system

Abstract

The system according to this embodiment aims to automatically generate and optimize high-quality creative content based on user requests. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, an optimization unit, a feedback unit, and a learning unit. The reception unit receives user requests. The generation unit generates content based on the information received by the reception unit. The optimization unit optimizes the content generated by the generation unit. The feedback unit receives user feedback to ensure the quality of the content optimized by the optimization unit. The learning unit allows the generation unit to learn from past data.

Description

Technical Field

[0006] , , , ,

[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, the method 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 that responds 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, the process of automatically generating and optimizing high-quality creative content based on user requests has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically generate and optimize high-quality creative content based on user requests.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, an optimization unit, a feedback unit, and a learning unit. The reception unit receives user requests. The generation unit generates content based on the information received by the reception unit. The optimization unit optimizes the content generated by the generation unit. The feedback unit receives user feedback to ensure the quality of the content optimized by the optimization unit. The learning unit allows the generation unit to learn from past data. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate and optimize high-quality creative content based on user requests. [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, and the like. 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) An automated creative content generation AI agent according to an embodiment of the present invention is an AI service that automatically generates high-quality creative content for marketing, advertising, design, publications, and social media. This automated creative content generation AI agent creates content with originality and creativity based on user requests, and further optimizes it for its intended use and target audience. First, the user inputs the type of content they want to generate (text, images, videos, etc.), its intended use, and target audience. Next, the generation AI analyzes this input information and generates the optimal creative content. The generated content is created with originality and creativity based on the user's requests and optimized for its intended use and target audience. For example, when generating an advertising image for a marketing campaign, the user inputs the purpose of the advertisement (e.g., promoting a new product), the target audience (e.g., young people), and other requirements (e.g., using brand colors). Based on this information, the generation AI generates the optimal advertising image. Similarly, when generating video content for social media, the user inputs the purpose of the video (e.g., increasing brand awareness), the target audience (e.g., users in a specific region), and other requirements (e.g., including a specific message). Based on this information, the generation AI generates optimal video content. In this way, the generation AI automatically generates high-quality creative content based on user requests, providing content optimized for its intended use and target audience. As a result, the automated creative content generation AI agent can automatically generate high-quality creative content based on user requests and provide optimized content.

[0029] The automated creative content generation AI agent according to this embodiment comprises a reception unit, a generation unit, an optimization unit, a feedback unit, and a learning unit. The reception unit receives user requests. User requests include, but are not limited to, requests in text format or audio format. The reception unit can, for example, allow the user to input the type of content they want to generate (text, image, video, etc.), its purpose of use, and target audience. The generation unit generates content based on the information received by the reception unit. The generation unit generates content with originality and creativity based on the user's requests, for example, using a generation AI. The generation unit analyzes the user's requests using the generation AI and generates optimal creative content. For example, when the generation unit generates an advertising image for a marketing campaign, the user inputs the purpose of the advertisement (e.g., promotion of a new product), the target audience (e.g., young people), and other requirements (e.g., use of brand colors). The generation unit generates the optimal advertising image based on this information. The optimization unit optimizes the content generated by the generation unit. The optimization unit optimizes the generated content for its intended use and target audience. The optimization unit optimizes the generated content based on user requests. For example, when the optimization unit generates video content for social media, the user inputs the purpose of the video (e.g., increasing brand awareness), target audience (e.g., users in a specific region), and other requirements (e.g., including a specific message). The optimization unit generates optimal video content based on this information. The feedback unit accepts user feedback to ensure the quality of the content optimized by the optimization unit. The feedback unit accepts user feedback and ensures the quality of the content. The feedback unit analyzes user feedback and identifies areas for improvement to enhance the quality of the content. The learning unit allows the generation unit to learn from past data. The learning unit learns from past user request data, past generated content data, etc., and reflects this in the content generation of the generation unit.The learning unit analyzes past data and optimizes the content generation algorithm of the generation unit. As a result, the automated creative content generation AI agent according to the embodiment can automatically generate high-quality creative content based on user requests and provide optimized content.

[0030] The reception unit receives user requests. User requests include, but are not limited to, requests in text format, audio format, etc. The reception unit can, for example, allow users to input the type of content they want to generate (text, images, videos, etc.), its purpose of use, and target audience. Specifically, the reception unit receives input from users through a user interface. The user interface can take the form of a web browser or mobile application and is designed to be intuitive for users to operate. Users can select the type of content they want to generate and detailed requirements using dropdown menus and checkboxes on the interface. It is also possible to accept voice input using speech recognition technology, allowing users to input requests simply by speaking into a microphone. The reception unit analyzes these inputs in real time to accurately understand user requests. Furthermore, the reception unit can refer to the user's past request history and make suggestions that take into account the user's preferences and tendencies. For example, it can suggest new images in a similar style to a user who has previously generated images in a particular style. This allows the reception unit to quickly and accurately receive user requests and provide appropriate information to the generation unit.

[0031] The generation unit generates content based on information received by the reception unit. The generation unit, for example, uses a generation AI to generate content that is unique and creative based on user requests. The generation unit uses the generation AI to analyze user requests and generate optimal creative content. Specifically, the generation unit utilizes natural language processing and image generation technologies to generate diverse content that meets user requests. For example, in the case of text content, the generation AI collects relevant information based on keywords and themes entered by the user and generates original text. In the case of image content, the generation AI generates original images based on the style and color scheme specified by the user. In the case of video content, the generation AI combines multiple video clips to generate a video based on the scenario and message specified by the user. By combining these technologies, the generation unit can respond to diverse user requests. Furthermore, the generation unit can evaluate the quality of the generated content and make corrections as needed. For example, it evaluates whether the generated text is grammatically correct and whether the generated images are visually appealing, and makes corrections as necessary. This allows the generation unit to provide high-quality creative content that meets user requests.

[0032] The optimization unit optimizes the content generated by the generation unit. For example, the optimization unit optimizes the generated content for its intended use and target audience. The optimization unit optimizes the generated content based on user requests. Specifically, the optimization unit adjusts the format and content of the generated content to make it best suited to the user's purpose. For example, when generating video content for social media, the optimization unit adjusts the video length and format to make it optimal for the specific platform. It can also adjust the tone and style of the content according to the target audience. For example, advertising content aimed at young people will adopt a casual and friendly tone and emphasize visual elements. On the other hand, business content will adopt a professional and reliable tone and emphasize the accuracy of information. By making these adjustments, the optimization unit ensures that the generated content is delivered in a form that best suits the user's purpose. Furthermore, the optimization unit continuously improves the quality of the generated content based on user feedback. For example, it analyzes user feedback, identifies areas for improvement in the content, and reflects them in the next generation. This allows the optimization unit to consistently provide high-quality content and improve user satisfaction.

[0033] The Feedback Unit accepts user feedback to ensure the quality of content optimized by the Optimization Unit. Specifically, the Feedback Unit collects feedback through the user interface. Users can input evaluations and suggestions for improvement regarding the generated content. The Feedback Unit collects and analyzes this feedback in real time. For example, if a user is dissatisfied with the color scheme or composition of a generated image, the Feedback Unit adjusts the color scheme and composition in the next generation based on that feedback. The Feedback Unit can also categorize user feedback to identify common problems and areas for improvement. This allows the Feedback Unit to take concrete actions to continuously improve the quality of the generated content. Furthermore, the Feedback Unit feeds user feedback back to the Generation and Optimization Units to improve the overall generation process. For example, it can be used to adjust the algorithms used by the Generation Unit or to improve the accuracy of adjustments made by the Optimization Unit. This allows the Feedback Unit to effectively utilize user feedback, guarantee the quality of the generated content, and improve user satisfaction.

[0034] The learning unit allows the generation unit to learn from past data. For example, the learning unit learns from past user request data and past generated content data, and incorporates this learning into the generation unit's content generation. Specifically, the learning unit builds a database for collecting and analyzing past data. This database includes user request history, generated content ratings, and feedback. The learning unit analyzes this data and extracts insights to optimize the generation unit's algorithms. For example, if a particular type of content receives high ratings, it strengthens the algorithm for generating that type of content. It can also analyze user request patterns and trends to prepare for future requests. Furthermore, the learning unit collaborates with the generation and optimization units to reflect learning results in real time. For example, it can introduce new algorithms and adjustment methods to improve the accuracy and efficiency of the entire generation process. This allows the learning unit to effectively utilize past data and continuously improve the generation unit's content generation capabilities. Additionally, the learning unit enhances the self-learning capabilities of the generation AI, realizing a content generation agent that evolves over time. This allows the learning unit to constantly incorporate the latest information and technologies, providing high-quality creative content that meets user demands.

[0035] The generation unit can generate content with originality and creativity based on user requests. For example, the generation unit uses generational AI to generate content with originality and creativity based on user requests. The generation unit's generational AI analyzes user requests and generates optimal creative content. For example, when the generation unit generates an advertising image for a marketing campaign, the user inputs the advertising objective (e.g., promoting a new product), target audience (e.g., young people), and other requirements (e.g., using brand colors). The generation unit generates the optimal advertising image based on this information. The generation unit's generational AI analyzes user requests and generates optimal creative content. For example, when the generation unit generates video content for social media, the user inputs the video objective (e.g., increasing brand awareness), target audience (e.g., users in a specific region), and other requirements (e.g., including a specific message). The generation unit generates the optimal video content based on this information. This allows the generation unit to generate content with originality and creativity based on user requests.

[0036] The optimization unit can optimize the generated content to its intended use and target audience. For example, the optimization unit optimizes the generated content to its intended use and target audience. The optimization unit optimizes the generated content based on user requests. For example, when the optimization unit generates video content for social media, the user inputs the video's purpose (e.g., increasing brand awareness), target audience (e.g., users in a specific region), and other requirements (e.g., including a specific message). Based on this information, the optimization unit generates the optimal video content. The optimization unit optimizes the generated content to its intended use and target audience. For example, when the optimization unit generates advertising images for a marketing campaign, the user inputs the advertising's purpose (e.g., promoting a new product), target audience (e.g., young people), and other requirements (e.g., using brand colors). Based on this information, the optimization unit generates the optimal advertising image. This allows the optimization unit to optimize the generated content to its intended use and target audience.

[0037] The feedback department can receive user feedback and guarantee the quality of the content. For example, the feedback department can receive user feedback and guarantee the quality of the content. The feedback department analyzes user feedback and identifies areas for improvement to enhance the quality of the content. The feedback department can receive user feedback and guarantee the quality of the content. For example, the feedback department can receive user feedback and guarantee the quality of the content. The feedback department analyzes user feedback and identifies areas for improvement to enhance the quality of the content. Thus, by receiving user feedback, the feedback department can guarantee the quality of the content.

[0038] The learning unit can learn from past data and reflect that learning in the content generation of the generation unit. For example, the learning unit can learn from past user request data, past generated content data, etc., and reflect that learning in the content generation of the generation unit. The learning unit analyzes past data and optimizes the content generation algorithm of the generation unit. The learning unit learns from past data and reflects that learning in the content generation of the generation unit. For example, the learning unit can learn from past user request data, past generated content data, etc., and reflect that learning in the content generation of the generation unit. The learning unit analyzes past data and optimizes the content generation algorithm of the generation unit. In this way, the learning unit can learn from past data and reflect that learning in the content generation of the generation unit.

[0039] The reception department can analyze the user's past request history and select the optimal reception method. For example, the reception department may prioritize suggesting reception methods that the user has frequently used in the past. The reception department can suggest the optimal reception method for a specific time period based on the user's past request history. The reception department can analyze the user's past request history and select the most efficient reception method. Thus, the reception department can select the optimal reception method by analyzing the user's past request history. Some or all of the above processing in the reception department may be performed using AI, for example, or without using AI.

[0040] The reception unit can filter requests based on the user's current projects and areas of interest when receiving them. For example, the reception unit can prioritize requests related to projects the user is currently working on. The reception unit can filter and accept relevant requests based on the user's areas of interest. The reception unit can accept the most relevant requests according to the progress of the user's current projects. In this way, the reception unit can prioritize receiving highly relevant requests by filtering requests based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI.

[0041] The reception unit can prioritize receiving requests based on their relevance, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize requests related to that region. If the user is on the move, the reception unit can prioritize requests based on their current location. If the user is participating in a specific event, the reception unit can prioritize requests related to that event. In this way, the reception unit can prioritize receiving requests based on their relevance by taking into account the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI.

[0042] The reception unit can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception unit can accept relevant requests based on information shared by the user on social media. The reception unit can prioritize requests based on the number of followers and influence of the user on social media. The reception unit can accept the most suitable requests based on the content of the user's social media posts. In this way, the reception unit can accept relevant requests by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI.

[0043] The generation unit can adjust the level of detail of the generated content based on the importance of the request. For example, for high-priority requests, the generation unit can generate text with detailed explanations. For low-priority requests, the generation unit can generate concise images. For medium-priority requests, the generation unit can generate videos of appropriate length. In this way, the generation unit can generate more appropriate content by adjusting the level of detail of the generated content based on the importance of the request. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0044] The generation unit can apply different generation algorithms depending on the category of the request when generating content. For example, the generation unit can apply an algorithm that enhances advertising effectiveness to marketing content. For design content, it can apply an algorithm that emphasizes visual appeal. For publication content, it can apply an algorithm that emphasizes readability. In this way, the generation unit can generate more appropriate content by applying different generation algorithms depending on the category of the request. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0045] The generation unit can determine the generation priority based on the submission timing of the requests when generating content. For example, the generation unit can generate content with the highest priority for urgent requests. The generation unit can also prioritize the generation of content for requests with approaching submission deadlines. The generation unit can postpone the generation of content for requests with distant submission deadlines. In this way, the generation unit can generate content in a more appropriate order by determining the generation priority based on the submission timing of the requests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0046] The generation unit can adjust the generation order based on the relevance of the requests when generating content. For example, the generation unit can prioritize generating content for requests with high relevance. For requests with moderate relevance, the generation unit can generate content in an appropriate order. For requests with low relevance, the generation unit can postpone the generation of content. In this way, the generation unit can generate content in a more appropriate order by adjusting the generation order based on the relevance of the requests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0047] The optimization unit can improve the accuracy of optimization by considering the interrelationships of content during the optimization process. For example, the optimization unit optimizes by linking related content together. The optimization unit can analyze the interrelationships of content and perform consistent optimization. The optimization unit can perform optimization that balances the overall system by considering the interrelationships of content. In this way, the optimization unit can improve the accuracy of optimization by considering the interrelationships of content. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without using AI.

[0048] The optimization unit can perform optimization while considering the attribute information of the content submitter. For example, the optimization unit can perform optimal optimization based on the submitter's expertise. The optimization unit can perform optimal optimization by referring to the submitter's past submission history. The optimization unit can perform optimal optimization by analyzing the submitter's attribute information. In this way, the optimization unit can perform optimal optimization by considering the attribute information of the content submitter. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI.

[0049] The optimization unit can perform optimization while considering the geographical distribution of content. For example, the optimization unit can prioritize optimizing content that is geographically close. The optimization unit can perform optimal optimization while considering geographical distribution. The optimization unit can perform optimization for each region based on geographical information. As a result, the optimization unit can perform optimal optimization for each region by considering the geographical distribution of content. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI.

[0050] The optimization unit can improve the accuracy of optimization by referring to relevant literature for the content during optimization. For example, the optimization unit performs optimal optimization based on relevant literature. The optimization unit can improve the accuracy of optimization by referring to relevant literature. The optimization unit can analyze relevant literature and perform consistent optimization. As a result, the optimization unit can improve the accuracy of optimization by referring to relevant literature for the content. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI.

[0051] The feedback unit can select the optimal feedback method by referring to past feedback data when receiving feedback. For example, the feedback unit can propose the optimal feedback method based on past feedback data. The feedback unit can analyze past feedback data and select the most efficient feedback method. The feedback unit can provide the user with the optimal feedback method by referring to past feedback data. In this way, the feedback unit can select the optimal feedback method by referring to past feedback data. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI.

[0052] The feedback unit can select the optimal feedback method when receiving feedback, taking into account the user's device information. For example, if the user is using a smartphone, the feedback unit can provide a simple feedback form. If the user is using a tablet, the feedback unit can provide a feedback form optimized for a larger screen. If the user is using a desktop, the feedback unit can provide a detailed feedback form. This allows the feedback unit to select the optimal feedback method by considering the user's device information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI.

[0053] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can analyze past learning data and improve the accuracy of the learning algorithm. The learning unit can adjust the parameters of the learning algorithm by referring to past learning data. In this way, the learning unit can optimize the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI.

[0054] The learning unit can weight the training data based on the submission date of the content during training. For example, the learning unit can give higher weight to content that has been submitted recently, and lower weight to content that has been submitted recently. The learning unit can adjust the weighting of the training data according to the submission date. This allows the learning unit to use more appropriate data for training by weighting the training data based on the submission date of the content. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can automatically prioritize user requests by referring to the user's past behavior history. For example, it can analyze the types and purposes of content that users have frequently requested in the past and prioritize similar requests if they are made again. It can also prioritize the application of content generation methods that users have previously given high ratings to. This allows the reception desk to process requests more efficiently and effectively by leveraging the user's past behavior history.

[0057] The content generation unit can consider current user trends and fads when generating content based on user requests, while maintaining originality and creativity. For example, the generation unit can collect the latest trend information from social media and news sites and reflect it in content generation. It can also analyze topics and keywords that users are interested in and generate content based on that. This allows the generation unit to provide content that incorporates the latest trends and meets user demands.

[0058] The optimization unit can incorporate user feedback in real time when optimizing generated content for its intended use and target audience. For example, if a user provides feedback on generated content, that feedback is immediately reflected in the optimization process. The optimization unit can also analyze user feedback, identify common areas for improvement, and incorporate them into future content creation. This allows the optimization unit to leverage user feedback to deliver higher quality content.

[0059] The feedback unit can automatically evaluate the importance of user feedback based on its content. For example, the feedback unit analyzes the content of user feedback and prioritizes processing high-priority feedback. Furthermore, the feedback unit can identify areas for improvement based on user feedback and provide this feedback to the generation and optimization units. This allows the feedback unit to effectively utilize user feedback to improve content quality.

[0060] The learning unit can utilize user feedback as training data when learning from past data and reflecting that learning in the content generation of the generation unit. For example, the learning unit can analyze user feedback and optimize the content generation algorithm of the generation unit. Furthermore, the learning unit can identify areas for improvement to enhance the quality of content generated by the generation unit based on user feedback. This allows the learning unit to continuously improve the content generation algorithm of the generation unit by leveraging user feedback.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives user requests. User requests may include text or audio formats. Users can input the type of content they want to generate (text, images, videos, etc.), its purpose, and target audience. Step 2: The generation unit generates content based on the information received by the reception unit. The generation unit uses generation AI to create content with originality and creativity based on user requests. For example, when generating advertising images for a marketing campaign, the user inputs the advertising objective, target audience, and other requirements, and the generation unit generates the optimal advertising image based on this information. Step 3: The optimization unit optimizes the content generated by the generation unit. The optimization unit optimizes the generated content for its intended use and target audience. For example, when generating video content for social media, the user inputs the video's purpose, target audience, and other requirements, and the optimization unit generates the optimal video content based on this information. Step 4: The Feedback Department receives user feedback to ensure the quality of the content optimized by the Optimization Department. The Feedback Department receives user feedback and ensures the quality of the content. The Feedback Department analyzes user feedback and identifies areas for improvement to enhance the quality of the content. Step 5: The learning unit learns from past data used by the generation unit. The learning unit learns from past user request data and past generated content data, and reflects this in the content generation of the generation unit. The learning unit analyzes past data and optimizes the content generation algorithm of the generation unit.

[0063] (Example of form 2) An automated creative content generation AI agent according to an embodiment of the present invention is an AI service that automatically generates high-quality creative content for marketing, advertising, design, publications, and social media. This automated creative content generation AI agent creates content with originality and creativity based on user requests, and further optimizes it for its intended use and target audience. First, the user inputs the type of content they want to generate (text, images, videos, etc.), its intended use, and target audience. Next, the generation AI analyzes this input information and generates the optimal creative content. The generated content is created with originality and creativity based on the user's requests and optimized for its intended use and target audience. For example, when generating an advertising image for a marketing campaign, the user inputs the purpose of the advertisement (e.g., promoting a new product), the target audience (e.g., young people), and other requirements (e.g., using brand colors). Based on this information, the generation AI generates the optimal advertising image. Similarly, when generating video content for social media, the user inputs the purpose of the video (e.g., increasing brand awareness), the target audience (e.g., users in a specific region), and other requirements (e.g., including a specific message). Based on this information, the generation AI generates optimal video content. In this way, the generation AI automatically generates high-quality creative content based on user requests, providing content optimized for its intended use and target audience. As a result, the automated creative content generation AI agent can automatically generate high-quality creative content based on user requests and provide optimized content.

[0064] The automated creative content generation AI agent according to this embodiment comprises a reception unit, a generation unit, an optimization unit, a feedback unit, and a learning unit. The reception unit receives user requests. User requests include, but are not limited to, requests in text format or audio format. The reception unit can, for example, allow the user to input the type of content they want to generate (text, image, video, etc.), its purpose of use, and target audience. The generation unit generates content based on the information received by the reception unit. The generation unit generates content with originality and creativity based on the user's requests, for example, using a generation AI. The generation unit analyzes the user's requests using the generation AI and generates optimal creative content. For example, when the generation unit generates an advertising image for a marketing campaign, the user inputs the purpose of the advertisement (e.g., promotion of a new product), the target audience (e.g., young people), and other requirements (e.g., use of brand colors). The generation unit generates the optimal advertising image based on this information. The optimization unit optimizes the content generated by the generation unit. The optimization unit optimizes the generated content for its intended use and target audience. The optimization unit optimizes the generated content based on user requests. For example, when the optimization unit generates video content for social media, the user inputs the purpose of the video (e.g., increasing brand awareness), target audience (e.g., users in a specific region), and other requirements (e.g., including a specific message). The optimization unit generates optimal video content based on this information. The feedback unit accepts user feedback to ensure the quality of the content optimized by the optimization unit. The feedback unit accepts user feedback and ensures the quality of the content. The feedback unit analyzes user feedback and identifies areas for improvement to enhance the quality of the content. The learning unit allows the generation unit to learn from past data. The learning unit learns from past user request data, past generated content data, etc., and reflects this in the content generation of the generation unit.The learning unit analyzes past data and optimizes the content generation algorithm of the generation unit. As a result, the automated creative content generation AI agent according to the embodiment can automatically generate high-quality creative content based on user requests and provide optimized content.

[0065] The reception unit receives user requests. User requests include, but are not limited to, requests in text format, audio format, etc. The reception unit can, for example, allow users to input the type of content they want to generate (text, images, videos, etc.), its purpose of use, and target audience. Specifically, the reception unit receives input from users through a user interface. The user interface can take the form of a web browser or mobile application and is designed to be intuitive for users to operate. Users can select the type of content they want to generate and detailed requirements using dropdown menus and checkboxes on the interface. It is also possible to accept voice input using speech recognition technology, allowing users to input requests simply by speaking into a microphone. The reception unit analyzes these inputs in real time to accurately understand user requests. Furthermore, the reception unit can refer to the user's past request history and make suggestions that take into account the user's preferences and tendencies. For example, it can suggest new images in a similar style to a user who has previously generated images in a particular style. This allows the reception unit to quickly and accurately receive user requests and provide appropriate information to the generation unit.

[0066] The generation unit generates content based on information received by the reception unit. The generation unit, for example, uses a generation AI to generate content that is unique and creative based on user requests. The generation unit uses the generation AI to analyze user requests and generate optimal creative content. Specifically, the generation unit utilizes natural language processing and image generation technologies to generate diverse content that meets user requests. For example, in the case of text content, the generation AI collects relevant information based on keywords and themes entered by the user and generates original text. In the case of image content, the generation AI generates original images based on the style and color scheme specified by the user. In the case of video content, the generation AI combines multiple video clips to generate a video based on the scenario and message specified by the user. By combining these technologies, the generation unit can respond to diverse user requests. Furthermore, the generation unit can evaluate the quality of the generated content and make corrections as needed. For example, it evaluates whether the generated text is grammatically correct and whether the generated images are visually appealing, and makes corrections as necessary. This allows the generation unit to provide high-quality creative content that meets user requests.

[0067] The optimization unit optimizes the content generated by the generation unit. For example, the optimization unit optimizes the generated content for its intended use and target audience. The optimization unit optimizes the generated content based on user requests. Specifically, the optimization unit adjusts the format and content of the generated content to make it best suited to the user's purpose. For example, when generating video content for social media, the optimization unit adjusts the video length and format to make it optimal for the specific platform. It can also adjust the tone and style of the content according to the target audience. For example, advertising content aimed at young people will adopt a casual and friendly tone and emphasize visual elements. On the other hand, business content will adopt a professional and reliable tone and emphasize the accuracy of information. By making these adjustments, the optimization unit ensures that the generated content is delivered in a form that best suits the user's purpose. Furthermore, the optimization unit continuously improves the quality of the generated content based on user feedback. For example, it analyzes user feedback, identifies areas for improvement in the content, and reflects them in the next generation. This allows the optimization unit to consistently provide high-quality content and improve user satisfaction.

[0068] The Feedback Unit accepts user feedback to ensure the quality of content optimized by the Optimization Unit. Specifically, the Feedback Unit collects feedback through the user interface. Users can input evaluations and suggestions for improvement regarding the generated content. The Feedback Unit collects and analyzes this feedback in real time. For example, if a user is dissatisfied with the color scheme or composition of a generated image, the Feedback Unit adjusts the color scheme and composition in the next generation based on that feedback. The Feedback Unit can also categorize user feedback to identify common problems and areas for improvement. This allows the Feedback Unit to take concrete actions to continuously improve the quality of the generated content. Furthermore, the Feedback Unit feeds user feedback back to the Generation and Optimization Units to improve the overall generation process. For example, it can be used to adjust the algorithms used by the Generation Unit or to improve the accuracy of adjustments made by the Optimization Unit. This allows the Feedback Unit to effectively utilize user feedback, guarantee the quality of the generated content, and improve user satisfaction.

[0069] The learning unit allows the generation unit to learn from past data. For example, the learning unit learns from past user request data and past generated content data, and incorporates this learning into the generation unit's content generation. Specifically, the learning unit builds a database for collecting and analyzing past data. This database includes user request history, generated content ratings, and feedback. The learning unit analyzes this data and extracts insights to optimize the generation unit's algorithms. For example, if a particular type of content receives high ratings, it strengthens the algorithm for generating that type of content. It can also analyze user request patterns and trends to prepare for future requests. Furthermore, the learning unit collaborates with the generation and optimization units to reflect learning results in real time. For example, it can introduce new algorithms and adjustment methods to improve the accuracy and efficiency of the entire generation process. This allows the learning unit to effectively utilize past data and continuously improve the generation unit's content generation capabilities. Additionally, the learning unit enhances the self-learning capabilities of the generation AI, realizing a content generation agent that evolves over time. This allows the learning unit to constantly incorporate the latest information and technologies, providing high-quality creative content that meets user demands.

[0070] The generation unit can generate content with originality and creativity based on user requests. For example, the generation unit uses generational AI to generate content with originality and creativity based on user requests. The generation unit's generational AI analyzes user requests and generates optimal creative content. For example, when the generation unit generates an advertising image for a marketing campaign, the user inputs the advertising objective (e.g., promoting a new product), target audience (e.g., young people), and other requirements (e.g., using brand colors). The generation unit generates the optimal advertising image based on this information. The generation unit's generational AI analyzes user requests and generates optimal creative content. For example, when the generation unit generates video content for social media, the user inputs the video objective (e.g., increasing brand awareness), target audience (e.g., users in a specific region), and other requirements (e.g., including a specific message). The generation unit generates the optimal video content based on this information. This allows the generation unit to generate content with originality and creativity based on user requests.

[0071] The optimization unit can optimize the generated content to its intended use and target audience. For example, the optimization unit optimizes the generated content to its intended use and target audience. The optimization unit optimizes the generated content based on user requests. For example, when the optimization unit generates video content for social media, the user inputs the video's purpose (e.g., increasing brand awareness), target audience (e.g., users in a specific region), and other requirements (e.g., including a specific message). Based on this information, the optimization unit generates the optimal video content. The optimization unit optimizes the generated content to its intended use and target audience. For example, when the optimization unit generates advertising images for a marketing campaign, the user inputs the advertising's purpose (e.g., promoting a new product), target audience (e.g., young people), and other requirements (e.g., using brand colors). Based on this information, the optimization unit generates the optimal advertising image. This allows the optimization unit to optimize the generated content to its intended use and target audience.

[0072] The feedback department can receive user feedback and guarantee the quality of the content. For example, the feedback department can receive user feedback and guarantee the quality of the content. The feedback department analyzes user feedback and identifies areas for improvement to enhance the quality of the content. The feedback department can receive user feedback and guarantee the quality of the content. For example, the feedback department can receive user feedback and guarantee the quality of the content. The feedback department analyzes user feedback and identifies areas for improvement to enhance the quality of the content. Thus, by receiving user feedback, the feedback department can guarantee the quality of the content.

[0073] The learning unit can learn from past data and reflect that learning in the content generation of the generation unit. For example, the learning unit can learn from past user request data, past generated content data, etc., and reflect that learning in the content generation of the generation unit. The learning unit analyzes past data and optimizes the content generation algorithm of the generation unit. The learning unit learns from past data and reflects that learning in the content generation of the generation unit. For example, the learning unit can learn from past user request data, past generated content data, etc., and reflect that learning in the content generation of the generation unit. The learning unit analyzes past data and optimizes the content generation algorithm of the generation unit. In this way, the learning unit can learn from past data and reflect that learning in the content generation of the generation unit.

[0074] The reception desk can estimate the user's emotions and adjust the timing of request acceptance based on the estimated emotions. For example, if the user is stressed, the reception desk can delay acceptance to allow the user to relax. If the user is in a hurry, the reception desk can accept the request immediately. If the user is relaxed, the reception desk can accept the request at the normal time. In this way, the reception desk can accept requests at a more appropriate time by adjusting the timing of request acceptance based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The reception department can analyze the user's past request history and select the optimal reception method. For example, the reception department may prioritize suggesting reception methods that the user has frequently used in the past. The reception department can suggest the optimal reception method for a specific time period based on the user's past request history. The reception department can analyze the user's past request history and select the most efficient reception method. Thus, the reception department can select the optimal reception method by analyzing the user's past request history. Some or all of the above processing in the reception department may be performed using AI, for example, or without using AI.

[0076] The reception unit can filter requests based on the user's current projects and areas of interest when receiving them. For example, the reception unit can prioritize requests related to projects the user is currently working on. The reception unit can filter and accept relevant requests based on the user's areas of interest. The reception unit can accept the most relevant requests according to the progress of the user's current projects. In this way, the reception unit can prioritize receiving highly relevant requests by filtering requests based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI.

[0077] The reception desk can estimate the user's emotions and determine the priority of requests to be received based on the estimated emotions. For example, if the user is stressed, the reception desk can postpone less important requests. If the user is relaxed, the reception desk can prioritize more important requests. If the user is in a hurry, the reception desk can prioritize urgent requests. In this way, the reception desk can receive requests in a more appropriate order by determining the priority of requests based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The reception unit can prioritize receiving requests based on their relevance, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize requests related to that region. If the user is on the move, the reception unit can prioritize requests based on their current location. If the user is participating in a specific event, the reception unit can prioritize requests related to that event. In this way, the reception unit can prioritize receiving requests based on their relevance by taking into account the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI.

[0079] The reception unit can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception unit can accept relevant requests based on information shared by the user on social media. The reception unit can prioritize requests based on the number of followers and influence of the user on social media. The reception unit can accept the most suitable requests based on the content of the user's social media posts. In this way, the reception unit can accept relevant requests by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI.

[0080] The generation unit can estimate the user's emotions and adjust the way it expresses the generated content based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate text in a soft tone. If the user is excited, the generation unit can generate images with energetic expressions. If the user is sad, the generation unit can generate videos with calming colors. In this way, the generation unit can generate more appropriate content by adjusting the way it expresses the content based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generation AI. Generation AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The generation unit can adjust the level of detail of the generated content based on the importance of the request. For example, for high-priority requests, the generation unit can generate text with detailed explanations. For low-priority requests, the generation unit can generate concise images. For medium-priority requests, the generation unit can generate videos of appropriate length. In this way, the generation unit can generate more appropriate content by adjusting the level of detail of the generated content based on the importance of the request. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0082] The generation unit can apply different generation algorithms depending on the category of the request when generating content. For example, the generation unit can apply an algorithm that enhances advertising effectiveness to marketing content. For design content, it can apply an algorithm that emphasizes visual appeal. For publication content, it can apply an algorithm that emphasizes readability. In this way, the generation unit can generate more appropriate content by applying different generation algorithms depending on the category of the request. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0083] The generation unit can estimate the user's emotions and adjust the length of the generated content based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise text. If the user is relaxed, the generation unit can generate a longer video with detailed explanations. If the user is excited, the generation unit can generate images with visually stimulating effects. This allows the generation unit to generate more appropriate content by adjusting the length of the content based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generation AI. Generation AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The generation unit can determine the generation priority based on the submission timing of the requests when generating content. For example, the generation unit can generate content with the highest priority for urgent requests. The generation unit can also prioritize the generation of content for requests with approaching submission deadlines. The generation unit can postpone the generation of content for requests with distant submission deadlines. In this way, the generation unit can generate content in a more appropriate order by determining the generation priority based on the submission timing of the requests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0085] The generation unit can adjust the generation order based on the relevance of the requests when generating content. For example, the generation unit can prioritize generating content for requests with high relevance. For requests with moderate relevance, the generation unit can generate content in an appropriate order. For requests with low relevance, the generation unit can postpone the generation of content. In this way, the generation unit can generate content in a more appropriate order by adjusting the generation order based on the relevance of the requests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0086] The optimization unit can estimate the user's emotions and adjust the optimization criteria based on the estimated emotions. For example, if the user is relaxed, the optimization unit can perform detailed optimization. If the user is in a hurry, the optimization unit can perform rapid optimization. If the user is excited, the optimization unit can perform visually appealing optimization. In this way, the optimization unit can perform more appropriate optimization by adjusting the optimization criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The optimization unit can improve the accuracy of optimization by considering the interrelationships of content during the optimization process. For example, the optimization unit optimizes by linking related content together. The optimization unit can analyze the interrelationships of content and perform consistent optimization. The optimization unit can perform optimization that balances the overall system by considering the interrelationships of content. In this way, the optimization unit can improve the accuracy of optimization by considering the interrelationships of content. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without using AI.

[0088] The optimization unit can perform optimization while considering the attribute information of the content submitter. For example, the optimization unit can perform optimal optimization based on the submitter's expertise. The optimization unit can perform optimal optimization by referring to the submitter's past submission history. The optimization unit can perform optimal optimization by analyzing the submitter's attribute information. In this way, the optimization unit can perform optimal optimization by considering the attribute information of the content submitter. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI.

[0089] The optimization unit can estimate the user's emotions and adjust the order in which the optimization results are displayed based on the estimated emotions. For example, if the user is relaxed, the optimization unit can prioritize displaying detailed optimization results. If the user is in a hurry, the optimization unit can prioritize displaying concise optimization results. If the user is excited, the optimization unit can prioritize displaying visually appealing optimization results. In this way, the optimization unit can display results in a more appropriate order by adjusting the order in which the optimization results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The optimization unit can perform optimization while considering the geographical distribution of content. For example, the optimization unit can prioritize optimizing content that is geographically close. The optimization unit can perform optimal optimization while considering geographical distribution. The optimization unit can perform optimization for each region based on geographical information. As a result, the optimization unit can perform optimal optimization for each region by considering the geographical distribution of content. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI.

[0091] The optimization unit can improve the accuracy of optimization by referring to relevant literature for the content during optimization. For example, the optimization unit performs optimal optimization based on relevant literature. The optimization unit can improve the accuracy of optimization by referring to relevant literature. The optimization unit can analyze relevant literature and perform consistent optimization. As a result, the optimization unit can improve the accuracy of optimization by referring to relevant literature for the content. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI.

[0092] The feedback unit can estimate the user's emotions and adjust how feedback is received based on the estimated emotions. For example, if the user is relaxed, the feedback unit may request detailed feedback. If the user is in a hurry, the feedback unit may request concise feedback. If the user is excited, the feedback unit may provide a visually appealing feedback form. This allows the feedback unit to receive feedback in a more appropriate way by adjusting how feedback is received based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The feedback unit can select the optimal feedback method by referring to past feedback data when receiving feedback. For example, the feedback unit can propose the optimal feedback method based on past feedback data. The feedback unit can analyze past feedback data and select the most efficient feedback method. The feedback unit can provide the user with the optimal feedback method by referring to past feedback data. In this way, the feedback unit can select the optimal feedback method by referring to past feedback data. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI.

[0094] The feedback unit can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is stressed, the feedback unit will postpone less important feedback. If the user is relaxed, the feedback unit can prioritize receiving more important feedback. If the user is in a hurry, the feedback unit can prioritize receiving urgent feedback. In this way, the feedback unit can receive feedback in a more appropriate order by prioritizing feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The feedback unit can select the optimal feedback method when receiving feedback, taking into account the user's device information. For example, if the user is using a smartphone, the feedback unit can provide a simple feedback form. If the user is using a tablet, the feedback unit can provide a feedback form optimized for a larger screen. If the user is using a desktop, the feedback unit can provide a detailed feedback form. This allows the feedback unit to select the optimal feedback method by considering the user's device information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI.

[0096] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit can select detailed training data. If the user is in a hurry, the learning unit can select concise training data. If the user is excited, the learning unit can select visually appealing training data. This allows the learning unit to use more appropriate data for training by selecting training data based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can analyze past learning data and improve the accuracy of the learning algorithm. The learning unit can adjust the parameters of the learning algorithm by referring to past learning data. In this way, the learning unit can optimize the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI.

[0098] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is relaxed, the learning unit will learn more frequently. If the user is in a hurry, the learning unit can reduce the learning frequency. If the user is excited, the learning unit can adjust the learning frequency. In this way, the learning unit can learn at a more appropriate frequency by adjusting the learning frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The learning unit can weight the training data based on the submission date of the content during training. For example, the learning unit can give higher weight to content that has been submitted recently, and lower weight to content that has been submitted recently. The learning unit can adjust the weighting of the training data according to the submission date. This allows the learning unit to use more appropriate data for training by weighting the training data based on the submission date of the content. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The reception desk can automatically prioritize user requests by referring to the user's past behavior history. For example, it can analyze the types and purposes of content that users have frequently requested in the past and prioritize similar requests if they are made again. It can also prioritize the application of content generation methods that users have previously given high ratings to. This allows the reception desk to process requests more efficiently and effectively by leveraging the user's past behavior history.

[0102] The content generation unit can consider current user trends and fads when generating content based on user requests, while maintaining originality and creativity. For example, the generation unit can collect the latest trend information from social media and news sites and reflect it in content generation. It can also analyze topics and keywords that users are interested in and generate content based on that. This allows the generation unit to provide content that incorporates the latest trends and meets user demands.

[0103] The optimization unit can incorporate user feedback in real time when optimizing generated content for its intended use and target audience. For example, if a user provides feedback on generated content, that feedback is immediately reflected in the optimization process. The optimization unit can also analyze user feedback, identify common areas for improvement, and incorporate them into future content creation. This allows the optimization unit to leverage user feedback to deliver higher quality content.

[0104] The feedback unit can automatically evaluate the importance of user feedback based on its content. For example, the feedback unit analyzes the content of user feedback and prioritizes processing high-priority feedback. Furthermore, the feedback unit can identify areas for improvement based on user feedback and provide this feedback to the generation and optimization units. This allows the feedback unit to effectively utilize user feedback to improve content quality.

[0105] The learning unit can utilize user feedback as training data when learning from past data and reflecting that learning in the content generation of the generation unit. For example, the learning unit can analyze user feedback and optimize the content generation algorithm of the generation unit. Furthermore, the learning unit can identify areas for improvement to enhance the quality of content generated by the generation unit based on user feedback. This allows the learning unit to continuously improve the content generation algorithm of the generation unit by leveraging user feedback.

[0106] The reception desk can estimate the user's emotions and adjust how requests are handled based on those estimates. For example, if a user is stressed, the reception desk can provide a relaxing interface. If a user is agitated, the reception desk can display guidance to calm the user. Furthermore, if a user is sad, the reception desk can display encouraging messages. In this way, the reception desk can provide an appropriate handling method that is tailored to the user's emotions.

[0107] The generation unit can estimate the user's emotions and adjust the tone of the generated content based on those emotions. For example, if the user is relaxed, the generation unit can generate text in a calm tone. If the user is excited, the generation unit can generate images in an energetic tone. Furthermore, if the user is sad, the generation unit can generate videos in a comforting tone. In this way, the generation unit can provide content with an appropriate tone that matches the user's emotions.

[0108] The optimization unit can estimate the user's emotions and adjust the optimization process based on those emotions. For example, if the user is relaxed, the optimization unit will perform a detailed optimization process. If the user is in a hurry, the optimization unit can perform a rapid optimization process. Furthermore, if the user is excited, the optimization unit can perform a visually appealing optimization process. In this way, the optimization unit can provide an appropriate optimization process that is tailored to the user's emotions.

[0109] The feedback unit can estimate the user's emotions and adjust how feedback is received based on those estimates. For example, if the user is relaxed, the feedback unit may request detailed feedback. If the user is in a hurry, the feedback unit may request concise feedback. Furthermore, if the user is excited, the feedback unit may provide a visually appealing feedback form. In this way, the feedback unit can provide an appropriate feedback method that is tailored to the user's emotions.

[0110] The learning unit can estimate the user's emotions and select training data based on those estimated emotions. For example, if the user is relaxed, the learning unit can select detailed training data. If the user is in a hurry, the learning unit can select concise training data. Furthermore, if the user is excited, the learning unit can select visually appealing training data. In this way, the learning unit can select appropriate training data according to the user's emotions.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk receives user requests. User requests may include text or audio formats. Users can input the type of content they want to generate (text, images, videos, etc.), its purpose, and target audience. Step 2: The generation unit generates content based on the information received by the reception unit. The generation unit uses generation AI to create content with originality and creativity based on user requests. For example, when generating advertising images for a marketing campaign, the user inputs the advertising objective, target audience, and other requirements, and the generation unit generates the optimal advertising image based on this information. Step 3: The optimization unit optimizes the content generated by the generation unit. The optimization unit optimizes the generated content for its intended use and target audience. For example, when generating video content for social media, the user inputs the video's purpose, target audience, and other requirements, and the optimization unit generates the optimal video content based on this information. Step 4: The Feedback Department receives user feedback to ensure the quality of the content optimized by the Optimization Department. The Feedback Department receives user feedback and ensures the quality of the content. The Feedback Department analyzes user feedback and identifies areas for improvement to enhance the quality of the content. Step 5: The learning unit learns from past data used by the generation unit. The learning unit learns from past user request data and past generated content data, and reflects this in the content generation of the generation unit. The learning unit analyzes past data and optimizes the content generation algorithm of the generation unit.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the reception unit, generation unit, optimization unit, feedback unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives user requests. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates content based on user requests. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the generated content. The feedback unit is implemented by the control unit 46A of the smart device 14 and receives user feedback. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns past data. 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.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the reception unit, generation unit, optimization unit, feedback unit, and learning 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 control unit 46A of the smart glasses 214 and receives user requests. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates content based on user requests. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the generated content. The feedback unit is implemented by the control unit 46A of the smart glasses 214 and receives user feedback. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns past data. 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.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the reception unit, generation unit, optimization unit, feedback unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives user requests. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates content based on user requests. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the generated content. The feedback unit is implemented by the control unit 46A of the headset terminal 314 and receives user feedback. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns past data. 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.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the reception unit, generation unit, optimization unit, feedback unit, and learning unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives user requests. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates content based on user requests. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the generated content. The feedback unit is implemented by the control unit 46A of the robot 414 and receives user feedback. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns past data. 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A reception desk that receives user requests, A generation unit that generates content based on the information received by the reception unit, An optimization unit that optimizes the content generated by the generation unit, A feedback unit that accepts user feedback in order to guarantee the quality of the content optimized by the optimization unit, The generation unit comprises a learning unit that learns past data. A system characterized by the following features. (Note 2) The generating unit is We create content with originality and creativity based on user requests. The system described in Appendix 1, characterized by the features described herein. (Note 3) The optimization unit, Optimize the generated content for its intended use and target audience. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is We accept user feedback and ensure content quality. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned learning unit, Learn from past data and reflect it in the content generation process. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of request acceptance based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past request history and select the optimal request processing method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When a request is received, it is filtered based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving a request, the system prioritizes requests that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When a request is received, the system analyzes the user's social media activity and accepts relevant requests. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates user emotions and adjusts how generated content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating content, adjust the level of detail based on the importance of the request. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating content, different generation algorithms are applied depending on the category of the request. The system described in Appendix 1, characterized by the features described herein. (Note 15) 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 16) The generating unit is When generating content, the generation priority is determined based on when the request was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating content, adjust the generation order based on the relevance of the requests. The system described in Appendix 1, characterized by the features described herein. (Note 18) The optimization unit, It estimates user sentiment and adjusts optimization criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The optimization unit, During optimization, the interrelationships of content are taken into consideration to improve the accuracy of the optimization. The system described in Appendix 1, characterized by the features described herein. (Note 20) The optimization unit, During optimization, the attribute information of the content submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, It estimates the user's emotions and adjusts the order in which the optimization results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, During optimization, the geographical distribution of the content is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, During optimization, we improve the accuracy of the optimization by referring to relevant literature for the content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is It estimates the user's emotions and adjusts the feedback collection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is When receiving feedback, the system will refer to past feedback data to select the most suitable method for receiving it. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is When receiving feedback, the system selects the most suitable method of receiving it, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned learning unit, During training, the training data is weighted based on when the content was submitted. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 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 receives user requests, A generation unit that generates content based on the information received by the reception unit, An optimization unit that optimizes the content generated by the generation unit, A feedback unit that accepts user feedback in order to guarantee the quality of the content optimized by the optimization unit, The generation unit comprises a learning unit that learns past data. A system characterized by the following features.

2. The generating unit is We create content with originality and creativity based on user requests. The system according to feature 1.

3. The optimization unit, Optimize the generated content for its intended use and target audience. The system according to feature 1.

4. The aforementioned feedback unit is We accept user feedback and ensure content quality. The system according to feature 1.

5. The aforementioned learning unit, Learn from past data and reflect it in the content generation of the generation unit. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of request acceptance based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past request history and select the optimal request processing method. The system according to feature 1.

8. The aforementioned reception unit is When a request is received, it is filtered based on the user's current projects and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to accept based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is When receiving a request, the system prioritizes requests that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

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