receive feedback during the rehearsal. This enables rehearsals to be conducted in front of a virtual audience by using VR technology. Some or all of the above processes in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit can construct a virtual environment using VR technology and allow users to rehearse.

[0040] The reception desk can analyze the user's past input history of themes and keywords and select the optimal input method. For example, the reception desk can automatically display keywords that the user has frequently used in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest themes and keywords that the user will use at specific times based on their past input history. In this way, the optimal input method can be selected by analyzing past input history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history data into AI, and the AI ​​can select the optimal input method.

[0041] The reception desk can filter themes and keywords based on the user's current areas of interest when they are entered. For example, the reception desk can prioritize displaying relevant themes and keywords based on topics the user has recently been interested in. The reception desk can also suggest relevant themes and keywords based on topics the user frequently mentions on social media. The reception desk can also filter relevant themes and keywords based on what the user has recently searched for. This allows users to enter highly relevant themes and keywords by filtering based on their current areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's areas of interest data into an AI, which then filters themes and keywords.

[0042] The reception system can prioritize the input of themes and keywords that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception system will prioritize themes and keywords related to that region. If the user is traveling, the reception system can also prioritize themes and keywords related to the travel destination. If the user is participating in a specific event, the reception system can also prioritize themes and keywords related to that event. This allows for the input of highly relevant themes and keywords by considering geographical location. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system may input the user's geographical location information into the AI, and the AI ​​may select themes and keywords.

[0043] The reception desk can analyze the user's social media activity and input relevant themes and keywords when the user enters themes and keywords. For example, the reception desk can input relevant themes and keywords based on topics that the user frequently mentions on social media. The reception desk can also analyze the content of posts from accounts that the user follows on social media and input relevant themes and keywords. The reception desk can also input relevant themes and keywords based on the activities of groups and communities that the user participates in on social media. In this way, relevant themes and keywords can be entered by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity data into AI, and the AI ​​will select themes and keywords.

[0044] The generation unit can adjust the level of detail generated based on the importance of themes and keywords during content generation. For example, the generation unit can generate detailed content based on important themes and keywords. The generation unit can also generate concise content based on general themes and keywords. The generation unit can also generate specialized content based on themes and keywords related to specific events or topics. This allows for the generation of more appropriate content by adjusting the level of detail based on the importance of themes and keywords. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the importance data of themes and keywords into the generation AI, and the generation AI adjusts the level of detail of the generation.

[0045] The generation unit can apply different generation algorithms depending on the theme and keyword category when generating material. For example, the generation unit can apply a generation algorithm that emphasizes humor to themes and keywords in the comedy category. It can also apply a generation algorithm that emphasizes emotion to themes and keywords in the drama category. It can also apply a generation algorithm that emphasizes facts to themes and keywords in the news category. By applying a generation algorithm according to the category, more appropriate material can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs theme and keyword category data into a generation AI, and the generation AI applies a generation algorithm.

[0046] The generation unit can determine the generation priority based on the submission timing of themes and keywords when generating content. For example, the generation unit can prioritize generating content based on urgent themes and keywords. It can also prioritize generating content based on themes and keywords with approaching submission deadlines. It can also prioritize generating content based on themes and keywords frequently used by users. This allows for content generation at a more appropriate time by determining priorities based on submission timing. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs theme and keyword submission timing data into the generation AI, and the generation AI determines the generation priority.

[0047] The generation unit can adjust the generation order based on the relevance of themes and keywords during content generation. For example, the generation unit can prioritize generating content based on highly relevant themes and keywords. The generation unit can also postpone generating content based on less relevant themes and keywords. The generation unit can also prioritize generating content based on highly relevant themes and keywords specified by the user. This allows for the generation of more appropriate content by adjusting the generation order based on relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs theme and keyword relevance data into the generation AI, and the generation AI adjusts the generation order.

[0048] The proposal department can adjust the level of detail of an idea based on the importance of themes and keywords. For example, the proposal department can propose detailed ideas based on important themes and keywords. It can also propose concise ideas based on general themes and keywords. It can also propose specialized ideas based on themes and keywords related to specific events or topics. This allows for the proposal of more appropriate ideas by adjusting the level of detail based on the importance of themes and keywords. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department inputs importance data for themes and keywords into the AI, and the AI ​​adjusts the level of detail of the proposal.

[0049] The suggestion function can apply different suggestion algorithms depending on the theme and keyword category when suggesting ideas. For example, the suggestion function can apply a suggestion algorithm that emphasizes humor to themes and keywords in the comedy category. It can also apply a suggestion algorithm that emphasizes emotion to themes and keywords in the drama category. It can also apply a suggestion algorithm that emphasizes facts to themes and keywords in the news category. By applying a suggestion algorithm according to the category, more appropriate ideas can be suggested. Some or all of the above processing in the suggestion function may be performed using AI or not. For example, the suggestion function inputs theme and keyword category data into the AI, and the AI ​​applies a suggestion algorithm.

[0050] The proposal department can prioritize proposals based on the submission timing of themes and keywords when submitting ideas. For example, the proposal department can prioritize ideas based on urgent themes and keywords. It can also prioritize ideas based on themes and keywords with approaching submission deadlines. It can also prioritize ideas based on themes and keywords frequently used by users. This allows for ideas to be proposed at a more appropriate time by prioritizing based on submission timing. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department inputs theme and keyword submission timing data into the AI, and the AI ​​determines the priority of proposals.

[0051] The suggestion function can adjust the order of suggestions based on the relevance of themes and keywords when proposing ideas. For example, the suggestion function can prioritize suggesting ideas based on highly relevant themes and keywords. It can also postpone suggesting ideas based on less relevant themes and keywords. The suggestion function can also prioritize suggesting ideas based on highly relevant themes and keywords specified by the user. This allows for the suggestion of more appropriate ideas by adjusting the order of suggestions based on relevance. Some or all of the above processing in the suggestion function may be performed using AI or not. For example, the suggestion function can input theme and keyword relevance data into the AI, and the AI ​​can adjust the order of suggestions.

[0052] The evaluation unit can analyze the user's past joke creation history during the evaluation process to select the optimal evaluation method. For example, the evaluation unit can select the optimal evaluation method based on the evaluation results of jokes the user has created in the past. The evaluation unit can also extract specific patterns from the user's past joke creation history and select an evaluation method based on those patterns. The evaluation unit can also analyze feedback the user has received in the past and select an evaluation method based on that. In this way, the optimal evaluation method can be selected by analyzing the past joke creation history. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the user's past joke creation history data into the AI, and the AI ​​can select the optimal evaluation method.

[0053] The evaluation unit can customize its evaluation methods based on the user's current areas of interest during the evaluation process. For example, the evaluation unit can customize its evaluation methods based on topics the user is currently interested in. The evaluation unit can also customize its evaluation methods based on topics the user frequently mentions on social media. The evaluation unit can also customize its evaluation methods based on content the user has recently searched for. This allows for the provision of more appropriate feedback by customizing the evaluation methods based on the user's current areas of interest. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input user area of ​​interest data into the AI, and the AI ​​can customize the evaluation methods.

[0054] The evaluation unit can select the optimal evaluation method by considering the user's geographical location information during the evaluation process. For example, if the user is in a specific region, the evaluation unit can select an evaluation method related to that region. If the user is traveling, the evaluation unit can also select an evaluation method related to the travel destination. If the user is participating in a specific event, the evaluation unit can also select an evaluation method related to that event. In this way, the optimal evaluation method can be selected by considering geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit inputs the user's geographical location data into the AI, and the AI ​​selects the optimal evaluation method.

[0055] The evaluation unit can analyze a user's social media activity during the evaluation process and propose evaluation methods. For example, the evaluation unit can propose evaluation methods based on topics that the user frequently mentions on social media. The evaluation unit can also analyze the content of posts from accounts that the user follows on social media and propose evaluation methods. The evaluation unit can also propose evaluation methods based on the activities of groups and communities that the user participates in on social media. This allows for the proposal of more appropriate evaluation methods by analyzing social media activity. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit inputs the user's social media activity data into an AI, and the AI ​​proposes evaluation methods.

[0056] The rehearsal unit can analyze the user's past rehearsal history during rehearsals to select the optimal rehearsal method. For example, the rehearsal unit can select the optimal rehearsal method based on the results of past rehearsals conducted by the user. The rehearsal unit can also extract specific patterns from the user's past rehearsal history and select a rehearsal method based on those patterns. The rehearsal unit can also analyze feedback the user has received in the past and select a rehearsal method based on that feedback. In this way, the optimal rehearsal method can be selected by analyzing past rehearsal history. Some or all of the above processes in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit can input the user's past rehearsal history data into AI, and the AI ​​can select the optimal rehearsal method.

[0057] The rehearsal unit can customize the rehearsal process based on the user's current areas of interest during the rehearsal. For example, the rehearsal unit can customize the rehearsal process based on topics the user is currently interested in. The rehearsal unit can also customize the rehearsal process based on topics the user frequently mentions on social media. The rehearsal unit can also customize the rehearsal process based on content the user has recently searched for. This allows for more appropriate rehearsals by customizing the rehearsal process based on the user's current areas of interest. Some or all of the above processes in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit can input the user's areas of interest data into the AI, and the AI ​​can customize the rehearsal process.

[0058] The rehearsal unit can select the optimal rehearsal method during rehearsals, taking into account the user's geographical location. For example, if the user is in a specific region, the rehearsal unit can select a rehearsal method relevant to that region. If the user is traveling, the rehearsal unit can also select a rehearsal method relevant to the travel destination. If the user is participating in a specific event, the rehearsal unit can also select a rehearsal method relevant to that event. In this way, the optimal rehearsal method can be selected by considering geographical location information. Some or all of the above processing in the rehearsal unit may be performed using AI, or not. For example, the rehearsal unit inputs the user's geographical location data into the AI, and the AI ​​selects the optimal rehearsal method.

[0059] The rehearsal department can analyze the user's social media activity during rehearsals and propose rehearsal methods. For example, the rehearsal department can propose rehearsal methods based on topics that the user frequently mentions on social media. The rehearsal department can also propose rehearsal methods by analyzing the content of posts from accounts that the user follows on social media. The rehearsal department can also propose rehearsal methods based on the activities of groups and communities that the user participates in on social media. In this way, by analyzing social media activity, more appropriate rehearsal methods can be proposed. Some or all of the above processing in the rehearsal department may be performed using AI or not. For example, the rehearsal department inputs the user's social media activity data into AI, and the AI ​​proposes rehearsal methods.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The comedy AI writer system may further include a generation unit that analyzes the user's past joke creation history and selects the optimal joke generation method. The generation unit may, for example, select the optimal generation method based on the evaluation results of jokes created by the user in the past. It may also extract specific patterns from the user's past joke creation history and select a generation method based on those patterns. It may also analyze feedback the user has received in the past and select a generation method based on that. In this way, the optimal generation method can be selected by analyzing the past joke creation history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit may input the user's past joke creation history data into the AI, and the AI ​​may select the optimal generation method.

[0062] The comedy AI writer system may further include a suggestion section that proposes ideas based on the user's current areas of interest. For example, the suggestion section might prioritize suggesting relevant ideas based on topics the user has recently been interested in. It could also suggest relevant ideas based on topics the user frequently mentions on social media. It could also suggest relevant ideas based on recent searches the user has conducted. This allows the system to provide highly relevant ideas by suggesting them based on the user's current areas of interest. Some or all of the above processing in the suggestion section may be performed using AI or not. For example, the suggestion section might input user area of ​​interest data into an AI, which then suggests ideas.

[0063] The comedy AI writer system may further include an evaluation unit that analyzes the user's past joke creation history and selects the optimal evaluation method. The evaluation unit may, for example, select the optimal evaluation method based on the evaluation results of jokes created by the user in the past. It may also extract specific patterns from the user's past joke creation history and select an evaluation method based on those patterns. It may also analyze feedback the user has received in the past and select an evaluation method based on that. In this way, the optimal evaluation method can be selected by analyzing the past joke creation history. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit may input the user's past joke creation history data into the AI, and the AI ​​may select the optimal evaluation method.

[0064] The comedy AI writer system may also include a rehearsal unit that analyzes the user's past rehearsal history and selects the optimal rehearsal method. The rehearsal unit may, for example, select the optimal rehearsal method based on the results of past rehearsals conducted by the user. It may also extract specific patterns from the user's past rehearsal history and select a rehearsal method based on those patterns. It may also analyze feedback the user has received in the past and select a rehearsal method based on that. In this way, the optimal rehearsal method can be selected by analyzing past rehearsal history. Some or all of the above processing in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit may input the user's past rehearsal history data into the AI, and the AI ​​may select the optimal rehearsal method.

[0065] The comedy AI writer system may further include a rehearsal unit that selects the optimal rehearsal method by considering the user's geographical location. For example, if the user is in a specific region, the rehearsal unit may select a rehearsal method related to that region. If the user is traveling, it may also select a rehearsal method related to the travel destination. If the user is participating in a specific event, it may also select a rehearsal method related to that event. This allows for the selection of the optimal rehearsal method by considering geographical location. Some or all of the above processing in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit may input the user's geographical location data into the AI, and the AI ​​may select the optimal rehearsal method.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk accepts themes and keywords entered by the user for generating content. Step 2: The generation unit uses a generation AI to generate content based on the themes and keywords received by the reception unit. For example, the generation AI receives a theme and keywords as input and generates content. The generation AI can generate content using a text generation AI (e.g., LLM). Step 3: The proposal unit proposes diverse ideas for the material generated by the generation unit. For example, it can utilize a vast database to propose diverse ideas for the generated material. Based on the database, the proposal unit can propose ideas from different perspectives and unique concepts. Step 4: The evaluation department provides objective evaluation and feedback based on the ideas proposed by the proposal department. For example, it may use speech recognition technology to analyze the voice of the user performing the material and provide real-time feedback. The evaluation department can also analyze real-time news and suggest current events as material. Step 5: The rehearsal team conducts a rehearsal in front of a virtual audience based on the feedback provided by the evaluation team. For example, by integrating with VR technology, users can rehearse in front of a virtual audience.

[0068] (Example of form 2) The comedy AI writer system according to an embodiment of the present invention is an innovative AI-assisted tool targeting professional comedians, students of comedy training schools, and ordinary people who enjoy comedy as a hobby. This comedy AI writer system reduces the time and effort required for joke creation, eliminates idea depletion, provides objective evaluation, and enables immediate response to current events. The comedy AI writer system utilizes generative AI technology to provide customization tailored to the user's personality and style, real-time feedback using speech recognition technology, high-quality joke generation that learns from past masterpiece skits and comedian styles, and a rehearsal function in front of a virtual audience through integration with VR technology. For example, the comedy AI writer system accepts the user inputting themes and keywords for joke creation. The generative AI quickly generates jokes based on the input themes and keywords. Next, the generative AI proposes diverse ideas using a vast database. Furthermore, the generative AI provides objective evaluation and feedback on the jokes created by the user. The generative AI also analyzes real-time news and proposes current events. Finally, through integration with VR technology, the user can rehearse in front of a virtual audience. This innovative AI-assisted tool reduces the time and effort required for creating jokes, eliminates idea depletion, provides objective evaluations, and enables immediate responses to current events. As a result, the AI ​​comedy writer system can streamline the user's joke creation process and deliver high-quality material.

[0069] The comedy AI writer system according to this embodiment comprises a reception unit, a generation unit, a proposal unit, an evaluation unit, and a rehearsal unit. The reception unit receives themes and keywords for joke creation from the user. The reception unit accepts themes and keywords entered by the user, for example. The generation unit generates jokes based on the themes and keywords received by the reception unit using a generation AI. The generation unit can generate jokes using a generation AI that receives themes and keywords as input. The generation AI can generate jokes using a text generation AI (e.g., LLM). The proposal unit proposes various ideas for the jokes generated by the generation unit. The proposal unit can propose various ideas for the generated jokes, for example, by utilizing a vast database. Based on the database, the proposal unit can propose ideas from different perspectives and unique ideas. The evaluation unit provides objective evaluation and feedback based on the ideas proposed by the proposal unit. The evaluation unit can analyze the voice of the user when performing the jokes using speech recognition technology and provide feedback in real time. The evaluation unit can also analyze real-time news and propose current events jokes. The rehearsal unit conducts rehearsals in front of a virtual audience based on feedback provided by the evaluation unit. The rehearsal unit can, for example, integrate with VR technology to allow users to rehearse in front of a virtual audience. This enables the comedy AI writer system according to this embodiment to streamline the user's joke creation process and provide high-quality jokes.

[0070] The reception desk receives themes and keywords for generating content. Specifically, users can input themes and keywords of interest through the system's interface. For example, if a user inputs themes such as "cats" or "space travel," the reception desk receives this information and passes it on to the next process. The reception desk not only accepts themes and keywords entered by users, but can also verify and correct the input. For example, it provides an interface for correcting keywords that the user has entered incorrectly, supporting users in entering accurate information. The reception desk also has a function to save the history of themes and keywords that the user has entered in the past, making them reusable. This allows users to create new content while referring to their past content creation history. Furthermore, the reception desk has a function to automatically suggest related themes and keywords based on the user's input. For example, if a user inputs "cats," it will suggest related keywords such as "pets" and "animals" to support the user's content creation. In this way, the reception desk can provide support to users in efficiently inputting themes and keywords and moving on to the next step.

[0071] The generation unit uses a generation AI to generate jokes based on themes and keywords received by the reception unit. Specifically, the generation AI receives themes and keywords entered by the user as prompts and generates comedy jokes based on them. The generation AI can generate jokes using a text generation AI (e.g., LLM). LLM has learned from a large amount of text data and has the ability to generate humor and jokes related to themes and keywords entered by the user. For example, if the user enters the keywords "cat" and "space travel," the generation AI can combine these keywords to generate a humorous joke such as "A cat goes on an adventure in a spaceship." The generation unit provides the jokes generated by the generation AI to the user and provides an interface that allows the user to review, modify, or add to the jokes. Furthermore, the generation unit also has the function to evaluate the quality of the jokes generated by the generation AI and regenerate them as needed. For example, if the generated joke does not meet the user's expectations, the generation unit will send a prompt to the generation AI again to generate new jokes. In this way, the generation unit can provide high-quality comedy jokes that satisfy the user.

[0072] The suggestion department proposes diverse ideas for the material generated by the generation department. Specifically, the suggestion department utilizes a vast database to suggest ideas from different perspectives and unique concepts for the generated material. For example, if the generated material is "A cat goes on an adventure in a spaceship," the suggestion department can propose additional ideas such as "An alien the cat encounters on the spaceship" or "The cat's troubles inside the spaceship." Based on the database, the suggestion department analyzes past successes and patterns of popular material, and generates new ideas based on that. Furthermore, the suggestion department can provide ideas from a wider range of perspectives by incorporating elements of humor from different cultures and regions. For example, incorporating jokes and satire from different countries can broaden the range of material. In addition, the suggestion department can propose individually customized ideas, taking into account the user's preferences and past material creation history. In this way, the suggestion department can support users in creating more diverse and unique material.

[0073] The evaluation department provides objective evaluations and feedback based on ideas proposed by the proposal department. Specifically, the evaluation department uses speech recognition technology to analyze the voice of the user performing the material and provides real-time feedback. For example, when a user actually performs a generated material, the evaluation department analyzes the voice and evaluates the clarity of pronunciation, tempo, and expression of emotion. The evaluation department can also analyze real-time news and suggest current events. This allows users to create material that incorporates the latest topics. The evaluation department provides specific areas for improvement and advice on the material performed by the user. For example, it may provide feedback such as, "It would be more effective if the tempo of this part were a little faster," or "Adjusting the timing of this joke would make it funnier." Furthermore, the evaluation department can analyze the user's past performance data and provide feedback to support long-term growth. This allows the evaluation department to support users in achieving higher quality performance.

[0074] The rehearsal department conducts rehearsals in front of a virtual audience based on feedback provided by the evaluation department. Specifically, the rehearsal department, in conjunction with VR technology, allows users to rehearse in front of a virtual audience. Users wear a VR headset, stand on a virtual stage, and perform their act while checking the audience's reactions in real time. The virtual audience provides realistic reactions to the user's performance, offering feedback such as laughter, applause, and boos. This allows users to refine their performance through rehearsals in a virtual environment before going on the actual stage. Furthermore, the rehearsal department has a function that records the user's performance data and allows them to review it later. This allows users to objectively review their performance and find areas for improvement. The rehearsal department can also simulate multiple scenarios. For example, by setting different audience groups and different stage environments and rehearsing in each scenario, users can prepare to handle various situations. In this way, the rehearsal department can provide support to help users prepare thoroughly for the actual performance and achieve a high-quality performance.

[0075] The generation unit can quickly generate content using a generation AI. For example, the generation unit can have the generation AI receive a theme or keywords as input and quickly generate content. The generation AI can generate content using a text generation AI (e.g., LLM). The generation unit uses the generation AI to generate content based on themes and keywords entered by the user. This allows for rapid content generation by using a generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit inputs a theme or keywords into the generation AI, and the generation AI generates content.

[0076] The proposal department can propose diverse ideas by utilizing a vast database. For example, the proposal department can propose diverse ideas for generated material by utilizing a vast database. Based on the database, the proposal department can propose ideas from different perspectives and unique ideas. The proposal department can propose diverse ideas by referring to past material and related topics contained in the database. In this way, by utilizing a vast database, diverse ideas can be proposed. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department inputs the database into AI, and the AI ​​proposes diverse ideas.

[0077] The evaluation unit can analyze the voice of the user performing a skit using speech recognition technology and provide real-time feedback. For example, the evaluation unit uses speech recognition technology to analyze the voice of the user performing a skit. The evaluation unit uses speech recognition technology to analyze the accuracy of the user's speech content and pronunciation and provides real-time feedback. The evaluation unit can also use speech recognition technology to analyze the tone and speed of the user's voice and provide real-time feedback. In this way, real-time feedback can be provided by using speech recognition technology. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit inputs the audio data acquired using speech recognition technology into the AI, the AI ​​analyzes the audio data and provides feedback.

[0078] The evaluation unit can analyze real-time news and suggest current events. For example, the evaluation unit analyzes real-time news and suggests current events. The evaluation unit acquires news feeds and extracts relevant current events using topic modeling techniques. The evaluation unit can also analyze the content of news and suggest current events that may be interesting to the user. In this way, current events can be suggested by analyzing real-time news. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit inputs a news feed into an AI, the AI ​​analyzes the news and suggests current events.

[0079] The rehearsal unit can conduct rehearsals in front of a virtual audience by integrating with VR technology. For example, the rehearsal unit uses VR technology to allow users to rehearse in front of a virtual audience. The rehearsal unit allows users to immerse themselves in a virtual environment and rehearse using a head-mounted display. The rehearsal unit can also simulate the reactions of the virtual audience and allow users to receive feedback during the rehearsal. This enables rehearsals to be conducted in front of a virtual audience by using VR technology. Some or all of the above processes in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit can construct a virtual environment using VR technology and allow users to rehearse.

[0080] The reception desk can estimate the user's emotions and adjust the timing of theme and keyword input based on the estimated emotions. For example, if the user is stressed, the reception desk can delay the input timing to provide time for relaxation. If the user is excited, the reception desk can speed up the input timing to allow for quick idea input. If the user is focused, the reception desk can adjust the input timing to maintain an optimal state of concentration. This allows for the input of themes and keywords at a more appropriate time by adjusting the input timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's facial expression data into a generative AI, which estimates the emotions and adjusts the input timing.

[0081] The reception desk can analyze the user's past input history of themes and keywords and select the optimal input method. For example, the reception desk can automatically display keywords that the user has frequently used in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest themes and keywords that the user will use at specific times based on their past input history. In this way, the optimal input method can be selected by analyzing past input history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history data into AI, and the AI ​​can select the optimal input method.

[0082] The reception desk can filter themes and keywords based on the user's current areas of interest when they are entered. For example, the reception desk can prioritize displaying relevant themes and keywords based on topics the user has recently been interested in. The reception desk can also suggest relevant themes and keywords based on topics the user frequently mentions on social media. The reception desk can also filter relevant themes and keywords based on what the user has recently searched for. This allows users to enter highly relevant themes and keywords by filtering based on their current areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's areas of interest data into an AI, which then filters themes and keywords.

[0083] The reception desk can estimate the user's emotions and determine the priority of themes and keywords to be entered based on the estimated emotions. For example, if the user is relaxed, the reception desk may prioritize detailed themes and keywords. If the user is in a hurry, the reception desk may also prioritize concise themes and keywords. If the user is excited, the reception desk may also prioritize stimulating themes and keywords. This allows for the input of more appropriate themes and keywords by prioritizing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input the user's facial expression data into a generative AI, which may estimate emotions and determine the priority of themes and keywords.

[0084] The reception system can prioritize the input of themes and keywords that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception system will prioritize themes and keywords related to that region. If the user is traveling, the reception system can also prioritize themes and keywords related to the travel destination. If the user is participating in a specific event, the reception system can also prioritize themes and keywords related to that event. This allows for the input of highly relevant themes and keywords by considering geographical location. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system may input the user's geographical location information into the AI, and the AI ​​may select themes and keywords.

[0085] The reception desk can analyze the user's social media activity and input relevant themes and keywords when the user enters themes and keywords. For example, the reception desk can input relevant themes and keywords based on topics that the user frequently mentions on social media. The reception desk can also analyze the content of posts from accounts that the user follows on social media and input relevant themes and keywords. The reception desk can also input relevant themes and keywords based on the activities of groups and communities that the user participates in on social media. In this way, relevant themes and keywords can be entered by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity data into AI, and the AI ​​will select themes and keywords.

[0086] The generation unit can estimate the user's emotions and adjust the method of generating content based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate content that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate short, concise content. If the user is excited, the generation unit can also generate content with visually stimulating effects. By adjusting the generation method based on the user's emotions, more appropriate content can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit inputs the user's facial expression data into the generation AI, which estimates the emotions and adjusts the method of generating content.

[0087] The generation unit can adjust the level of detail generated based on the importance of themes and keywords during content generation. For example, the generation unit can generate detailed content based on important themes and keywords. The generation unit can also generate concise content based on general themes and keywords. The generation unit can also generate specialized content based on themes and keywords related to specific events or topics. This allows for the generation of more appropriate content by adjusting the level of detail based on the importance of themes and keywords. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the importance data of themes and keywords into the generation AI, and the generation AI adjusts the level of detail of the generation.

[0088] The generation unit can apply different generation algorithms depending on the theme and keyword category when generating material. For example, the generation unit can apply a generation algorithm that emphasizes humor to themes and keywords in the comedy category. It can also apply a generation algorithm that emphasizes emotion to themes and keywords in the drama category. It can also apply a generation algorithm that emphasizes facts to themes and keywords in the news category. By applying a generation algorithm according to the category, more appropriate material can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs theme and keyword category data into a generation AI, and the generation AI applies a generation algorithm.

[0089] The generation unit can estimate the user's emotions and adjust the length of the generated material based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate longer material. If the user is in a hurry, the generation unit can also generate shorter, more concise material. If the user is excited, the generation unit can also generate material with visually stimulating effects. By adjusting the length of the material based on the user's emotions, more appropriate material can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit inputs the user's facial expression data into the generation AI, which estimates the emotions and adjusts the length of the material.

[0090] The generation unit can determine the generation priority based on the submission timing of themes and keywords when generating content. For example, the generation unit can prioritize generating content based on urgent themes and keywords. It can also prioritize generating content based on themes and keywords with approaching submission deadlines. It can also prioritize generating content based on themes and keywords frequently used by users. This allows for content generation at a more appropriate time by determining priorities based on submission timing. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs theme and keyword submission timing data into the generation AI, and the generation AI determines the generation priority.

[0091] The generation unit can adjust the generation order based on the relevance of themes and keywords during content generation. For example, the generation unit can prioritize generating content based on highly relevant themes and keywords. The generation unit can also postpone generating content based on less relevant themes and keywords. The generation unit can also prioritize generating content based on highly relevant themes and keywords specified by the user. This allows for the generation of more appropriate content by adjusting the generation order based on relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs theme and keyword relevance data into the generation AI, and the generation AI adjusts the generation order.

[0092] The suggestion unit can estimate the user's emotions and adjust the way ideas are suggested based on those emotions. For example, if the user is relaxed, the suggestion unit may suggest detailed ideas. If the user is in a hurry, the suggestion unit may suggest concise ideas. If the user is excited, the suggestion unit may suggest visually stimulating ideas. By adjusting the suggestion method based on the user's emotions, more appropriate ideas can be suggested. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit inputs user facial expression data into a generative AI, which estimates emotions and adjusts the way ideas are suggested.

[0093] The proposal department can adjust the level of detail of an idea based on the importance of themes and keywords. For example, the proposal department can propose detailed ideas based on important themes and keywords. It can also propose concise ideas based on general themes and keywords. It can also propose specialized ideas based on themes and keywords related to specific events or topics. This allows for the proposal of more appropriate ideas by adjusting the level of detail based on the importance of themes and keywords. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department inputs importance data for themes and keywords into the AI, and the AI ​​adjusts the level of detail of the proposal.

[0094] The suggestion function can apply different suggestion algorithms depending on the theme and keyword category when suggesting ideas. For example, the suggestion function can apply a suggestion algorithm that emphasizes humor to themes and keywords in the comedy category. It can also apply a suggestion algorithm that emphasizes emotion to themes and keywords in the drama category. It can also apply a suggestion algorithm that emphasizes facts to themes and keywords in the news category. By applying a suggestion algorithm according to the category, more appropriate ideas can be suggested. Some or all of the above processing in the suggestion function may be performed using AI or not. For example, the suggestion function inputs theme and keyword category data into the AI, and the AI ​​applies a suggestion algorithm.

[0095] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can offer longer suggestions. If the user is in a hurry, the suggestion unit can offer shorter, more concise suggestions. If the user is excited, the suggestion unit can offer suggestions with visually stimulating effects. By adjusting the length of suggestions based on the user's emotions, more appropriate ideas can be suggested. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit inputs user facial expression data into a generative AI, which estimates the emotions and adjusts the length of the suggestions.

[0096] The proposal department can prioritize proposals based on the submission timing of themes and keywords when submitting ideas. For example, the proposal department can prioritize ideas based on urgent themes and keywords. It can also prioritize ideas based on themes and keywords with approaching submission deadlines. It can also prioritize ideas based on themes and keywords frequently used by users. This allows for ideas to be proposed at a more appropriate time by prioritizing based on submission timing. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department inputs theme and keyword submission timing data into the AI, and the AI ​​determines the priority of proposals.

[0097] The suggestion function can adjust the order of suggestions based on the relevance of themes and keywords when proposing ideas. For example, the suggestion function can prioritize suggesting ideas based on highly relevant themes and keywords. It can also postpone suggesting ideas based on less relevant themes and keywords. The suggestion function can also prioritize suggesting ideas based on highly relevant themes and keywords specified by the user. This allows for the suggestion of more appropriate ideas by adjusting the order of suggestions based on relevance. Some or all of the above processing in the suggestion function may be performed using AI or not. For example, the suggestion function can input theme and keyword relevance data into the AI, and the AI ​​can adjust the order of suggestions.

[0098] The evaluation unit can estimate the user's emotions and adjust the evaluation method based on the estimated emotions. For example, if the user is relaxed, the evaluation unit can provide detailed feedback. If the user is in a hurry, the evaluation unit can also provide concise feedback. If the user is excited, the evaluation unit can also provide visually stimulating feedback. This allows for more appropriate feedback to be provided by adjusting the evaluation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit inputs the user's facial expression data into the generative AI, which estimates the emotions and adjusts the evaluation method.

[0099] The evaluation unit can analyze the user's past joke creation history during the evaluation process to select the optimal evaluation method. For example, the evaluation unit can select the optimal evaluation method based on the evaluation results of jokes the user has created in the past. The evaluation unit can also extract specific patterns from the user's past joke creation history and select an evaluation method based on those patterns. The evaluation unit can also analyze feedback the user has received in the past and select an evaluation method based on that. In this way, the optimal evaluation method can be selected by analyzing the past joke creation history. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the user's past joke creation history data into the AI, and the AI ​​can select the optimal evaluation method.

[0100] The evaluation unit can customize its evaluation methods based on the user's current areas of interest during the evaluation process. For example, the evaluation unit can customize its evaluation methods based on topics the user is currently interested in. The evaluation unit can also customize its evaluation methods based on topics the user frequently mentions on social media. The evaluation unit can also customize its evaluation methods based on content the user has recently searched for. This allows for the provision of more appropriate feedback by customizing the evaluation methods based on the user's current areas of interest. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input user area of ​​interest data into the AI, and the AI ​​can customize the evaluation methods.

[0101] The evaluation unit can estimate the user's emotions and determine the priority of evaluations based on the estimated emotions. For example, if the user is relaxed, the evaluation unit may prioritize detailed evaluations. If the user is in a hurry, the evaluation unit may also prioritize concise evaluations. If the user is excited, the evaluation unit may also prioritize visually stimulating evaluations. This allows for more appropriate feedback to be provided by prioritizing evaluations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit inputs user facial expression data into a generative AI, which estimates emotions and determines the priority of evaluations.

[0102] The evaluation unit can select the optimal evaluation method by considering the user's geographical location information during the evaluation process. For example, if the user is in a specific region, the evaluation unit can select an evaluation method related to that region. If the user is traveling, the evaluation unit can also select an evaluation method related to the travel destination. If the user is participating in a specific event, the evaluation unit can also select an evaluation method related to that event. In this way, the optimal evaluation method can be selected by considering geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit inputs the user's geographical location data into the AI, and the AI ​​selects the optimal evaluation method.

[0103] The evaluation unit can analyze a user's social media activity during the evaluation process and propose evaluation methods. For example, the evaluation unit can propose evaluation methods based on topics that the user frequently mentions on social media. The evaluation unit can also analyze the content of posts from accounts that the user follows on social media and propose evaluation methods. The evaluation unit can also propose evaluation methods based on the activities of groups and communities that the user participates in on social media. This allows for the proposal of more appropriate evaluation methods by analyzing social media activity. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit inputs the user's social media activity data into an AI, and the AI ​​proposes evaluation methods.

[0104] The rehearsal unit can estimate the user's emotions and adjust the rehearsal method based on the estimated emotions. For example, if the user is relaxed, the rehearsal unit will conduct a rehearsal at a relaxed pace. If the user is in a hurry, the rehearsal unit can conduct a short, to-the-point rehearsal. If the user is excited, the rehearsal unit can also conduct a rehearsal with visually stimulating effects. This allows for a more appropriate rehearsal by adjusting the rehearsal method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit inputs the user's facial expression data into the generative AI, which estimates the emotions and adjusts the rehearsal method.

[0105] The rehearsal unit can analyze the user's past rehearsal history during rehearsals to select the optimal rehearsal method. For example, the rehearsal unit can select the optimal rehearsal method based on the results of past rehearsals conducted by the user. The rehearsal unit can also extract specific patterns from the user's past rehearsal history and select a rehearsal method based on those patterns. The rehearsal unit can also analyze feedback the user has received in the past and select a rehearsal method based on that feedback. In this way, the optimal rehearsal method can be selected by analyzing past rehearsal history. Some or all of the above processes in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit can input the user's past rehearsal history data into AI, and the AI ​​can select the optimal rehearsal method.

[0106] The rehearsal unit can customize the rehearsal process based on the user's current areas of interest during the rehearsal. For example, the rehearsal unit can customize the rehearsal process based on topics the user is currently interested in. The rehearsal unit can also customize the rehearsal process based on topics the user frequently mentions on social media. The rehearsal unit can also customize the rehearsal process based on content the user has recently searched for. This allows for more appropriate rehearsals by customizing the rehearsal process based on the user's current areas of interest. Some or all of the above processes in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit can input the user's areas of interest data into the AI, and the AI ​​can customize the rehearsal process.

[0107] The rehearsal unit can estimate the user's emotions and determine the priority of rehearsals based on the estimated emotions. For example, if the user is relaxed, the rehearsal unit may prioritize detailed rehearsals. If the user is in a hurry, the rehearsal unit may also prioritize concise rehearsals. If the user is excited, the rehearsal unit may also prioritize visually stimulating rehearsals. This allows for more appropriate rehearsals by prioritizing rehearsals based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit inputs user facial expression data into a generative AI, which estimates emotions and determines the priority of rehearsals.

[0108] The rehearsal unit can select the optimal rehearsal method during rehearsals, taking into account the user's geographical location. For example, if the user is in a specific region, the rehearsal unit can select a rehearsal method relevant to that region. If the user is traveling, the rehearsal unit can also select a rehearsal method relevant to the travel destination. If the user is participating in a specific event, the rehearsal unit can also select a rehearsal method relevant to that event. In this way, the optimal rehearsal method can be selected by considering geographical location information. Some or all of the above processing in the rehearsal unit may be performed using AI, or not. For example, the rehearsal unit inputs the user's geographical location data into the AI, and the AI ​​selects the optimal rehearsal method.

[0109] The rehearsal department can analyze the user's social media activity during rehearsals and propose rehearsal methods. For example, the rehearsal department can propose rehearsal methods based on topics that the user frequently mentions on social media. The rehearsal department can also propose rehearsal methods by analyzing the content of posts from accounts that the user follows on social media. The rehearsal department can also propose rehearsal methods based on the activities of groups and communities that the user participates in on social media. In this way, by analyzing social media activity, more appropriate rehearsal methods can be proposed. Some or all of the above processing in the rehearsal department may be performed using AI or not. For example, the rehearsal department inputs the user's social media activity data into AI, and the AI ​​proposes rehearsal methods.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The comedy AI writer system may further include a generation unit that estimates the user's emotions and adjusts the joke generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit may generate jokes that proceed at a leisurely pace. If the user is in a hurry, it may also generate short, concise jokes. If the user is excited, it may also generate jokes with visually stimulating effects. This allows for the generation of more appropriate jokes by adjusting the generation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit inputs the user's facial expression data into the generation AI, which estimates the emotions and adjusts the joke generation method.

[0112] The comedy AI writer system may further include a generation unit that analyzes the user's past joke creation history and selects the optimal joke generation method. The generation unit may, for example, select the optimal generation method based on the evaluation results of jokes created by the user in the past. It may also extract specific patterns from the user's past joke creation history and select a generation method based on those patterns. It may also analyze feedback the user has received in the past and select a generation method based on that. In this way, the optimal generation method can be selected by analyzing the past joke creation history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit may input the user's past joke creation history data into the AI, and the AI ​​may select the optimal generation method.

[0113] The comedy AI writer system may further include a suggestion unit that estimates the user's emotions and adjusts the way ideas are suggested based on those emotions. For example, if the user is relaxed, the suggestion unit may suggest detailed ideas. If the user is in a hurry, it may suggest concise ideas. If the user is excited, it may suggest visually stimulating ideas. This allows for the suggestion of more appropriate ideas by adjusting the suggestion method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit inputs the user's facial expression data into the generative AI, which estimates the emotions and adjusts the way ideas are suggested.

[0114] The comedy AI writer system may further include a suggestion section that proposes ideas based on the user's current areas of interest. For example, the suggestion section might prioritize suggesting relevant ideas based on topics the user has recently been interested in. It could also suggest relevant ideas based on topics the user frequently mentions on social media. It could also suggest relevant ideas based on recent searches the user has conducted. This allows the system to provide highly relevant ideas by suggesting them based on the user's current areas of interest. Some or all of the above processing in the suggestion section may be performed using AI or not. For example, the suggestion section might input user area of ​​interest data into an AI, which then suggests ideas.

[0115] The comedy AI writer system may further include an evaluation unit that estimates the user's emotions and adjusts the evaluation method based on the estimated emotions. For example, the evaluation unit may provide detailed feedback if the user is relaxed, concise feedback if the user is in a hurry, or visually stimulating feedback if the user is excited. This allows for more appropriate feedback to be provided by adjusting the evaluation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit inputs the user's facial expression data into the generative AI, which estimates the emotions and adjusts the evaluation method.

[0116] The comedy AI writer system may further include an evaluation unit that analyzes the user's past joke creation history and selects the optimal evaluation method. The evaluation unit may, for example, select the optimal evaluation method based on the evaluation results of jokes created by the user in the past. It may also extract specific patterns from the user's past joke creation history and select an evaluation method based on those patterns. It may also analyze feedback the user has received in the past and select an evaluation method based on that. In this way, the optimal evaluation method can be selected by analyzing the past joke creation history. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit may input the user's past joke creation history data into the AI, and the AI ​​may select the optimal evaluation method.

[0117] The comedy AI writer system may further include a rehearsal unit that estimates the user's emotions and adjusts the rehearsal method based on the estimated emotions. For example, if the user is relaxed, the rehearsal unit may conduct a rehearsal at a relaxed pace. If the user is in a hurry, it may conduct a short, to-the-point rehearsal. If the user is excited, it may conduct a rehearsal with visually stimulating effects. This allows for more appropriate rehearsals by adjusting the rehearsal method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit inputs the user's facial expression data into the generative AI, which estimates the emotions and adjusts the rehearsal method.

[0118] The comedy AI writer system may also include a rehearsal unit that analyzes the user's past rehearsal history and selects the optimal rehearsal method. The rehearsal unit may, for example, select the optimal rehearsal method based on the results of past rehearsals conducted by the user. It may also extract specific patterns from the user's past rehearsal history and select a rehearsal method based on those patterns. It may also analyze feedback the user has received in the past and select a rehearsal method based on that. In this way, the optimal rehearsal method can be selected by analyzing past rehearsal history. Some or all of the above processing in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit may input the user's past rehearsal history data into the AI, and the AI ​​may select the optimal rehearsal method.

[0119] The comedy AI writer system may further include a rehearsal unit that estimates the user's emotions and determines the priority of rehearsals based on the estimated emotions. For example, if the user is relaxed, the rehearsal unit may prioritize detailed rehearsals. If the user is in a hurry, it may prioritize concise rehearsals. If the user is excited, it may prioritize visually stimulating rehearsals. This allows for more appropriate rehearsals by determining the priority of rehearsals based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit inputs the user's facial expression data into the generative AI, which estimates the emotions and determines the priority of rehearsals.

[0120] The comedy AI writer system may further include a rehearsal unit that selects the optimal rehearsal method by considering the user's geographical location. For example, if the user is in a specific region, the rehearsal unit may select a rehearsal method related to that region. If the user is traveling, it may also select a rehearsal method related to the travel destination. If the user is participating in a specific event, it may also select a rehearsal method related to that event. This allows for the selection of the optimal rehearsal method by considering geographical location. Some or all of the above processing in the rehearsal unit may be performed using AI or not. For example, the rehearsal unit may input the user's geographical location data into the AI, and the AI ​​may select the optimal rehearsal method.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk accepts themes and keywords entered by the user for generating content. Step 2: The generation unit uses a generation AI to generate content based on the themes and keywords received by the reception unit. For example, the generation AI receives a theme and keywords as input and generates content. The generation AI can generate content using a text generation AI (e.g., LLM). Step 3: The proposal unit proposes diverse ideas for the material generated by the generation unit. For example, it can utilize a vast database to propose diverse ideas for the generated material. Based on the database, the proposal unit can propose ideas from different perspectives and unique concepts. Step 4: The evaluation department provides objective evaluation and feedback based on the ideas proposed by the proposal department. For example, it may use speech recognition technology to analyze the voice of the user performing the material and provide real-time feedback. The evaluation department can also analyze real-time news and suggest current events as material. Step 5: The rehearsal team conducts a rehearsal in front of a virtual audience based on the feedback provided by the evaluation team. For example, by integrating with VR technology, users can rehearse in front of a virtual audience.

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0126] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, evaluation unit, and rehearsal unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives themes and keywords entered by the user. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the generation AI receives themes and keywords as input and generates material. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes diverse ideas for the generated material using a vast database. The evaluation unit is implemented, for example, by the control unit 46A of the smart device 14, where it analyzes the voice of the user when they perform the material using speech recognition technology and provides real-time feedback. The rehearsal unit allows the user to rehearse in front of a virtual audience, for example, by coordinating with the output device 40 of the smart device 14 and VR technology. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, evaluation unit, and rehearsal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives themes and keywords entered by the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and the generation AI receives themes and keywords as input and generates material. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes various ideas for the generated material using a vast database. The evaluation unit is implemented by the control unit 46A of the smart glasses 214 and uses speech recognition technology to analyze the voice of the user when performing the material and provides real-time feedback. The rehearsal unit allows the user to rehearse in front of a virtual audience by coordinating the speaker 240 of the smart glasses 214 with VR technology. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, evaluation unit, and rehearsal unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives themes and keywords entered by the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the generation AI receives themes and keywords as input and generates material. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes diverse ideas for the generated material using a vast database. The evaluation unit is implemented by, for example, the control unit 46A of the headset terminal 314, which analyzes the voice of the user when they perform the material using speech recognition technology and provides real-time feedback. The rehearsal unit allows the user to rehearse in front of a virtual audience by, for example, the display 343 of the headset terminal 314 and in cooperation with VR technology. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 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.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, evaluation unit, and rehearsal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives themes and keywords entered by the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and the generation AI receives themes and keywords as input and generates material. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes various ideas for the generated material using a vast database. The evaluation unit is implemented by, for example, the control unit 46A of the robot 414, and uses speech recognition technology to analyze the voice of the user when performing the material and provides real-time feedback. The rehearsal unit allows the user to rehearse in front of a virtual audience by, for example, the speaker 240 of the robot 414 and in cooperation with VR technology. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A reception desk that accepts themes and keywords for generating content, A generation unit that generates material based on themes and keywords received by the reception unit, A proposal unit that proposes various ideas for the material generated by the generation unit, An evaluation unit provides objective evaluation and feedback based on the ideas proposed by the aforementioned proposal unit, The system includes a rehearsal unit that performs a rehearsal in front of a virtual audience based on feedback provided by the evaluation unit. A system characterized by the following features. (Note 2) The generating unit is Generate jokes quickly using a generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose diverse ideas by utilizing a vast database. The system described in Appendix 1, characterized by the features described herein. (Note 4) The evaluation unit described above, Using speech recognition technology, the system analyzes the user's voice while they perform their routine and provides real-time feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The evaluation unit described above, We analyze real-time news and suggest current events topics. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned rehearsal section, Rehearsals are conducted in front of a virtual audience through integration with VR technology. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of theme and keyword input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system analyzes the user's past theme and keyword input history to select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter themes or keywords, the system filters them based on their current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input themes and keywords based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter themes or keywords, the system prioritizes themes and keywords that are most relevant to their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users enter themes or keywords, the system analyzes their social media activity and inputs relevant themes and keywords. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is We estimate the user's emotions and adjust the content generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating content, adjust the level of detail based on the importance of the theme and keywords. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating content, different generation algorithms are applied depending on the theme and keyword category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the generated content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating content ideas, prioritize the generation based on the timing of theme and keyword submissions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating content ideas, the order of generation is adjusted based on the relevance of themes and keywords. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the idea suggestion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When proposing ideas, adjust the level of detail in the proposal based on the importance of the theme and keywords. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When proposing ideas, different suggestion algorithms are applied depending on the theme and keyword categories. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting ideas, prioritize proposals based on the timing of theme and keyword submissions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When proposing ideas, adjust the order of proposals based on the relevance of themes and keywords. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit described above, It estimates the user's emotions and adjusts the evaluation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The evaluation unit described above, During the evaluation process, the system analyzes the user's past content creation history to select the most suitable evaluation method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The evaluation unit described above, During the evaluation process, the evaluation method is customized based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The evaluation unit described above, It estimates the user's emotions and determines the priority of evaluations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The evaluation unit described above, During evaluation, the optimal evaluation method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The evaluation unit described above, During the evaluation process, we will analyze users' social media activity and propose evaluation methods. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned rehearsal section, The system estimates the user's emotions and adjusts the rehearsal method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned rehearsal section, During rehearsals, the system analyzes the user's past rehearsal history to select the optimal rehearsal method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned rehearsal section, During rehearsals, the rehearsal methods are customized based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned rehearsal section, The system estimates the user's emotions and prioritizes rehearsals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned rehearsal section, During rehearsals, the optimal rehearsal method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned rehearsal section, During rehearsals, we analyze users' social media activity and propose rehearsal methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts themes and keywords for generating content, A generation unit that generates material based on themes and keywords received by the reception unit, A proposal unit that proposes various ideas for the material generated by the generation unit, An evaluation unit provides objective evaluation and feedback based on the ideas proposed by the aforementioned proposal unit, The system includes a rehearsal unit that performs a rehearsal in front of a virtual audience based on feedback provided by the evaluation unit. A system characterized by the following features.

2. The generating unit is Generate jokes quickly using AI. The system according to feature 1.

3. The aforementioned proposal section is, We propose diverse ideas by utilizing a vast database. The system according to feature 1.

4. The evaluation unit, Using speech recognition technology, the system analyzes the user's voice while they perform their skit and provides real-time feedback. The system according to feature 1.

5. The evaluation unit, We analyze real-time news and suggest current events topics. The system according to feature 1.

6. The aforementioned rehearsal section, Rehearsals are conducted in front of a virtual audience through integration with VR technology. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of theme and keyword input based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is The system analyzes the user's past theme and keyword input history to select the optimal input method. The system according to feature 1.

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