system

Abstract

We provide the system. [Solution] A means of receiving requests from users and analyzing the information, A means for automatically creating creative information based on specified information using a generation algorithm, A means of presenting the created information to users and collecting their evaluations, A means of improving information by analyzing collected evaluations and reflecting them in the generation algorithm, A means that operates on an information processing device and is accessible from a mobile information terminal, A system that provides generated creative information to a visual display device in high resolution and includes means for enabling partial modification.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern enterprises and businesses, there are problems such as spending a great deal of time and resources on the production of high-quality creative content and being difficult to progress work efficiently. Furthermore, it takes time to quickly reflect feedback on the generated content, resulting in further delays and inefficiencies. Such a situation has become a factor in reducing competitiveness.

Means for Solving the Problems

[0005] The present invention provides a system that includes means for receiving requests from users and analyzing their content, means for automatically generating creative content based on specified content using a generative model, means for presenting the generated content to the user and collecting feedback, and means for analyzing that feedback and reflecting it in the generative model to improve the content. This streamlines the content generation process and enables improved quality and rapid reflection of feedback.

[0006] "User" refers to an individual or company that requests the creation of creative content using this system.

[0007] A "request" refers to the specifications and requirements for the creative content that a user wants to generate, which are submitted to the server.

[0008] A "generative model" refers to a system that includes an algorithm that uses AI technology to automatically generate content based on user requests.

[0009] "Creative content" refers to a variety of information formats used for marketing and promotion, such as design proposals, text content, and advertising materials.

[0010] "Feedback" refers to information from users indicating their evaluation of the generated content and their suggestions for improvement.

[0011] "Means of analysis" refers to the data processing mechanism within the server that understands user requests and feedback and performs appropriate processing.

[0012] "Means of automatic generation" refers to a function that executes a process of mechanically generating creative content using a generative model.

[0013] "Means of presentation" refers to the function of sending generated content to the user and making it available for visual or auditory confirmation.

[0014] "Means of improvement" refers to the process of analyzing collected feedback and using the results to create future content. [Brief explanation of the drawing]

[0015] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 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.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] The 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.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] The creative assistant system of the present invention is centered around a generative AI model and automatically generates creative content based on user requests. The system employs a client-server model, and users can access the system from their terminals via the internet.

[0037] User interface

[0038] Users access the system via a web or mobile application to request the creation of creative content. The user interface provides forms for entering the type of request and preferences. For example, a user might specify detailed specifications such as a color theme and font style for creating an advertising poster for a new product.

[0039] Server-based processing

[0040] The server analyzes requests received from users and invokes an AI model to generate creative content based on the specified information. For example, if poster design proposals are to be generated, the AI ​​model automatically generates multiple designs by combining colors, text placement, and images. This generated content is then presented to the user through the user interface.

[0041] Feedback and Improvement

[0042] After users review the generated content, they can provide feedback. This feedback includes areas for improvement and new requests. The server collects this feedback and incorporates it into the AI ​​model to improve accuracy in subsequent content generation processes. Through this cycle, the system continuously learns and can provide higher quality creative content.

[0043] For example, an advertising agency can use this system to quickly generate multi-platform visual content for a new campaign—without using existing designer resources. After users review the initial design draft, they can use the feedback function to specify any changes they want to make to specific color schemes or layouts. The resulting content, being fast and high-quality, contributes to improving a company's marketing efficiency.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] Users use their devices to submit requests for creative content creation. These requests include information such as the type, style, and purpose of the content.

[0047] Step 2:

[0048] The server receives a request from the user and parses its contents. The parsing process identifies the type and specifications of the requested content and sets the parameters required by the generative model.

[0049] Step 3:

[0050] The server invokes a generative model to automatically generate creative content based on user requests. Specifically, it uses an AI model to generate design proposals and text content. At this stage, it creates a variety of proposals while adhering to the initially set specifications.

[0051] Step 4:

[0052] The server sends the generated creative content to the user in a format viewable on the device. The user can preview the results and check the details.

[0053] Step 5:

[0054] Users input feedback on the presented content via their device and send it to the server. This feedback includes suggestions for improvements and additional comments.

[0055] Step 6:

[0056] The server analyzes the feedback it receives, adjusts the generative model based on that information, and regenerates the improved content. This adjustment improves the quality of subsequent generation processes.

[0057] Step 7:

[0058] The server presents the improved content to the user again and asks for feedback again. This cycle is repeated until the feedback is satisfactory.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] Modern information generation systems are required to accurately understand users' specific needs and provide high-quality, creative information immediately. However, conventional methods often fail to accurately reflect user intent, requiring manual adjustments. Furthermore, the lack of mechanisms for continuously improving the quality of generated information makes it difficult to efficiently provide advanced, creative information.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for sending requests from the user to a computer via a fault, analyzing data, and generating prompt statements; means for automatically forming creative information based on the analyzed data using a generation AI model; and means for displaying the created information to the user and accumulating evaluation information. This makes it possible to accurately and quickly reflect user requests and continuously provide high-quality creative information.

[0064] A "user" refers to an individual who accesses the system, submits requests, and receives the creative information generated.

[0065] "Sending a request to a computer through a network" refers to the process of transmitting a request for information generation from a user's device to a server via a network.

[0066] "Analyzing data to generate prompts" refers to the process of interpreting the requests received from the user and forming instructions in a format that the AI ​​model can understand.

[0067] A "generative AI model" refers to an artificial intelligence system that automatically generates creative information based on specified conditions and requirements.

[0068] "Automatically generating creative information" refers to the process by which a generative AI model independently generates information and content that meets user requests, based on analyzed data.

[0069] "Accumulating evaluation information" refers to the act of collecting user feedback on generated information and saving it for future improvements.

[0070] A "cloud infrastructure" refers to a platform on which services and systems operate in a digital environment provided via the internet.

[0071] This creative assistant system is designed to allow users to easily generate creative information. It primarily operates on a cloud-based infrastructure, and users can access the system via the internet.

[0072] User actions:

[0073] Users access the system via a web or mobile application using their device. The interface includes a form for creating creative content, where users enter prompts. These prompts contain specific requests, which are then sent to the system.

[0074] Server operation:

[0075] The server receives requests from users and uses a generative AI model to automatically generate creative content based on the user's requests. The server uses a high-performance processor to analyze the received data and generate prompt sentences to input into the AI ​​model. The generative AI model forms information by combining colors, text placement, and images based on the conditions specified by the user.

[0076] The generated content is presented directly to the user, who can then rate it. When feedback is sent to the server, the server analyzes this data and adjusts the generating AI model to improve the accuracy of future content generation.

[0077] Specific example:

[0078] Advertising agencies can leverage this system when generating visual content usable across multiple platforms for new campaigns. Users can enter prompts such as, "Please generate an advertising poster for a new product. I'd like a blue and white color theme, a modern font style, and center the text with the image in the background," and quickly receive design proposals.

[0079] In this way, the system supports the user's creative process and efficiently provides high-quality information.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user uses their device to access a web or mobile application and enters a prompt request regarding the desired creative content. The entered prompt includes specific requests, such as "Generate an advertising poster for a new product. The color theme should be blue and white, and the font style should be modern. The text should be centered, with the image as the background." The input data is sent directly to the server. The output is a prompt for the server to begin processing.

[0083] Step 2:

[0084] The server analyzes the prompt text received from the user and converts it into a format suitable for the generating AI model. Specifically, the server uses natural language processing techniques to break down the prompt text and identify elements such as color, layout, and font. The input for analysis is the prompt text from the user, and the output is data in a format that the AI ​​model can understand (tagned attribute information).

[0085] Step 3:

[0086] The server inputs the analyzed data into the AI ​​model and generates creative content based on the specified specifications. Here, the AI ​​model generates content using templates and design rules according to the given attribute information. The input is the analyzed data format, and the output is the generated creative content. This process automatically handles color selection, font settings, and layout determination.

[0087] Step 4:

[0088] The generated creative content is sent from the server to the user's terminal and presented through the user interface. The user visually reviews multiple options on the terminal. Specifically, the user compares and evaluates the different design options and decides to select the final option or request improvements. The output is the design proposal provided to the user.

[0089] Step 5:

[0090] Users send ratings and feedback on generated content to the server via their devices. This feedback includes suggestions for improvement and additional requests. The input is the feedback information sent by the user, and the output is the information stored in the server's feedback database.

[0091] Step 6:

[0092] The server analyzes accumulated feedback and applies it to the generative AI model to improve the creative content generation process for future generations. Specifically, the server analyzes trends extracted from the feedback and adjusts the generative algorithms and parameters of the AI ​​model. The input is the analyzed feedback data, and the output is the improved generative model.

[0093] (Application Example 1)

[0094] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0095] Traditional creative information creation processes require significant time and skilled personnel, and are particularly inefficient in creating visual advertising information. Furthermore, there is the difficulty of making real-time partial revisions from mobile devices.

[0096] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0097] In this invention, the server includes means for receiving requests from users and analyzing information, means for automatically creating creative information based on specified information using a generation algorithm, and means for presenting the created information to the user and collecting evaluations. This makes it possible to efficiently create high-quality creative information while making partial corrections in real time from a mobile information terminal.

[0098] "Means for receiving requests from users and analyzing information" refers to a function that receives information entered by a user and converts its content into a format suitable for understanding and processing.

[0099] "A means of automatically creating creative information based on specified information using a generation algorithm" refers to a function that mechanically generates new information according to conditions specified by the user, using a pre-set calculation method.

[0100] "A means of presenting generated information to users and collecting their feedback" refers to a function that shows generated information to users and collects their reactions and opinions.

[0101] "Means that operate on an information processing device and are accessible from a mobile information terminal" refers to functions that operate using computing equipment and can be accessed through mobile phones or portable devices.

[0102] "A means of providing generated creative information to a visual display device in high resolution and enabling partial modification" refers to a function that provides the created information to a screen that displays it in high resolution and further enables specific modifications to that information.

[0103] The system that implements this application example operates using an information processing device, and users can access it from their mobile devices. The server is equipped with a backend using Python and Django, which receives and parses user requests. User requests are sent from the terminal via a REST API.

[0104] The server runs a generative AI model based on TENSORFLOW® to generate new creative information. The generated information is displayed in high resolution on a mobile device, and a user interface implemented using React Native allows the user to view and modify the information with touch operations.

[0105] Of particular importance is the ability for users to provide feedback on the generated creative information after it has been presented to them. This feedback is then incorporated into subsequent content creation processes, leading to continuous improvement of the information.

[0106] For example, when a company plans an advertising campaign for a new product, users can input their preferences regarding theme colors and target markets. The AI ​​model then quickly generates multiple design options, which can be modified and adjusted on a mobile device.

[0107] The generative AI model accepts prompt text such as "A poster for a food delivery service's spring campaign. It features a colorful and refreshing design, targeting women in their 20s and 30s," and makes predictions and generates content based on that input.

[0108] Therefore, this system can provide users with a comfortable and efficient solution for creative mass generation.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] Users launch a web or mobile application from their mobile device and input information such as advertising campaign details. This input includes title, target audience, and color scheme. The device formats the user's input information in JSON format and sends it to the server via a REST API.

[0112] Step 2:

[0113] The server parses the received JSON data. The data analysis module reads the input information and extracts the necessary parameters. In this process, the server uses Python and Django to process the request. This process performs necessary syntax checks and parameter validity checks to ensure that the data is in the correct format as input to the generated AI model.

[0114] Step 3:

[0115] The server uses a generative AI model based on TensorFlow to generate creative information that matches specified conditions. The model references a pre-trained dataset and generates design proposals that best match the user's input. The input here is a prompt based on the user's request, and the output is the generated advertisement design proposal.

[0116] Step 4:

[0117] The generated design proposals are formatted again in JSON format and sent to the device to present the results to the user. The device's user interface is implemented with React Native and displays the generated information in high resolution. The user can intuitively review the displayed design proposals and consider what needs to be corrected at that stage.

[0118] Step 5:

[0119] Users can input modifications and additional feedback via touch gestures on the interface. For example, they can specify changes to the design's colors and fonts. The device then converts this feedback information back into JSON format and sends it to the server.

[0120] Step 6:

[0121] The server analyzes user feedback and updates the generated AI model. This feedback is used to improve the model's accuracy in subsequent generation processes. In this way, the server continuously improves the model, enabling it to provide even higher quality creative information.

[0122] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0123] This invention relates to a system that recognizes user emotions in real time and generates and optimizes creative content based on those emotions. The system has a configuration that combines an emotion engine and a generative AI model and operates on a cloud-based platform.

[0124] User interface

[0125] Users access the system via web or mobile applications. The user interface provides forms for submitting requests for creative content and sentiment recognition capabilities to capture user responses. For example, if a user requests a promotional video for a specific advertising campaign, sentiment data is collected from the user's facial expressions and tone of voice during the process.

[0126] Server-based processing

[0127] The server first receives a request from the user and analyzes its content. Simultaneously, the emotion engine analyzes the user's emotional data, combines this data with the request analysis results, and sends it to the AI ​​model. This allows the generative model to generate creative content that is appropriate to the user's emotional state.

[0128] Emotion-based optimization

[0129] Even as generated content is presented to the user and the user reacts accordingly, the sentiment engine continues to collect data in real time. The server uses this data to optimize content suggestions and immediately makes new suggestions as needed. If the user's sentiment shifts in a positive direction, content improvements are made to maintain that direction.

[0130] For example, if a marketing professional uses this system to develop a promotional strategy for a new product, and the emotion engine indicates that the initial user response is positive, all generated creative ideas will be fine-tuned to reinforce that emotion. This not only maximizes marketing effectiveness but also improves consumer engagement.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user uses their device to enter a request for creative content into the application. The request includes details such as the type, purpose, and style of content needed.

[0134] Step 2:

[0135] The server receives the user's request and parses its contents. The parsing results include the requested design elements and keywords.

[0136] Step 3:

[0137] The emotion engine simultaneously acquires data to understand the user's emotions. This data is collected through the user's camera feed and voice tone.

[0138] Step 4:

[0139] Based on requests and sentiment data analyzed by the server, a generative model is used to automatically generate creative content. The model constructs content using an approach optimized for the user's emotional state.

[0140] Step 5:

[0141] The device presents the generated creative content to the user, and the emotion engine continuously monitors the user's reactions.

[0142] Step 6:

[0143] While users review content and provide feedback, the sentiment engine also sends collected data to the server.

[0144] Step 7:

[0145] The server analyzes feedback and sentiment data, adjusts the generative model, and generates new content. This process improves content generation to better match user needs and sentiments.

[0146] Step 8:

[0147] This process is repeated multiple times, ultimately delivering creative content that satisfies the user.

[0148] (Example 2)

[0149] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0150] In modern information processing, there is a need to quickly generate content that appropriately reflects the user's emotional state and to optimize it in real time to meet the user's needs. However, conventional methods lack the accuracy and speed to reflect emotions, making it difficult to maximize the appeal and effectiveness of content.

[0151] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0152] In this invention, the server includes means for receiving user input and analyzing content and sentiment data, means for automatically generating creative information based on the input content and sentiment data using a generative AI model, and means for presenting the generated information to the user and continuously collecting sentiment data. This enables the generation of content that is tailored to the user's emotional state and real-time optimization.

[0153] "User input" refers to the information and requests that a user provides to the system, which are transmitted to the terminal through a specific interface.

[0154] "Emotional data" is a collection of information that indicates a user's emotional state, gathered from things like their facial expressions and tone of voice.

[0155] A "generative AI model" refers to artificial intelligence technology that generates diverse creative information based on a given input prompt.

[0156] "Creative information" refers to creative content and materials that are automatically generated by generative AI models.

[0157] "Information optimization" is the process of adjusting generated creative information based on user sentiment data to improve it and make it more effective.

[0158] A "cloud infrastructure" refers to a network-based computing environment that enables remote access and provides computing resources and data storage.

[0159] This invention is a system that leverages user emotions to generate and optimize creative information in real time. This system operates on a cloud infrastructure and can be accessed by users via web or mobile applications.

[0160] Overview of program processing

[0161] The user inputs requests and desired content types to the system through the terminal. This input relates to specific creative information, such as promotional videos, and is done using the terminal's interface. Simultaneously with the input, the terminal uses the user's camera and microphone to sense facial expressions and voice tone, collecting emotional data.

[0162] The server analyzes both the request content and sentiment data sent from the terminal. This analysis includes not only the type of content but also the user's emotional state. Based on this data, the server sends a prompt to the generative AI model. The generative AI model uses specific algorithms and machine learning techniques to generate creative content based on the prompt. For example, a prompt such as "Generate an energetic promotional video showing the user's joy" might be generated.

[0163] The generated content is sent to the device via the server and presented to the user. The user reviews this information and then shows an emotional response. Based on this reaction, the device collects new emotional data. Based on this feedback, the server repeatedly optimizes the generated content. For example, in a marketing context, it is expected that user engagement will improve as the generated proposals are refined based on the positive responses shown by the user.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The user inputs a request for creative information via a web or mobile application interface through their device. This input could, for example, be a request to generate a promotional video. The device receives this input and simultaneously uses its camera and microphone to sense the user's facial expressions and tone of voice, collecting emotional data. The output of this step is the request content and initial emotional data.

[0167] Step 2:

[0168] The terminal sends the collected request details and emotion data to the server. The server receives this data and first analyzes the request details. Next, it analyzes the emotion data in real time using an emotion engine to determine the user's current emotional state. The output of this step is the analyzed request details and emotion state data.

[0169] Step 3:

[0170] The server creates prompts for the generative AI model based on the analyzed data. For example, if the user has expressed positive emotions, the prompt might say, "Generate a promotional video that highlights the positive emotions the user has expressed." The server then sends this prompt to the generative AI model. The output of this step is a specific prompt that the generative AI model can understand.

[0171] Step 4:

[0172] The generative AI model receives a prompt and generates the specified creative information. The generative AI model uses machine learning techniques to create materials such as videos and images that are suitable for the prompt. The output of this step is the generated creative information.

[0173] Step 5:

[0174] The server sends the generated information to the terminal and presents it to the user. The user views the provided information and expresses an emotional response to its content. The output of this step is data of the user's new emotional response.

[0175] Step 6:

[0176] The device detects a new emotional response from the user and collects that data again. The device sends the newly collected emotional data to the server. The output of this step is the updated emotional data.

[0177] Step 7:

[0178] The server analyzes the updated sentiment data and, if necessary, uses a generative AI model to readjust the creative information. It also optimizes the generated information to maximize its effectiveness, potentially improving user satisfaction. The output of this step is the adjusted or optimized creative information.

[0179] (Application Example 2)

[0180] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0181] In modern society, there is a demand for advertising that reaches diverse users, but conventional systems do not adequately optimize content in real time based on users' emotional responses. Therefore, providing a system that can quickly respond to changes in users' interests and emotions is a challenge.

[0182] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0183] In this invention, the server includes means for receiving requests from users and analyzing their content, means for automatically generating creative content based on the specified content using a generative model, and means for recognizing the user's emotions in real time and optimizing the creative content based on that data. This enables the server to immediately adapt to the user's emotional state and provide more effective advertising content.

[0184] "Means for receiving and analyzing user requests" refers to a system that receives requests submitted by users and interprets the intent and purpose of those requests.

[0185] "Means for automatically generating creative content based on specified content using a generative model" refers to a function that automatically generates content using a machine learning model in accordance with user requests.

[0186] "Means for presenting generated content to users and collecting feedback" refers to a system that displays generated content to users and records user reactions.

[0187] "A means of improving content by analyzing collected feedback and reflecting it in the generative model" refers to a function that analyzes user feedback and improves the output of the generative model based on that analysis.

[0188] "A means of recognizing user emotions in real time and optimizing creative content based on that data" refers to a function that instantly grasps the user's emotional state and adjusts and optimizes the content based on that information.

[0189] "Performing tasks on the cloud" refers to executing processes in a virtualized environment provided via the internet, and is operated in a manner that does not depend on physical local hardware.

[0190] This system is designed to provide real-time optimized advertising content based on user emotions. The following components are used in its implementation:

[0191] The server first receives requests sent from the user's device and analyzes their content. This analysis uses natural language processing techniques to understand the user's intent. The user's device collects emotional data from facial expressions and voice via its camera and microphone, and this information is analyzed in real time by emotion recognition software. Emotion recognition engines such as the Affectiva SDK are utilized in this process.

[0192] Next, the server uses a generative AI model running on the cloud to generate creative content based on the analyzed requests and sentiment data. This process is implemented using AI platforms such as Google Cloud AI or Amazon SageMaker. The generated content is then presented to the user.

[0193] While the user is viewing content, the device continuously collects emotional data. This data is sent to a server and analyzed as feedback. The server uses this feedback to adjust the generative model and optimize the content. This optimization involves improving the content so that the user's emotional state moves in a positive direction.

[0194] As a concrete example, suppose a user is viewing a travel package advertisement on their smartphone and the system detects a smile. In that case, the system switches to an advertisement that includes beach footage emphasizing relaxation. Another example of a specific prompt message would be, "If the user smiles, generate a promotional video centered on beautiful sunset footage of a resort."

[0195] This makes it possible to respond immediately to the user's emotional state and maximize the effectiveness of advertisements.

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The terminal receives a request from the user. The user enters information about the content they want to generate, and this data is sent to the terminal. The input data is saved as a text-based prompt.

[0199] Step 2:

[0200] The device uses a camera and microphone to capture the user's facial expressions and voice, collecting emotional data in real time. This emotional data is analyzed using the Affectiva SDK to generate output data indicating the emotions the user is experiencing.

[0201] Step 3:

[0202] The server receives user requests and sentiment data sent from the terminal. Using this data as input, the server analyzes the request content using natural language processing technology and processes the data as needed to understand the user's intent.

[0203] Step 4:

[0204] The server invokes a generative AI model located in the cloud and generates creative content based on the analyzed request and sentiment data. The generation process utilizes Google Cloud AI and Amazon SageMaker to output engaging visual content and other elements from the input data.

[0205] Step 5:

[0206] The generated content is sent to the device and presented to the user. The user views this content in real time and receives personalized feedback that differs from the input data.

[0207] Step 6:

[0208] The device continuously monitors the user's emotions while they are viewing content and sends real-time emotion data to the server. This data is output as user feedback.

[0209] Step 7:

[0210] The server analyzes feedback data sent from the terminal and reflects it in the generative model to optimize content. It analyzes changes in emotion and performs data calculations to regenerate the optimal content that matches the user's emotional state.

[0211] 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.

[0212] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0213] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] 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.

[0217] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0218] 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.

[0219] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0220] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0221] 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.

[0222] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0223] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0224] The 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.

[0225] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0226] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0227] The creative assistant system of the present invention is centered around a generative AI model and automatically generates creative content based on user requests. The system employs a client-server model, and users can access the system from their terminals via the internet.

[0228] User interface

[0229] Users access the system via a web or mobile application to request the creation of creative content. The user interface provides forms for entering the type of request and preferences. For example, a user might specify detailed specifications such as a color theme and font style for creating an advertising poster for a new product.

[0230] Server-based processing

[0231] The server analyzes requests received from users and invokes an AI model to generate creative content based on the specified information. For example, if poster design proposals are to be generated, the AI ​​model automatically generates multiple designs by combining colors, text placement, and images. This generated content is then presented to the user through the user interface.

[0232] Feedback and Improvement

[0233] After users review the generated content, they can provide feedback. This feedback includes areas for improvement and new requests. The server collects this feedback and incorporates it into the AI ​​model to improve accuracy in subsequent content generation processes. Through this cycle, the system continuously learns and can provide higher quality creative content.

[0234] For example, an advertising agency can use this system to quickly generate multi-platform visual content for a new campaign—without using existing designer resources. After users review the initial design draft, they can use the feedback function to specify any changes they want to make to specific color schemes or layouts. The resulting content, being fast and high-quality, contributes to improving a company's marketing efficiency.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] Users use their devices to submit requests for creative content creation. These requests include information such as the type, style, and purpose of the content.

[0238] Step 2:

[0239] The server receives a request from the user and parses its contents. The parsing process identifies the type and specifications of the requested content and sets the parameters required by the generative model.

[0240] Step 3:

[0241] The server invokes a generative model to automatically generate creative content based on user requests. Specifically, it uses an AI model to generate design proposals and text content. At this stage, it creates a variety of proposals while adhering to the initially set specifications.

[0242] Step 4:

[0243] The server sends the generated creative content to the user in a format viewable on the device. The user can preview the results and check the details.

[0244] Step 5:

[0245] Users input feedback on the presented content via their device and send it to the server. This feedback includes suggestions for improvements and additional comments.

[0246] Step 6:

[0247] The server analyzes the feedback it receives, adjusts the generative model based on that information, and regenerates the improved content. This adjustment improves the quality of subsequent generation processes.

[0248] Step 7:

[0249] The server presents the improved content to the user again and asks for feedback again. This cycle is repeated until the feedback is satisfactory.

[0250] (Example 1)

[0251] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0252] Modern information generation systems are required to accurately understand users' specific needs and provide high-quality, creative information immediately. However, conventional methods often fail to accurately reflect user intent, requiring manual adjustments. Furthermore, the lack of mechanisms for continuously improving the quality of generated information makes it difficult to efficiently provide advanced, creative information.

[0253] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0254] In this invention, the server includes means for sending requests from the user to a computer via a fault, analyzing data, and generating prompt statements; means for automatically forming creative information based on the analyzed data using a generation AI model; and means for displaying the created information to the user and accumulating evaluation information. This makes it possible to accurately and quickly reflect user requests and continuously provide high-quality creative information.

[0255] A "user" refers to an individual who accesses the system, submits requests, and receives the creative information generated.

[0256] "Sending a request to a computer through a network" refers to the process of transmitting a request for information generation from a user's device to a server via a network.

[0257] "Analyzing data to generate prompts" refers to the process of interpreting the requests received from the user and forming instructions in a format that the AI ​​model can understand.

[0258] A "generative AI model" refers to an artificial intelligence system that automatically generates creative information based on specified conditions and requirements.

[0259] "Automatically generating creative information" refers to the process by which a generative AI model independently generates information and content that meets user requests, based on analyzed data.

[0260] "Accumulating evaluation information" refers to the act of collecting user feedback on generated information and saving it for future improvements.

[0261] A "cloud infrastructure" refers to a platform on which services and systems operate in a digital environment provided via the internet.

[0262] This creative assistant system is designed to allow users to easily generate creative information. It primarily operates on a cloud-based infrastructure, and users can access the system via the internet.

[0263] User actions:

[0264] Users access the system via a web or mobile application using their device. The interface includes a form for creating creative content, where users enter prompts. These prompts contain specific requests, which are then sent to the system.

[0265] Server operation:

[0266] The server receives requests from users and uses a generative AI model to automatically generate creative content based on the user's requests. The server uses a high-performance processor to analyze the received data and generate prompt sentences to input into the AI ​​model. The generative AI model forms information by combining colors, text placement, and images based on the conditions specified by the user.

[0267] The generated content is presented directly to the user, who can then rate it. When feedback is sent to the server, the server analyzes this data and adjusts the generating AI model to improve the accuracy of future content generation.

[0268] Specific example:

[0269] Advertising agencies can leverage this system when generating visual content usable across multiple platforms for new campaigns. Users can enter prompts such as, "Please generate an advertising poster for a new product. I'd like a blue and white color theme, a modern font style, and center the text with the image in the background," and quickly receive design proposals.

[0270] In this way, the system supports the user's creative process and efficiently provides high-quality information.

[0271] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0272] Step 1:

[0273] The user uses their device to access a web or mobile application and enters a prompt request regarding the desired creative content. The entered prompt includes specific requests, such as "Generate an advertising poster for a new product. The color theme should be blue and white, and the font style should be modern. The text should be centered, with the image as the background." The input data is sent directly to the server. The output is a prompt for the server to begin processing.

[0274] Step 2:

[0275] The server analyzes the prompt text received from the user and converts it into a format suitable for the generating AI model. Specifically, the server uses natural language processing techniques to break down the prompt text and identify elements such as color, layout, and font. The input for analysis is the prompt text from the user, and the output is data in a format that the AI ​​model can understand (tagned attribute information).

[0276] Step 3:

[0277] The server inputs the analyzed data into the AI ​​model and generates creative content based on the specified specifications. Here, the AI ​​model generates content using templates and design rules according to the given attribute information. The input is the analyzed data format, and the output is the generated creative content. This process automatically handles color selection, font settings, and layout determination.

[0278] Step 4:

[0279] The generated creative content is sent from the server to the user terminal and presented through the user interface. The user visually checks multiple proposals on the terminal. As a specific operation, the user evaluates while comparing different design proposals and selects the final proposal or decides to request improvements. The output is the design proposal provided to the user.

[0280] Step 5:

[0281] The user sends evaluations and feedback on the generated content to the server through the terminal. The feedback includes points for improvement and additional wishes. The input is the feedback information sent from the user, and the output is the information stored in the server's feedback database.

[0282] Step 6:

[0283] The server analyzes the accumulated feedback and applies it to the generation AI model to improve the creative content generation process for subsequent times. As a specific operation, the server analyzes the trends extracted from the feedback and adjusts the generation algorithm and parameters of the AI model. The input is the analyzed feedback data, and the output is the improved generation model.

[0284] (Application Example 1)

[0285] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0286] The conventional creative information creation process requires a lot of time and skilled personnel, and there is a problem that it is particularly inefficient in creating visual advertising information. There is also a problem that it is difficult to perform partial corrections in real time from a mobile information terminal.

[0287] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0288] In this invention, the server includes means for receiving requests from users and analyzing information, means for automatically creating creative information based on specified information using a generation algorithm, and means for presenting the created information to the user and collecting evaluations. This makes it possible to efficiently create high-quality creative information while making partial corrections in real time from a mobile information terminal.

[0289] "Means for receiving requests from users and analyzing information" refers to a function that receives information entered by a user and converts its content into a format suitable for understanding and processing.

[0290] "A means of automatically creating creative information based on specified information using a generation algorithm" refers to a function that mechanically generates new information according to conditions specified by the user, using a pre-set calculation method.

[0291] "A means of presenting generated information to users and collecting their feedback" refers to a function that shows generated information to users and collects their reactions and opinions.

[0292] "Means that operate on an information processing device and are accessible from a mobile information terminal" refers to functions that operate using computing equipment and can be accessed through mobile phones or portable devices.

[0293] "A means of providing generated creative information to a visual display device in high resolution and enabling partial modification" refers to a function that provides the created information to a screen that displays it in high resolution and further enables specific modifications to that information.

[0294] The system that implements this application example operates using an information processing device, and users can access it from their mobile devices. The server is equipped with a backend using Python and Django, which receives and parses user requests. User requests are sent from the terminal via a REST API.

[0295] The server runs a TensorFlow-based generative AI model to generate new creative information. The generated information is displayed in high resolution on a mobile device, and a user interface implemented using React Native allows the user to view and modify the information using touch controls.

[0296] Of particular importance is the ability for users to provide feedback on the generated creative information after it has been presented to them. This feedback is then incorporated into subsequent content creation processes, leading to continuous improvement of the information.

[0297] For example, when a company plans an advertising campaign for a new product, users can input their preferences regarding theme colors and target markets. The AI ​​model then quickly generates multiple design options, which can be modified and adjusted on a mobile device.

[0298] The generative AI model accepts prompt text such as "A poster for a food delivery service's spring campaign. It features a colorful and refreshing design, targeting women in their 20s and 30s," and makes predictions and generates content based on that input.

[0299] Therefore, this system can provide users with a comfortable and efficient solution for creative mass generation.

[0300] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0301] Step 1:

[0302] Users launch a web or mobile application from their mobile device and input information such as advertising campaign details. This input includes title, target audience, and color scheme. The device formats the user's input information in JSON format and sends it to the server via a REST API.

[0303] Step 2:

[0304] The server analyzes the received JSON data. The data analysis module reads the input information and extracts the necessary parameters. At this time, the server uses Python and Django to process the request. In this process, necessary syntax checks and parameter sanity checks are performed to ensure that the data is in the correct format for input to the generative AI model.

[0305] Step 3:

[0306] The server uses a generative AI model based on TensorFlow to generate creative information that meets the specified conditions. The model refers to a pre-trained dataset and generates a design proposal that best matches the input from the user. The input here is a prompt sentence based on the user's request, and the output is the generated advertisement design proposal.

[0307] Step 4:

[0308] The generated design proposal is reshaped into JSON format again and sent to the terminal to present the results to the user. The user interface of the terminal is implemented in React Native and displays the information generated in high quality. The user can intuitively check the displayed design proposal and consider parts that should be corrected at that stage.

[0309] Step 5:

[0310] The user can input corrections or additional feedback through touch operations on the interface. For example, it is possible to give specific instructions for changes to the color or font of the design. The terminal converts this feedback information back into JSON format and sends it to the server.

[0311] Step 6:

[0312] The server analyzes user feedback and updates the generated AI model. This feedback is used to improve the model's accuracy in subsequent generation processes. In this way, the server continuously improves the model, enabling it to provide even higher quality creative information.

[0313] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0314] This invention relates to a system that recognizes user emotions in real time and generates and optimizes creative content based on those emotions. The system has a configuration that combines an emotion engine and a generative AI model and operates on a cloud-based platform.

[0315] User interface

[0316] Users access the system via web or mobile applications. The user interface provides forms for submitting requests for creative content and sentiment recognition capabilities to capture user responses. For example, if a user requests a promotional video for a specific advertising campaign, sentiment data is collected from the user's facial expressions and tone of voice during the process.

[0317] Server-based processing

[0318] The server first receives a request from the user and analyzes its content. Simultaneously, the emotion engine analyzes the user's emotional data, combines this data with the request analysis results, and sends it to the AI ​​model. This allows the generative model to generate creative content that is appropriate to the user's emotional state.

[0319] Emotion-based optimization

[0320] Even as generated content is presented to the user and the user reacts accordingly, the sentiment engine continues to collect data in real time. The server uses this data to optimize content suggestions and immediately makes new suggestions as needed. If the user's sentiment shifts in a positive direction, content improvements are made to maintain that direction.

[0321] For example, if a marketing professional uses this system to develop a promotional strategy for a new product, and the emotion engine indicates that the initial user response is positive, all generated creative ideas will be fine-tuned to reinforce that emotion. This not only maximizes marketing effectiveness but also improves consumer engagement.

[0322] The following describes the processing flow.

[0323] Step 1:

[0324] The user uses their device to enter a request for creative content into the application. The request includes details such as the type, purpose, and style of content needed.

[0325] Step 2:

[0326] The server receives the user's request and parses its contents. The parsing results include the requested design elements and keywords.

[0327] Step 3:

[0328] The emotion engine simultaneously acquires data to understand the user's emotions. This data is collected through the user's camera feed and voice tone.

[0329] Step 4:

[0330] Based on requests and sentiment data analyzed by the server, a generative model is used to automatically generate creative content. The model constructs content using an approach optimized for the user's emotional state.

[0331] Step 5:

[0332] The device presents the generated creative content to the user, and the emotion engine continuously monitors the user's reactions.

[0333] Step 6:

[0334] While users review content and provide feedback, the sentiment engine also sends collected data to the server.

[0335] Step 7:

[0336] The server analyzes feedback and sentiment data, adjusts the generative model, and generates new content. This process improves content generation to better match user needs and sentiments.

[0337] Step 8:

[0338] This process is repeated multiple times, ultimately delivering creative content that satisfies the user.

[0339] (Example 2)

[0340] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0341] In modern information processing, there is a need to quickly generate content that appropriately reflects the user's emotional state and to optimize it in real time to meet the user's needs. However, conventional methods lack the accuracy and speed to reflect emotions, making it difficult to maximize the appeal and effectiveness of content.

[0342] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0343] In this invention, the server includes means for receiving user input and analyzing content and sentiment data, means for automatically generating creative information based on the input content and sentiment data using a generative AI model, and means for presenting the generated information to the user and continuously collecting sentiment data. This enables the generation of content that is tailored to the user's emotional state and real-time optimization.

[0344] "User input" refers to the information and requests that a user provides to the system, which are transmitted to the terminal through a specific interface.

[0345] "Emotional data" is a collection of information that indicates a user's emotional state, gathered from things like their facial expressions and tone of voice.

[0346] A "generative AI model" refers to artificial intelligence technology that generates diverse creative information based on a given input prompt.

[0347] "Creative information" refers to creative content and materials that are automatically generated by generative AI models.

[0348] "Information optimization" is the process of adjusting generated creative information based on user sentiment data to improve it and make it more effective.

[0349] A "cloud infrastructure" refers to a network-based computing environment that enables remote access and provides computing resources and data storage.

[0350] This invention is a system that leverages user emotions to generate and optimize creative information in real time. This system operates on a cloud infrastructure and can be accessed by users via web or mobile applications.

[0351] Overview of program processing

[0352] The user inputs requests and desired content types to the system through the terminal. This input relates to specific creative information, such as promotional videos, and is done using the terminal's interface. Simultaneously with the input, the terminal uses the user's camera and microphone to sense facial expressions and voice tone, collecting emotional data.

[0353] The server analyzes both the request content and sentiment data sent from the terminal. This analysis includes not only the type of content but also the user's emotional state. Based on this data, the server sends a prompt to the generative AI model. The generative AI model uses specific algorithms and machine learning techniques to generate creative content based on the prompt. For example, a prompt such as "Generate an energetic promotional video showing the user's joy" might be generated.

[0354] The generated content is sent to the device via the server and presented to the user. The user reviews this information and then shows an emotional response. Based on this reaction, the device collects new emotional data. Based on this feedback, the server repeatedly optimizes the generated content. For example, in a marketing context, it is expected that user engagement will improve as the generated proposals are refined based on the positive responses shown by the user.

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] The user inputs a request for creative information via a web or mobile application interface through their device. This input could, for example, be a request to generate a promotional video. The device receives this input and simultaneously uses its camera and microphone to sense the user's facial expressions and tone of voice, collecting emotional data. The output of this step is the request content and initial emotional data.

[0358] Step 2:

[0359] The terminal sends the collected request details and emotion data to the server. The server receives this data and first analyzes the request details. Next, it analyzes the emotion data in real time using an emotion engine to determine the user's current emotional state. The output of this step is the analyzed request details and emotion state data.

[0360] Step 3:

[0361] The server creates prompts for the generative AI model based on the analyzed data. For example, if the user has expressed positive emotions, the prompt might say, "Generate a promotional video that highlights the positive emotions the user has expressed." The server then sends this prompt to the generative AI model. The output of this step is a specific prompt that the generative AI model can understand.

[0362] Step 4:

[0363] The generative AI model receives a prompt and generates the specified creative information. The generative AI model uses machine learning techniques to create materials such as videos and images that are suitable for the prompt. The output of this step is the generated creative information.

[0364] Step 5:

[0365] The server sends the generated information to the terminal and presents it to the user. The user views the provided information and expresses an emotional response to its content. The output of this step is data of the user's new emotional response.

[0366] Step 6:

[0367] The device detects a new emotional response from the user and collects that data again. The device sends the newly collected emotional data to the server. The output of this step is the updated emotional data.

[0368] Step 7:

[0369] The server analyzes the updated sentiment data and, if necessary, uses a generative AI model to readjust the creative information. It also optimizes the generated information to maximize its effectiveness, potentially improving user satisfaction. The output of this step is the adjusted or optimized creative information.

[0370] (Application Example 2)

[0371] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0372] In modern society, there is a demand for advertising that reaches diverse users, but conventional systems do not adequately optimize content in real time based on users' emotional responses. Therefore, providing a system that can quickly respond to changes in users' interests and emotions is a challenge.

[0373] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0374] In this invention, the server includes means for receiving requests from users and analyzing their content, means for automatically generating creative content based on the specified content using a generative model, and means for recognizing the user's emotions in real time and optimizing the creative content based on that data. This enables the server to immediately adapt to the user's emotional state and provide more effective advertising content.

[0375] "Means for receiving and analyzing user requests" refers to a system that receives requests submitted by users and interprets the intent and purpose of those requests.

[0376] "Means for automatically generating creative content based on specified content using a generative model" refers to a function that automatically generates content using a machine learning model in accordance with user requests.

[0377] "Means for presenting generated content to users and collecting feedback" refers to a system that displays generated content to users and records user reactions.

[0378] "A means of improving content by analyzing collected feedback and reflecting it in the generative model" refers to a function that analyzes user feedback and improves the output of the generative model based on that analysis.

[0379] "A means of recognizing user emotions in real time and optimizing creative content based on that data" refers to a function that instantly grasps the user's emotional state and adjusts and optimizes the content based on that information.

[0380] "Performing tasks on the cloud" refers to executing processes in a virtualized environment provided via the internet, and is operated in a manner that does not depend on physical local hardware.

[0381] This system is designed to provide real-time optimized advertising content based on user emotions. The following components are used in its implementation:

[0382] The server first receives requests sent from the user's device and analyzes their content. This analysis uses natural language processing techniques to understand the user's intent. The user's device collects emotional data from facial expressions and voice via its camera and microphone, and this information is analyzed in real time by emotion recognition software. Emotion recognition engines such as the Affectiva SDK are utilized in this process.

[0383] Next, the server uses a generative AI model running in the cloud to generate creative content based on the analyzed requests and sentiment data. This process is implemented using AI platforms such as Google Cloud AI or Amazon SageMaker. The generated content is then presented to the user.

[0384] While the user is viewing content, the device continuously collects emotional data. This data is sent to a server and analyzed as feedback. The server uses this feedback to adjust the generative model and optimize the content. This optimization involves improving the content so that the user's emotional state moves in a positive direction.

[0385] As a concrete example, suppose a user is viewing a travel package advertisement on their smartphone and the system detects a smile. In that case, the system switches to an advertisement that includes beach footage emphasizing relaxation. Another example of a specific prompt message would be, "If the user smiles, generate a promotional video centered on beautiful sunset footage of a resort."

[0386] This makes it possible to respond immediately to the user's emotional state and maximize the effectiveness of advertisements.

[0387] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0388] Step 1:

[0389] The terminal receives a request from the user. The user enters information about the content they want to generate, and this data is sent to the terminal. The input data is saved as a text-based prompt.

[0390] Step 2:

[0391] The device uses a camera and microphone to capture the user's facial expressions and voice, collecting emotional data in real time. This emotional data is analyzed using the Affectiva SDK to generate output data indicating the emotions the user is experiencing.

[0392] Step 3:

[0393] The server receives user requests and sentiment data sent from the terminal. Using this data as input, the server analyzes the request content using natural language processing technology and processes the data as needed to understand the user's intent.

[0394] Step 4:

[0395] The server invokes a generative AI model located in the cloud and generates creative content based on the analyzed request and sentiment data. The generation process utilizes Google Cloud AI and Amazon SageMaker to output engaging visual content and other elements from the input data.

[0396] Step 5:

[0397] The generated content is sent to the device and presented to the user. The user views this content in real time and receives personalized feedback that differs from the input data.

[0398] Step 6:

[0399] The device continuously monitors the user's emotions while they are viewing content and sends real-time emotion data to the server. This data is output as user feedback.

[0400] Step 7:

[0401] The server analyzes feedback data sent from the terminal and reflects it in the generative model to optimize content. It analyzes changes in emotion and performs data calculations to regenerate the optimal content that matches the user's emotional state.

[0402] 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.

[0403] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0404] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] 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.

[0408] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0409] 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.

[0410] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0411] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0412] 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.

[0413] 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.

[0414] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0415] The 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.

[0416] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0417] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0418] The creative assistant system of the present invention is centered around a generative AI model and automatically generates creative content based on user requests. The system employs a client-server model, and users can access the system from their terminals via the internet.

[0419] User interface

[0420] Users access the system via a web or mobile application to request the creation of creative content. The user interface provides forms for entering the type of request and preferences. For example, a user might specify detailed specifications such as a color theme and font style for creating an advertising poster for a new product.

[0421] Server-based processing

[0422] The server analyzes requests received from users and invokes an AI model to generate creative content based on the specified information. For example, if poster design proposals are to be generated, the AI ​​model automatically generates multiple designs by combining colors, text placement, and images. This generated content is then presented to the user through the user interface.

[0423] Feedback and Improvement

[0424] After users review the generated content, they can provide feedback. This feedback includes areas for improvement and new requests. The server collects this feedback and incorporates it into the AI ​​model to improve accuracy in subsequent content generation processes. Through this cycle, the system continuously learns and can provide higher quality creative content.

[0425] For example, an advertising agency can use this system to quickly generate multi-platform visual content for a new campaign—without using existing designer resources. After users review the initial design draft, they can use the feedback function to specify any changes they want to make to specific color schemes or layouts. The resulting content, being fast and high-quality, contributes to improving a company's marketing efficiency.

[0426] The following describes the processing flow.

[0427] Step 1:

[0428] Users use their devices to submit requests for creative content creation. These requests include information such as the type, style, and purpose of the content.

[0429] Step 2:

[0430] The server receives a request from the user and parses its contents. The parsing process identifies the type and specifications of the requested content and sets the parameters required by the generative model.

[0431] Step 3:

[0432] The server invokes a generative model to automatically generate creative content based on user requests. Specifically, it uses an AI model to generate design proposals and text content. At this stage, it creates a variety of proposals while adhering to the initially set specifications.

[0433] Step 4:

[0434] The server sends the generated creative content to the user in a format viewable on the device. The user can preview the results and check the details.

[0435] Step 5:

[0436] Users input feedback on the presented content via their device and send it to the server. This feedback includes suggestions for improvements and additional comments.

[0437] Step 6:

[0438] The server analyzes the feedback it receives, adjusts the generative model based on that information, and regenerates the improved content. This adjustment improves the quality of subsequent generation processes.

[0439] Step 7:

[0440] The server presents the improved content to the user again and asks for feedback again. This cycle is repeated until the feedback is satisfactory.

[0441] (Example 1)

[0442] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0443] Modern information generation systems are required to accurately understand users' specific needs and provide high-quality, creative information immediately. However, conventional methods often fail to accurately reflect user intent, requiring manual adjustments. Furthermore, the lack of mechanisms for continuously improving the quality of generated information makes it difficult to efficiently provide advanced, creative information.

[0444] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0445] In this invention, the server includes means for sending requests from the user to a computer via a fault, analyzing data, and generating prompt statements; means for automatically forming creative information based on the analyzed data using a generation AI model; and means for displaying the created information to the user and accumulating evaluation information. This makes it possible to accurately and quickly reflect user requests and continuously provide high-quality creative information.

[0446] A "user" refers to an individual who accesses the system, submits requests, and receives the creative information generated.

[0447] "Sending a request to a computer through a network" refers to the process of transmitting a request for information generation from a user's device to a server via a network.

[0448] "Analyzing data to generate prompts" refers to the process of interpreting the requests received from the user and forming instructions in a format that the AI ​​model can understand.

[0449] A "generative AI model" refers to an artificial intelligence system that automatically generates creative information based on specified conditions and requirements.

[0450] "Automatically generating creative information" refers to the process by which a generative AI model independently generates information and content that meets user requests, based on analyzed data.

[0451] "Accumulating evaluation information" refers to the act of collecting user feedback on generated information and saving it for future improvements.

[0452] A "cloud infrastructure" refers to a platform on which services and systems operate in a digital environment provided via the internet.

[0453] This creative assistant system is designed to allow users to easily generate creative information. It primarily operates on a cloud-based infrastructure, and users can access the system via the internet.

[0454] User actions:

[0455] Users access the system via a web or mobile application using their device. The interface includes a form for creating creative content, where users enter prompts. These prompts contain specific requests, which are then sent to the system.

[0456] Server operation:

[0457] The server receives requests from users and uses a generative AI model to automatically generate creative content based on the user's requests. The server uses a high-performance processor to analyze the received data and generate prompt sentences to input into the AI ​​model. The generative AI model forms information by combining colors, text placement, and images based on the conditions specified by the user.

[0458] The generated content is presented directly to the user, who can then rate it. When feedback is sent to the server, the server analyzes this data and adjusts the generating AI model to improve the accuracy of future content generation.

[0459] Specific example:

[0460] Advertising agencies can leverage this system when generating visual content usable across multiple platforms for new campaigns. Users can enter prompts such as, "Please generate an advertising poster for a new product. I'd like a blue and white color theme, a modern font style, and center the text with the image in the background," and quickly receive design proposals.

[0461] In this way, the system supports the user's creative process and efficiently provides high-quality information.

[0462] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0463] Step 1:

[0464] The user uses their device to access a web or mobile application and enters a prompt request regarding the desired creative content. The entered prompt includes specific requests, such as "Generate an advertising poster for a new product. The color theme should be blue and white, and the font style should be modern. The text should be centered, with the image as the background." The input data is sent directly to the server. The output is a prompt for the server to begin processing.

[0465] Step 2:

[0466] The server analyzes the prompt text received from the user and converts it into a format suitable for the generating AI model. Specifically, the server uses natural language processing techniques to break down the prompt text and identify elements such as color, layout, and font. The input for analysis is the prompt text from the user, and the output is data in a format that the AI ​​model can understand (tagned attribute information).

[0467] Step 3:

[0468] The server inputs the analyzed data into the AI ​​model and generates creative content based on the specified specifications. Here, the AI ​​model generates content using templates and design rules according to the given attribute information. The input is the analyzed data format, and the output is the generated creative content. This process automatically handles color selection, font settings, and layout determination.

[0469] Step 4:

[0470] The generated creative content is sent from the server to the user's terminal and presented through the user interface. The user visually reviews multiple options on the terminal. Specifically, the user compares and evaluates the different design options and decides to select the final option or request improvements. The output is the design proposal provided to the user.

[0471] Step 5:

[0472] Users send ratings and feedback on generated content to the server via their devices. This feedback includes suggestions for improvement and additional requests. The input is the feedback information sent by the user, and the output is the information stored in the server's feedback database.

[0473] Step 6:

[0474] The server analyzes accumulated feedback and applies it to the generative AI model to improve the creative content generation process for future generations. Specifically, the server analyzes trends extracted from the feedback and adjusts the generative algorithms and parameters of the AI ​​model. The input is the analyzed feedback data, and the output is the improved generative model.

[0475] (Application Example 1)

[0476] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0477] Traditional creative information creation processes require significant time and skilled personnel, and are particularly inefficient in creating visual advertising information. Furthermore, there is the difficulty of making real-time partial revisions from mobile devices.

[0478] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0479] In this invention, the server includes means for receiving requests from users and analyzing information, means for automatically creating creative information based on specified information using a generation algorithm, and means for presenting the created information to the user and collecting evaluations. This makes it possible to efficiently create high-quality creative information while making partial corrections in real time from a mobile information terminal.

[0480] "Means for receiving requests from users and analyzing information" refers to a function that receives information entered by a user and converts its content into a format suitable for understanding and processing.

[0481] "A means of automatically creating creative information based on specified information using a generation algorithm" refers to a function that mechanically generates new information according to conditions specified by the user, using a pre-set calculation method.

[0482] "A means of presenting generated information to users and collecting their feedback" refers to a function that shows generated information to users and collects their reactions and opinions.

[0483] "Means that operate on an information processing device and are accessible from a mobile information terminal" refers to functions that operate using computing equipment and can be accessed through mobile phones or portable devices.

[0484] "A means of providing generated creative information to a visual display device in high resolution and enabling partial modification" refers to a function that provides the created information to a screen that displays it in high resolution and further enables specific modifications to that information.

[0485] The system that implements this application example operates using an information processing device, and users can access it from their mobile devices. The server is equipped with a backend using Python and Django, which receives and parses user requests. User requests are sent from the terminal via a REST API.

[0486] The server runs a TensorFlow-based generative AI model to generate new creative information. The generated information is displayed in high resolution on a mobile device, and a user interface implemented using React Native allows the user to view and modify the information using touch controls.

[0487] Of particular importance is the ability for users to provide feedback on the generated creative information after it has been presented to them. This feedback is then incorporated into subsequent content creation processes, leading to continuous improvement of the information.

[0488] For example, when a company plans an advertising campaign for a new product, users can input their preferences regarding theme colors and target markets. The AI ​​model then quickly generates multiple design options, which can be modified and adjusted on a mobile device.

[0489] The generative AI model accepts prompt text such as "A poster for a food delivery service's spring campaign. It features a colorful and refreshing design, targeting women in their 20s and 30s," and makes predictions and generates content based on that input.

[0490] Therefore, this system can provide users with a comfortable and efficient solution for creative mass generation.

[0491] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0492] Step 1:

[0493] Users launch a web or mobile application from their mobile device and input information such as advertising campaign details. This input includes title, target audience, and color scheme. The device formats the user's input information in JSON format and sends it to the server via a REST API.

[0494] Step 2:

[0495] The server parses the received JSON data. The data analysis module reads the input information and extracts the necessary parameters. In this process, the server uses Python and Django to process the request. This process performs necessary syntax checks and parameter validity checks to ensure that the data is in the correct format as input to the generated AI model.

[0496] Step 3:

[0497] The server uses a generative AI model based on TensorFlow to generate creative information that matches specified conditions. The model references a pre-trained dataset and generates design proposals that best match the user's input. The input here is a prompt based on the user's request, and the output is the generated advertisement design proposal.

[0498] Step 4:

[0499] The generated design proposals are formatted again in JSON format and sent to the device to present the results to the user. The device's user interface is implemented with React Native and displays the generated information in high resolution. The user can intuitively review the displayed design proposals and consider what needs to be corrected at that stage.

[0500] Step 5:

[0501] Users can input modifications and additional feedback via touch gestures on the interface. For example, they can specify changes to the design's colors and fonts. The device then converts this feedback information back into JSON format and sends it to the server.

[0502] Step 6:

[0503] The server analyzes user feedback and updates the generated AI model. This feedback is used to improve the model's accuracy in subsequent generation processes. In this way, the server continuously improves the model, enabling it to provide even higher quality creative information.

[0504] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0505] This invention relates to a system that recognizes user emotions in real time and generates and optimizes creative content based on those emotions. The system has a configuration that combines an emotion engine and a generative AI model and operates on a cloud-based platform.

[0506] User interface

[0507] Users access the system via web or mobile applications. The user interface provides forms for submitting requests for creative content and sentiment recognition capabilities to capture user responses. For example, if a user requests a promotional video for a specific advertising campaign, sentiment data is collected from the user's facial expressions and tone of voice during the process.

[0508] Server-based processing

[0509] The server first receives a request from the user and analyzes its content. Simultaneously, the emotion engine analyzes the user's emotional data, combines this data with the request analysis results, and sends it to the AI ​​model. This allows the generative model to generate creative content that is appropriate to the user's emotional state.

[0510] Emotion-based optimization

[0511] Even as generated content is presented to the user and the user reacts accordingly, the sentiment engine continues to collect data in real time. The server uses this data to optimize content suggestions and immediately makes new suggestions as needed. If the user's sentiment shifts in a positive direction, content improvements are made to maintain that direction.

[0512] For example, if a marketing professional uses this system to develop a promotional strategy for a new product, and the emotion engine indicates that the initial user response is positive, all generated creative ideas will be fine-tuned to reinforce that emotion. This not only maximizes marketing effectiveness but also improves consumer engagement.

[0513] The following describes the processing flow.

[0514] Step 1:

[0515] The user uses their device to enter a request for creative content into the application. The request includes details such as the type, purpose, and style of content needed.

[0516] Step 2:

[0517] The server receives the user's request and parses its contents. The parsing results include the requested design elements and keywords.

[0518] Step 3:

[0519] The emotion engine simultaneously acquires data to understand the user's emotions. This data is collected through the user's camera feed and voice tone.

[0520] Step 4:

[0521] Based on requests and sentiment data analyzed by the server, a generative model is used to automatically generate creative content. The model constructs content using an approach optimized for the user's emotional state.

[0522] Step 5:

[0523] The device presents the generated creative content to the user, and the emotion engine continuously monitors the user's reactions.

[0524] Step 6:

[0525] While users review content and provide feedback, the sentiment engine also sends collected data to the server.

[0526] Step 7:

[0527] The server analyzes feedback and sentiment data, adjusts the generative model, and generates new content. This process improves content generation to better match user needs and sentiments.

[0528] Step 8:

[0529] This process is repeated multiple times, ultimately delivering creative content that satisfies the user.

[0530] (Example 2)

[0531] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0532] In modern information processing, there is a need to quickly generate content that appropriately reflects the user's emotional state and to optimize it in real time to meet the user's needs. However, conventional methods lack the accuracy and speed to reflect emotions, making it difficult to maximize the appeal and effectiveness of content.

[0533] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0534] In this invention, the server includes means for receiving user input and analyzing content and sentiment data, means for automatically generating creative information based on the input content and sentiment data using a generative AI model, and means for presenting the generated information to the user and continuously collecting sentiment data. This enables the generation of content that is tailored to the user's emotional state and real-time optimization.

[0535] "User input" refers to the information and requests that a user provides to the system, which are transmitted to the terminal through a specific interface.

[0536] "Emotional data" is a collection of information that indicates a user's emotional state, gathered from things like their facial expressions and tone of voice.

[0537] A "generative AI model" refers to artificial intelligence technology that generates diverse creative information based on a given input prompt.

[0538] "Creative information" refers to creative content and materials that are automatically generated by generative AI models.

[0539] "Information optimization" is the process of adjusting generated creative information based on user sentiment data to improve it and make it more effective.

[0540] A "cloud infrastructure" refers to a network-based computing environment that enables remote access and provides computing resources and data storage.

[0541] This invention is a system that leverages user emotions to generate and optimize creative information in real time. This system operates on a cloud infrastructure and can be accessed by users via web or mobile applications.

[0542] Overview of program processing

[0543] The user inputs requests and desired content types to the system through the terminal. This input relates to specific creative information, such as promotional videos, and is done using the terminal's interface. Simultaneously with the input, the terminal uses the user's camera and microphone to sense facial expressions and voice tone, collecting emotional data.

[0544] The server analyzes both the request content and sentiment data sent from the terminal. This analysis includes not only the type of content but also the user's emotional state. Based on this data, the server sends a prompt to the generative AI model. The generative AI model uses specific algorithms and machine learning techniques to generate creative content based on the prompt. For example, a prompt such as "Generate an energetic promotional video showing the user's joy" might be generated.

[0545] The generated content is sent to the device via the server and presented to the user. The user reviews this information and then shows an emotional response. Based on this reaction, the device collects new emotional data. Based on this feedback, the server repeatedly optimizes the generated content. For example, in a marketing context, it is expected that user engagement will improve as the generated proposals are refined based on the positive responses shown by the user.

[0546] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0547] Step 1:

[0548] The user inputs a request for creative information via a web or mobile application interface through their device. This input could, for example, be a request to generate a promotional video. The device receives this input and simultaneously uses its camera and microphone to sense the user's facial expressions and tone of voice, collecting emotional data. The output of this step is the request content and initial emotional data.

[0549] Step 2:

[0550] The terminal sends the collected request details and emotion data to the server. The server receives this data and first analyzes the request details. Next, it analyzes the emotion data in real time using an emotion engine to determine the user's current emotional state. The output of this step is the analyzed request details and emotion state data.

[0551] Step 3:

[0552] The server creates prompts for the generative AI model based on the analyzed data. For example, if the user has expressed positive emotions, the prompt might say, "Generate a promotional video that highlights the positive emotions the user has expressed." The server then sends this prompt to the generative AI model. The output of this step is a specific prompt that the generative AI model can understand.

[0553] Step 4:

[0554] The generative AI model receives a prompt and generates the specified creative information. The generative AI model uses machine learning techniques to create materials such as videos and images that are suitable for the prompt. The output of this step is the generated creative information.

[0555] Step 5:

[0556] The server sends the generated information to the terminal and presents it to the user. The user views the provided information and expresses an emotional response to its content. The output of this step is data of the user's new emotional response.

[0557] Step 6:

[0558] The device detects a new emotional response from the user and collects that data again. The device sends the newly collected emotional data to the server. The output of this step is the updated emotional data.

[0559] Step 7:

[0560] The server analyzes the updated sentiment data and, if necessary, uses a generative AI model to readjust the creative information. It also optimizes the generated information to maximize its effectiveness, potentially improving user satisfaction. The output of this step is the adjusted or optimized creative information.

[0561] (Application Example 2)

[0562] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0563] In modern society, there is a demand for advertising that reaches diverse users, but conventional systems do not adequately optimize content in real time based on users' emotional responses. Therefore, providing a system that can quickly respond to changes in users' interests and emotions is a challenge.

[0564] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0565] In this invention, the server includes means for receiving requests from users and analyzing their content, means for automatically generating creative content based on the specified content using a generative model, and means for recognizing the user's emotions in real time and optimizing the creative content based on that data. This enables the server to immediately adapt to the user's emotional state and provide more effective advertising content.

[0566] "Means for receiving and analyzing user requests" refers to a system that receives requests submitted by users and interprets the intent and purpose of those requests.

[0567] "Means for automatically generating creative content based on specified content using a generative model" refers to a function that automatically generates content using a machine learning model in accordance with user requests.

[0568] "Means for presenting generated content to users and collecting feedback" refers to a system that displays generated content to users and records user reactions.

[0569] "A means of improving content by analyzing collected feedback and reflecting it in the generative model" refers to a function that analyzes user feedback and improves the output of the generative model based on that analysis.

[0570] "A means of recognizing user emotions in real time and optimizing creative content based on that data" refers to a function that instantly grasps the user's emotional state and adjusts and optimizes the content based on that information.

[0571] "Performing tasks on the cloud" refers to executing processes in a virtualized environment provided via the internet, and is operated in a manner that does not depend on physical local hardware.

[0572] This system is designed to provide real-time optimized advertising content based on user emotions. The following components are used in its implementation:

[0573] The server first receives requests sent from the user's device and analyzes their content. This analysis uses natural language processing techniques to understand the user's intent. The user's device collects emotional data from facial expressions and voice via its camera and microphone, and this information is analyzed in real time by emotion recognition software. Emotion recognition engines such as the Affectiva SDK are utilized in this process.

[0574] Next, the server uses a generative AI model running in the cloud to generate creative content based on the analyzed requests and sentiment data. This process is implemented using AI platforms such as Google Cloud AI or Amazon SageMaker. The generated content is then presented to the user.

[0575] While the user is viewing content, the device continuously collects emotional data. This data is sent to a server and analyzed as feedback. The server uses this feedback to adjust the generative model and optimize the content. This optimization involves improving the content so that the user's emotional state moves in a positive direction.

[0576] As a concrete example, suppose a user is viewing a travel package advertisement on their smartphone and the system detects a smile. In that case, the system switches to an advertisement that includes beach footage emphasizing relaxation. Another example of a specific prompt message would be, "If the user smiles, generate a promotional video centered on beautiful sunset footage of a resort."

[0577] This makes it possible to respond immediately to the user's emotional state and maximize the effectiveness of advertisements.

[0578] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0579] Step 1:

[0580] The terminal receives a request from the user. The user enters information about the content they want to generate, and this data is sent to the terminal. The input data is saved as a text-based prompt.

[0581] Step 2:

[0582] The device uses a camera and microphone to capture the user's facial expressions and voice, collecting emotional data in real time. This emotional data is analyzed using the Affectiva SDK to generate output data indicating the emotions the user is experiencing.

[0583] Step 3:

[0584] The server receives user requests and sentiment data sent from the terminal. Using this data as input, the server analyzes the request content using natural language processing technology and processes the data as needed to understand the user's intent.

[0585] Step 4:

[0586] The server invokes a generative AI model located in the cloud and generates creative content based on the analyzed request and sentiment data. The generation process utilizes Google Cloud AI and Amazon SageMaker to output engaging visual content and other elements from the input data.

[0587] Step 5:

[0588] The generated content is sent to the device and presented to the user. The user views this content in real time and receives personalized feedback that differs from the input data.

[0589] Step 6:

[0590] The device continuously monitors the user's emotions while they are viewing content and sends real-time emotion data to the server. This data is output as user feedback.

[0591] Step 7:

[0592] The server analyzes feedback data sent from the terminal and reflects it in the generative model to optimize content. It analyzes changes in emotion and performs data calculations to regenerate the optimal content that matches the user's emotional state.

[0593] 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.

[0594] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0595] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0596] [Fourth Embodiment]

[0597] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0598] 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.

[0599] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0600] 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.

[0601] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0602] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0603] 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.

[0604] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0605] 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.

[0606] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0607] The 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.

[0608] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0609] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0610] The creative assistant system of the present invention is centered around a generative AI model and automatically generates creative content based on user requests. The system employs a client-server model, and users can access the system from their terminals via the internet.

[0611] User interface

[0612] Users access the system via a web or mobile application to request the creation of creative content. The user interface provides forms for entering the type of request and preferences. For example, a user might specify detailed specifications such as a color theme and font style for creating an advertising poster for a new product.

[0613] Server-based processing

[0614] The server analyzes requests received from users and invokes an AI model to generate creative content based on the specified information. For example, if poster design proposals are to be generated, the AI ​​model automatically generates multiple designs by combining colors, text placement, and images. This generated content is then presented to the user through the user interface.

[0615] Feedback and Improvement

[0616] After users review the generated content, they can provide feedback. This feedback includes areas for improvement and new requests. The server collects this feedback and incorporates it into the AI ​​model to improve accuracy in subsequent content generation processes. Through this cycle, the system continuously learns and can provide higher quality creative content.

[0617] For example, an advertising agency can use this system to quickly generate multi-platform visual content for a new campaign—without using existing designer resources. After users review the initial design draft, they can use the feedback function to specify any changes they want to make to specific color schemes or layouts. The resulting content, being fast and high-quality, contributes to improving a company's marketing efficiency.

[0618] The following describes the processing flow.

[0619] Step 1:

[0620] Users use their devices to submit requests for creative content creation. These requests include information such as the type, style, and purpose of the content.

[0621] Step 2:

[0622] The server receives a request from the user and parses its contents. The parsing process identifies the type and specifications of the requested content and sets the parameters required by the generative model.

[0623] Step 3:

[0624] The server invokes a generative model to automatically generate creative content based on user requests. Specifically, it uses an AI model to generate design proposals and text content. At this stage, it creates a variety of proposals while adhering to the initially set specifications.

[0625] Step 4:

[0626] The server sends the generated creative content to the user in a format viewable on the device. The user can preview the results and check the details.

[0627] Step 5:

[0628] Users input feedback on the presented content via their device and send it to the server. This feedback includes suggestions for improvements and additional comments.

[0629] Step 6:

[0630] The server analyzes the feedback it receives, adjusts the generative model based on that information, and regenerates the improved content. This adjustment improves the quality of subsequent generation processes.

[0631] Step 7:

[0632] The server presents the improved content to the user again and asks for feedback again. This cycle is repeated until the feedback is satisfactory.

[0633] (Example 1)

[0634] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0635] Modern information generation systems are required to accurately understand users' specific needs and provide high-quality, creative information immediately. However, conventional methods often fail to accurately reflect user intent, requiring manual adjustments. Furthermore, the lack of mechanisms for continuously improving the quality of generated information makes it difficult to efficiently provide advanced, creative information.

[0636] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0637] In this invention, the server includes means for sending requests from the user to a computer via a fault, analyzing data, and generating prompt statements; means for automatically forming creative information based on the analyzed data using a generation AI model; and means for displaying the created information to the user and accumulating evaluation information. This makes it possible to accurately and quickly reflect user requests and continuously provide high-quality creative information.

[0638] A "user" refers to an individual who accesses the system, submits requests, and receives the creative information generated.

[0639] "Sending a request to a computer through a network" refers to the process of transmitting a request for information generation from a user's device to a server via a network.

[0640] "Analyzing data to generate prompts" refers to the process of interpreting the requests received from the user and forming instructions in a format that the AI ​​model can understand.

[0641] A "generative AI model" refers to an artificial intelligence system that automatically generates creative information based on specified conditions and requirements.

[0642] "Automatically generating creative information" refers to the process by which a generative AI model independently generates information and content that meets user requests, based on analyzed data.

[0643] "Accumulating evaluation information" refers to the act of collecting user feedback on generated information and saving it for future improvements.

[0644] A "cloud infrastructure" refers to a platform on which services and systems operate in a digital environment provided via the internet.

[0645] This creative assistant system is designed to allow users to easily generate creative information. It primarily operates on a cloud-based infrastructure, and users can access the system via the internet.

[0646] User actions:

[0647] Users access the system via a web or mobile application using their device. The interface includes a form for creating creative content, where users enter prompts. These prompts contain specific requests, which are then sent to the system.

[0648] Server operation:

[0649] The server receives requests from users and uses a generative AI model to automatically generate creative content based on the user's requests. The server uses a high-performance processor to analyze the received data and generate prompt sentences to input into the AI ​​model. The generative AI model forms information by combining colors, text placement, and images based on the conditions specified by the user.

[0650] The generated content is presented directly to the user, who can then rate it. When feedback is sent to the server, the server analyzes this data and adjusts the generating AI model to improve the accuracy of future content generation.

[0651] Specific example:

[0652] Advertising agencies can leverage this system when generating visual content usable across multiple platforms for new campaigns. Users can enter prompts such as, "Please generate an advertising poster for a new product. I'd like a blue and white color theme, a modern font style, and center the text with the image in the background," and quickly receive design proposals.

[0653] In this way, the system supports the user's creative process and efficiently provides high-quality information.

[0654] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0655] Step 1:

[0656] The user uses their device to access a web or mobile application and enters a prompt request regarding the desired creative content. The entered prompt includes specific requests, such as "Generate an advertising poster for a new product. The color theme should be blue and white, and the font style should be modern. The text should be centered, with the image as the background." The input data is sent directly to the server. The output is a prompt for the server to begin processing.

[0657] Step 2:

[0658] The server analyzes the prompt text received from the user and converts it into a format suitable for the generating AI model. Specifically, the server uses natural language processing techniques to break down the prompt text and identify elements such as color, layout, and font. The input for analysis is the prompt text from the user, and the output is data in a format that the AI ​​model can understand (tagned attribute information).

[0659] Step 3:

[0660] The server inputs the analyzed data into the AI ​​model and generates creative content based on the specified specifications. Here, the AI ​​model generates content using templates and design rules according to the given attribute information. The input is the analyzed data format, and the output is the generated creative content. This process automatically handles color selection, font settings, and layout determination.

[0661] Step 4:

[0662] The generated creative content is sent from the server to the user's terminal and presented through the user interface. The user visually reviews multiple options on the terminal. Specifically, the user compares and evaluates the different design options and decides to select the final option or request improvements. The output is the design proposal provided to the user.

[0663] Step 5:

[0664] Users send ratings and feedback on generated content to the server via their devices. This feedback includes suggestions for improvement and additional requests. The input is the feedback information sent by the user, and the output is the information stored in the server's feedback database.

[0665] Step 6:

[0666] The server analyzes accumulated feedback and applies it to the generative AI model to improve the creative content generation process for future generations. Specifically, the server analyzes trends extracted from the feedback and adjusts the generative algorithms and parameters of the AI ​​model. The input is the analyzed feedback data, and the output is the improved generative model.

[0667] (Application Example 1)

[0668] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0669] Traditional creative information creation processes require significant time and skilled personnel, and are particularly inefficient in creating visual advertising information. Furthermore, there is the difficulty of making real-time partial revisions from mobile devices.

[0670] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0671] In this invention, the server includes means for receiving requests from users and analyzing information, means for automatically creating creative information based on specified information using a generation algorithm, and means for presenting the created information to the user and collecting evaluations. This makes it possible to efficiently create high-quality creative information while making partial corrections in real time from a mobile information terminal.

[0672] "Means for receiving requests from users and analyzing information" refers to a function that receives information entered by a user and converts its content into a format suitable for understanding and processing.

[0673] "A means of automatically creating creative information based on specified information using a generation algorithm" refers to a function that mechanically generates new information according to conditions specified by the user, using a pre-set calculation method.

[0674] "A means of presenting generated information to users and collecting their feedback" refers to a function that shows generated information to users and collects their reactions and opinions.

[0675] "Means that operate on an information processing device and are accessible from a mobile information terminal" refers to functions that operate using computing equipment and can be accessed through mobile phones or portable devices.

[0676] "A means of providing generated creative information to a visual display device in high resolution and enabling partial modification" refers to a function that provides the created information to a screen that displays it in high resolution and further enables specific modifications to that information.

[0677] The system that implements this application example operates using an information processing device, and users can access it from their mobile devices. The server is equipped with a backend using Python and Django, which receives and parses user requests. User requests are sent from the terminal via a REST API.

[0678] The server runs a TensorFlow-based generative AI model to generate new creative information. The generated information is displayed in high resolution on a mobile device, and a user interface implemented using React Native allows the user to view and modify the information using touch controls.

[0679] Of particular importance is the ability for users to provide feedback on the generated creative information after it has been presented to them. This feedback is then incorporated into subsequent content creation processes, leading to continuous improvement of the information.

[0680] For example, when a company plans an advertising campaign for a new product, users can input their preferences regarding theme colors and target markets. The AI ​​model then quickly generates multiple design options, which can be modified and adjusted on a mobile device.

[0681] The generative AI model accepts prompt text such as "A poster for a food delivery service's spring campaign. It features a colorful and refreshing design, targeting women in their 20s and 30s," and makes predictions and generates content based on that input.

[0682] Therefore, this system can provide users with a comfortable and efficient solution for creative mass generation.

[0683] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0684] Step 1:

[0685] Users launch a web or mobile application from their mobile device and input information such as advertising campaign details. This input includes title, target audience, and color scheme. The device formats the user's input information in JSON format and sends it to the server via a REST API.

[0686] Step 2:

[0687] The server parses the received JSON data. The data analysis module reads the input information and extracts the necessary parameters. In this process, the server uses Python and Django to process the request. This process performs necessary syntax checks and parameter validity checks to ensure that the data is in the correct format as input to the generated AI model.

[0688] Step 3:

[0689] The server uses a generative AI model based on TensorFlow to generate creative information that matches specified conditions. The model references a pre-trained dataset and generates design proposals that best match the user's input. The input here is a prompt based on the user's request, and the output is the generated advertisement design proposal.

[0690] Step 4:

[0691] The generated design proposals are formatted again in JSON format and sent to the device to present the results to the user. The device's user interface is implemented with React Native and displays the generated information in high resolution. The user can intuitively review the displayed design proposals and consider what needs to be corrected at that stage.

[0692] Step 5:

[0693] Users can input modifications and additional feedback via touch gestures on the interface. For example, they can specify changes to the design's colors and fonts. The device then converts this feedback information back into JSON format and sends it to the server.

[0694] Step 6:

[0695] The server analyzes user feedback and updates the generated AI model. This feedback is used to improve the model's accuracy in subsequent generation processes. In this way, the server continuously improves the model, enabling it to provide even higher quality creative information.

[0696] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0697] This invention relates to a system that recognizes user emotions in real time and generates and optimizes creative content based on those emotions. The system has a configuration that combines an emotion engine and a generative AI model and operates on a cloud-based platform.

[0698] User interface

[0699] Users access the system via web or mobile applications. The user interface provides forms for submitting requests for creative content and sentiment recognition capabilities to capture user responses. For example, if a user requests a promotional video for a specific advertising campaign, sentiment data is collected from the user's facial expressions and tone of voice during the process.

[0700] Server-based processing

[0701] The server first receives a request from the user and analyzes its content. Simultaneously, the emotion engine analyzes the user's emotional data, combines this data with the request analysis results, and sends it to the AI ​​model. This allows the generative model to generate creative content that is appropriate to the user's emotional state.

[0702] Emotion-based optimization

[0703] Even as generated content is presented to the user and the user reacts accordingly, the sentiment engine continues to collect data in real time. The server uses this data to optimize content suggestions and immediately makes new suggestions as needed. If the user's sentiment shifts in a positive direction, content improvements are made to maintain that direction.

[0704] For example, if a marketing professional uses this system to develop a promotional strategy for a new product, and the emotion engine indicates that the initial user response is positive, all generated creative ideas will be fine-tuned to reinforce that emotion. This not only maximizes marketing effectiveness but also improves consumer engagement.

[0705] The following describes the processing flow.

[0706] Step 1:

[0707] The user uses their device to enter a request for creative content into the application. The request includes details such as the type, purpose, and style of content needed.

[0708] Step 2:

[0709] The server receives the user's request and parses its contents. The parsing results include the requested design elements and keywords.

[0710] Step 3:

[0711] The emotion engine simultaneously acquires data to understand the user's emotions. This data is collected through the user's camera feed and voice tone.

[0712] Step 4:

[0713] Based on requests and sentiment data analyzed by the server, a generative model is used to automatically generate creative content. The model constructs content using an approach optimized for the user's emotional state.

[0714] Step 5:

[0715] The device presents the generated creative content to the user, and the emotion engine continuously monitors the user's reactions.

[0716] Step 6:

[0717] While users review content and provide feedback, the sentiment engine also sends collected data to the server.

[0718] Step 7:

[0719] The server analyzes feedback and sentiment data, adjusts the generative model, and generates new content. This process improves content generation to better match user needs and sentiments.

[0720] Step 8:

[0721] This process is repeated multiple times, ultimately delivering creative content that satisfies the user.

[0722] (Example 2)

[0723] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0724] In modern information processing, there is a need to quickly generate content that appropriately reflects the user's emotional state and to optimize it in real time to meet the user's needs. However, conventional methods lack the accuracy and speed to reflect emotions, making it difficult to maximize the appeal and effectiveness of content.

[0725] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0726] In this invention, the server includes means for receiving user input and analyzing content and sentiment data, means for automatically generating creative information based on the input content and sentiment data using a generative AI model, and means for presenting the generated information to the user and continuously collecting sentiment data. This enables the generation of content that is tailored to the user's emotional state and real-time optimization.

[0727] "User input" refers to the information and requests that a user provides to the system, which are transmitted to the terminal through a specific interface.

[0728] "Emotional data" is a collection of information that indicates a user's emotional state, gathered from things like their facial expressions and tone of voice.

[0729] A "generative AI model" refers to artificial intelligence technology that generates diverse creative information based on a given input prompt.

[0730] "Creative information" refers to creative content and materials that are automatically generated by generative AI models.

[0731] "Information optimization" is the process of adjusting generated creative information based on user sentiment data to improve it and make it more effective.

[0732] A "cloud infrastructure" refers to a network-based computing environment that enables remote access and provides computing resources and data storage.

[0733] This invention is a system that leverages user emotions to generate and optimize creative information in real time. This system operates on a cloud infrastructure and can be accessed by users via web or mobile applications.

[0734] Overview of program processing

[0735] The user inputs requests and desired content types to the system through the terminal. This input relates to specific creative information, such as promotional videos, and is done using the terminal's interface. Simultaneously with the input, the terminal uses the user's camera and microphone to sense facial expressions and voice tone, collecting emotional data.

[0736] The server analyzes both the request content and sentiment data sent from the terminal. This analysis includes not only the type of content but also the user's emotional state. Based on this data, the server sends a prompt to the generative AI model. The generative AI model uses specific algorithms and machine learning techniques to generate creative content based on the prompt. For example, a prompt such as "Generate an energetic promotional video showing the user's joy" might be generated.

[0737] The generated content is sent to the device via the server and presented to the user. The user reviews this information and then shows an emotional response. Based on this reaction, the device collects new emotional data. Based on this feedback, the server repeatedly optimizes the generated content. For example, in a marketing context, it is expected that user engagement will improve as the generated proposals are refined based on the positive responses shown by the user.

[0738] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0739] Step 1:

[0740] The user inputs a request for creative information via a web or mobile application interface through their device. This input could, for example, be a request to generate a promotional video. The device receives this input and simultaneously uses its camera and microphone to sense the user's facial expressions and tone of voice, collecting emotional data. The output of this step is the request content and initial emotional data.

[0741] Step 2:

[0742] The terminal sends the collected request details and emotion data to the server. The server receives this data and first analyzes the request details. Next, it analyzes the emotion data in real time using an emotion engine to determine the user's current emotional state. The output of this step is the analyzed request details and emotion state data.

[0743] Step 3:

[0744] The server creates prompts for the generative AI model based on the analyzed data. For example, if the user has expressed positive emotions, the prompt might say, "Generate a promotional video that highlights the positive emotions the user has expressed." The server then sends this prompt to the generative AI model. The output of this step is a specific prompt that the generative AI model can understand.

[0745] Step 4:

[0746] The generative AI model receives a prompt and generates the specified creative information. The generative AI model uses machine learning techniques to create materials such as videos and images that are suitable for the prompt. The output of this step is the generated creative information.

[0747] Step 5:

[0748] The server sends the generated information to the terminal and presents it to the user. The user views the provided information and expresses an emotional response to its content. The output of this step is data of the user's new emotional response.

[0749] Step 6:

[0750] The device detects a new emotional response from the user and collects that data again. The device sends the newly collected emotional data to the server. The output of this step is the updated emotional data.

[0751] Step 7:

[0752] The server analyzes the updated sentiment data and, if necessary, uses a generative AI model to readjust the creative information. It also optimizes the generated information to maximize its effectiveness, potentially improving user satisfaction. The output of this step is the adjusted or optimized creative information.

[0753] (Application Example 2)

[0754] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0755] In modern society, there is a demand for advertising that reaches diverse users, but conventional systems do not adequately optimize content in real time based on users' emotional responses. Therefore, providing a system that can quickly respond to changes in users' interests and emotions is a challenge.

[0756] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0757] In this invention, the server includes means for receiving requests from users and analyzing their content, means for automatically generating creative content based on the specified content using a generative model, and means for recognizing the user's emotions in real time and optimizing the creative content based on that data. This enables the server to immediately adapt to the user's emotional state and provide more effective advertising content.

[0758] "Means for receiving and analyzing user requests" refers to a system that receives requests submitted by users and interprets the intent and purpose of those requests.

[0759] "Means for automatically generating creative content based on specified content using a generative model" refers to a function that automatically generates content using a machine learning model in accordance with user requests.

[0760] "Means for presenting generated content to users and collecting feedback" refers to a system that displays generated content to users and records user reactions.

[0761] "A means of improving content by analyzing collected feedback and reflecting it in the generative model" refers to a function that analyzes user feedback and improves the output of the generative model based on that analysis.

[0762] "A means of recognizing user emotions in real time and optimizing creative content based on that data" refers to a function that instantly grasps the user's emotional state and adjusts and optimizes the content based on that information.

[0763] "Performing tasks on the cloud" refers to executing processes in a virtualized environment provided via the internet, and is operated in a manner that does not depend on physical local hardware.

[0764] This system is designed to provide real-time optimized advertising content based on user emotions. The following components are used in its implementation:

[0765] The server first receives requests sent from the user's device and analyzes their content. This analysis uses natural language processing techniques to understand the user's intent. The user's device collects emotional data from facial expressions and voice via its camera and microphone, and this information is analyzed in real time by emotion recognition software. Emotion recognition engines such as the Affectiva SDK are utilized in this process.

[0766] Next, the server uses a generative AI model running in the cloud to generate creative content based on the analyzed requests and sentiment data. This process is implemented using AI platforms such as Google Cloud AI or Amazon SageMaker. The generated content is then presented to the user.

[0767] While the user is viewing content, the device continuously collects emotional data. This data is sent to a server and analyzed as feedback. The server uses this feedback to adjust the generative model and optimize the content. This optimization involves improving the content so that the user's emotional state moves in a positive direction.

[0768] As a concrete example, suppose a user is viewing a travel package advertisement on their smartphone and the system detects a smile. In that case, the system switches to an advertisement that includes beach footage emphasizing relaxation. Another example of a specific prompt message would be, "If the user smiles, generate a promotional video centered on beautiful sunset footage of a resort."

[0769] This makes it possible to respond immediately to the user's emotional state and maximize the effectiveness of advertisements.

[0770] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0771] Step 1:

[0772] The terminal receives a request from the user. The user enters information about the content they want to generate, and this data is sent to the terminal. The input data is saved as a text-based prompt.

[0773] Step 2:

[0774] The device uses a camera and microphone to capture the user's facial expressions and voice, collecting emotional data in real time. This emotional data is analyzed using the Affectiva SDK to generate output data indicating the emotions the user is experiencing.

[0775] Step 3:

[0776] The server receives user requests and sentiment data sent from the terminal. Using this data as input, the server analyzes the request content using natural language processing technology and processes the data as needed to understand the user's intent.

[0777] Step 4:

[0778] The server invokes a generative AI model located in the cloud and generates creative content based on the analyzed request and sentiment data. The generation process utilizes Google Cloud AI and Amazon SageMaker to output engaging visual content and other elements from the input data.

[0779] Step 5:

[0780] The generated content is sent to the device and presented to the user. The user views this content in real time and receives personalized feedback that differs from the input data.

[0781] Step 6:

[0782] The device continuously monitors the user's emotions while they are viewing content and sends real-time emotion data to the server. This data is output as user feedback.

[0783] Step 7:

[0784] The server analyzes feedback data sent from the terminal and reflects it in the generative model to optimize content. It analyzes changes in emotion and performs data calculations to regenerate the optimal content that matches the user's emotional state.

[0785] 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.

[0786] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0787] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0788] 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.

[0789] Figure 9 shows an 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.

[0790] 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.

[0791] 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.

[0792] 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, motorcycles, etc., 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, for example, based 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.

[0793] 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."

[0794] 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.

[0795] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0796] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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 the like 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.

[0805] 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.

[0806] The following is further disclosed regarding the embodiments described above.

[0807] (Claim 1)

[0808] A means of receiving requests from users and analyzing their content,

[0809] A means for automatically generating creative content based on specified content using a generative model,

[0810] A means of presenting the generated content to the user and collecting feedback,

[0811] A system that includes means to improve content by analyzing collected feedback and reflecting it in a generative model.

[0812] (Claim 2)

[0813] The system according to claim 1, wherein the content improvement cycle is repeated based on multiple feedbacks.

[0814] (Claim 3)

[0815] The system according to claim 1, which operates on a cloud-based platform and is accessible to users in a remote environment.

[0816] "Example 1"

[0817] (Claim 1)

[0818] A means for a user to send a request to a computer through a fault, analyze the data, and generate a prompt statement,

[0819] A means of automatically forming creative information based on analyzed data using a generative AI model,

[0820] A means of displaying created information to users and accumulating evaluation information,

[0821] A system that includes means for optimizing information by analyzing accumulated evaluation data and reflecting it in the generated AI model.

[0822] (Claim 2)

[0823] The system according to claim 1, wherein the information optimization cycle is repeated based on multiple evaluations.

[0824] (Claim 3)

[0825] The system according to claim 1, which operates on a cloud infrastructure that can be operated by a user in a remote environment.

[0826] "Application Example 1"

[0827] (Claim 1)

[0828] A means of receiving requests from users and analyzing the information,

[0829] A means for automatically creating creative information based on specified information using a generation algorithm,

[0830] A means of presenting the created information to users and collecting their evaluations,

[0831] A means of improving information by analyzing collected evaluations and reflecting them in the generation algorithm,

[0832] A means that operates on an information processing device and is accessible from a mobile information terminal,

[0833] A system that provides generated creative information to a visual display device in high resolution and includes means for enabling partial modification.

[0834] (Claim 2)

[0835] The system according to claim 1, wherein the information improvement process is repeated based on multiple evaluations.

[0836] (Claim 3)

[0837] The system according to claim 1, which operates on a remote information system that can be accessed by users in a remote environment.

[0838] "Example 2 of combining an emotion engine"

[0839] (Claim 1)

[0840] A means for receiving user input and analyzing its content and sentiment data,

[0841] A means of automatically generating creative information based on input content and sentiment data using a generative AI model,

[0842] A means of presenting generated information to the user and continuously collecting emotional data,

[0843] A means of optimizing information by analyzing collected emotional data and reflecting it in a generative AI model,

[0844] A system that includes means for improving information suggestions in real time in response to changes in the user's emotions.

[0845] (Claim 2)

[0846] The system according to claim 1, wherein the information optimization cycle is repeated based on multiple sets of sentiment data.

[0847] (Claim 3)

[0848] The system according to claim 1, which operates on a cloud infrastructure accessible to users in a remote environment.

[0849] "Application example 2 when combining with an emotional engine"

[0850] (Claim 1)

[0851] A means of receiving requests from users and analyzing their content,

[0852] A means for automatically generating creative content based on specified content using a generative model,

[0853] A means of presenting the generated content to the user and collecting feedback,

[0854] By analyzing the collected feedback and reflecting it in the generative model, a means of improving content is provided.

[0855] A means of recognizing user emotions in real time and optimizing creative content based on that data,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, wherein the content improvement cycle is repeated based on multiple feedbacks and takes into account the user's emotional state.

[0859] (Claim 3)

[0860] The system according to claim 1, which is accessible to users in a remote environment and processes emotional data on the cloud. [Explanation of Symbols]

[0861] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving requests from users and analyzing the information, A means for automatically creating creative information based on specified information using a generation algorithm, A means of presenting the created information to users and collecting their evaluations, A means of improving information by analyzing collected evaluations and reflecting them in the generation algorithm, A means that operates on an information processing device and is accessible from a mobile information terminal, A system that provides generated creative information to a visual display device in high resolution and includes means for enabling partial modification.

2. The system according to claim 1, wherein the information improvement process is repeated based on multiple evaluations.

3. The system according to claim 1, which operates on a remote information system that can be accessed by users in a remote location.

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