system

Abstract

Provide a system. 【Solution means】 Means for receiving input data, Means for analyzing the input data using natural language processing technology and extracting relevant keywords and concepts, Means for selecting an appropriate template based on the analysis result, Means for automatically generating graphs and charts using an image generation algorithm, Means for combining the selected template and the generated content to form slides, Means for transmitting the generated slides to the user terminal and receiving feedback, Means for optimizing the system using the received feedback, A system including the above.

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 creating a business presentation, it is difficult to create high-quality materials in a short time, and there is a large variation in the quality of materials depending on the skills and experience of the creator. The problem to be solved is to address this issue.

Means for Solving the Problems

[0005] The present invention solves the aforementioned problems through a system that includes means for receiving input data and extracting relevant keywords and concepts by analyzing it using natural language processing technology, means for selecting an appropriate template based on the analysis results, means for automatically generating graphs and charts using an image generation algorithm, means for composing slides by combining the selected template and generated content, means for sending the generated slides to a user terminal and receiving feedback, and means for optimizing the system using the received feedback.

[0006] "Input data" refers to information received from users, including themes and objectives, and serves as the basis for the system's analysis.

[0007] "Natural language processing technology" is a technology that enables computers to understand, analyze, and use human language, and is used to analyze the meaning of user-input data.

[0008] "Keywords" are important words or phrases that represent a specific theme or concept, and are information extracted from input data.

[0009] A "concept" is an abstract idea or theme, and it is an element that a system interprets from input data.

[0010] A "template" is a predefined format for the layout and design of presentation slides, providing a framework that enables rapid document creation.

[0011] An "image generation algorithm" is a computational method for automatically generating visual content such as graphs and charts, and is a technology used to improve the quality of presentations.

[0012] A "slide" is a page that makes up part of a presentation document, and it is a medium for effectively conveying information by combining text and images.

[0013] A "user terminal" is a device used by users to view presentation materials and interact with the system, and includes personal computers and tablets.

[0014] "Feedback" refers to the opinions and requests that users provide to the system, and is used to improve and customize the generated materials.

[0015] "Optimizing the system" means improving the system's processing and generation processes based on user feedback, and making adjustments to provide more accurate results. [Brief explanation of the drawing]

[0016] [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]Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Also, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] 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.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention relates to a business presentation creation support system utilizing generative AI technology. The system's main components consist of a server and user terminals. The server plays a central role, analyzing data entered by the user and automatically generating presentation materials.

[0038] The user inputs the presentation theme and purpose using a terminal. This input is sent to the server. The server first analyzes the input data using natural language processing technology and extracts relevant keywords and concepts. Based on this analysis, the server selects the most appropriate template from its template database. For example, if the theme is "Annual Sales Report," a template that emphasizes business data will be selected.

[0039] Next, the server uses an image generation algorithm to automatically generate visual content such as graphs and charts. In this process, the generated content is incorporated into a template and structured as slides. As a result, the presentation becomes visually consistent and engaging.

[0040] The generated presentation materials are sent to the user's device. The user reviews the materials and provides feedback to the server as needed. For example, if they want to change the color of a graph or the font size, they can enter specific requests. The server receives this feedback and uses it to optimize the entire system. This includes using machine learning algorithms to analyze feedback patterns and make adjustments to better match the user's preferences in future material generation.

[0041] In this way, the system provides high-quality presentation materials in a short amount of time, supporting efficient decision-making in business processes.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users input themes and objectives for creating presentations via their devices and send them to the server. Examples of inputs include "Annual Sales Report" and "New Product Market Analysis."

[0045] Step 2:

[0046] The server applies natural language processing techniques to the received input data to extract relevant keywords and concepts. In this step, key elements such as "sales," "annual data," and "market trends" are identified.

[0047] Step 3:

[0048] The server selects an appropriate template from the template database based on the analysis. For example, a template for a business data presentation suitable for sales reporting might be selected.

[0049] Step 4:

[0050] The server uses an image generation algorithm to automatically generate visual content such as graphs and charts necessary for the presentation. The generated content is then applied to the template in a consistent manner.

[0051] Step 5:

[0052] The server combines the selected template with the generated visual content to construct a presentation in slide format. At this stage, the layout and order of the slides are optimized.

[0053] Step 6:

[0054] The completed presentation materials are sent to the user's device. The user reviews the materials and provides feedback to the server if any corrections or additions are needed.

[0055] Step 7:

[0056] The server analyzes user feedback and optimizes the system using machine learning. This process allows for adjustments to better match user preferences in future material generation.

[0057] (Example 1)

[0058] 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."

[0059] Traditional presentation creation methods required a tremendous amount of time and effort to prepare materials, and it was particularly difficult to create visually consistent, high-quality materials in a short amount of time. Furthermore, while there is a need to effectively incorporate user feedback to improve the quality of materials, there has been a lack of methods to enable this.

[0060] 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.

[0061] In this invention, the server includes means for acquiring information from the user, means for processing the input information using generative AI technology and identifying relevant words and concepts, and means for determining the optimal template based on the identified information. This enables the rapid generation of high-quality presentation materials and flexible customization to meet the user's needs.

[0062] "Input information" refers to data such as the purpose and theme that users provide when creating presentation materials.

[0063] "Generative AI technology" is a technology that uses artificial intelligence to analyze natural language and generate visual information, thereby efficiently processing user input.

[0064] "Words and concepts" refer to keywords and important semantic content related to the presentation's theme, extracted from the input information through natural language processing.

[0065] A "template" is a pre-defined framework for the structure and design of presentation materials, serving as a foundation into which specific content and visuals are incorporated.

[0066] An "image generation model" is an algorithm or program that automatically creates visual information such as graphs and charts based on specified conditions.

[0067] "Materials" refers to a collection of presentation slides composed of generated templates and visual information.

[0068] The system of this invention aims to automatically generate presentation materials using generative AI technology. Its main components include a server and a user terminal. The user provides input information, including a clear theme and purpose, using the terminal. This information is immediately transmitted to the server.

[0069] The server analyzes the received data using generative AI technology, specifically natural language processing technology. The technology used is a commonly used generative AI model. The analysis identifies keywords and concepts from the presentation, which form the basis for template selection. The server then selects the most suitable template from the template database.

[0070] Next, the server automatically generates visual content such as graphs and charts using an image generation model. This adds consistency and visual impact to the presentation materials. The generated visual content is then incorporated into a selected template and finally compiled into a document.

[0071] This document is sent from the server to the user's terminal, where the user reviews its contents. If necessary, the user can provide feedback, which the server uses to optimize the entire system.

[0072] As a concrete example, if a user is creating a presentation on the theme of "New Product Marketing Plan for Next Year," the user would input this as a prompt. Based on an example prompt such as, "Please create a marketing plan for a new product targeting increased sales for next year. Please make it a visually appealing presentation that includes a comparison graph with competing products," the system will automatically generate appropriate templates and visual materials.

[0073] As described above, the present invention provides a practical means for quickly and efficiently creating high-quality presentation materials by utilizing generative AI technology.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user uses a terminal to input the presentation theme and objectives. This information is sent to the server as input data. The terminal considers that the input data may include background information on the theme and specific instructions, and sends this to the server.

[0077] Step 2:

[0078] The server analyzes the received input data using a generating AI model. In this step, the server utilizes natural language processing techniques to extract key keywords and concepts from the input data. As a result of this analysis, the extracted keywords are sent to the next step as output data.

[0079] Step 3:

[0080] The server selects the most suitable template from the template database based on keywords and concepts. In this process, the server selects the template's structure and style information as output data and passes it on to the next visual content generation step.

[0081] Step 4:

[0082] The server uses an image generation algorithm to generate graphs and charts that fit the selected template. During this process, numerical data is transformed into visual data as part of the data calculation. The generated visual content is then compiled as part of the slides and configured as output data.

[0083] Step 5:

[0084] The server sends the configured presentation materials to the user's terminal. The user can view the sent materials and utilize features for visual evaluation. They can check the quality of the materials and provide feedback.

[0085] Step 6:

[0086] Users enter feedback on their devices. This feedback includes desired improvements and additional requests. The user's feedback data is sent to the server.

[0087] Step 7:

[0088] The server analyzes the received feedback. Through machine learning algorithms, it analyzes patterns derived from the feedback and optimizes the system. This ensures that the next presentation generated will be more tailored to the user's preferences.

[0089] (Application Example 1)

[0090] 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."

[0091] Creating visually appealing and effective display plans is crucial for product placement and promotional activities in physical stores, but it requires time and specialized knowledge. Therefore, store staff need ways to use their time efficiently and create product displays with expert support.

[0092] 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.

[0093] In this invention, the server includes means for receiving input data, means for analyzing the input data using natural language processing technology and extracting relevant keywords and concepts, and means for proposing product layout plans based on the received information. This enables store staff to quickly generate effective and creative product displays.

[0094] "Means for receiving input data" refers to a function that takes in information provided by the user into the system and prepares it for processing.

[0095] "A means of analyzing input data using natural language processing technology and extracting relevant keywords and concepts" refers to a technical method that analyzes linguistic information provided by the user to identify and extract important words and concepts.

[0096] "The means of selecting the appropriate template" refers to a function that selects the most suitable format from pre-configured templates based on the results of data analysis.

[0097] "Methods for automatically generating visual content using image generation algorithms" refer to technologies that use computer programs to automatically create visual elements such as graphs and tables based on presented data.

[0098] "Means of combining selected templates and generated content to construct visual materials" refers to the process of integrating selected templates and generated visual content to prepare completed visual materials.

[0099] "A means of transmitting generated visual materials to an electronic device and receiving feedback" refers to a system that transmits created visual information to the user's device and collects their usage results and opinions.

[0100] "Methods for optimizing the system using received feedback" refers to the process of improving the entire system and enhancing future performance based on opinions and reactions gathered from users.

[0101] "A means of proposing product placement plans based on received information" refers to a function that designs and provides effective product placement methods based on the information collected.

[0102] This invention is a system that efficiently supports product displays and promotional activities in physical stores. The system primarily utilizes a server and the user's electronic devices. The server employs a generative AI model to process information provided by the user.

[0103] First, the user's device inputs product details and campaign themes. This input is sent to the server, which analyzes it using natural language processing technology. Based on the keywords and concepts extracted through the analysis, the server selects an appropriate template and then uses an image generation algorithm to create visual content.

[0104] Visual content and selected templates are combined to create visual materials. These visual materials are sent to the user's device, and the resulting feedback is sent back to the server. The server analyzes the feedback and optimizes the system. Furthermore, it also has a function to suggest product placement options based on the received information.

[0105] The hardware and software used include programs to run OpenAI's generative models, as well as Matplotlib and PIL for generating visual content. This makes it possible to quickly create effective product placement and display ideas.

[0106] For example, consider a campaign promoting a newly released confectionery product. In this case, the user inputs themes such as "Newly released 12-pack of dark chocolate" and "Valentine's Day dessert," and the server generates appropriate display suggestions and sends them back to the terminal. These display suggestions can then be used to determine how products are displayed in stores and to create point-of-purchase (POP) advertisements.

[0107] An example of a prompt message would be entered in the following format:

[0108] Product Information: Newly released 12-pack of dark chocolates. Campaign Theme: Valentine's Day Desserts. Please suggest a suitable display idea.

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

[0110] Step 1:

[0111] The user uses a terminal to enter product information and a campaign theme. The entered data is formed into a prompt message and sent to the server. The input in this step is the text data entered by the user into the terminal, and the output is the prompt message sent to the server.

[0112] Step 2:

[0113] The server uses natural language processing techniques to analyze the received prompt text. Specifically, it analyzes the input text data and extracts relevant keywords and concepts. In this process, the server utilizes a generative AI model. The input is the prompt text, and the output is the extracted keywords and concepts.

[0114] Step 3:

[0115] The server selects an appropriate template based on the extracted keywords and concepts. This template is chosen from the template database to be the most suitable format. The input is the analysis result from step 2, and the output is the selected template.

[0116] Step 4:

[0117] The server automatically generates visual content using an image generation algorithm. In this step, graphs and charts are designed and generated using extracted keywords and template information. The input is keywords and template information, and the output is visual content.

[0118] Step 5:

[0119] The server combines the selected template and generated visual content to construct the completed visual document. This results in a visually organized document. The input is the template and visual content, and the output is the constructed visual document.

[0120] Step 6:

[0121] The server sends the generated visual materials to the user's terminal. The user then reviews the materials and provides feedback. This feedback is sent back to the server. The input is the visual materials, and the output is the feedback on the terminal.

[0122] Step 7:

[0123] The server uses the received feedback to optimize the system. Machine learning algorithms are used to analyze the feedback, which is then used to improve the overall system. The input is the feedback, and the output is the optimized system.

[0124] Step 8:

[0125] The server then proposes a product placement plan based on the information it receives. This placement plan is then sent back to the terminal as a visual representation. The input is the optimized system and feedback, and the output is the placement plan for the terminal.

[0126] 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.

[0127] This invention combines a business presentation creation support system utilizing generative AI technology with an emotion engine that recognizes user emotions. The system is centered around a server and a user terminal, and automatically generates presentation materials that reflect the data and emotions entered by the user.

[0128] The user inputs the presentation theme and intent through their device. During this process, the emotion engine analyzes the user's emotions based on the input data and user interaction. For example, if a user presents a presentation on "Success Stories of New Products" while also expressing feelings of joy and anticipation, the emotion engine will detect this.

[0129] The server analyzes the received input data using natural language processing technology and extracts relevant keywords and concepts. Simultaneously, it takes into account the emotional information analyzed by the emotion engine and selects a template from the template database that is more suitable for the user's emotions. For example, if the emotion of joy is recognized, a template with a bright and cheerful design will be selected.

[0130] Next, the server uses an image generation algorithm to automatically generate visual content such as graphs and charts. The generated visual content also reflects the results of emotion recognition by the emotion engine, and is adjusted so that it influences the overall tone and design of the presentation.

[0131] The completed slides are sent to the user's device, where the user reviews the materials. The user can provide feedback; for example, if they request that the design's color scheme be brighter, the server uses machine learning algorithms based on this feedback to optimize the entire system.

[0132] In this way, the present invention enables the rapid generation of high-quality presentation materials that resonate with the user's emotions, and allows for flexible material creation tailored to the user's needs.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The user uses the device to input the presentation theme and intent, while simultaneously engaging in natural dialogue and operations. During this process, the emotion engine recognizes emotions from the user's expressions and actions. For example, it analyzes emotions such as tension and anticipation from the keywords and tone of voice being entered.

[0136] Step 2:

[0137] The server applies natural language processing techniques to the text data received from the user to extract relevant keywords and concepts. In this step, important themes such as "market analysis" and "new products" are identified.

[0138] Step 3:

[0139] Based on the emotional information recognized by the emotion engine, the server selects a template from the database that is appropriate to the analysis results. For example, if the emotion of expectation is detected, a template with a lively color scheme will be selected.

[0140] Step 4:

[0141] The server uses image generation algorithms to generate graphs and charts based on the analysis results. These visual contents are integrated into templates and designed to resonate with the user's emotions.

[0142] Step 5:

[0143] The generated presentation slides are adjusted as needed to match the user's emotions. For example, colors and font sizes are optimized based on the results of emotion recognition.

[0144] Step 6:

[0145] The server sends the completed slides to the user's terminal. The user reviews the finished material and provides visual and content-based feedback.

[0146] Step 7:

[0147] The server receives user feedback and adjusts the entire system through machine learning algorithms to improve the accuracy and emotional relevance of future presentations.

[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 as the "terminal".

[0150] Conventional presentation material creation systems provide standardized content and design without considering user emotions, making it difficult to generate materials that resonate with users' intentions and feelings in a short amount of time. As a result, it was also difficult to create materials that maximized the effectiveness of presentations. Furthermore, there was insufficient mechanism for effectively utilizing user feedback on generated materials to improve the system.

[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 input data at a user terminal and analyzing the user's emotions, means for analyzing the input data using natural language processing technology and extracting relevant keywords and concepts, and means for selecting an appropriate template from a database based on the emotion recognition information and analysis results. This makes it possible to generate high-quality presentation materials that reflect the user's emotions in a short time, and further optimize the entire system by incorporating user feedback.

[0153] "Input data" refers to information that a user provides to the system via their device in order to express the theme and intent of their presentation.

[0154] An "emotion engine" is a technology that analyzes user input data and interactions to identify the user's underlying emotions.

[0155] "Natural language processing technology" is a method for analyzing input data as structural information and extracting relevant keywords and concepts.

[0156] A "template database" is a storage device that stores and provides various pre-designed presentation templates.

[0157] An "image generation algorithm" is a technology that automatically generates visual content based on input data and prompt text.

[0158] A "machine learning algorithm" is a mathematical method used to improve system performance by leveraging user feedback.

[0159] This invention relates to a system that utilizes generative AI technology to automatically generate presentation materials based on the user's emotions. The system consists of a user terminal and a server.

[0160] The user inputs the presentation theme and intent through the terminal. The terminal is equipped with an emotion engine that analyzes emotions from text input and user interaction. This process uses NLP-related libraries in the Python language (e.g., NLTK and spaCy). For example, if emotions such as joy and anticipation are expressed when the theme is "Success Stories of New Products," the emotion engine detects this and sends the information to the server.

[0161] The server analyzes the received data using natural language processing techniques to extract important keywords and concepts. The Gensim library is commonly used for this processing. Next, considering the analysis results and sentiment information, an appropriate template is selected from the template database. The selected template is then automatically enhanced with visual content using a generative AI model (e.g., Stable Diffusion or DALL-E). Keywords such as "sustainability" and "eco-friendly" can be provided as prompts during this process.

[0162] The generated slides are adjusted in tone and design based on emotion recognition. The server sends the completed presentation materials to the user's terminal. The user can review the received materials and provide feedback. Based on this feedback, the server continuously optimizes the entire system using machine learning algorithms (e.g., Scikit-learn).

[0163] In this way, it is possible to generate high-quality presentation materials that reflect user emotions and respond quickly and flexibly.

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

[0165] Step 1:

[0166] The user inputs the presentation theme and intent through the device. The device passes this input data to an emotion engine, which analyzes the user's emotions based on their input and word choices. The emotion engine uses natural language processing technology to identify emotions such as joy and anticipation from the text. The output of this process is the analyzed emotion information.

[0167] Step 2:

[0168] The terminal sends the analyzed sentiment information and input data to the server. The server analyzes the received input data using natural language processing techniques and extracts keywords and concepts. This process applies a pre-configured algorithm to extract highly relevant information. The output is a list of the extracted keywords and concepts.

[0169] Step 3:

[0170] The server selects an appropriate template from the template database based on the emotion information and analysis results. For example, if the emotion engine detects joy, a template with bright colors will be selected. The output is the selected template.

[0171] Step 4:

[0172] The server automatically generates visual content using a generative AI model based on the selected template and extracted keywords. At this stage, keywords such as "success stories" and "growth" are input to the AI ​​model as prompts, and appropriate images and charts are generated. The output is the generated visual content.

[0173] Step 5:

[0174] The server combines the generated visual content with the selected template to create the final slide presentation. The overall design and tone of the slides are also adjusted based on emotional information. The output is the completed presentation slides.

[0175] Step 6:

[0176] The server sends the completed slides to the user's device. The user reviews the received slides and provides feedback as needed. This feedback includes any changes or improvements the user would like to see.

[0177] Step 7:

[0178] The server receives user feedback and optimizes the system using machine learning algorithms. Here, the feedback is analyzed, and system parameters are adjusted to improve future generation. The output is the optimized system configuration.

[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 advertising production, a challenge exists in quickly generating effective advertisements that appeal to the target audience's emotions because the design of visual content generally fails to adequately reflect emotional elements. Furthermore, traditional advertising production processes are slow to incorporate feedback, hindering rapid improvement.

[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 input data, means for analyzing the input data using natural language processing technology and extracting relevant keywords and concepts, and means for selecting an appropriate template based on the analysis results and user sentiment data. This makes it possible to analyze user sentiment and reflect it in the content design of advertisements. Furthermore, based on user feedback on the generated advertising materials, the system can be rapidly optimized using machine learning algorithms, enabling the immediate provision of targeted advertisements.

[0184] "Means for receiving input data" refers to the function that incorporates information about advertising themes and goals provided by the user into the system.

[0185] "A means of analyzing input data using natural language processing technology and extracting relevant keywords and concepts" refers to the process of analyzing received input data and identifying important terms and ideas related to the main point of the advertisement.

[0186] "A means of selecting an appropriate template based on user sentiment data" refers to a function that selects the optimal template that matches the user's emotions based on the analysis results and the user's sentiment information.

[0187] "Methods for automatically generating visual content using image generation algorithms" refers to the process of automatically creating the graphic elements necessary for advertising using AI technology.

[0188] "Methods for composing advertising materials by combining selected templates and generated content" refers to the process of combining selected templates and automatically generated content to create a single advertising material.

[0189] "A means of sending generated advertising materials to a device and receiving feedback" refers to a method of delivering created advertising materials to a user's device and obtaining user evaluations and improvement requests.

[0190] "Methods for optimizing the system using received feedback" refers to the process of improving the system's functionality by utilizing AI technology based on advice and requests received from users.

[0191] The system that realizes this invention includes a process for automatically generating visual content that reflects user emotions in advertising production. This makes it possible to quickly produce effective advertisements for the target audience.

[0192] The server receives advertising themes and goals entered by the user through their device. The entered data is analyzed using Google Cloud's natural language processing API to extract key keywords and concepts. Next, the server uses a TENSORFLOW® model to obtain user sentiment data and selects the most suitable template for the advertisement based on the analysis results. Based on the selected template, the server automatically generates the necessary visual content using an image generation algorithm.

[0193] The generated advertising materials are sent to the user's device. Users can view them and provide feedback. The received feedback is used to optimize the entire system using machine learning algorithms. This cyclical process generates more sophisticated advertisements.

[0194] As a concrete example, when a furniture retailer creates an advertisement themed around "comfortable living spaces," the system can automatically select design elements that evoke a sense of happiness and display them as visual content. An example of a prompt message is as follows:

[0195] Theme: Advertisements for a comfortable living space

[0196] User emotion: Happiness

[0197] Desired color tone: Soft

[0198] In this way, the aim is to make the content of the advertisement appeal more effectively to the emotions of the recipient.

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The user inputs advertising themes and goals via their device. The device then sends this information to the server. The input data includes themes, user intent, and desired design elements. The output is the transfer of data to the server.

[0202] Step 2:

[0203] The server analyzes the received input data using Google Cloud's natural language processing API. Based on the input data, it extracts relevant keywords and concepts. This analysis includes data processing using a natural language model, and the analyzed information is output.

[0204] Step 3:

[0205] The server, along with the analysis results, also considers user sentiment data using a TensorFlow model. The input consists of keywords and sentiment indicators, and based on these, it selects an appropriate design template. The output is the selected template data.

[0206] Step 4:

[0207] The server uses an image generation algorithm to automatically generate visual content based on the selected template. Template and keyword information are input, and a graphic design is created based on this. The output is a completed advertising material.

[0208] Step 5:

[0209] The server sends the generated advertising materials to the user's device. The user reviews the advertising materials on their device and provides feedback. The transmission serves as output, and the feedback serves as input for the next stage.

[0210] Step 6:

[0211] User feedback is received by the server and used to optimize the system using machine learning algorithms. The input is feedback data, and data calculations and system adjustments are performed based on this data. The output is the improved system parameters.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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".

[0228] This invention relates to a business presentation creation support system utilizing generative AI technology. The system's main components consist of a server and user terminals. The server plays a central role, analyzing data entered by the user and automatically generating presentation materials.

[0229] The user inputs the presentation theme and purpose using a terminal. This input is sent to the server. The server first analyzes the input data using natural language processing technology and extracts relevant keywords and concepts. Based on this analysis, the server selects the most appropriate template from its template database. For example, if the theme is "Annual Sales Report," a template that emphasizes business data will be selected.

[0230] Next, the server uses an image generation algorithm to automatically generate visual content such as graphs and charts. In this process, the generated content is incorporated into a template and structured as slides. As a result, the presentation becomes visually consistent and engaging.

[0231] The generated presentation materials are sent to the user's device. The user reviews the materials and provides feedback to the server as needed. For example, if they want to change the color of a graph or the font size, they can enter specific requests. The server receives this feedback and uses it to optimize the entire system. This includes using machine learning algorithms to analyze feedback patterns and make adjustments to better match the user's preferences in future material generation.

[0232] In this way, the system provides high-quality presentation materials in a short amount of time, supporting efficient decision-making in business processes.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] Users input themes and objectives for creating presentations via their devices and send them to the server. Examples of inputs include "Annual Sales Report" and "New Product Market Analysis."

[0236] Step 2:

[0237] The server applies natural language processing techniques to the received input data to extract relevant keywords and concepts. In this step, key elements such as "sales," "annual data," and "market trends" are identified.

[0238] Step 3:

[0239] The server selects an appropriate template from the template database based on the analysis. For example, a template for a business data presentation suitable for sales reporting might be selected.

[0240] Step 4:

[0241] The server uses an image generation algorithm to automatically generate visual content such as graphs and charts necessary for the presentation. The generated content is then applied to the template in a consistent manner.

[0242] Step 5:

[0243] The server combines the selected template with the generated visual content to construct a presentation in slide format. At this stage, the layout and order of the slides are optimized.

[0244] Step 6:

[0245] The completed presentation materials are sent to the user's device. The user reviews the materials and provides feedback to the server if any corrections or additions are needed.

[0246] Step 7:

[0247] The server analyzes user feedback and optimizes the system using machine learning. This process allows for adjustments to better match user preferences in future material generation.

[0248] (Example 1)

[0249] 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."

[0250] Traditional presentation creation methods required a tremendous amount of time and effort to prepare materials, and it was particularly difficult to create visually consistent, high-quality materials in a short amount of time. Furthermore, while there is a need to effectively incorporate user feedback to improve the quality of materials, there has been a lack of methods to enable this.

[0251] 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.

[0252] In this invention, the server includes means for acquiring information from the user, means for processing the input information using generative AI technology and identifying relevant words and concepts, and means for determining the optimal template based on the identified information. This enables the rapid generation of high-quality presentation materials and flexible customization to meet the user's needs.

[0253] "Input information" refers to data such as the purpose and theme that users provide when creating presentation materials.

[0254] "Generative AI technology" is a technology that uses artificial intelligence to analyze natural language and generate visual information, thereby efficiently processing user input.

[0255] "Words and concepts" refer to keywords and important semantic content related to the presentation's theme, extracted from the input information through natural language processing.

[0256] A "template" is a pre-defined framework for the structure and design of presentation materials, serving as a foundation into which specific content and visuals are incorporated.

[0257] An "image generation model" is an algorithm or program that automatically creates visual information such as graphs and charts based on specified conditions.

[0258] "Materials" refers to a collection of presentation slides composed of generated templates and visual information.

[0259] The system of this invention aims to automatically generate presentation materials using generative AI technology. Its main components include a server and a user terminal. The user provides input information, including a clear theme and purpose, using the terminal. This information is immediately transmitted to the server.

[0260] The server analyzes the received data using generative AI technology, specifically natural language processing technology. The technology used is a commonly used generative AI model. The analysis identifies keywords and concepts from the presentation, which form the basis for template selection. The server then selects the most suitable template from the template database.

[0261] Next, the server automatically generates visual content such as graphs and charts using an image generation model. This adds consistency and visual impact to the presentation materials. The generated visual content is then incorporated into a selected template and finally compiled into a document.

[0262] This document is sent from the server to the user's terminal, where the user reviews its contents. If necessary, the user can provide feedback, which the server uses to optimize the entire system.

[0263] As a concrete example, if a user is creating a presentation on the theme of "New Product Marketing Plan for Next Year," the user would input this as a prompt. Based on an example prompt such as, "Please create a marketing plan for a new product targeting increased sales for next year. Please make it a visually appealing presentation that includes a comparison graph with competing products," the system will automatically generate appropriate templates and visual materials.

[0264] As described above, the present invention provides a practical means for quickly and efficiently creating high-quality presentation materials by utilizing generative AI technology.

[0265] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0266] Step 1:

[0267] The user uses a terminal to input the presentation theme and objectives. This information is sent to the server as input data. The terminal considers that the input data may include background information on the theme and specific instructions, and sends this to the server.

[0268] Step 2:

[0269] The server analyzes the received input data using a generating AI model. In this step, the server utilizes natural language processing techniques to extract key keywords and concepts from the input data. As a result of this analysis, the extracted keywords are sent to the next step as output data.

[0270] Step 3:

[0271] The server selects the most suitable template from the template database based on keywords and concepts. In this process, the server selects the template's structure and style information as output data and passes it on to the next visual content generation step.

[0272] Step 4:

[0273] The server uses an image generation algorithm to generate graphs and charts that fit the selected template. During this process, numerical data is transformed into visual data as part of the data calculation. The generated visual content is then compiled as part of the slides and configured as output data.

[0274] Step 5:

[0275] The server sends the configured presentation materials to the user's terminal. The user can view the sent materials and utilize features for visual evaluation. They can check the quality of the materials and provide feedback.

[0276] Step 6:

[0277] Users enter feedback on their devices. This feedback includes desired improvements and additional requests. The user's feedback data is sent to the server.

[0278] Step 7:

[0279] The server analyzes the received feedback. Through machine learning algorithms, it analyzes patterns derived from the feedback and optimizes the system. This ensures that the next presentation generated will be more tailored to the user's preferences.

[0280] (Application Example 1)

[0281] 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 glasses 214 will be referred to as the "terminal."

[0282] In physical stores, when it comes to product placement and promotional activities, creating a visual and effective display plan is important, but it requires time and specialized knowledge. Therefore, store staff need a way to create product displays while using time efficiently and receiving specialized support.

[0283] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0284] In this invention, the server includes means for receiving input data, means for analyzing the input data using natural language processing technology and extracting relevant keywords and concepts, and means for proposing a product placement plan based on the received information. As a result, store staff can quickly generate effective and creative product displays.

[0285] The "means for receiving input data" is a function that takes in information provided by the user into the system and prepares it for processing.

[0286] The "means for analyzing input data using natural language processing technology and extracting relevant keywords and concepts" is a technical method for analyzing the language information provided by the user to identify and extract important words and concepts.

[0287] The "means for selecting an appropriate template" is a function that selects the most suitable format from pre-set templates based on the results of data analysis.

[0288] The "means for automatically generating visual content using an image generation algorithm" is a technology that uses a computer program to automatically create visual elements such as graphs and tables based on the presented data.

[0289] "Means of combining selected templates and generated content to construct visual materials" refers to the process of integrating selected templates and generated visual content to prepare completed visual materials.

[0290] "A means of transmitting generated visual materials to an electronic device and receiving feedback" refers to a system that transmits created visual information to the user's device and collects their usage results and opinions.

[0291] "Methods for optimizing the system using received feedback" refers to the process of improving the entire system and enhancing future performance based on opinions and reactions gathered from users.

[0292] "A means of proposing product placement plans based on received information" refers to a function that designs and provides effective product placement methods based on the information collected.

[0293] This invention is a system that efficiently supports product displays and promotional activities in physical stores. The system primarily utilizes a server and the user's electronic devices. The server employs a generative AI model to process information provided by the user.

[0294] First, the user's device inputs product details and campaign themes. This input is sent to the server, which analyzes it using natural language processing technology. Based on the keywords and concepts extracted through the analysis, the server selects an appropriate template and then uses an image generation algorithm to create visual content.

[0295] Visual content and selected templates are combined to create visual materials. These visual materials are sent to the user's device, and the resulting feedback is sent back to the server. The server analyzes the feedback and optimizes the system. Furthermore, it also has a function to suggest product placement options based on the received information.

[0296] The hardware and software used include programs to run OpenAI's generative models, as well as Matplotlib and PIL for generating visual content. This makes it possible to quickly create effective product placement and display ideas.

[0297] For example, consider a campaign promoting a newly released confectionery product. In this case, the user inputs themes such as "Newly released 12-pack of dark chocolate" and "Valentine's Day dessert," and the server generates appropriate display suggestions and sends them back to the terminal. These display suggestions can then be used to determine how products are displayed in stores and to create point-of-purchase (POP) advertisements.

[0298] An example of a prompt message would be entered in the following format:

[0299] Product Information: Newly released 12-pack of dark chocolates. Campaign Theme: Valentine's Day Desserts. Please suggest a suitable display idea.

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

[0301] Step 1:

[0302] The user uses a terminal to enter product information and a campaign theme. The entered data is formed into a prompt message and sent to the server. The input in this step is the text data entered by the user into the terminal, and the output is the prompt message sent to the server.

[0303] Step 2:

[0304] The server performs analysis using natural language processing technology based on the received prompt text. Specifically, it analyzes the input text data to extract relevant keywords and concepts. In this process, the server utilizes a generative AI model. The input is the prompt text, and the output is the extracted keywords and concepts.

[0305] Step 3:

[0306] The server selects an appropriate template based on the extracted keywords and concepts. The most suitable format is selected from the template database. The input is the analysis result of Step 2, and the output is the selected template.

[0307] Step 4:

[0308] The server automatically generates visual content using an image generation algorithm. In this step, the extracted keywords and template information are used to design and generate graphs and charts. The input is the keywords and template information, and the output is the visual content.

[0309] Step 5:

[0310] The server combines the selected template and the generated visual content to compose the completed visual material. As a result, a visually organized material is prepared. The input is the template and the visual content, and the output is the composed visual material.

[0311] Step 6:

[0312] The server sends the generated visual material to the user's terminal. At that time, the user checks the material and inputs feedback. This feedback is sent back to the server. The input is the visual material, and the output is the feedback on the terminal.

[0313] Step 7:

[0314] The server uses the received feedback to optimize the system. Machine learning algorithms are used to analyze the feedback, which is then used to improve the overall system. The input is the feedback, and the output is the optimized system.

[0315] Step 8:

[0316] The server then proposes a product placement plan based on the information it receives. This placement plan is then sent back to the terminal as a visual representation. The input is the optimized system and feedback, and the output is the placement plan for the terminal.

[0317] 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.

[0318] This invention combines a business presentation creation support system utilizing generative AI technology with an emotion engine that recognizes user emotions. The system is centered around a server and a user terminal, and automatically generates presentation materials that reflect the data and emotions entered by the user.

[0319] The user inputs the presentation theme and intent through their device. During this process, the emotion engine analyzes the user's emotions based on the input data and user interaction. For example, if a user presents a presentation on "Success Stories of New Products" while also expressing feelings of joy and anticipation, the emotion engine will detect this.

[0320] The server analyzes the received input data using natural language processing technology and extracts relevant keywords and concepts. Simultaneously, it takes into account the emotional information analyzed by the emotion engine and selects a template from the template database that is more suitable for the user's emotions. For example, if the emotion of joy is recognized, a template with a bright and cheerful design will be selected.

[0321] Next, the server uses an image generation algorithm to automatically generate visual content such as graphs and charts. The generated visual content also reflects the results of emotion recognition by the emotion engine, and is adjusted so that it influences the overall tone and design of the presentation.

[0322] The completed slides are sent to the user's device, where the user reviews the materials. The user can provide feedback; for example, if they request that the design's color scheme be brighter, the server uses machine learning algorithms based on this feedback to optimize the entire system.

[0323] In this way, the present invention enables the rapid generation of high-quality presentation materials that resonate with the user's emotions, and allows for flexible material creation tailored to the user's needs.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] The user uses the device to input the presentation theme and intent, while simultaneously engaging in natural dialogue and operations. During this process, the emotion engine recognizes emotions from the user's expressions and actions. For example, it analyzes emotions such as tension and anticipation from the keywords and tone of voice being entered.

[0327] Step 2:

[0328] The server applies natural language processing techniques to the text data received from the user to extract relevant keywords and concepts. In this step, important themes such as "market analysis" and "new products" are identified.

[0329] Step 3:

[0330] Based on the emotional information recognized by the emotion engine, the server selects a template from the database that is appropriate to the analysis results. For example, if the emotion of expectation is detected, a template with a lively color scheme will be selected.

[0331] Step 4:

[0332] The server uses image generation algorithms to generate graphs and charts based on the analysis results. These visual contents are integrated into templates and designed to resonate with the user's emotions.

[0333] Step 5:

[0334] The generated presentation slides are adjusted as needed to match the user's emotions. For example, colors and font sizes are optimized based on the results of emotion recognition.

[0335] Step 6:

[0336] The server sends the completed slides to the user's terminal. The user reviews the finished material and provides visual and content-based feedback.

[0337] Step 7:

[0338] The server receives user feedback and adjusts the entire system through machine learning algorithms to improve the accuracy and emotional relevance of future presentations.

[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] Conventional presentation material creation systems provide standardized content and design without considering user emotions, making it difficult to generate materials that resonate with users' intentions and feelings in a short amount of time. As a result, it was also difficult to create materials that maximized the effectiveness of presentations. Furthermore, there was insufficient mechanism for effectively utilizing user feedback on generated materials to improve the system.

[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 input data at a user terminal and analyzing the user's emotions, means for analyzing the input data using natural language processing technology and extracting relevant keywords and concepts, and means for selecting an appropriate template from a database based on the emotion recognition information and analysis results. This makes it possible to generate high-quality presentation materials that reflect the user's emotions in a short time, and further optimize the entire system by incorporating user feedback.

[0344] "Input data" refers to information that a user provides to the system via their device in order to express the theme and intent of their presentation.

[0345] An "emotion engine" is a technology that analyzes user input data and interactions to identify the user's underlying emotions.

[0346] "Natural language processing technology" is a method for analyzing input data as structural information and extracting relevant keywords and concepts.

[0347] A "template database" is a storage device that stores and provides various pre-designed presentation templates.

[0348] An "image generation algorithm" is a technology that automatically generates visual content based on input data and prompt text.

[0349] A "machine learning algorithm" is a mathematical method used to improve system performance by leveraging user feedback.

[0350] This invention relates to a system that utilizes generative AI technology to automatically generate presentation materials based on the user's emotions. The system consists of a user terminal and a server.

[0351] The user inputs the presentation theme and intent through the terminal. The terminal is equipped with an emotion engine that analyzes emotions from text input and user interaction. This process uses NLP-related libraries in the Python language (e.g., NLTK and spaCy). For example, if emotions such as joy and anticipation are expressed when the theme is "Success Stories of New Products," the emotion engine detects this and sends the information to the server.

[0352] The server analyzes the received data using natural language processing techniques to extract important keywords and concepts. The Gensim library is commonly used for this processing. Next, considering the analysis results and sentiment information, an appropriate template is selected from the template database. The selected template is then automatically enhanced with visual content using a generative AI model (e.g., Stable Diffusion or DALL-E). Keywords such as "sustainability" and "eco-friendly" can be provided as prompts during this process.

[0353] The generated slides are adjusted in tone and design based on emotion recognition. The server sends the completed presentation materials to the user's terminal. The user can review the received materials and provide feedback. Based on this feedback, the server continuously optimizes the entire system using machine learning algorithms (e.g., Scikit-learn).

[0354] In this way, it is possible to generate high-quality presentation materials that reflect user emotions and respond quickly and flexibly.

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

[0356] Step 1:

[0357] The user inputs the presentation theme and intent through the device. The device passes this input data to an emotion engine, which analyzes the user's emotions based on their input and word choices. The emotion engine uses natural language processing technology to identify emotions such as joy and anticipation from the text. The output of this process is the analyzed emotion information.

[0358] Step 2:

[0359] The terminal sends the analyzed sentiment information and input data to the server. The server analyzes the received input data using natural language processing techniques and extracts keywords and concepts. This process applies a pre-configured algorithm to extract highly relevant information. The output is a list of the extracted keywords and concepts.

[0360] Step 3:

[0361] The server selects an appropriate template from the template database based on the emotion information and analysis results. For example, if the emotion engine detects joy, a template with bright colors will be selected. The output is the selected template.

[0362] Step 4:

[0363] The server automatically generates visual content using a generative AI model based on the selected template and extracted keywords. At this stage, keywords such as "success stories" and "growth" are input to the AI ​​model as prompts, and appropriate images and charts are generated. The output is the generated visual content.

[0364] Step 5:

[0365] The server combines the generated visual content with the selected template to create the final slide presentation. The overall design and tone of the slides are also adjusted based on emotional information. The output is the completed presentation slides.

[0366] Step 6:

[0367] The server sends the completed slides to the user's device. The user reviews the received slides and provides feedback as needed. This feedback includes any changes or improvements the user would like to see.

[0368] Step 7:

[0369] The server receives user feedback and optimizes the system using machine learning algorithms. Here, the feedback is analyzed, and system parameters are adjusted to improve future generation. The output is the optimized system configuration.

[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 advertising production, a challenge exists in quickly generating effective advertisements that appeal to the target audience's emotions because the design of visual content generally fails to adequately reflect emotional elements. Furthermore, traditional advertising production processes are slow to incorporate feedback, hindering rapid improvement.

[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 input data, means for analyzing the input data using natural language processing technology and extracting relevant keywords and concepts, and means for selecting an appropriate template based on the analysis results and user sentiment data. This makes it possible to analyze user sentiment and reflect it in the content design of advertisements. Furthermore, based on user feedback on the generated advertising materials, the system can be rapidly optimized using machine learning algorithms, enabling the immediate provision of targeted advertisements.

[0375] "Means for receiving input data" refers to the function that incorporates information about advertising themes and goals provided by the user into the system.

[0376] "A means of analyzing input data using natural language processing technology and extracting relevant keywords and concepts" refers to the process of analyzing received input data and identifying important terms and ideas related to the main point of the advertisement.

[0377] "A means of selecting an appropriate template based on user sentiment data" refers to a function that selects the optimal template that matches the user's emotions based on the analysis results and the user's sentiment information.

[0378] "Methods for automatically generating visual content using image generation algorithms" refers to the process of automatically creating the graphic elements necessary for advertising using AI technology.

[0379] "Methods for composing advertising materials by combining selected templates and generated content" refers to the process of combining selected templates and automatically generated content to create a single advertising material.

[0380] "A means of sending generated advertising materials to a device and receiving feedback" refers to a method of delivering created advertising materials to a user's device and obtaining user evaluations and improvement requests.

[0381] "Methods for optimizing the system using received feedback" refers to the process of improving the system's functionality by utilizing AI technology based on advice and requests received from users.

[0382] The system that realizes this invention includes a process for automatically generating visual content that reflects user emotions in advertising production. This makes it possible to quickly produce effective advertisements for the target audience.

[0383] The server receives advertising themes and goals entered by the user through their device. The input data is analyzed using Google Cloud's natural language processing API to extract key keywords and concepts. Next, a TensorFlow model is used to obtain user sentiment data, and together with the analysis results, the server selects the most suitable template for the advertisement. Based on the selected template, an image generation algorithm is used to automatically generate the necessary visual content.

[0384] The generated advertising materials are sent to the user's device. Users can view them and provide feedback. The received feedback is used to optimize the entire system using machine learning algorithms. This cyclical process generates more sophisticated advertisements.

[0385] As a concrete example, when a furniture retailer creates an advertisement themed around "comfortable living spaces," the system can automatically select design elements that evoke a sense of happiness and display them as visual content. An example of a prompt message is as follows:

[0386] Theme: Advertisements for a comfortable living space

[0387] User emotion: Happiness

[0388] Desired color tone: Soft

[0389] In this way, the aim is to make the content of the advertisement appeal more effectively to the emotions of the recipient.

[0390] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0391] Step 1:

[0392] The user inputs advertising themes and goals via their device. The device then sends this information to the server. The input data includes themes, user intent, and desired design elements. The output is the transfer of data to the server.

[0393] Step 2:

[0394] The server analyzes the received input data using Google Cloud's natural language processing API. Based on the input data, it extracts relevant keywords and concepts. This analysis includes data processing using a natural language model, and the analyzed information is output.

[0395] Step 3:

[0396] The server, along with the analysis results, also considers user sentiment data using a TensorFlow model. The input consists of keywords and sentiment indicators, and based on these, it selects an appropriate design template. The output is the selected template data.

[0397] Step 4:

[0398] The server uses an image generation algorithm to automatically generate visual content based on the selected template. Template and keyword information are input, and a graphic design is created based on this. The output is a completed advertising material.

[0399] Step 5:

[0400] The server sends the generated advertising materials to the user's device. The user reviews the advertising materials on their device and provides feedback. The transmission serves as output, and the feedback serves as input for the next stage.

[0401] Step 6:

[0402] User feedback is received by the server and used to optimize the system using machine learning algorithms. The input is feedback data, and data calculations and system adjustments are performed based on this data. The output is the improved system parameters.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] [Third Embodiment]

[0407] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0408] 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.

[0409] 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).

[0410] 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.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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".

[0419] This invention relates to a business presentation creation support system utilizing generative AI technology. The system's main components consist of a server and user terminals. The server plays a central role, analyzing data entered by the user and automatically generating presentation materials.

[0420] The user inputs the presentation theme and purpose using a terminal. This input is sent to the server. The server first analyzes the input data using natural language processing technology and extracts relevant keywords and concepts. Based on this analysis, the server selects the most appropriate template from its template database. For example, if the theme is "Annual Sales Report," a template that emphasizes business data will be selected.

[0421] Next, the server uses an image generation algorithm to automatically generate visual content such as graphs and charts. In this process, the generated content is incorporated into a template and structured as slides. As a result, the presentation becomes visually consistent and engaging.

[0422] The generated presentation materials are sent to the user's device. The user reviews the materials and provides feedback to the server as needed. For example, if they want to change the color of a graph or the font size, they can enter specific requests. The server receives this feedback and uses it to optimize the entire system. This includes using machine learning algorithms to analyze feedback patterns and make adjustments to better match the user's preferences in future material generation.

[0423] In this way, the system provides high-quality presentation materials in a short amount of time, supporting efficient decision-making in business processes.

[0424] The following describes the processing flow.

[0425] Step 1:

[0426] Users input themes and objectives for creating presentations via their devices and send them to the server. Examples of inputs include "Annual Sales Report" and "New Product Market Analysis."

[0427] Step 2:

[0428] The server applies natural language processing techniques to the received input data to extract relevant keywords and concepts. In this step, key elements such as "sales," "annual data," and "market trends" are identified.

[0429] Step 3:

[0430] The server selects an appropriate template from the template database based on the analysis. For example, a template for a business data presentation suitable for sales reporting might be selected.

[0431] Step 4:

[0432] The server uses an image generation algorithm to automatically generate visual content such as graphs and charts necessary for the presentation. The generated content is then applied to the template in a consistent manner.

[0433] Step 5:

[0434] The server combines the selected template with the generated visual content to construct a presentation in slide format. At this stage, the layout and order of the slides are optimized.

[0435] Step 6:

[0436] The completed presentation materials are sent to the user's device. The user reviews the materials and provides feedback to the server if any corrections or additions are needed.

[0437] Step 7:

[0438] The server analyzes user feedback and optimizes the system using machine learning. This process allows for adjustments to better match user preferences in future material generation.

[0439] (Example 1)

[0440] 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."

[0441] Traditional presentation creation methods required a tremendous amount of time and effort to prepare materials, and it was particularly difficult to create visually consistent, high-quality materials in a short amount of time. Furthermore, while there is a need to effectively incorporate user feedback to improve the quality of materials, there has been a lack of methods to enable this.

[0442] 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.

[0443] In this invention, the server includes means for acquiring information from the user, means for processing the input information using generative AI technology and identifying relevant words and concepts, and means for determining the optimal template based on the identified information. This enables the rapid generation of high-quality presentation materials and flexible customization to meet the user's needs.

[0444] "Input information" refers to data such as the purpose and theme that users provide when creating presentation materials.

[0445] "Generative AI technology" is a technology that uses artificial intelligence to analyze natural language and generate visual information, thereby efficiently processing user input.

[0446] "Words and concepts" refer to keywords and important semantic content related to the presentation's theme, extracted from the input information through natural language processing.

[0447] A "template" is a pre-defined framework for the structure and design of presentation materials, serving as a foundation into which specific content and visuals are incorporated.

[0448] An "image generation model" is an algorithm or program that automatically creates visual information such as graphs and charts based on specified conditions.

[0449] "Materials" refers to a collection of presentation slides composed of generated templates and visual information.

[0450] The system of this invention aims to automatically generate presentation materials using generative AI technology. Its main components include a server and a user terminal. The user provides input information, including a clear theme and purpose, using the terminal. This information is immediately transmitted to the server.

[0451] The server analyzes the received data using generative AI technology, specifically natural language processing technology. The technology used is a commonly used generative AI model. The analysis identifies keywords and concepts from the presentation, which form the basis for template selection. The server then selects the most suitable template from the template database.

[0452] Next, the server automatically generates visual content such as graphs and charts using an image generation model. This adds consistency and visual impact to the presentation materials. The generated visual content is then incorporated into a selected template and finally compiled into a document.

[0453] This document is sent from the server to the user's terminal, where the user reviews its contents. If necessary, the user can provide feedback, which the server uses to optimize the entire system.

[0454] As a concrete example, if a user is creating a presentation on the theme of "New Product Marketing Plan for Next Year," the user would input this as a prompt. Based on an example prompt such as, "Please create a marketing plan for a new product targeting increased sales for next year. Please make it a visually appealing presentation that includes a comparison graph with competing products," the system will automatically generate appropriate templates and visual materials.

[0455] As described above, the present invention provides a practical means for quickly and efficiently creating high-quality presentation materials by utilizing generative AI technology.

[0456] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0457] Step 1:

[0458] The user uses a terminal to input the presentation theme and objectives. This information is sent to the server as input data. The terminal considers that the input data may include background information on the theme and specific instructions, and sends this to the server.

[0459] Step 2:

[0460] The server analyzes the received input data using a generating AI model. In this step, the server utilizes natural language processing techniques to extract key keywords and concepts from the input data. As a result of this analysis, the extracted keywords are sent to the next step as output data.

[0461] Step 3:

[0462] The server selects the most suitable template from the template database based on keywords and concepts. In this process, the server selects the template's structure and style information as output data and passes it on to the next visual content generation step.

[0463] Step 4:

[0464] The server uses an image generation algorithm to generate graphs and charts that fit the selected template. During this process, numerical data is transformed into visual data as part of the data calculation. The generated visual content is then compiled as part of the slides and configured as output data.

[0465] Step 5:

[0466] The server sends the configured presentation materials to the user's terminal. The user can view the sent materials and utilize features for visual evaluation. They can check the quality of the materials and provide feedback.

[0467] Step 6:

[0468] Users enter feedback on their devices. This feedback includes desired improvements and additional requests. The user's feedback data is sent to the server.

[0469] Step 7:

[0470] The server analyzes the received feedback. Through machine learning algorithms, it analyzes patterns derived from the feedback and optimizes the system. This ensures that the next presentation generated will be more tailored to the user's preferences.

[0471] (Application Example 1)

[0472] 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."

[0473] Creating visually appealing and effective display plans is crucial for product placement and promotional activities in physical stores, but it requires time and specialized knowledge. Therefore, store staff need ways to use their time efficiently and create product displays with expert support.

[0474] 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.

[0475] In this invention, the server includes means for receiving input data, means for analyzing the input data using natural language processing technology and extracting relevant keywords and concepts, and means for proposing product layout plans based on the received information. This enables store staff to quickly generate effective and creative product displays.

[0476] "Means for receiving input data" refers to a function that takes in information provided by the user into the system and prepares it for processing.

[0477] "A means of analyzing input data using natural language processing technology and extracting relevant keywords and concepts" refers to a technical method that analyzes linguistic information provided by the user to identify and extract important words and concepts.

[0478] "The means of selecting the appropriate template" refers to a function that selects the most suitable format from pre-configured templates based on the results of data analysis.

[0479] "Methods for automatically generating visual content using image generation algorithms" refer to technologies that use computer programs to automatically create visual elements such as graphs and tables based on presented data.

[0480] "Means of combining selected templates and generated content to construct visual materials" refers to the process of integrating selected templates and generated visual content to prepare completed visual materials.

[0481] "A means of transmitting generated visual materials to an electronic device and receiving feedback" refers to a system that transmits created visual information to the user's device and collects their usage results and opinions.

[0482] "Methods for optimizing the system using received feedback" refers to the process of improving the entire system and enhancing future performance based on opinions and reactions gathered from users.

[0483] "A means of proposing product placement plans based on received information" refers to a function that designs and provides effective product placement methods based on the information collected.

[0484] This invention is a system that efficiently supports product displays and promotional activities in physical stores. The system primarily utilizes a server and the user's electronic devices. The server employs a generative AI model to process information provided by the user.

[0485] First, the user's device inputs product details and campaign themes. This input is sent to the server, which analyzes it using natural language processing technology. Based on the keywords and concepts extracted through the analysis, the server selects an appropriate template and then uses an image generation algorithm to create visual content.

[0486] Visual content and selected templates are combined to create visual materials. These visual materials are sent to the user's device, and the resulting feedback is sent back to the server. The server analyzes the feedback and optimizes the system. Furthermore, it also has a function to suggest product placement options based on the received information.

[0487] The hardware and software used include programs to run OpenAI's generative models, as well as Matplotlib and PIL for generating visual content. This makes it possible to quickly create effective product placement and display ideas.

[0488] For example, consider a campaign promoting a newly released confectionery product. In this case, the user inputs themes such as "Newly released 12-pack of dark chocolate" and "Valentine's Day dessert," and the server generates appropriate display suggestions and sends them back to the terminal. These display suggestions can then be used to determine how products are displayed in stores and to create point-of-purchase (POP) advertisements.

[0489] An example of a prompt message would be entered in the following format:

[0490] Product Information: Newly released 12-pack of dark chocolates. Campaign Theme: Valentine's Day Desserts. Please suggest a suitable display idea.

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

[0492] Step 1:

[0493] The user uses a terminal to enter product information and a campaign theme. The entered data is formed into a prompt message and sent to the server. The input in this step is the text data entered by the user into the terminal, and the output is the prompt message sent to the server.

[0494] Step 2:

[0495] The server uses natural language processing techniques to analyze the received prompt text. Specifically, it analyzes the input text data and extracts relevant keywords and concepts. In this process, the server utilizes a generative AI model. The input is the prompt text, and the output is the extracted keywords and concepts.

[0496] Step 3:

[0497] The server selects an appropriate template based on the extracted keywords and concepts. This template is chosen from the template database to be the most suitable format. The input is the analysis result from step 2, and the output is the selected template.

[0498] Step 4:

[0499] The server automatically generates visual content using an image generation algorithm. In this step, graphs and charts are designed and generated using extracted keywords and template information. The input is keywords and template information, and the output is visual content.

[0500] Step 5:

[0501] The server combines the selected template and generated visual content to construct the completed visual document. This results in a visually organized document. The input is the template and visual content, and the output is the constructed visual document.

[0502] Step 6:

[0503] The server sends the generated visual materials to the user's terminal. The user then reviews the materials and provides feedback. This feedback is sent back to the server. The input is the visual materials, and the output is the feedback on the terminal.

[0504] Step 7:

[0505] The server uses the received feedback to optimize the system. Machine learning algorithms are used to analyze the feedback, which is then used to improve the overall system. The input is the feedback, and the output is the optimized system.

[0506] Step 8:

[0507] The server then proposes a product placement plan based on the information it receives. This placement plan is then sent back to the terminal as a visual representation. The input is the optimized system and feedback, and the output is the placement plan for the terminal.

[0508] 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.

[0509] This invention combines a business presentation creation support system utilizing generative AI technology with an emotion engine that recognizes user emotions. The system is centered around a server and a user terminal, and automatically generates presentation materials that reflect the data and emotions entered by the user.

[0510] The user inputs the presentation theme and intent through their device. During this process, the emotion engine analyzes the user's emotions based on the input data and user interaction. For example, if a user presents a presentation on "Success Stories of New Products" while also expressing feelings of joy and anticipation, the emotion engine will detect this.

[0511] The server analyzes the received input data using natural language processing technology and extracts relevant keywords and concepts. Simultaneously, it takes into account the emotional information analyzed by the emotion engine and selects a template from the template database that is more suitable for the user's emotions. For example, if the emotion of joy is recognized, a template with a bright and cheerful design will be selected.

[0512] Next, the server uses an image generation algorithm to automatically generate visual content such as graphs and charts. The generated visual content also reflects the results of emotion recognition by the emotion engine, and is adjusted so that it influences the overall tone and design of the presentation.

[0513] The completed slides are sent to the user's device, where the user reviews the materials. The user can provide feedback; for example, if they request that the design's color scheme be brighter, the server uses machine learning algorithms based on this feedback to optimize the entire system.

[0514] In this way, the present invention enables the rapid generation of high-quality presentation materials that resonate with the user's emotions, and allows for flexible material creation tailored to the user's needs.

[0515] The following describes the processing flow.

[0516] Step 1:

[0517] The user uses the device to input the presentation theme and intent, while simultaneously engaging in natural dialogue and operations. During this process, the emotion engine recognizes emotions from the user's expressions and actions. For example, it analyzes emotions such as tension and anticipation from the keywords and tone of voice being entered.

[0518] Step 2:

[0519] The server applies natural language processing techniques to the text data received from the user to extract relevant keywords and concepts. In this step, important themes such as "market analysis" and "new products" are identified.

[0520] Step 3:

[0521] Based on the emotional information recognized by the emotion engine, the server selects a template from the database that is appropriate to the analysis results. For example, if the emotion of expectation is detected, a template with a lively color scheme will be selected.

[0522] Step 4:

[0523] The server uses image generation algorithms to generate graphs and charts based on the analysis results. These visual contents are integrated into templates and designed to resonate with the user's emotions.

[0524] Step 5:

[0525] The generated presentation slides are adjusted as needed to match the user's emotions. For example, colors and font sizes are optimized based on the results of emotion recognition.

[0526] Step 6:

[0527] The server sends the completed slides to the user's terminal. The user reviews the finished material and provides visual and content-based feedback.

[0528] Step 7:

[0529] The server receives user feedback and adjusts the entire system through machine learning algorithms to improve the accuracy and emotional relevance of future presentations.

[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] Conventional presentation material creation systems provide standardized content and design without considering user emotions, making it difficult to generate materials that resonate with users' intentions and feelings in a short amount of time. As a result, it was also difficult to create materials that maximized the effectiveness of presentations. Furthermore, there was insufficient mechanism for effectively utilizing user feedback on generated materials to improve the system.

[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 input data at a user terminal and analyzing the user's emotions, means for analyzing the input data using natural language processing technology and extracting relevant keywords and concepts, and means for selecting an appropriate template from a database based on the emotion recognition information and analysis results. This makes it possible to generate high-quality presentation materials that reflect the user's emotions in a short time, and further optimize the entire system by incorporating user feedback.

[0535] "Input data" refers to information that a user provides to the system via their device in order to express the theme and intent of their presentation.

[0536] An "emotion engine" is a technology that analyzes user input data and interactions to identify the user's underlying emotions.

[0537] "Natural language processing technology" is a method for analyzing input data as structural information and extracting relevant keywords and concepts.

[0538] A "template database" is a storage device that stores and provides various pre-designed presentation templates.

[0539] An "image generation algorithm" is a technology that automatically generates visual content based on input data and prompt text.

[0540] A "machine learning algorithm" is a mathematical method used to improve system performance by leveraging user feedback.

[0541] This invention relates to a system that utilizes generative AI technology to automatically generate presentation materials based on the user's emotions. The system consists of a user terminal and a server.

[0542] The user inputs the presentation theme and intent through the terminal. The terminal is equipped with an emotion engine that analyzes emotions from text input and user interaction. This process uses NLP-related libraries in the Python language (e.g., NLTK and spaCy). For example, if emotions such as joy and anticipation are expressed when the theme is "Success Stories of New Products," the emotion engine detects this and sends the information to the server.

[0543] The server analyzes the received data using natural language processing techniques to extract important keywords and concepts. The Gensim library is commonly used for this processing. Next, considering the analysis results and sentiment information, an appropriate template is selected from the template database. The selected template is then automatically enhanced with visual content using a generative AI model (e.g., Stable Diffusion or DALL-E). Keywords such as "sustainability" and "eco-friendly" can be provided as prompts during this process.

[0544] The generated slides are adjusted in tone and design based on emotion recognition. The server sends the completed presentation materials to the user's terminal. The user can review the received materials and provide feedback. Based on this feedback, the server continuously optimizes the entire system using machine learning algorithms (e.g., Scikit-learn).

[0545] In this way, it is possible to generate high-quality presentation materials that reflect user emotions and respond quickly and flexibly.

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

[0547] Step 1:

[0548] The user inputs the presentation theme and intent through the device. The device passes this input data to an emotion engine, which analyzes the user's emotions based on their input and word choices. The emotion engine uses natural language processing technology to identify emotions such as joy and anticipation from the text. The output of this process is the analyzed emotion information.

[0549] Step 2:

[0550] The terminal sends the analyzed sentiment information and input data to the server. The server analyzes the received input data using natural language processing techniques and extracts keywords and concepts. This process applies a pre-configured algorithm to extract highly relevant information. The output is a list of the extracted keywords and concepts.

[0551] Step 3:

[0552] The server selects an appropriate template from the template database based on the emotion information and analysis results. For example, if the emotion engine detects joy, a template with bright colors will be selected. The output is the selected template.

[0553] Step 4:

[0554] The server automatically generates visual content using a generative AI model based on the selected template and extracted keywords. At this stage, keywords such as "success stories" and "growth" are input to the AI ​​model as prompts, and appropriate images and charts are generated. The output is the generated visual content.

[0555] Step 5:

[0556] The server combines the generated visual content with the selected template to create the final slide presentation. The overall design and tone of the slides are also adjusted based on emotional information. The output is the completed presentation slides.

[0557] Step 6:

[0558] The server sends the completed slides to the user's device. The user reviews the received slides and provides feedback as needed. This feedback includes any changes or improvements the user would like to see.

[0559] Step 7:

[0560] The server receives user feedback and optimizes the system using machine learning algorithms. Here, the feedback is analyzed, and system parameters are adjusted to improve future generation. The output is the optimized system configuration.

[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 advertising production, a challenge exists in quickly generating effective advertisements that appeal to the target audience's emotions because the design of visual content generally fails to adequately reflect emotional elements. Furthermore, traditional advertising production processes are slow to incorporate feedback, hindering rapid improvement.

[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 input data, means for analyzing the input data using natural language processing technology and extracting relevant keywords and concepts, and means for selecting an appropriate template based on the analysis results and user sentiment data. This makes it possible to analyze user sentiment and reflect it in the content design of advertisements. Furthermore, based on user feedback on the generated advertising materials, the system can be rapidly optimized using machine learning algorithms, enabling the immediate provision of targeted advertisements.

[0566] "Means for receiving input data" refers to the function that incorporates information about advertising themes and goals provided by the user into the system.

[0567] "A means of analyzing input data using natural language processing technology and extracting relevant keywords and concepts" refers to the process of analyzing received input data and identifying important terms and ideas related to the main point of the advertisement.

[0568] "A means of selecting an appropriate template based on user sentiment data" refers to a function that selects the optimal template that matches the user's emotions based on the analysis results and the user's sentiment information.

[0569] "Methods for automatically generating visual content using image generation algorithms" refers to the process of automatically creating the graphic elements necessary for advertising using AI technology.

[0570] "Methods for composing advertising materials by combining selected templates and generated content" refers to the process of combining selected templates and automatically generated content to create a single advertising material.

[0571] "A means of sending generated advertising materials to a device and receiving feedback" refers to a method of delivering created advertising materials to a user's device and obtaining user evaluations and improvement requests.

[0572] "Methods for optimizing the system using received feedback" refers to the process of improving the system's functionality by utilizing AI technology based on advice and requests received from users.

[0573] The system that realizes this invention includes a process for automatically generating visual content that reflects user emotions in advertising production. This makes it possible to quickly produce effective advertisements for the target audience.

[0574] The server receives advertising themes and goals entered by the user through their device. The input data is analyzed using Google Cloud's natural language processing API to extract key keywords and concepts. Next, a TensorFlow model is used to obtain user sentiment data, and together with the analysis results, the server selects the most suitable template for the advertisement. Based on the selected template, an image generation algorithm is used to automatically generate the necessary visual content.

[0575] The generated advertising materials are sent to the user's device. Users can view them and provide feedback. The received feedback is used to optimize the entire system using machine learning algorithms. This cyclical process generates more sophisticated advertisements.

[0576] As a concrete example, when a furniture retailer creates an advertisement themed around "comfortable living spaces," the system can automatically select design elements that evoke a sense of happiness and display them as visual content. An example of a prompt message is as follows:

[0577] Theme: Advertisements for a comfortable living space

[0578] User emotion: Happiness

[0579] Desired color tone: Soft

[0580] In this way, the aim is to make the content of the advertisement appeal more effectively to the emotions of the recipient.

[0581] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0582] Step 1:

[0583] The user inputs advertising themes and goals via their device. The device then sends this information to the server. The input data includes themes, user intent, and desired design elements. The output is the transfer of data to the server.

[0584] Step 2:

[0585] The server analyzes the received input data using Google Cloud's natural language processing API. Based on the input data, it extracts relevant keywords and concepts. This analysis includes data processing using a natural language model, and the analyzed information is output.

[0586] Step 3:

[0587] The server, along with the analysis results, also considers user sentiment data using a TensorFlow model. The input consists of keywords and sentiment indicators, and based on these, it selects an appropriate design template. The output is the selected template data.

[0588] Step 4:

[0589] The server uses an image generation algorithm to automatically generate visual content based on the selected template. Template and keyword information are input, and a graphic design is created based on this. The output is a completed advertising material.

[0590] Step 5:

[0591] The server sends the generated advertising materials to the user's device. The user reviews the advertising materials on their device and provides feedback. The transmission serves as output, and the feedback serves as input for the next stage.

[0592] Step 6:

[0593] User feedback is received by the server and used to optimize the system using machine learning algorithms. The input is feedback data, and data calculations and system adjustments are performed based on this data. The output is the improved system parameters.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] [Fourth Embodiment]

[0598] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0599] 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.

[0600] 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).

[0601] 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.

[0602] 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.

[0603] 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).

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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".

[0611] This invention relates to a business presentation creation support system utilizing generative AI technology. The system's main components consist of a server and user terminals. The server plays a central role, analyzing data entered by the user and automatically generating presentation materials.

[0612] The user inputs the presentation theme and purpose using a terminal. This input is sent to the server. The server first analyzes the input data using natural language processing technology and extracts relevant keywords and concepts. Based on this analysis, the server selects the most appropriate template from its template database. For example, if the theme is "Annual Sales Report," a template that emphasizes business data will be selected.

[0613] Next, the server uses an image generation algorithm to automatically generate visual content such as graphs and charts. In this process, the generated content is incorporated into a template and structured as slides. As a result, the presentation becomes visually consistent and engaging.

[0614] The generated presentation materials are sent to the user's device. The user reviews the materials and provides feedback to the server as needed. For example, if they want to change the color of a graph or the font size, they can enter specific requests. The server receives this feedback and uses it to optimize the entire system. This includes using machine learning algorithms to analyze feedback patterns and make adjustments to better match the user's preferences in future material generation.

[0615] In this way, the system provides high-quality presentation materials in a short amount of time, supporting efficient decision-making in business processes.

[0616] The following describes the processing flow.

[0617] Step 1:

[0618] Users input themes and objectives for creating presentations via their devices and send them to the server. Examples of inputs include "Annual Sales Report" and "New Product Market Analysis."

[0619] Step 2:

[0620] The server applies natural language processing techniques to the received input data to extract relevant keywords and concepts. In this step, key elements such as "sales," "annual data," and "market trends" are identified.

[0621] Step 3:

[0622] The server selects an appropriate template from the template database based on the analysis. For example, a template for a business data presentation suitable for sales reporting might be selected.

[0623] Step 4:

[0624] The server uses an image generation algorithm to automatically generate visual content such as graphs and charts necessary for the presentation. The generated content is then applied to the template in a consistent manner.

[0625] Step 5:

[0626] The server combines the selected template with the generated visual content to construct a presentation in slide format. At this stage, the layout and order of the slides are optimized.

[0627] Step 6:

[0628] The completed presentation materials are sent to the user's device. The user reviews the materials and provides feedback to the server if any corrections or additions are needed.

[0629] Step 7:

[0630] The server analyzes user feedback and optimizes the system using machine learning. This process allows for adjustments to better match user preferences in future material generation.

[0631] (Example 1)

[0632] 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".

[0633] Traditional presentation creation methods required a tremendous amount of time and effort to prepare materials, and it was particularly difficult to create visually consistent, high-quality materials in a short amount of time. Furthermore, while there is a need to effectively incorporate user feedback to improve the quality of materials, there has been a lack of methods to enable this.

[0634] 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.

[0635] In this invention, the server includes means for acquiring information from the user, means for processing the input information using generative AI technology and identifying relevant words and concepts, and means for determining the optimal template based on the identified information. This enables the rapid generation of high-quality presentation materials and flexible customization to meet the user's needs.

[0636] "Input information" refers to data such as the purpose and theme that users provide when creating presentation materials.

[0637] "Generative AI technology" is a technology that uses artificial intelligence to analyze natural language and generate visual information, thereby efficiently processing user input.

[0638] "Words and concepts" refer to keywords and important semantic content related to the presentation's theme, extracted from the input information through natural language processing.

[0639] A "template" is a pre-defined framework for the structure and design of presentation materials, serving as a foundation into which specific content and visuals are incorporated.

[0640] An "image generation model" is an algorithm or program that automatically creates visual information such as graphs and charts based on specified conditions.

[0641] "Materials" refers to a collection of presentation slides composed of generated templates and visual information.

[0642] The system of this invention aims to automatically generate presentation materials using generative AI technology. Its main components include a server and a user terminal. The user provides input information, including a clear theme and purpose, using the terminal. This information is immediately transmitted to the server.

[0643] The server analyzes the received data using generative AI technology, specifically natural language processing technology. The technology used is a commonly used generative AI model. The analysis identifies keywords and concepts from the presentation, which form the basis for template selection. The server then selects the most suitable template from the template database.

[0644] Next, the server automatically generates visual content such as graphs and charts using an image generation model. This adds consistency and visual impact to the presentation materials. The generated visual content is then incorporated into a selected template and finally compiled into a document.

[0645] This document is sent from the server to the user's terminal, where the user reviews its contents. If necessary, the user can provide feedback, which the server uses to optimize the entire system.

[0646] As a concrete example, if a user is creating a presentation on the theme of "New Product Marketing Plan for Next Year," the user would input this as a prompt. Based on an example prompt such as, "Please create a marketing plan for a new product targeting increased sales for next year. Please make it a visually appealing presentation that includes a comparison graph with competing products," the system will automatically generate appropriate templates and visual materials.

[0647] As described above, the present invention provides a practical means for quickly and efficiently creating high-quality presentation materials by utilizing generative AI technology.

[0648] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0649] Step 1:

[0650] The user uses a terminal to input the presentation theme and objectives. This information is sent to the server as input data. The terminal considers that the input data may include background information on the theme and specific instructions, and sends this to the server.

[0651] Step 2:

[0652] The server analyzes the received input data using a generating AI model. In this step, the server utilizes natural language processing techniques to extract key keywords and concepts from the input data. As a result of this analysis, the extracted keywords are sent to the next step as output data.

[0653] Step 3:

[0654] The server selects the most suitable template from the template database based on keywords and concepts. In this process, the server selects the template's structure and style information as output data and passes it on to the next visual content generation step.

[0655] Step 4:

[0656] The server uses an image generation algorithm to generate graphs and charts that fit the selected template. During this process, numerical data is transformed into visual data as part of the data calculation. The generated visual content is then compiled as part of the slides and configured as output data.

[0657] Step 5:

[0658] The server sends the configured presentation materials to the user's terminal. The user can view the sent materials and utilize features for visual evaluation. They can check the quality of the materials and provide feedback.

[0659] Step 6:

[0660] Users enter feedback on their devices. This feedback includes desired improvements and additional requests. The user's feedback data is sent to the server.

[0661] Step 7:

[0662] The server analyzes the received feedback. Through machine learning algorithms, it analyzes patterns derived from the feedback and optimizes the system. This ensures that the next presentation generated will be more tailored to the user's preferences.

[0663] (Application Example 1)

[0664] 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".

[0665] Creating visually appealing and effective display plans is crucial for product placement and promotional activities in physical stores, but it requires time and specialized knowledge. Therefore, store staff need ways to use their time efficiently and create product displays with expert support.

[0666] 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.

[0667] In this invention, the server includes means for receiving input data, means for analyzing the input data using natural language processing technology and extracting relevant keywords and concepts, and means for proposing product layout plans based on the received information. This enables store staff to quickly generate effective and creative product displays.

[0668] "Means for receiving input data" refers to a function that takes in information provided by the user into the system and prepares it for processing.

[0669] "A means of analyzing input data using natural language processing technology and extracting relevant keywords and concepts" refers to a technical method that analyzes linguistic information provided by the user to identify and extract important words and concepts.

[0670] "The means of selecting the appropriate template" refers to a function that selects the most suitable format from pre-configured templates based on the results of data analysis.

[0671] "Methods for automatically generating visual content using image generation algorithms" refer to technologies that use computer programs to automatically create visual elements such as graphs and tables based on presented data.

[0672] "Means of combining selected templates and generated content to construct visual materials" refers to the process of integrating selected templates and generated visual content to prepare completed visual materials.

[0673] "A means of transmitting generated visual materials to an electronic device and receiving feedback" refers to a system that transmits created visual information to the user's device and collects their usage results and opinions.

[0674] "Methods for optimizing the system using received feedback" refers to the process of improving the entire system and enhancing future performance based on opinions and reactions gathered from users.

[0675] "A means of proposing product placement plans based on received information" refers to a function that designs and provides effective product placement methods based on the information collected.

[0676] This invention is a system that efficiently supports product displays and promotional activities in physical stores. The system primarily utilizes a server and the user's electronic devices. The server employs a generative AI model to process information provided by the user.

[0677] First, the user's device inputs product details and campaign themes. This input is sent to the server, which analyzes it using natural language processing technology. Based on the keywords and concepts extracted through the analysis, the server selects an appropriate template and then uses an image generation algorithm to create visual content.

[0678] Visual content and selected templates are combined to create visual materials. These visual materials are sent to the user's device, and the resulting feedback is sent back to the server. The server analyzes the feedback and optimizes the system. Furthermore, it also has a function to suggest product placement options based on the received information.

[0679] The hardware and software used include programs to run OpenAI's generative models, as well as Matplotlib and PIL for generating visual content. This makes it possible to quickly create effective product placement and display ideas.

[0680] For example, consider a campaign promoting a newly released confectionery product. In this case, the user inputs themes such as "Newly released 12-pack of dark chocolate" and "Valentine's Day dessert," and the server generates appropriate display suggestions and sends them back to the terminal. These display suggestions can then be used to determine how products are displayed in stores and to create point-of-purchase (POP) advertisements.

[0681] An example of a prompt message would be entered in the following format:

[0682] Product Information: Newly released 12-pack of dark chocolates. Campaign Theme: Valentine's Day Desserts. Please suggest a suitable display idea.

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

[0684] Step 1:

[0685] The user uses a terminal to enter product information and a campaign theme. The entered data is formed into a prompt message and sent to the server. The input in this step is the text data entered by the user into the terminal, and the output is the prompt message sent to the server.

[0686] Step 2:

[0687] The server uses natural language processing techniques to analyze the received prompt text. Specifically, it analyzes the input text data and extracts relevant keywords and concepts. In this process, the server utilizes a generative AI model. The input is the prompt text, and the output is the extracted keywords and concepts.

[0688] Step 3:

[0689] The server selects an appropriate template based on the extracted keywords and concepts. This template is chosen from the template database to be the most suitable format. The input is the analysis result from step 2, and the output is the selected template.

[0690] Step 4:

[0691] The server automatically generates visual content using an image generation algorithm. In this step, graphs and charts are designed and generated using extracted keywords and template information. The input is keywords and template information, and the output is visual content.

[0692] Step 5:

[0693] The server combines the selected template and generated visual content to construct the completed visual document. This results in a visually organized document. The input is the template and visual content, and the output is the constructed visual document.

[0694] Step 6:

[0695] The server sends the generated visual materials to the user's terminal. The user then reviews the materials and provides feedback. This feedback is sent back to the server. The input is the visual materials, and the output is the feedback on the terminal.

[0696] Step 7:

[0697] The server uses the received feedback to optimize the system. Machine learning algorithms are used to analyze the feedback, which is then used to improve the overall system. The input is the feedback, and the output is the optimized system.

[0698] Step 8:

[0699] The server then proposes a product placement plan based on the information it receives. This placement plan is then sent back to the terminal as a visual representation. The input is the optimized system and feedback, and the output is the placement plan for the terminal.

[0700] 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.

[0701] This invention combines a business presentation creation support system utilizing generative AI technology with an emotion engine that recognizes user emotions. The system is centered around a server and a user terminal, and automatically generates presentation materials that reflect the data and emotions entered by the user.

[0702] The user inputs the presentation theme and intent through their device. During this process, the emotion engine analyzes the user's emotions based on the input data and user interaction. For example, if a user presents a presentation on "Success Stories of New Products" while also expressing feelings of joy and anticipation, the emotion engine will detect this.

[0703] The server analyzes the received input data using natural language processing technology and extracts relevant keywords and concepts. Simultaneously, it takes into account the emotional information analyzed by the emotion engine and selects a template from the template database that is more suitable for the user's emotions. For example, if the emotion of joy is recognized, a template with a bright and cheerful design will be selected.

[0704] Next, the server uses an image generation algorithm to automatically generate visual content such as graphs and charts. The generated visual content also reflects the results of emotion recognition by the emotion engine, and is adjusted so that it influences the overall tone and design of the presentation.

[0705] The completed slides are sent to the user's device, where the user reviews the materials. The user can provide feedback; for example, if they request that the design's color scheme be brighter, the server uses machine learning algorithms based on this feedback to optimize the entire system.

[0706] In this way, the present invention enables the rapid generation of high-quality presentation materials that resonate with the user's emotions, and allows for flexible material creation tailored to the user's needs.

[0707] The following describes the processing flow.

[0708] Step 1:

[0709] The user uses the device to input the presentation theme and intent, while simultaneously engaging in natural dialogue and operations. During this process, the emotion engine recognizes emotions from the user's expressions and actions. For example, it analyzes emotions such as tension and anticipation from the keywords and tone of voice being entered.

[0710] Step 2:

[0711] The server applies natural language processing techniques to the text data received from the user to extract relevant keywords and concepts. In this step, important themes such as "market analysis" and "new products" are identified.

[0712] Step 3:

[0713] Based on the emotional information recognized by the emotion engine, the server selects a template from the database that is appropriate to the analysis results. For example, if the emotion of expectation is detected, a template with a lively color scheme will be selected.

[0714] Step 4:

[0715] The server uses image generation algorithms to generate graphs and charts based on the analysis results. These visual contents are integrated into templates and designed to resonate with the user's emotions.

[0716] Step 5:

[0717] The generated presentation slides are adjusted as needed to match the user's emotions. For example, colors and font sizes are optimized based on the results of emotion recognition.

[0718] Step 6:

[0719] The server sends the completed slides to the user's terminal. The user reviews the finished material and provides visual and content-based feedback.

[0720] Step 7:

[0721] The server receives user feedback and adjusts the entire system through machine learning algorithms to improve the accuracy and emotional relevance of future presentations.

[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] Conventional presentation material creation systems provide standardized content and design without considering user emotions, making it difficult to generate materials that resonate with users' intentions and feelings in a short amount of time. As a result, it was also difficult to create materials that maximized the effectiveness of presentations. Furthermore, there was insufficient mechanism for effectively utilizing user feedback on generated materials to improve the system.

[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 input data at a user terminal and analyzing the user's emotions, means for analyzing the input data using natural language processing technology and extracting relevant keywords and concepts, and means for selecting an appropriate template from a database based on the emotion recognition information and analysis results. This makes it possible to generate high-quality presentation materials that reflect the user's emotions in a short time, and further optimize the entire system by incorporating user feedback.

[0727] "Input data" refers to information that a user provides to the system via their device in order to express the theme and intent of their presentation.

[0728] An "emotion engine" is a technology that analyzes user input data and interactions to identify the user's underlying emotions.

[0729] "Natural language processing technology" is a method for analyzing input data as structural information and extracting relevant keywords and concepts.

[0730] A "template database" is a storage device that stores and provides various pre-designed presentation templates.

[0731] An "image generation algorithm" is a technology that automatically generates visual content based on input data and prompt text.

[0732] A "machine learning algorithm" is a mathematical method used to improve system performance by leveraging user feedback.

[0733] This invention relates to a system that utilizes generative AI technology to automatically generate presentation materials based on the user's emotions. The system consists of a user terminal and a server.

[0734] The user inputs the presentation theme and intent through the terminal. The terminal is equipped with an emotion engine that analyzes emotions from text input and user interaction. This process uses NLP-related libraries in the Python language (e.g., NLTK and spaCy). For example, if emotions such as joy and anticipation are expressed when the theme is "Success Stories of New Products," the emotion engine detects this and sends the information to the server.

[0735] The server analyzes the received data using natural language processing techniques to extract important keywords and concepts. The Gensim library is commonly used for this processing. Next, considering the analysis results and sentiment information, an appropriate template is selected from the template database. The selected template is then automatically enhanced with visual content using a generative AI model (e.g., Stable Diffusion or DALL-E). Keywords such as "sustainability" and "eco-friendly" can be provided as prompts during this process.

[0736] The generated slides are adjusted in tone and design based on emotion recognition. The server sends the completed presentation materials to the user's terminal. The user can review the received materials and provide feedback. Based on this feedback, the server continuously optimizes the entire system using machine learning algorithms (e.g., Scikit-learn).

[0737] In this way, it is possible to generate high-quality presentation materials that reflect user emotions and respond quickly and flexibly.

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

[0739] Step 1:

[0740] The user inputs the presentation theme and intent through the device. The device passes this input data to an emotion engine, which analyzes the user's emotions based on their input and word choices. The emotion engine uses natural language processing technology to identify emotions such as joy and anticipation from the text. The output of this process is the analyzed emotion information.

[0741] Step 2:

[0742] The terminal sends the analyzed sentiment information and input data to the server. The server analyzes the received input data using natural language processing techniques and extracts keywords and concepts. This process applies a pre-configured algorithm to extract highly relevant information. The output is a list of the extracted keywords and concepts.

[0743] Step 3:

[0744] The server selects an appropriate template from the template database based on the emotion information and analysis results. For example, if the emotion engine detects joy, a template with bright colors will be selected. The output is the selected template.

[0745] Step 4:

[0746] The server automatically generates visual content using a generative AI model based on the selected template and extracted keywords. At this stage, keywords such as "success stories" and "growth" are input to the AI ​​model as prompts, and appropriate images and charts are generated. The output is the generated visual content.

[0747] Step 5:

[0748] The server combines the generated visual content with the selected template to create the final slide presentation. The overall design and tone of the slides are also adjusted based on emotional information. The output is the completed presentation slides.

[0749] Step 6:

[0750] The server sends the completed slides to the user's device. The user reviews the received slides and provides feedback as needed. This feedback includes any changes or improvements the user would like to see.

[0751] Step 7:

[0752] The server receives user feedback and optimizes the system using machine learning algorithms. Here, the feedback is analyzed, and system parameters are adjusted to improve future generation. The output is the optimized system configuration.

[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 advertising production, a challenge exists in quickly generating effective advertisements that appeal to the target audience's emotions because the design of visual content generally fails to adequately reflect emotional elements. Furthermore, traditional advertising production processes are slow to incorporate feedback, hindering rapid improvement.

[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 input data, means for analyzing the input data using natural language processing technology and extracting relevant keywords and concepts, and means for selecting an appropriate template based on the analysis results and user sentiment data. This makes it possible to analyze user sentiment and reflect it in the content design of advertisements. Furthermore, based on user feedback on the generated advertising materials, the system can be rapidly optimized using machine learning algorithms, enabling the immediate provision of targeted advertisements.

[0758] "Means for receiving input data" refers to the function that incorporates information about advertising themes and goals provided by the user into the system.

[0759] "A means of analyzing input data using natural language processing technology and extracting relevant keywords and concepts" refers to the process of analyzing received input data and identifying important terms and ideas related to the main point of the advertisement.

[0760] "A means of selecting an appropriate template based on user sentiment data" refers to a function that selects the optimal template that matches the user's emotions based on the analysis results and the user's sentiment information.

[0761] "Methods for automatically generating visual content using image generation algorithms" refers to the process of automatically creating the graphic elements necessary for advertising using AI technology.

[0762] "Methods for composing advertising materials by combining selected templates and generated content" refers to the process of combining selected templates and automatically generated content to create a single advertising material.

[0763] "A means of sending generated advertising materials to a device and receiving feedback" refers to a method of delivering created advertising materials to a user's device and obtaining user evaluations and improvement requests.

[0764] "Methods for optimizing the system using received feedback" refers to the process of improving the system's functionality by utilizing AI technology based on advice and requests received from users.

[0765] The system that realizes this invention includes a process for automatically generating visual content that reflects user emotions in advertising production. This makes it possible to quickly produce effective advertisements for the target audience.

[0766] The server receives advertising themes and goals entered by the user through their device. The input data is analyzed using Google Cloud's natural language processing API to extract key keywords and concepts. Next, a TensorFlow model is used to obtain user sentiment data, and together with the analysis results, the server selects the most suitable template for the advertisement. Based on the selected template, an image generation algorithm is used to automatically generate the necessary visual content.

[0767] The generated advertising materials are sent to the user's device. Users can view them and provide feedback. The received feedback is used to optimize the entire system using machine learning algorithms. This cyclical process generates more sophisticated advertisements.

[0768] As a concrete example, when a furniture retailer creates an advertisement themed around "comfortable living spaces," the system can automatically select design elements that evoke a sense of happiness and display them as visual content. An example of a prompt message is as follows:

[0769] Theme: Advertisements for a comfortable living space

[0770] User emotion: Happiness

[0771] Desired color tone: Soft

[0772] In this way, the aim is to make the content of the advertisement appeal more effectively to the emotions of the recipient.

[0773] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0774] Step 1:

[0775] The user inputs advertising themes and goals via their device. The device then sends this information to the server. The input data includes themes, user intent, and desired design elements. The output is the transfer of data to the server.

[0776] Step 2:

[0777] The server analyzes the received input data using Google Cloud's natural language processing API. Based on the input data, it extracts relevant keywords and concepts. This analysis includes data processing using a natural language model, and the analyzed information is output.

[0778] Step 3:

[0779] The server, along with the analysis results, also considers user sentiment data using a TensorFlow model. The input consists of keywords and sentiment indicators, and based on these, it selects an appropriate design template. The output is the selected template data.

[0780] Step 4:

[0781] The server uses an image generation algorithm to automatically generate visual content based on the selected template. Template and keyword information are input, and a graphic design is created based on this. The output is a completed advertising material.

[0782] Step 5:

[0783] The server sends the generated advertising materials to the user's device. The user reviews the advertising materials on their device and provides feedback. The transmission serves as output, and the feedback serves as input for the next stage.

[0784] Step 6:

[0785] User feedback is received by the server and used to optimize the system using machine learning algorithms. The input is feedback data, and data calculations and system adjustments are performed based on this data. The output is the improved system parameters.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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."

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] The following is further disclosed regarding the embodiments described above.

[0808] (Claim 1)

[0809] A means for receiving input data,

[0810] A means for analyzing input data using natural language processing technology and extracting relevant keywords and concepts,

[0811] A means of selecting an appropriate template based on the analysis results,

[0812] A method for automatically generating graphs and charts using an image generation algorithm,

[0813] A means of composing slides by combining selected templates and generated content,

[0814] A means for sending the generated slides to the user's terminal and receiving feedback,

[0815] Using the received feedback, a system optimization means is used,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, which analyzes user feedback and adjusts the system through a machine learning algorithm.

[0819] (Claim 3)

[0820] The system according to claim 1, which provides templates suitable for various types of presentations using a template database.

[0821] "Example 1"

[0822] (Claim 1)

[0823] Means of obtaining information from users,

[0824] A means for processing input information using generative AI technology and identifying related words and concepts,

[0825] A means for determining the optimal template based on identified information,

[0826] A means of generating visual information using an image generation model,

[0827] A means of integrating the determined template and generated visual information to create a document,

[0828] A means for transferring the created document to the user's device and receiving a response,

[0829] A means of adjusting the system based on the response received,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, which processes user responses and optimizes the system using a learning algorithm.

[0833] (Claim 3)

[0834] The system according to claim 1, which uses a template information holding device to supply templates corresponding to various types of documents.

[0835] "Application Example 1"

[0836] (Claim 1)

[0837] A means for receiving input data,

[0838] A means for analyzing input data using natural language processing technology and extracting relevant keywords and concepts,

[0839] A means of selecting an appropriate template based on the analysis results,

[0840] A means of automatically generating visual content using an image generation algorithm,

[0841] A means of constructing visual materials by combining selected templates and generated content,

[0842] A means for transmitting generated visual materials to an electronic device and receiving feedback,

[0843] Using the received feedback, a system optimization means is used,

[0844] A means of proposing product placement plans based on received information,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, which analyzes user feedback and adjusts the system through machine learning techniques.

[0848] (Claim 3)

[0849] The system according to claim 1, which provides templates suitable for various types of visual materials using a template database.

[0850] "Example 2 of combining an emotion engine"

[0851] (Claim 1)

[0852] A means of receiving input data on a user terminal and analyzing the user's emotions,

[0853] A means for analyzing input data using natural language processing technology and extracting relevant keywords and concepts,

[0854] A means for selecting an appropriate template from a database based on emotion recognition information and analysis results,

[0855] A method for automatically generating visual content using a generation algorithm and adjusting the tone of a presentation to match the emotion,

[0856] A means of composing slides by combining selected templates and generated content,

[0857] A means for sending the generated slides to the user's terminal and receiving feedback from the user,

[0858] A system that includes means for optimizing the system using machine learning algorithms based on received feedback and analysis results.

[0859] (Claim 2)

[0860] The system according to claim 1, which analyzes user feedback, adjusts the system through machine learning algorithms, and improves the output of the generated AI model.

[0861] (Claim 3)

[0862] The system according to claim 1, which uses a template database to select templates that fit various presentation categories based on emotional information.

[0863] "Application example 2 when combining with an emotional engine"

[0864] (Claim 1)

[0865] A means for receiving input data,

[0866] A means for analyzing input data using natural language processing technology and extracting relevant keywords and concepts,

[0867] A means for selecting an appropriate template based on analysis results and user sentiment data,

[0868] A means of automatically generating visual content using an image generation algorithm,

[0869] A means of composing advertising materials by combining selected templates and generated content,

[0870] A means of sending the generated advertising materials to a device and receiving feedback,

[0871] Using the received feedback, a system optimization means is used,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, which analyzes user feedback and adjusts the system through a machine learning algorithm.

[0875] (Claim 3)

[0876] The system according to claim 1, which provides templates suitable for various types of advertisements using a template database. [Explanation of Symbols]

[0877] 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 for receiving input data, A means for analyzing input data using natural language processing technology and extracting relevant keywords and concepts, A means of selecting an appropriate template based on the analysis results, A method for automatically generating graphs and charts using an image generation algorithm, A means of composing slides by combining selected templates and generated content, A means for sending the generated slides to the user's terminal and receiving feedback, Using the received feedback, a system optimization means is used, A system that includes this.

2. The system according to claim 1, which analyzes user feedback and adjusts the system through a machine learning algorithm.

3. The system according to claim 1, which provides templates suitable for various types of presentations using a template database.

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