system

Abstract

We provide the system. [Solution] An information processing device includes means for receiving information input from a user in natural language, A natural language processing means for analyzing the received information and extracting key points and important information fragments, A plan generation means that determines the structure of the presentation materials based on the extracted information fragments, A document generation means that automatically generates charts, slides, and related visual information according to the document structure plan, Means for providing the generated materials to the user terminal, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] There is a problem that good ideas held by businesspersons are not effectively communicated. One of the reasons is that differences in presentation creation ability affect the evaluation of the value of ideas. In particular, creating visual and easy-to-understand presentation materials requires a lot of time and specialized knowledge, resulting in a situation where the original ideas are not correctly evaluated. Therefore, it is necessary to provide an environment where the essential value of ideas can be accurately communicated and fairly evaluated without being influenced by presentation ability or knowledge.

Means for Solving the Problems

[0005] This invention utilizes natural language processing technology to receive information input from a user in natural language using an information processing device, analyze that information, and automatically extract key points and important information fragments. Furthermore, it provides a system that determines the structure of presentation materials based on the extracted information fragments and automatically generates visual materials accordingly. This reduces the time and effort users spend on presentations, and by optimizing visual effects using a speech script generation function and a template engine, it ensures that ideas are communicated with their full value.

[0006] An "information processing device" is a computer system, including its hardware and software, used for receiving, analyzing, and processing data.

[0007] A "user" refers to an individual or group that uses an information processing device to input ideas and request the generation of presentation materials.

[0008] "Natural language" refers to the language that humans use on a daily basis, and includes text data that can be analyzed and processed by computers.

[0009] "Natural language processing" refers to methods and techniques for analyzing natural language using computers to understand its meaning and extract necessary information.

[0010] "Key points" refer to the parts of information or data that are considered particularly important, or the key points that are essential for understanding the whole picture.

[0011] "Information fragment" is a term that refers to important elements or topics selected from analyzed data.

[0012] "Presentation materials" are slides or documents used to effectively convey information using visuals and text.

[0013] "Plan generation method" refers to a function that plans the structure and layout of presentation materials based on information provided by the user.

[0014] "Document generation means" refers to tools or methods that automatically create visual documents based on a decided plan.

[0015] The "speech script generation function" is a system that automatically generates key points and phrases to be spoken, making it easier for users to understand the content of their presentation.

[0016] A "template engine" refers to a tool or program that unifies the design and automatically performs visual optimization when generating documents. [Brief explanation of the drawing]

[0017] [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] It 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 Embodiment 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 Embodiment 2 when the 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 the emotion engine is combined.

Mode for Carrying Out the Invention

[0018] 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.

[0019] First, the terms used in the following description will be described.

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] 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.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] In implementing this invention, the server efficiently processes natural language information input by the user and constructs a system that automatically generates presentation materials to effectively convey ideas. When a user inputs their thoughts in natural language on a terminal, the terminal transmits that data to the server via the network.

[0039] The server applies natural language processing (NLP) techniques to analyze the natural language data it receives. First, the server preprocesses the input, splitting the text and removing unnecessary information. Then, it uses NLP algorithms to extract key keywords and topics. The server uses these elements to clarify the logical flow of ideas and determine an efficient presentation structure.

[0040] Next, the server begins the process of visualizing the presentation materials based on the determined configuration. The server utilizes a template engine to automatically generate charts, graphs, and slides. It optimizes the information to be presented in a way that is easy for the audience to understand, taking visual effects and design into consideration. Furthermore, the server generates a speech script, which is provided as a guide for the user during the presentation.

[0041] Ultimately, the server sends the generated materials to the user's terminal, allowing the user to review, edit, and use them. For example, if a user inputs an idea about "energy efficiency strategies," the server uses that information to create slides that incorporate relevant statistical data and case studies, and use charts and graphs to make them intuitively understandable. In this way, the user can prepare a fair presentation in which the value of their idea is directly evaluated.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user uses a device to input their ideas in natural language. Once input is complete, the device sends the data to the server.

[0045] Step 2:

[0046] The server receives natural language data sent from the terminal. The received data is first preprocessed, with unnecessary characters being removed and text normalization being performed.

[0047] Step 3:

[0048] The server passes the pre-processed data to a natural language processing (NLP) module, which analyzes the meaning of the text. This involves tokenization, part-of-speech tagging, and syntactic analysis.

[0049] Step 4:

[0050] The server extracts important keywords and topics from the input based on the analysis information obtained by NLP. Topic modeling and keyword extraction algorithms are used for this purpose.

[0051] Step 5:

[0052] The server determines the structure of the presentation materials based on extracted keywords and topics. It consults the template library to formulate the most effective flow of the materials.

[0053] Step 6:

[0054] The server generates visual materials according to the predetermined presentation structure. Using a template engine, it automatically creates slides, charts, and graphs, and optimizes visual effects.

[0055] Step 7:

[0056] The server creates a speech script related to the generated presentation materials, providing the user with instructions and key points for delivering the presentation.

[0057] Step 8:

[0058] The server sends the completed materials and speech script to the user's terminal. The user reviews them, makes any necessary adjustments, and prepares the final version.

[0059] (Example 1)

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

[0061] Creating audiovisual materials efficiently and effectively is difficult for many users, particularly the process of visualizing ideas expressed in natural language, which often requires considerable time and effort. Furthermore, specialized skills are frequently required to create visually clear materials. Therefore, there is a need for a means to easily generate audiovisual materials from natural language and effectively communicate users' ideas.

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

[0063] In this invention, the server includes means for receiving information input from a user in natural language, language processing means for analyzing the received information and extracting key points and important information elements, and plan generation means for determining the structure of audiovisual material based on the extracted information elements. This makes it possible for users to efficiently generate audiovisual material that can be intuitively understood from natural language, even without advanced specialized knowledge.

[0064] A "user" is the entity that uses this system to input information in natural language.

[0065] "Natural language" refers to the language that humans use on a daily basis, that is, a language that is not artificial.

[0066] "Information" refers to the content that users input into the system and that is processed.

[0067] A "receiving device" is a device used by the user to review and edit generated materials.

[0068] "Language processing means" refers to technologies that analyze natural language input and have the function of extracting key points and important information.

[0069] A "plan generation means" is a technology that has the function of determining the logical structure of audiovisual materials from the analyzed information.

[0070] "Document generation means" refers to technology that has the function of automatically creating visual documents based on a predetermined structure.

[0071] A "composition engine" is a general term for technologies used to automatically optimize visual effects and design.

[0072] This invention relates to a system that generates visual presentation materials based on information input in natural language. Specifically, this system operates around three elements: the user, the terminal, and the server.

[0073] The user first uses a terminal to input their ideas and thoughts in natural language. The terminal then transmits the information entered by the user to a server via the network. This process involves data communication using the internet.

[0074] The server is equipped with language processing capabilities for processing the received data. Specifically, it analyzes the input data using natural language processing techniques and extracts key points and important information elements. At this stage, general text processing libraries (e.g., NLTK and spaCy) and generative AI models are utilized.

[0075] Based on the extracted information elements, the server uses a planning generation mechanism to determine the logical structure of the presentation materials. Following this structure, the material generation mechanism automatically generates the visual elements. This process utilizes a structure engine to generate charts and slides. A template engine is also used to automatically optimize visual effects and design.

[0076] The generated presentation materials are sent to the terminal, where the user can review, edit, and use them. To improve user convenience, the server also generates a speech transcript and provides it as a guide during the presentation.

[0077] For example, if a user enters the prompt "Create a presentation on an innovative strategy for energy efficiency," the server will incorporate relevant statistical data and case studies, and generate slides that are intuitively understandable using charts and graphs. In this way, the system can quickly provide high-quality presentation materials to effectively communicate the user's ideas.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The user inputs information using natural language via the device. Specifically, they input prompts such as "plan for improving energy efficiency" via keyboard or voice input. The input information is saved on the device as text data. This data is the first to be processed.

[0081] Step 2:

[0082] The terminal sends the entered text data to the server over the network. Security protocols are applied during this transmission process to protect the confidentiality of the data. Once the transmitted data reaches the server, the next processing step begins.

[0083] Step 3:

[0084] The server analyzes the received text data using language processing tools. Specifically, it uses natural language processing techniques (e.g., text analysis libraries and generative AI models) to divide the text data into phrases and remove noise (unnecessary words and phrases). This process extracts appropriate topics and keywords. The output is a list of the extracted keywords and topics.

[0085] Step 4:

[0086] Based on the extracted keywords and topics, the server uses a planning generation mechanism to determine the structure of the presentation materials. It determines the order in which each topic should be presented, which information is important and should be emphasized, and constructs a logical framework. The output is represented as a structural plan for the entire presentation.

[0087] Step 5:

[0088] The server automatically generates visual materials using a material generation system based on the determined configuration. A template engine is used in this process to design slides corresponding to each topic. Relevant charts and statistical data are incorporated to create visually impactful materials. This output is provided in the format of the final presentation.

[0089] Step 6:

[0090] The server sends the generated presentation materials to the terminal. The user can receive these materials on the terminal and review their contents. Furthermore, they can edit the materials if necessary and use them in the final presentation. The output of this step is an editable document presented to the user.

[0091] (Application Example 1)

[0092] 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."

[0093] In the advertising industry, creating effective presentation materials quickly and efficiently is crucial. However, traditional methods are time-consuming and labor-intensive, and require specialized knowledge to optimize visual effects and design. Furthermore, scriptwriting is necessary to properly convey the presentation content, which can lead to inconsistent materials.

[0094] 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.

[0095] In this invention, the server includes, in an information processing device, means for receiving information input from a user in natural language; natural language processing means for analyzing the received information and extracting key points and important information fragments; plan generation means for determining the structure of presentation materials based on the extracted information fragments; and advertising creation means for automatically generating advertising slides and scripts. This enables the rapid creation of presentation materials for advertising campaigns and the optimization of their visual effects and design.

[0096] An "information processing device" is a device that receives and analyzes data and generates specific results based on that data.

[0097] "Natural language" refers to the language that humans use on a daily basis, and is primarily a means of expressing information in written and conversational forms.

[0098] "Natural language processing" is a technology that uses computers to analyze human language, understand its content, and process it.

[0099] An "information fragment" refers to an important element or unit of data extracted from received data.

[0100] A "plan generation mechanism" is a system that has the function of determining materials and structures for a specific purpose based on extracted information.

[0101] An "advertising creation tool" is a device that has the function of automatically generating presentation materials for advertising campaigns.

[0102] A "slide" is a page or screen used to visually present information in a presentation.

[0103] A "script" refers to a document that contains a written plan or spoken words used during a presentation.

[0104] "Visual effects" are techniques that use visual elements to convey information more effectively.

[0105] "Design" is a general term for anything that is visually organized and laid out to effectively convey information.

[0106] This invention is a system that uses an information processing device to receive natural language input information from a user, analyze it, and automatically generate presentation materials. Specifically, the server receives advertising ideas written in natural language from the user's terminal. The server analyzes this information using natural language processing technology and extracts key points and important information. Natural language processing libraries such as SpaCy and NLTK are applied to this analysis.

[0107] Next, the server automatically generates presentation slides and scripts based on the extracted information. For generating advertising slides and scripts, a template engine utilizing LaTeX and the Google® Slides API is used to automatically optimize visual effects and design. Finally, the generated materials are delivered to the user's terminal via the network, allowing the user to review and edit them as needed.

[0108] For example, if a user inputs an idea for a "targeting strategy for a new product," the server uses that information to generate presentation materials based on detailed consumer segmentation and market analysis, creating visually clear slides and a consistent script. In this way, users can prepare an effective advertising campaign presentation in a short amount of time.

[0109] An example of a prompt is, "Please tell us about the objectives and target audience of your advertising campaign. Example: 'Promote new eco-friendly products to young people of Gen Z.'" Based on appropriate idea input in response to this prompt, the system will automatically generate materials.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] Users input advertising campaign ideas into their devices using natural language. The input data is formatted by the device and sent to the server. This input includes specific campaign objectives and target audience information.

[0113] Step 2:

[0114] The server analyzes the received natural language data. Using natural language processing libraries (e.g., SpaCy, NLTK), it segments the data and removes unnecessary information. Important keywords and topics are extracted from the input data, and a data structure is generated based on them. The output is structured information containing the key points of the data.

[0115] Step 3:

[0116] The server determines the structure of the presentation materials based on the extracted key points. Using a generative AI model, it plans the effective order and content of the slides while considering the flow of the advertisement. At this stage, it is decided which information will be presented as the climax. The output is a plan of the materials as a draft structure.

[0117] Step 4:

[0118] The server automatically generates charts, graphs, and slides using a template engine (e.g., LaTeX, Google Slides API). A visually appealing design is applied based on the presentation plan. Inputs include the order and theme of the slides, and the output is a completed slide file.

[0119] Step 5:

[0120] The server uses the generated slides to create a speech script. The script includes example prompts and guides the flow of speech during the presentation. The output is the script to be used during the presentation.

[0121] Step 6:

[0122] The server sends the final output—slides and scripts—to the user's device. The user can then review the received materials and edit or modify them as needed. The output is the presentation material displayed on the user's device.

[0123] 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.

[0124] This invention provides a system that not only processes user natural language input using an information processing device but also combines it with an emotion engine to automatically generate richer presentation materials. To implement this system, the user inputs ideas in natural language via a terminal, and that data is processed on a server.

[0125] First, the natural language data entered by the user on the device is sent to the server. The server receives this data, applies natural language processing techniques, and extracts important keywords and topics from the input. Furthermore, the emotion engine recognizes the user's emotions based on this analyzed data and extracts emotion-based context.

[0126] The server incorporates this emotional information into the structure of the presentation materials, adjusting the tone and style of the materials. Using a template engine, it optimizes the visual design according to the emotional context, so that the generated materials more accurately reflect the user's intent.

[0127] Specifically, for example, if a user is preparing a presentation on "the benefits of introducing a new product within the company," the server extracts information from the natural language content and analyzes the emotions expressed in that language. If the analysis reveals enthusiasm or anticipation, it reflects a more positive design and proactive expression in the document. This makes the document more appealing and leaves a stronger impression on the audience.

[0128] The final generated document is sent to the user's device, where they can review and make adjustments. Because the system even considers emotional nuances, the user's original intent is faithfully reproduced, improving the effectiveness of the presentation.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The user inputs their desired idea in natural language via a device. Once the input is complete, the device sends that natural language data to the server.

[0132] Step 2:

[0133] The server receives natural language data sent from the terminal, passes the data to a preprocessing module to remove unnecessary information, and then normalizes the text.

[0134] Step 3:

[0135] The server passes the pre-processed data to a natural language processing (NLP) module. The NLP module analyzes the text and extracts keywords and topics.

[0136] Step 4:

[0137] The server inputs the analyzed data into the emotion engine, which then performs emotion analysis. The emotion engine identifies the user's emotional state from the text input and extracts emotions such as joy, sadness, and surprise.

[0138] Step 5:

[0139] The server matches extracted keywords, topics, and sentiment information to create a structure for the presentation materials. This structure includes the message and overall tone the material aims to convey.

[0140] Step 6:

[0141] The server uses a template engine to adjust the tone and style of the materials and automatically generate visual information such as charts, graphs, and slides. Design selections are made based on emotional information.

[0142] Step 7:

[0143] The server automatically generates a speech script to support the user's presentation, along with the generated presentation materials. The script includes expressions based on emotional information.

[0144] Step 8:

[0145] The server sends the final created materials and scripts to the user's terminal. The user reviews them, makes any necessary corrections, and prepares for the presentation.

[0146] (Example 2)

[0147] 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".

[0148] Conventional presentation material generation systems, while capable of extracting information based on user natural language input, struggled to automatically generate materials that appropriately reflected the user's emotions and intentions. Therefore, maximizing the effectiveness of presentations required manual adjustments, resulting in a time-consuming and laborious process.

[0149] 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.

[0150] In this invention, the server includes means for receiving data input from a user in natural language, natural language processing means for analyzing the received data and extracting key points and important information, and sentiment analysis means for analyzing the user's emotions based on the extracted information and generating an emotion-based context. This enables the automatic generation of presentation materials that reflect the user's intentions and emotions.

[0151] An "information processing device" is a machine or system that can input, process, and output data, and performs specific tasks or calculations according to the user's requests.

[0152] "Natural language" refers to the language that humans use on a daily basis, which develops naturally without being influenced by specific programs or algorithms, and is a linguistic form used for communication.

[0153] "Natural language processing means" refers to techniques or methods for analyzing natural language data such as text and speech, and for extracting its structure and semantic information.

[0154] "Sentiment analysis means" refers to a technology or method for recognizing emotions from user text or other input data and for evaluating or classifying information based on these emotions.

[0155] A "template engine" is a software component that uses reusable templates to dynamically generate content, producing standardized output based on different data.

[0156] This invention is a system that analyzes emotions using data entered by a user in natural language via an information processing device, and automatically generates presentation materials based on that analysis. This system makes it possible to efficiently create engaging materials that reflect the user's intentions and emotions.

[0157] First, the user uses a terminal to input their presentation ideas and content in natural language. For example, they might input, "I want to create a presentation about the economic effects of introducing a new product." This input data is then sent to the server via the network.

[0158] The server analyzes the received data using natural language processing techniques. Specifically, for example, it uses the natural language processing library SpaCy to extract important keywords from the text. The server also utilizes an emotion analysis engine to evaluate the user's emotions. This emotion analysis can utilize technologies such as emotion analysis APIs.

[0159] Based on the analysis results, the server determines the structure of the presentation materials. The design and visual style of the materials are optimized using the Jinja2 template engine. Because the materials are automatically generated based on the prompts entered by the user, the content and tone can accurately reflect the user's intent.

[0160] Through this invention, users can quickly obtain professional presentation materials that perfectly match the emotions and intentions they express, saving them considerable time and effort. Furthermore, by applying various generative AI models, it is expected that user satisfaction will be enhanced and its use in business settings will be further promoted.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The user inputs an idea using natural language on their device. For example, they might input, "I want to create a presentation about the economic effects of introducing a new product." The input data is sent to the server in its original format. Here, the input data is in text form, and the output is the data sent to the server.

[0164] Step 2:

[0165] The server receives natural language data from the terminal. The received data is then analyzed using natural language processing techniques. Specifically, a natural language processing library (e.g., SpaCy) is used to analyze important keywords and topics from the data. The input for this step is text data, and the output is the analyzed information (keywords and topics).

[0166] Step 3:

[0167] The server performs sentiment analysis based on the analyzed information. For example, it might use a sentiment analysis API. Using the already analyzed information as input, it recognizes emotions and outputs the results as data. This output is contextual information that reflects the user's emotions and intentions.

[0168] Step 4:

[0169] The server determines the structure of the presentation materials based on the results of sentiment analysis. A template engine (e.g., Jinja2) is used to structure the materials, optimizing the design according to the sentiment. The input for this step is sentiment-based contextual information, and the output is the presentation materials to which the template has been applied.

[0170] Step 5:

[0171] The server sends the final generated presentation materials to the user's terminal. The user receives the materials on their terminal, reviews the content as needed, and makes any necessary adjustments. Here, the input is the generated materials, and the output is the finalized materials provided to the user.

[0172] Through this processing flow, users can easily obtain presentation materials that reflect emotions and intentions, and effectively utilize them in business and other settings.

[0173] (Application Example 2)

[0174] 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 device 14 will be referred to as the "terminal."

[0175] A challenge in generating presentation materials is the inability to adequately reflect the user's intentions and emotions. Furthermore, the generated materials tend to be uniform, making it difficult to leave a strong impression on the audience. Therefore, there is a need to automatically generate individually optimized materials that take into account the emotional characteristics of each user.

[0176] 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.

[0177] In this invention, the server includes means for receiving information input from a user in natural language, natural language processing means for analyzing the received information and extracting key points and important information fragments, and plan generation means for determining the structure of presentation materials based on the extracted information fragments and the user's emotional characteristics. This enables the automatic generation of presentation materials that take the user's emotions into consideration.

[0178] "Information entered in natural language" refers to data in the language format used by users when speaking or writing, and is the subject of processing by the system.

[0179] "Natural language processing means" refers to technologies or devices used by computers to understand and analyze the language that humans use on a daily basis.

[0180] "Emotional characteristics" are data obtained by analyzing the nuances of emotions expressed through a user's language and expressions.

[0181] A "plan generation means" is a function or device that designs how to structure data and present it based on the analyzed information.

[0182] "Material generation means" refers to a function or device that creates specific visual materials according to a predetermined configuration.

[0183] A "template engine" is software or a device that efficiently generates visual materials using pre-prepared design templates.

[0184] The system implementing this invention consists of a user terminal, a server operating on the cloud, and various software components for processing data. The user inputs information in natural language using a smartphone or smart glasses. This information is then transmitted from the user terminal to the server.

[0185] The server uses natural language processing technology developed with Python (e.g., spaCy) to extract important keywords and topics from the received information. At the same time, it analyzes emotional characteristics using deep learning frameworks such as TENSORFLOW® to understand the user's emotional state.

[0186] Next, the server uses a template engine (e.g., Jinja2) to determine an optimized visual design based on emotion and automatically generate presentation materials. These materials can then be presented to the user, for example, as a virtual tour of a virtual store.

[0187] The generated materials are returned to the user's terminal, where they can visually review and make further adjustments. These materials, generated with the user's emotions in mind, support more engaging and effective presentations.

[0188] As a concrete example, when a user passionately introduces a new eco-bag, this system generates a trendy and energetic tour. An example of a prompt given to the generating AI model might be: "While the user is excitedly introducing the new eco-bag, analyze their emotions and generate a trendy and energetic tour."

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The user uses a device to input information in natural language. This input is either in the form of speech recognition or text input and is sent from the device to the server. During this process, the user's spoken content is captured as digital data.

[0192] Step 2:

[0193] The server uses spaCy to analyze the received natural language data. It extracts important keywords and topics from the input data. This data processing extracts the key points of the information.

[0194] Step 3:

[0195] The server analyzes emotional characteristics from the information extracted using TensorFlow. This process detects the intensity and type of emotion based on the user's statements. The output is the emotional state expressed by the user.

[0196] Step 4:

[0197] The server uses Jinja2 based on the analysis results to determine the visual design of the presentation materials. By applying templates, it generates visual content with a tone and style that matches the desired emotion. The output is a visually optimized document.

[0198] Step 5:

[0199] The server sends the generated presentation materials to the user's terminal. The user can view these materials and make adjustments as needed. The content displayed on the terminal is the final output for the user.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] [Second Embodiment]

[0204] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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".

[0216] In implementing this invention, the server efficiently processes natural language information input by the user and constructs a system that automatically generates presentation materials to effectively convey ideas. When a user inputs their thoughts in natural language on a terminal, the terminal transmits that data to the server via the network.

[0217] The server applies natural language processing (NLP) techniques to analyze the natural language data it receives. First, the server preprocesses the input, splitting the text and removing unnecessary information. Then, it uses NLP algorithms to extract key keywords and topics. The server uses these elements to clarify the logical flow of ideas and determine an efficient presentation structure.

[0218] Next, the server begins the process of visualizing the presentation materials based on the determined configuration. The server utilizes a template engine to automatically generate charts, graphs, and slides. It optimizes the information to be presented in a way that is easy for the audience to understand, taking visual effects and design into consideration. Furthermore, the server generates a speech script, which is provided as a guide for the user during the presentation.

[0219] Ultimately, the server sends the generated materials to the user's terminal, allowing the user to review, edit, and use them. For example, if a user inputs an idea about "energy efficiency strategies," the server uses that information to create slides that incorporate relevant statistical data and case studies, and use charts and graphs to make them intuitively understandable. In this way, the user can prepare a fair presentation in which the value of their idea is directly evaluated.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] The user uses a device to input their ideas in natural language. Once input is complete, the device sends the data to the server.

[0223] Step 2:

[0224] The server receives natural language data sent from the terminal. The received data is first preprocessed, with unnecessary characters being removed and text normalization being performed.

[0225] Step 3:

[0226] The server passes the pre-processed data to a natural language processing (NLP) module, which analyzes the meaning of the text. This involves tokenization, part-of-speech tagging, and syntactic analysis.

[0227] Step 4:

[0228] The server extracts important keywords and topics from the input based on the analysis information obtained by NLP. Topic modeling and keyword extraction algorithms are used for this purpose.

[0229] Step 5:

[0230] The server determines the structure of the presentation materials based on extracted keywords and topics. It consults the template library to formulate the most effective flow of the materials.

[0231] Step 6:

[0232] The server generates visual materials according to the predetermined presentation structure. Using a template engine, it automatically creates slides, charts, and graphs, and optimizes visual effects.

[0233] Step 7:

[0234] The server creates a speech script related to the generated presentation materials, providing the user with instructions and key points for delivering the presentation.

[0235] Step 8:

[0236] The server sends the completed materials and speech script to the user's terminal. The user reviews them, makes any necessary adjustments, and prepares the final version.

[0237] (Example 1)

[0238] 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."

[0239] Creating audiovisual materials efficiently and effectively is difficult for many users, particularly the process of visualizing ideas expressed in natural language, which often requires considerable time and effort. Furthermore, specialized skills are frequently required to create visually clear materials. Therefore, there is a need for a means to easily generate audiovisual materials from natural language and effectively communicate users' ideas.

[0240] 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.

[0241] In this invention, the server includes means for receiving information input from a user in natural language, language processing means for analyzing the received information and extracting key points and important information elements, and plan generation means for determining the structure of audiovisual material based on the extracted information elements. This makes it possible for users to efficiently generate audiovisual material that can be intuitively understood from natural language, even without advanced specialized knowledge.

[0242] A "user" is the entity that uses this system to input information in natural language.

[0243] "Natural language" refers to the language that humans use on a daily basis, that is, a language that is not artificial.

[0244] "Information" refers to the content that users input into the system and that is processed.

[0245] A "receiving device" is a device used by the user to review and edit generated materials.

[0246] "Language processing means" refers to technologies that analyze natural language input and have the function of extracting key points and important information.

[0247] A "plan generation means" is a technology that has the function of determining the logical structure of audiovisual materials from the analyzed information.

[0248] "Document generation means" refers to technology that has the function of automatically creating visual documents based on a predetermined structure.

[0249] A "composition engine" is a general term for technologies used to automatically optimize visual effects and design.

[0250] This invention relates to a system that generates visual presentation materials based on information input in natural language. Specifically, this system operates around three elements: the user, the terminal, and the server.

[0251] The user first uses a terminal to input their ideas and thoughts in natural language. The terminal then transmits the information entered by the user to a server via the network. This process involves data communication using the internet.

[0252] The server is equipped with language processing capabilities for processing the received data. Specifically, it analyzes the input data using natural language processing techniques and extracts key points and important information elements. At this stage, general text processing libraries (e.g., NLTK and spaCy) and generative AI models are utilized.

[0253] Based on the extracted information elements, the server uses a planning generation mechanism to determine the logical structure of the presentation materials. Following this structure, the material generation mechanism automatically generates the visual elements. This process utilizes a structure engine to generate charts and slides. A template engine is also used to automatically optimize visual effects and design.

[0254] The generated presentation materials are sent to the terminal, where the user can review, edit, and use them. To improve user convenience, the server also generates a speech transcript and provides it as a guide during the presentation.

[0255] For example, if a user enters the prompt "Create a presentation on an innovative strategy for energy efficiency," the server will incorporate relevant statistical data and case studies, and generate slides that are intuitively understandable using charts and graphs. In this way, the system can quickly provide high-quality presentation materials to effectively communicate the user's ideas.

[0256] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0257] Step 1:

[0258] The user inputs information using natural language via the device. Specifically, they input prompts such as "plan for improving energy efficiency" via keyboard or voice input. The input information is saved on the device as text data. This data is the first to be processed.

[0259] Step 2:

[0260] The terminal sends the entered text data to the server over the network. Security protocols are applied during this transmission process to protect the confidentiality of the data. Once the transmitted data reaches the server, the next processing step begins.

[0261] Step 3:

[0262] The server analyzes the received text data using language processing tools. Specifically, it uses natural language processing techniques (e.g., text analysis libraries and generative AI models) to divide the text data into phrases and remove noise (unnecessary words and phrases). This process extracts appropriate topics and keywords. The output is a list of the extracted keywords and topics.

[0263] Step 4:

[0264] Based on the extracted keywords and topics, the server uses a planning generation mechanism to determine the structure of the presentation materials. It determines the order in which each topic should be presented, which information is important and should be emphasized, and constructs a logical framework. The output is represented as a structural plan for the entire presentation.

[0265] Step 5:

[0266] The server automatically generates visual materials using a material generation system based on the determined configuration. A template engine is used in this process to design slides corresponding to each topic. Relevant charts and statistical data are incorporated to create visually impactful materials. This output is provided in the format of the final presentation.

[0267] Step 6:

[0268] The server sends the generated presentation materials to the terminal. The user can receive these materials on the terminal and review their contents. Furthermore, they can edit the materials if necessary and use them in the final presentation. The output of this step is an editable document presented to the user.

[0269] (Application Example 1)

[0270] 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."

[0271] In the advertising industry, creating effective presentation materials quickly and efficiently is crucial. However, traditional methods are time-consuming and labor-intensive, and require specialized knowledge to optimize visual effects and design. Furthermore, scriptwriting is necessary to properly convey the presentation content, which can lead to inconsistent materials.

[0272] 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.

[0273] In this invention, the server includes, in an information processing device, means for receiving information input from a user in natural language; natural language processing means for analyzing the received information and extracting key points and important information fragments; plan generation means for determining the structure of presentation materials based on the extracted information fragments; and advertising creation means for automatically generating advertising slides and scripts. This enables the rapid creation of presentation materials for advertising campaigns and the optimization of their visual effects and design.

[0274] An "information processing device" is a device that receives and analyzes data and generates specific results based on that data.

[0275] "Natural language" refers to the language that humans use on a daily basis, and is primarily a means of expressing information in written and conversational forms.

[0276] "Natural language processing" is a technology that uses computers to analyze human language, understand its content, and process it.

[0277] An "information fragment" refers to an important element or unit of data extracted from received data.

[0278] A "plan generation mechanism" is a system that has the function of determining materials and structures for a specific purpose based on extracted information.

[0279] The "advertisement creation means" is a device having a function of automatically generating presentation materials for an advertising campaign.

[0280] A "slide" is a page or screen for visually presenting information in a presentation.

[0281] A "script" is a document referring to a script or dialogue to be used in a presentation.

[0282] A "visual effect" is a technology for more effectively conveying information by using visual elements.

[0283] "Design" is a general term for things that are visually organized and laid out to effectively convey information.

[0284] This invention is a system that receives input information in the natural language of a user using an information processing device, analyzes it, and automatically generates presentation materials. Specifically, the server receives an idea for an advertisement described in natural language from the user's terminal. The server analyzes this information using natural language processing technology and extracts key points and important information. For this analysis, natural language processing libraries such as SpaCy and NLTK are applied.

[0285] Next, based on the extracted information, the server automatically generates presentation slides and scripts. For generating advertisement slides and scripts, a template engine that utilizes LaTeX or the Google Slides API is used so that visual effects and designs are automatically optimized. Finally, the generated materials are provided to the user's terminal via a network, and the user can view the materials and edit them as needed.

[0286] For example, when a user inputs an idea regarding the "targeting strategy for new products", the server generates presentation materials based on detailed consumer segmentation and market analysis using this information, and creates visually easy-to-understand slides and a consistent script. In this way, the user can prepare an effective advertising campaign presentation in a short time.

[0287] As an example of a prompt sentence, "Please tell me about the purpose and target of the advertising campaign. Example: 'Promote new eco-friendly products to young people in Generation Z.'" can be cited. Based on an appropriate idea input for this prompt, the system automatically generates materials.

[0288] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0289] Step 1:

[0290] The user inputs an idea for an advertising campaign in natural language on the terminal. The input data is formatted by the terminal and sent to the server. This input includes information on specific campaign purposes and target audiences.

[0291] Step 2:

[0292] The server analyzes the received natural language data. Using a natural language processing library (e.g., SpaCy, NLTK), the data is split and unnecessary information is removed. Important keywords and topics are extracted from the input data, and a data structure is generated based on them. As output, structured information containing the main points of the data is obtained.

[0293] Step 3:

[0294] The server determines the structure of the presentation materials based on the extracted key points. Using a generative AI model, it plans the effective order and content of the slides while considering the flow of the advertisement. At this stage, it is decided which information will be presented as the climax. The output is a plan of the materials as a draft structure.

[0295] Step 4:

[0296] The server automatically generates charts, graphs, and slides using a template engine (e.g., LaTeX, Google Slides API). A visually appealing design is applied based on the presentation plan. Inputs include the order and theme of the slides, and the output is a completed slide file.

[0297] Step 5:

[0298] The server uses the generated slides to create a speech script. The script includes example prompts and guides the flow of speech during the presentation. The output is the script to be used during the presentation.

[0299] Step 6:

[0300] The server sends the final output—slides and scripts—to the user's device. The user can then review the received materials and edit or modify them as needed. The output is the presentation material displayed on the user's device.

[0301] 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.

[0302] This invention provides a system that not only processes a user's natural language input using an information processing device, but also automatically generates richer presentation materials by combining an emotion engine. To implement this system, the user inputs ideas in natural language via a terminal, and the data is processed on the server.

[0303] First, the natural language data input by the user on the terminal is sent to the server. The server receives this and applies natural language processing technology to extract important keywords and topics from the input. Furthermore, the emotion engine recognizes the user's emotion based on this analyzed data and extracts emotion-based context.

[0304] The server incorporates this emotion information into the composition of the presentation materials and adjusts the tone and style of the materials. Using a template engine, it optimizes the visual design according to the emotion context, so that the generated materials can more accurately reflect the user's intention.

[0305] Specifically, for example, when the user is preparing a presentation on "the merits of new product introduction within the company", the server extracts information from the natural language content and analyzes the emotion from the language. If enthusiasm or expectation is felt as a result of the analysis, a more positive design and positive expressions are reflected in the materials. This makes the materials more attractive and can give a strong impression to the audience.

[0306] The finally generated materials are sent to the user's terminal, and the user can review and make fine adjustments. In this system, because nuances of emotion are also considered, the user's original intention is faithfully reproduced and the effect of the presentation is improved.

[0307] The following explains the processing flow.

[0308] Step 1:

[0309] The user inputs their desired idea in natural language via a device. Once the input is complete, the device sends that natural language data to the server.

[0310] Step 2:

[0311] The server receives natural language data sent from the terminal, passes the data to a preprocessing module to remove unnecessary information, and then normalizes the text.

[0312] Step 3:

[0313] The server passes the pre-processed data to a natural language processing (NLP) module. The NLP module analyzes the text and extracts keywords and topics.

[0314] Step 4:

[0315] The server inputs the analyzed data into the emotion engine, which then performs emotion analysis. The emotion engine identifies the user's emotional state from the text input and extracts emotions such as joy, sadness, and surprise.

[0316] Step 5:

[0317] The server matches extracted keywords, topics, and sentiment information to create a structure for the presentation materials. This structure includes the message and overall tone the material aims to convey.

[0318] Step 6:

[0319] The server uses a template engine to adjust the tone and style of the materials and automatically generate visual information such as charts, graphs, and slides. Design selections are made based on emotional information.

[0320] Step 7:

[0321] The server automatically generates a speech script to support the user's presentation, along with the generated presentation materials. The script includes expressions based on emotional information.

[0322] Step 8:

[0323] The server sends the final created materials and scripts to the user's terminal. The user reviews them, makes any necessary corrections, and prepares for the presentation.

[0324] (Example 2)

[0325] 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".

[0326] Conventional presentation material generation systems, while capable of extracting information based on user natural language input, struggled to automatically generate materials that appropriately reflected the user's emotions and intentions. Therefore, maximizing the effectiveness of presentations required manual adjustments, resulting in a time-consuming and laborious process.

[0327] 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.

[0328] In this invention, the server includes means for receiving data input from a user in natural language, natural language processing means for analyzing the received data and extracting key points and important information, and sentiment analysis means for analyzing the user's emotions based on the extracted information and generating an emotion-based context. This enables the automatic generation of presentation materials that reflect the user's intentions and emotions.

[0329] An "information processing device" is a machine or system that can input, process, and output data, and performs specific tasks or calculations according to the user's requests.

[0330] "Natural language" refers to the language that humans use on a daily basis, which develops naturally without being influenced by specific programs or algorithms, and is a linguistic form used for communication.

[0331] "Natural language processing means" refers to techniques or methods for analyzing natural language data such as text and speech, and for extracting its structure and semantic information.

[0332] "Sentiment analysis means" refers to a technology or method for recognizing emotions from user text or other input data and for evaluating or classifying information based on these emotions.

[0333] A "template engine" is a software component that uses reusable templates to dynamically generate content, producing standardized output based on different data.

[0334] This invention is a system that analyzes emotions using data entered by a user in natural language via an information processing device, and automatically generates presentation materials based on that analysis. This system makes it possible to efficiently create engaging materials that reflect the user's intentions and emotions.

[0335] First, the user uses a terminal to input their presentation ideas and content in natural language. For example, they might input, "I want to create a presentation about the economic effects of introducing a new product." This input data is then sent to the server via the network.

[0336] The server analyzes the received data using natural language processing techniques. Specifically, for example, it uses the natural language processing library SpaCy to extract important keywords from the text. The server also utilizes an emotion analysis engine to evaluate the user's emotions. This emotion analysis can utilize technologies such as emotion analysis APIs.

[0337] Based on the analysis results, the server determines the structure of the presentation materials. The design and visual style of the materials are optimized using the Jinja2 template engine. Because the materials are automatically generated based on the prompts entered by the user, the content and tone can accurately reflect the user's intent.

[0338] Through this invention, users can quickly obtain professional presentation materials that perfectly match the emotions and intentions they express, saving them considerable time and effort. Furthermore, by applying various generative AI models, it is expected that user satisfaction will be enhanced and its use in business settings will be further promoted.

[0339] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0340] Step 1:

[0341] The user inputs an idea using natural language on their device. For example, they might input, "I want to create a presentation about the economic effects of introducing a new product." The input data is sent to the server in its original format. Here, the input data is in text form, and the output is the data sent to the server.

[0342] Step 2:

[0343] The server receives natural language data from the terminal. The received data is then analyzed using natural language processing techniques. Specifically, a natural language processing library (e.g., SpaCy) is used to analyze important keywords and topics from the data. The input for this step is text data, and the output is the analyzed information (keywords and topics).

[0344] Step 3:

[0345] The server performs sentiment analysis based on the analyzed information. For example, it might use a sentiment analysis API. Using the already analyzed information as input, it recognizes emotions and outputs the results as data. This output is contextual information that reflects the user's emotions and intentions.

[0346] Step 4:

[0347] The server determines the structure of the presentation materials based on the results of sentiment analysis. A template engine (e.g., Jinja2) is used to structure the materials, optimizing the design according to the sentiment. The input for this step is sentiment-based contextual information, and the output is the presentation materials to which the template has been applied.

[0348] Step 5:

[0349] The server sends the final generated presentation materials to the user's terminal. The user receives the materials on their terminal, reviews the content as needed, and makes any necessary adjustments. Here, the input is the generated materials, and the output is the finalized materials provided to the user.

[0350] Through this processing flow, users can easily obtain presentation materials that reflect emotions and intentions, and effectively utilize them in business and other settings.

[0351] (Application Example 2)

[0352] 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."

[0353] A challenge in generating presentation materials is the inability to adequately reflect the user's intentions and emotions. Furthermore, the generated materials tend to be uniform, making it difficult to leave a strong impression on the audience. Therefore, there is a need to automatically generate individually optimized materials that take into account the emotional characteristics of each user.

[0354] 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.

[0355] In this invention, the server includes means for receiving information input from a user in natural language, natural language processing means for analyzing the received information and extracting key points and important information fragments, and plan generation means for determining the structure of presentation materials based on the extracted information fragments and the user's emotional characteristics. This enables the automatic generation of presentation materials that take the user's emotions into consideration.

[0356] "Information entered in natural language" refers to data in the language format used by users when speaking or writing, and is the subject of processing by the system.

[0357] "Natural language processing means" refers to technologies or devices used by computers to understand and analyze the language that humans use on a daily basis.

[0358] "Emotional characteristics" are data obtained by analyzing the nuances of emotions expressed through a user's language and expressions.

[0359] A "plan generation means" is a function or device that designs how to structure data and present it based on the analyzed information.

[0360] "Material generation means" refers to a function or device that creates specific visual materials according to a predetermined configuration.

[0361] A "template engine" is software or a device that efficiently generates visual materials using pre-prepared design templates.

[0362] The system implementing this invention consists of a user terminal, a server operating on the cloud, and various software components for processing data. The user inputs information in natural language using a smartphone or smart glasses. This information is then transmitted from the user terminal to the server.

[0363] The server uses natural language processing technology developed with Python (e.g., spaCy) to extract important keywords and topics from the received information. At the same time, it analyzes sentiment features using deep learning frameworks such as TensorFlow to understand the user's emotional state.

[0364] Next, the server uses a template engine (e.g., Jinja2) to determine an optimized visual design based on emotion and automatically generate presentation materials. These materials can then be presented to the user, for example, as a virtual tour of a virtual store.

[0365] The generated materials are returned to the user's terminal, where they can visually review and make further adjustments. These materials, generated with the user's emotions in mind, support more engaging and effective presentations.

[0366] As a concrete example, when a user passionately introduces a new eco-bag, this system generates a trendy and energetic tour. An example of a prompt given to the generating AI model might be: "While the user is excitedly introducing the new eco-bag, analyze their emotions and generate a trendy and energetic tour."

[0367] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0368] Step 1:

[0369] The user uses a device to input information in natural language. This input is either in the form of speech recognition or text input and is sent from the device to the server. During this process, the user's spoken content is captured as digital data.

[0370] Step 2:

[0371] The server uses spaCy to analyze the received natural language data. It extracts important keywords and topics from the input data. This data processing extracts the key points of the information.

[0372] Step 3:

[0373] The server analyzes emotional characteristics from the information extracted using TensorFlow. This process detects the intensity and type of emotion based on the user's statements. The output is the emotional state expressed by the user.

[0374] Step 4:

[0375] The server uses Jinja2 based on the analysis results to determine the visual design of the presentation materials. By applying templates, it generates visual content with a tone and style that matches the desired emotion. The output is a visually optimized document.

[0376] Step 5:

[0377] The server sends the generated presentation materials to the user's terminal. The user can view these materials and make adjustments as needed. The content displayed on the terminal is the final output for the user.

[0378] 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.

[0379] 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.

[0380] 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.

[0381] [Third Embodiment]

[0382] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0383] 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.

[0384] 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).

[0385] 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.

[0386] 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.

[0387] 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).

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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".

[0394] In implementing this invention, the server efficiently processes natural language information input by the user and constructs a system that automatically generates presentation materials to effectively convey ideas. When a user inputs their thoughts in natural language on a terminal, the terminal transmits that data to the server via the network.

[0395] The server applies natural language processing (NLP) techniques to analyze the natural language data it receives. First, the server preprocesses the input, splitting the text and removing unnecessary information. Then, it uses NLP algorithms to extract key keywords and topics. The server uses these elements to clarify the logical flow of ideas and determine an efficient presentation structure.

[0396] Next, the server begins the process of visualizing the presentation materials based on the determined configuration. The server utilizes a template engine to automatically generate charts, graphs, and slides. It optimizes the information to be presented in a way that is easy for the audience to understand, taking visual effects and design into consideration. Furthermore, the server generates a speech script, which is provided as a guide for the user during the presentation.

[0397] Ultimately, the server sends the generated materials to the user's terminal, allowing the user to review, edit, and use them. For example, if a user inputs an idea about "energy efficiency strategies," the server uses that information to create slides that incorporate relevant statistical data and case studies, and use charts and graphs to make them intuitively understandable. In this way, the user can prepare a fair presentation in which the value of their idea is directly evaluated.

[0398] The following describes the processing flow.

[0399] Step 1:

[0400] The user uses a device to input their ideas in natural language. Once input is complete, the device sends the data to the server.

[0401] Step 2:

[0402] The server receives natural language data sent from the terminal. The received data is first preprocessed, with unnecessary characters being removed and text normalization being performed.

[0403] Step 3:

[0404] The server passes the pre-processed data to a natural language processing (NLP) module, which analyzes the meaning of the text. This involves tokenization, part-of-speech tagging, and syntactic analysis.

[0405] Step 4:

[0406] The server extracts important keywords and topics from the input based on the analysis information obtained by NLP. Topic modeling and keyword extraction algorithms are used for this purpose.

[0407] Step 5:

[0408] The server determines the structure of the presentation materials based on extracted keywords and topics. It consults the template library to formulate the most effective flow of the materials.

[0409] Step 6:

[0410] The server generates visual materials according to the predetermined presentation structure. Using a template engine, it automatically creates slides, charts, and graphs, and optimizes visual effects.

[0411] Step 7:

[0412] The server creates a speech script related to the generated presentation materials, providing the user with instructions and key points for delivering the presentation.

[0413] Step 8:

[0414] The server sends the completed materials and speech script to the user's terminal. The user reviews them, makes any necessary adjustments, and prepares the final version.

[0415] (Example 1)

[0416] 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."

[0417] Creating audiovisual materials efficiently and effectively is difficult for many users, particularly the process of visualizing ideas expressed in natural language, which often requires considerable time and effort. Furthermore, specialized skills are frequently required to create visually clear materials. Therefore, there is a need for a means to easily generate audiovisual materials from natural language and effectively communicate users' ideas.

[0418] 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.

[0419] In this invention, the server includes means for receiving information input from a user in natural language, language processing means for analyzing the received information and extracting key points and important information elements, and plan generation means for determining the structure of audiovisual material based on the extracted information elements. This makes it possible for users to efficiently generate audiovisual material that can be intuitively understood from natural language, even without advanced specialized knowledge.

[0420] A "user" is the entity that uses this system to input information in natural language.

[0421] "Natural language" refers to the language that humans use on a daily basis, that is, a language that is not artificial.

[0422] "Information" refers to the content that users input into the system and that is processed.

[0423] A "receiving device" is a device used by the user to review and edit generated materials.

[0424] "Language processing means" refers to technologies that analyze natural language input and have the function of extracting key points and important information.

[0425] A "plan generation means" is a technology that has the function of determining the logical structure of audiovisual materials from the analyzed information.

[0426] "Document generation means" refers to technology that has the function of automatically creating visual documents based on a predetermined structure.

[0427] A "composition engine" is a general term for technologies used to automatically optimize visual effects and design.

[0428] This invention relates to a system that generates visual presentation materials based on information input in natural language. Specifically, this system operates around three elements: the user, the terminal, and the server.

[0429] The user first uses a terminal to input their ideas and thoughts in natural language. The terminal then transmits the information entered by the user to a server via the network. This process involves data communication using the internet.

[0430] The server is equipped with language processing capabilities for processing the received data. Specifically, it analyzes the input data using natural language processing techniques and extracts key points and important information elements. At this stage, general text processing libraries (e.g., NLTK and spaCy) and generative AI models are utilized.

[0431] Based on the extracted information elements, the server uses a planning generation mechanism to determine the logical structure of the presentation materials. Following this structure, the material generation mechanism automatically generates the visual elements. This process utilizes a structure engine to generate charts and slides. A template engine is also used to automatically optimize visual effects and design.

[0432] The generated presentation materials are sent to the terminal, where the user can review, edit, and use them. To improve user convenience, the server also generates a speech transcript and provides it as a guide during the presentation.

[0433] For example, if a user enters the prompt "Create a presentation on an innovative strategy for energy efficiency," the server will incorporate relevant statistical data and case studies, and generate slides that are intuitively understandable using charts and graphs. In this way, the system can quickly provide high-quality presentation materials to effectively communicate the user's ideas.

[0434] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0435] Step 1:

[0436] The user inputs information using natural language via the device. Specifically, they input prompts such as "plan for improving energy efficiency" via keyboard or voice input. The input information is saved on the device as text data. This data is the first to be processed.

[0437] Step 2:

[0438] The terminal sends the entered text data to the server over the network. Security protocols are applied during this transmission process to protect the confidentiality of the data. Once the transmitted data reaches the server, the next processing step begins.

[0439] Step 3:

[0440] The server analyzes the received text data using language processing tools. Specifically, it uses natural language processing techniques (e.g., text analysis libraries and generative AI models) to divide the text data into phrases and remove noise (unnecessary words and phrases). This process extracts appropriate topics and keywords. The output is a list of the extracted keywords and topics.

[0441] Step 4:

[0442] Based on the extracted keywords and topics, the server uses a planning generation mechanism to determine the structure of the presentation materials. It determines the order in which each topic should be presented, which information is important and should be emphasized, and constructs a logical framework. The output is represented as a structural plan for the entire presentation.

[0443] Step 5:

[0444] The server automatically generates visual materials using a material generation system based on the determined configuration. A template engine is used in this process to design slides corresponding to each topic. Relevant charts and statistical data are incorporated to create visually impactful materials. This output is provided in the format of the final presentation.

[0445] Step 6:

[0446] The server sends the generated presentation materials to the terminal. The user can receive these materials on the terminal and review their contents. Furthermore, they can edit the materials if necessary and use them in the final presentation. The output of this step is an editable document presented to the user.

[0447] (Application Example 1)

[0448] 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."

[0449] In the advertising industry, creating effective presentation materials quickly and efficiently is crucial. However, traditional methods are time-consuming and labor-intensive, and require specialized knowledge to optimize visual effects and design. Furthermore, scriptwriting is necessary to properly convey the presentation content, which can lead to inconsistent materials.

[0450] 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.

[0451] In this invention, the server includes, in an information processing device, means for receiving information input from a user in natural language; natural language processing means for analyzing the received information and extracting key points and important information fragments; plan generation means for determining the structure of presentation materials based on the extracted information fragments; and advertising creation means for automatically generating advertising slides and scripts. This enables the rapid creation of presentation materials for advertising campaigns and the optimization of their visual effects and design.

[0452] An "information processing device" is a device that receives and analyzes data and generates specific results based on that data.

[0453] "Natural language" refers to the language that humans use on a daily basis, and is primarily a means of expressing information in written and conversational forms.

[0454] "Natural language processing" is a technology that uses computers to analyze human language, understand its content, and process it.

[0455] An "information fragment" refers to an important element or unit of data extracted from received data.

[0456] A "plan generation mechanism" is a system that has the function of determining materials and structures for a specific purpose based on extracted information.

[0457] An "advertising creation tool" is a device that has the function of automatically generating presentation materials for advertising campaigns.

[0458] A "slide" is a page or screen used to visually present information in a presentation.

[0459] A "script" refers to a document that contains a written plan or spoken words used during a presentation.

[0460] "Visual effects" are techniques that use visual elements to convey information more effectively.

[0461] "Design" is a general term for anything that is visually organized and laid out to effectively convey information.

[0462] This invention is a system that uses an information processing device to receive natural language input information from a user, analyze it, and automatically generate presentation materials. Specifically, the server receives advertising ideas written in natural language from the user's terminal. The server analyzes this information using natural language processing technology and extracts key points and important information. Natural language processing libraries such as SpaCy and NLTK are applied to this analysis.

[0463] Next, the server automatically generates presentation slides and scripts based on the extracted information. For generating advertising slides and scripts, a template engine utilizing LaTeX and the Google Slides API is used to automatically optimize visual effects and design. Finally, the generated materials are delivered to the user's terminal via the network, allowing the user to review and edit them as needed.

[0464] For example, if a user inputs an idea for a "targeting strategy for a new product," the server uses that information to generate presentation materials based on detailed consumer segmentation and market analysis, creating visually clear slides and a consistent script. In this way, users can prepare an effective advertising campaign presentation in a short amount of time.

[0465] An example of a prompt is, "Please tell us about the objectives and target audience of your advertising campaign. Example: 'Promote new eco-friendly products to young people of Gen Z.'" Based on appropriate idea input in response to this prompt, the system will automatically generate materials.

[0466] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0467] Step 1:

[0468] Users input advertising campaign ideas into their devices using natural language. The input data is formatted by the device and sent to the server. This input includes specific campaign objectives and target audience information.

[0469] Step 2:

[0470] The server analyzes the received natural language data. Using natural language processing libraries (e.g., SpaCy, NLTK), it segments the data and removes unnecessary information. Important keywords and topics are extracted from the input data, and a data structure is generated based on them. The output is structured information containing the key points of the data.

[0471] Step 3:

[0472] The server determines the structure of the presentation materials based on the extracted key points. Using a generative AI model, it plans the effective order and content of the slides while considering the flow of the advertisement. At this stage, it is decided which information will be presented as the climax. The output is a plan of the materials as a draft structure.

[0473] Step 4:

[0474] The server automatically generates charts, graphs, and slides using a template engine (e.g., LaTeX, Google Slides API). A visually appealing design is applied based on the presentation plan. Inputs include the order and theme of the slides, and the output is a completed slide file.

[0475] Step 5:

[0476] The server uses the generated slides to create a speech script. The script includes example prompts and guides the flow of speech during the presentation. The output is the script to be used during the presentation.

[0477] Step 6:

[0478] The server sends the final output—slides and scripts—to the user's device. The user can then review the received materials and edit or modify them as needed. The output is the presentation material displayed on the user's device.

[0479] 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.

[0480] This invention provides a system that not only processes user natural language input using an information processing device but also combines it with an emotion engine to automatically generate richer presentation materials. To implement this system, the user inputs ideas in natural language via a terminal, and that data is processed on a server.

[0481] First, the natural language data entered by the user on the device is sent to the server. The server receives this data, applies natural language processing techniques, and extracts important keywords and topics from the input. Furthermore, the emotion engine recognizes the user's emotions based on this analyzed data and extracts emotion-based context.

[0482] The server incorporates this emotional information into the structure of the presentation materials, adjusting the tone and style of the materials. Using a template engine, it optimizes the visual design according to the emotional context, so that the generated materials more accurately reflect the user's intent.

[0483] Specifically, for example, if a user is preparing a presentation on "the benefits of introducing a new product within the company," the server extracts information from the natural language content and analyzes the emotions expressed in that language. If the analysis reveals enthusiasm or anticipation, it reflects a more positive design and proactive expression in the document. This makes the document more appealing and leaves a stronger impression on the audience.

[0484] The final generated document is sent to the user's device, where they can review and make adjustments. Because the system even considers emotional nuances, the user's original intent is faithfully reproduced, improving the effectiveness of the presentation.

[0485] The following describes the processing flow.

[0486] Step 1:

[0487] The user inputs their desired idea in natural language via a device. Once the input is complete, the device sends that natural language data to the server.

[0488] Step 2:

[0489] The server receives natural language data sent from the terminal, passes the data to a preprocessing module to remove unnecessary information, and then normalizes the text.

[0490] Step 3:

[0491] The server passes the pre-processed data to a natural language processing (NLP) module. The NLP module analyzes the text and extracts keywords and topics.

[0492] Step 4:

[0493] The server inputs the analyzed data into the emotion engine, which then performs emotion analysis. The emotion engine identifies the user's emotional state from the text input and extracts emotions such as joy, sadness, and surprise.

[0494] Step 5:

[0495] The server matches extracted keywords, topics, and sentiment information to create a structure for the presentation materials. This structure includes the message and overall tone the material aims to convey.

[0496] Step 6:

[0497] The server uses a template engine to adjust the tone and style of the materials and automatically generate visual information such as charts, graphs, and slides. Design selections are made based on emotional information.

[0498] Step 7:

[0499] The server automatically generates a speech script to support the user's presentation, along with the generated presentation materials. The script includes expressions based on emotional information.

[0500] Step 8:

[0501] The server sends the final created materials and scripts to the user's terminal. The user reviews them, makes any necessary corrections, and prepares for the presentation.

[0502] (Example 2)

[0503] 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."

[0504] Conventional presentation material generation systems, while capable of extracting information based on user natural language input, struggled to automatically generate materials that appropriately reflected the user's emotions and intentions. Therefore, maximizing the effectiveness of presentations required manual adjustments, resulting in a time-consuming and laborious process.

[0505] 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.

[0506] In this invention, the server includes means for receiving data input from a user in natural language, natural language processing means for analyzing the received data and extracting key points and important information, and sentiment analysis means for analyzing the user's emotions based on the extracted information and generating an emotion-based context. This enables the automatic generation of presentation materials that reflect the user's intentions and emotions.

[0507] An "information processing device" is a machine or system that can input, process, and output data, and performs specific tasks or calculations according to the user's requests.

[0508] "Natural language" refers to the language that humans use on a daily basis, which develops naturally without being influenced by specific programs or algorithms, and is a linguistic form used for communication.

[0509] "Natural language processing means" refers to techniques or methods for analyzing natural language data such as text and speech, and for extracting its structure and semantic information.

[0510] "Sentiment analysis means" refers to a technology or method for recognizing emotions from user text or other input data and for evaluating or classifying information based on these emotions.

[0511] A "template engine" is a software component that uses reusable templates to dynamically generate content, producing standardized output based on different data.

[0512] This invention is a system that analyzes emotions using data entered by a user in natural language via an information processing device, and automatically generates presentation materials based on that analysis. This system makes it possible to efficiently create engaging materials that reflect the user's intentions and emotions.

[0513] First, the user uses a terminal to input their presentation ideas and content in natural language. For example, they might input, "I want to create a presentation about the economic effects of introducing a new product." This input data is then sent to the server via the network.

[0514] The server analyzes the received data using natural language processing techniques. Specifically, for example, it uses the natural language processing library SpaCy to extract important keywords from the text. The server also utilizes an emotion analysis engine to evaluate the user's emotions. This emotion analysis can utilize technologies such as emotion analysis APIs.

[0515] Based on the analysis results, the server determines the structure of the presentation materials. The design and visual style of the materials are optimized using the Jinja2 template engine. Because the materials are automatically generated based on the prompts entered by the user, the content and tone can accurately reflect the user's intent.

[0516] Through this invention, users can quickly obtain professional presentation materials that perfectly match the emotions and intentions they express, saving them considerable time and effort. Furthermore, by applying various generative AI models, it is expected that user satisfaction will be enhanced and its use in business settings will be further promoted.

[0517] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0518] Step 1:

[0519] The user inputs an idea using natural language on their device. For example, they might input, "I want to create a presentation about the economic effects of introducing a new product." The input data is sent to the server in its original format. Here, the input data is in text form, and the output is the data sent to the server.

[0520] Step 2:

[0521] The server receives natural language data from the terminal. The received data is then analyzed using natural language processing techniques. Specifically, a natural language processing library (e.g., SpaCy) is used to analyze important keywords and topics from the data. The input for this step is text data, and the output is the analyzed information (keywords and topics).

[0522] Step 3:

[0523] The server performs sentiment analysis based on the analyzed information. For example, it might use a sentiment analysis API. Using the already analyzed information as input, it recognizes emotions and outputs the results as data. This output is contextual information that reflects the user's emotions and intentions.

[0524] Step 4:

[0525] The server determines the structure of the presentation materials based on the results of sentiment analysis. A template engine (e.g., Jinja2) is used to structure the materials, optimizing the design according to the sentiment. The input for this step is sentiment-based contextual information, and the output is the presentation materials to which the template has been applied.

[0526] Step 5:

[0527] The server sends the final generated presentation materials to the user's terminal. The user receives the materials on their terminal, reviews the content as needed, and makes any necessary adjustments. Here, the input is the generated materials, and the output is the finalized materials provided to the user.

[0528] Through this processing flow, users can easily obtain presentation materials that reflect emotions and intentions, and effectively utilize them in business and other settings.

[0529] (Application Example 2)

[0530] 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."

[0531] A challenge in generating presentation materials is the inability to adequately reflect the user's intentions and emotions. Furthermore, the generated materials tend to be uniform, making it difficult to leave a strong impression on the audience. Therefore, there is a need to automatically generate individually optimized materials that take into account the emotional characteristics of each user.

[0532] 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.

[0533] In this invention, the server includes means for receiving information input from a user in natural language, natural language processing means for analyzing the received information and extracting key points and important information fragments, and plan generation means for determining the structure of presentation materials based on the extracted information fragments and the user's emotional characteristics. This enables the automatic generation of presentation materials that take the user's emotions into consideration.

[0534] "Information entered in natural language" refers to data in the language format used by users when speaking or writing, and is the subject of processing by the system.

[0535] "Natural language processing means" refers to technologies or devices used by computers to understand and analyze the language that humans use on a daily basis.

[0536] "Emotional characteristics" are data obtained by analyzing the nuances of emotions expressed through a user's language and expressions.

[0537] A "plan generation means" is a function or device that designs how to structure data and present it based on the analyzed information.

[0538] "Material generation means" refers to a function or device that creates specific visual materials according to a predetermined configuration.

[0539] A "template engine" is software or a device that efficiently generates visual materials using pre-prepared design templates.

[0540] The system implementing this invention consists of a user terminal, a server operating on the cloud, and various software components for processing data. The user inputs information in natural language using a smartphone or smart glasses. This information is then transmitted from the user terminal to the server.

[0541] The server uses natural language processing technology developed with Python (e.g., spaCy) to extract important keywords and topics from the received information. At the same time, it analyzes sentiment features using deep learning frameworks such as TensorFlow to understand the user's emotional state.

[0542] Next, the server uses a template engine (e.g., Jinja2) to determine an optimized visual design based on emotion and automatically generate presentation materials. These materials can then be presented to the user, for example, as a virtual tour of a virtual store.

[0543] The generated materials are returned to the user's terminal, where they can visually review and make further adjustments. These materials, generated with the user's emotions in mind, support more engaging and effective presentations.

[0544] As a concrete example, when a user passionately introduces a new eco-bag, this system generates a trendy and energetic tour. An example of a prompt given to the generating AI model might be: "While the user is excitedly introducing the new eco-bag, analyze their emotions and generate a trendy and energetic tour."

[0545] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0546] Step 1:

[0547] The user uses a device to input information in natural language. This input is either in the form of speech recognition or text input and is sent from the device to the server. During this process, the user's spoken content is captured as digital data.

[0548] Step 2:

[0549] The server uses spaCy to analyze the received natural language data. It extracts important keywords and topics from the input data. This data processing extracts the key points of the information.

[0550] Step 3:

[0551] The server analyzes emotional characteristics from the information extracted using TensorFlow. This process detects the intensity and type of emotion based on the user's statements. The output is the emotional state expressed by the user.

[0552] Step 4:

[0553] The server uses Jinja2 based on the analysis results to determine the visual design of the presentation materials. By applying templates, it generates visual content with a tone and style that matches the desired emotion. The output is a visually optimized document.

[0554] Step 5:

[0555] The server sends the generated presentation materials to the user's terminal. The user can view these materials and make adjustments as needed. The content displayed on the terminal is the final output for the user.

[0556] 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.

[0557] 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.

[0558] 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.

[0559] [Fourth Embodiment]

[0560] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0561] 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.

[0562] 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).

[0563] 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.

[0564] 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.

[0565] 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).

[0566] 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.

[0567] 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.

[0568] 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.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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".

[0573] In implementing this invention, the server efficiently processes natural language information input by the user and constructs a system that automatically generates presentation materials to effectively convey ideas. When a user inputs their thoughts in natural language on a terminal, the terminal transmits that data to the server via the network.

[0574] The server applies natural language processing (NLP) techniques to analyze the natural language data it receives. First, the server preprocesses the input, splitting the text and removing unnecessary information. Then, it uses NLP algorithms to extract key keywords and topics. The server uses these elements to clarify the logical flow of ideas and determine an efficient presentation structure.

[0575] Next, the server begins the process of visualizing the presentation materials based on the determined configuration. The server utilizes a template engine to automatically generate charts, graphs, and slides. It optimizes the information to be presented in a way that is easy for the audience to understand, taking visual effects and design into consideration. Furthermore, the server generates a speech script, which is provided as a guide for the user during the presentation.

[0576] Ultimately, the server sends the generated materials to the user's terminal, allowing the user to review, edit, and use them. For example, if a user inputs an idea about "energy efficiency strategies," the server uses that information to create slides that incorporate relevant statistical data and case studies, and use charts and graphs to make them intuitively understandable. In this way, the user can prepare a fair presentation in which the value of their idea is directly evaluated.

[0577] The following describes the processing flow.

[0578] Step 1:

[0579] The user uses a device to input their ideas in natural language. Once input is complete, the device sends the data to the server.

[0580] Step 2:

[0581] The server receives natural language data sent from the terminal. The received data is first preprocessed, with unnecessary characters being removed and text normalization being performed.

[0582] Step 3:

[0583] The server passes the pre-processed data to a natural language processing (NLP) module, which analyzes the meaning of the text. This involves tokenization, part-of-speech tagging, and syntactic analysis.

[0584] Step 4:

[0585] The server extracts important keywords and topics from the input based on the analysis information obtained by NLP. Topic modeling and keyword extraction algorithms are used for this purpose.

[0586] Step 5:

[0587] The server determines the structure of the presentation materials based on extracted keywords and topics. It consults the template library to formulate the most effective flow of the materials.

[0588] Step 6:

[0589] The server generates visual materials according to the predetermined presentation structure. Using a template engine, it automatically creates slides, charts, and graphs, and optimizes visual effects.

[0590] Step 7:

[0591] The server creates a speech script related to the generated presentation materials, providing the user with instructions and key points for delivering the presentation.

[0592] Step 8:

[0593] The server sends the completed materials and speech script to the user's terminal. The user reviews them, makes any necessary adjustments, and prepares the final version.

[0594] (Example 1)

[0595] 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".

[0596] Creating audiovisual materials efficiently and effectively is difficult for many users, particularly the process of visualizing ideas expressed in natural language, which often requires considerable time and effort. Furthermore, specialized skills are frequently required to create visually clear materials. Therefore, there is a need for a means to easily generate audiovisual materials from natural language and effectively communicate users' ideas.

[0597] 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.

[0598] In this invention, the server includes means for receiving information input from a user in natural language, language processing means for analyzing the received information and extracting key points and important information elements, and plan generation means for determining the structure of audiovisual material based on the extracted information elements. This makes it possible for users to efficiently generate audiovisual material that can be intuitively understood from natural language, even without advanced specialized knowledge.

[0599] A "user" is the entity that uses this system to input information in natural language.

[0600] "Natural language" refers to the language that humans use on a daily basis, that is, a language that is not artificial.

[0601] "Information" refers to the content that users input into the system and that is processed.

[0602] A "receiving device" is a device used by the user to review and edit generated materials.

[0603] "Language processing means" refers to technologies that analyze natural language input and have the function of extracting key points and important information.

[0604] A "plan generation means" is a technology that has the function of determining the logical structure of audiovisual materials from the analyzed information.

[0605] "Document generation means" refers to technology that has the function of automatically creating visual documents based on a predetermined structure.

[0606] A "composition engine" is a general term for technologies used to automatically optimize visual effects and design.

[0607] This invention relates to a system that generates visual presentation materials based on information input in natural language. Specifically, this system operates around three elements: the user, the terminal, and the server.

[0608] The user first uses a terminal to input their ideas and thoughts in natural language. The terminal then transmits the information entered by the user to a server via the network. This process involves data communication using the internet.

[0609] The server is equipped with language processing capabilities for processing the received data. Specifically, it analyzes the input data using natural language processing techniques and extracts key points and important information elements. At this stage, general text processing libraries (e.g., NLTK and spaCy) and generative AI models are utilized.

[0610] Based on the extracted information elements, the server uses a planning generation mechanism to determine the logical structure of the presentation materials. Following this structure, the material generation mechanism automatically generates the visual elements. This process utilizes a structure engine to generate charts and slides. A template engine is also used to automatically optimize visual effects and design.

[0611] The generated presentation materials are sent to the terminal, where the user can review, edit, and use them. To improve user convenience, the server also generates a speech transcript and provides it as a guide during the presentation.

[0612] For example, if a user enters the prompt "Create a presentation on an innovative strategy for energy efficiency," the server will incorporate relevant statistical data and case studies, and generate slides that are intuitively understandable using charts and graphs. In this way, the system can quickly provide high-quality presentation materials to effectively communicate the user's ideas.

[0613] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0614] Step 1:

[0615] The user inputs information using natural language via the device. Specifically, they input prompts such as "plan for improving energy efficiency" via keyboard or voice input. The input information is saved on the device as text data. This data is the first to be processed.

[0616] Step 2:

[0617] The terminal sends the entered text data to the server over the network. Security protocols are applied during this transmission process to protect the confidentiality of the data. Once the transmitted data reaches the server, the next processing step begins.

[0618] Step 3:

[0619] The server analyzes the received text data using language processing tools. Specifically, it uses natural language processing techniques (e.g., text analysis libraries and generative AI models) to divide the text data into phrases and remove noise (unnecessary words and phrases). This process extracts appropriate topics and keywords. The output is a list of the extracted keywords and topics.

[0620] Step 4:

[0621] Based on the extracted keywords and topics, the server uses a planning generation mechanism to determine the structure of the presentation materials. It determines the order in which each topic should be presented, which information is important and should be emphasized, and constructs a logical framework. The output is represented as a structural plan for the entire presentation.

[0622] Step 5:

[0623] The server automatically generates visual materials using a material generation system based on the determined configuration. A template engine is used in this process to design slides corresponding to each topic. Relevant charts and statistical data are incorporated to create visually impactful materials. This output is provided in the format of the final presentation.

[0624] Step 6:

[0625] The server sends the generated presentation materials to the terminal. The user can receive these materials on the terminal and review their contents. Furthermore, they can edit the materials if necessary and use them in the final presentation. The output of this step is an editable document presented to the user.

[0626] (Application Example 1)

[0627] 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".

[0628] In the advertising industry, creating effective presentation materials quickly and efficiently is crucial. However, traditional methods are time-consuming and labor-intensive, and require specialized knowledge to optimize visual effects and design. Furthermore, scriptwriting is necessary to properly convey the presentation content, which can lead to inconsistent materials.

[0629] 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.

[0630] In this invention, the server includes, in an information processing device, means for receiving information input from a user in natural language; natural language processing means for analyzing the received information and extracting key points and important information fragments; plan generation means for determining the structure of presentation materials based on the extracted information fragments; and advertising creation means for automatically generating advertising slides and scripts. This enables the rapid creation of presentation materials for advertising campaigns and the optimization of their visual effects and design.

[0631] An "information processing device" is a device that receives and analyzes data and generates specific results based on that data.

[0632] "Natural language" refers to the language that humans use on a daily basis, and is primarily a means of expressing information in written and conversational forms.

[0633] "Natural language processing" is a technology that uses computers to analyze human language, understand its content, and process it.

[0634] An "information fragment" refers to an important element or unit of data extracted from received data.

[0635] A "plan generation mechanism" is a system that has the function of determining materials and structures for a specific purpose based on extracted information.

[0636] An "advertising creation tool" is a device that has the function of automatically generating presentation materials for advertising campaigns.

[0637] A "slide" is a page or screen used to visually present information in a presentation.

[0638] A "script" refers to a document that contains a written plan or spoken words used during a presentation.

[0639] "Visual effects" are techniques that use visual elements to convey information more effectively.

[0640] "Design" is a general term for anything that is visually organized and laid out to effectively convey information.

[0641] This invention is a system that uses an information processing device to receive natural language input information from a user, analyze it, and automatically generate presentation materials. Specifically, the server receives advertising ideas written in natural language from the user's terminal. The server analyzes this information using natural language processing technology and extracts key points and important information. Natural language processing libraries such as SpaCy and NLTK are applied to this analysis.

[0642] Next, the server automatically generates presentation slides and scripts based on the extracted information. For generating advertising slides and scripts, a template engine utilizing LaTeX and the Google Slides API is used to automatically optimize visual effects and design. Finally, the generated materials are delivered to the user's terminal via the network, allowing the user to review and edit them as needed.

[0643] For example, if a user inputs an idea for a "targeting strategy for a new product," the server uses that information to generate presentation materials based on detailed consumer segmentation and market analysis, creating visually clear slides and a consistent script. In this way, users can prepare an effective advertising campaign presentation in a short amount of time.

[0644] An example of a prompt is, "Please tell us about the objectives and target audience of your advertising campaign. Example: 'Promote new eco-friendly products to young people of Gen Z.'" Based on appropriate idea input in response to this prompt, the system will automatically generate materials.

[0645] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0646] Step 1:

[0647] Users input advertising campaign ideas into their devices using natural language. The input data is formatted by the device and sent to the server. This input includes specific campaign objectives and target audience information.

[0648] Step 2:

[0649] The server analyzes the received natural language data. Using natural language processing libraries (e.g., SpaCy, NLTK), it segments the data and removes unnecessary information. Important keywords and topics are extracted from the input data, and a data structure is generated based on them. The output is structured information containing the key points of the data.

[0650] Step 3:

[0651] The server determines the structure of the presentation materials based on the extracted key points. Using a generative AI model, it plans the effective order and content of the slides while considering the flow of the advertisement. At this stage, it is decided which information will be presented as the climax. The output is a plan of the materials as a draft structure.

[0652] Step 4:

[0653] The server automatically generates charts, graphs, and slides using a template engine (e.g., LaTeX, Google Slides API). A visually appealing design is applied based on the presentation plan. Inputs include the order and theme of the slides, and the output is a completed slide file.

[0654] Step 5:

[0655] The server uses the generated slides to create a speech script. The script includes example prompts and guides the flow of speech during the presentation. The output is the script to be used during the presentation.

[0656] Step 6:

[0657] The server sends the final output—slides and scripts—to the user's device. The user can then review the received materials and edit or modify them as needed. The output is the presentation material displayed on the user's device.

[0658] 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.

[0659] This invention provides a system that not only processes user natural language input using an information processing device but also combines it with an emotion engine to automatically generate richer presentation materials. To implement this system, the user inputs ideas in natural language via a terminal, and that data is processed on a server.

[0660] First, the natural language data entered by the user on the device is sent to the server. The server receives this data, applies natural language processing techniques, and extracts important keywords and topics from the input. Furthermore, the emotion engine recognizes the user's emotions based on this analyzed data and extracts emotion-based context.

[0661] The server incorporates this emotional information into the structure of the presentation materials, adjusting the tone and style of the materials. Using a template engine, it optimizes the visual design according to the emotional context, so that the generated materials more accurately reflect the user's intent.

[0662] Specifically, for example, if a user is preparing a presentation on "the benefits of introducing a new product within the company," the server extracts information from the natural language content and analyzes the emotions expressed in that language. If the analysis reveals enthusiasm or anticipation, it reflects a more positive design and proactive expression in the document. This makes the document more appealing and leaves a stronger impression on the audience.

[0663] The final generated document is sent to the user's device, where they can review and make adjustments. Because the system even considers emotional nuances, the user's original intent is faithfully reproduced, improving the effectiveness of the presentation.

[0664] The following describes the processing flow.

[0665] Step 1:

[0666] The user inputs their desired idea in natural language via a device. Once the input is complete, the device sends that natural language data to the server.

[0667] Step 2:

[0668] The server receives natural language data sent from the terminal, passes the data to a preprocessing module to remove unnecessary information, and then normalizes the text.

[0669] Step 3:

[0670] The server passes the pre-processed data to a natural language processing (NLP) module. The NLP module analyzes the text and extracts keywords and topics.

[0671] Step 4:

[0672] The server inputs the analyzed data into the emotion engine, which then performs emotion analysis. The emotion engine identifies the user's emotional state from the text input and extracts emotions such as joy, sadness, and surprise.

[0673] Step 5:

[0674] The server matches extracted keywords, topics, and sentiment information to create a structure for the presentation materials. This structure includes the message and overall tone the material aims to convey.

[0675] Step 6:

[0676] The server uses a template engine to adjust the tone and style of the materials and automatically generate visual information such as charts, graphs, and slides. Design selections are made based on emotional information.

[0677] Step 7:

[0678] The server automatically generates a speech script to support the user's presentation, along with the generated presentation materials. The script includes expressions based on emotional information.

[0679] Step 8:

[0680] The server sends the final created materials and scripts to the user's terminal. The user reviews them, makes any necessary corrections, and prepares for the presentation.

[0681] (Example 2)

[0682] 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".

[0683] Conventional presentation material generation systems, while capable of extracting information based on user natural language input, struggled to automatically generate materials that appropriately reflected the user's emotions and intentions. Therefore, maximizing the effectiveness of presentations required manual adjustments, resulting in a time-consuming and laborious process.

[0684] 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.

[0685] In this invention, the server includes means for receiving data input from a user in natural language, natural language processing means for analyzing the received data and extracting key points and important information, and sentiment analysis means for analyzing the user's emotions based on the extracted information and generating an emotion-based context. This enables the automatic generation of presentation materials that reflect the user's intentions and emotions.

[0686] An "information processing device" is a machine or system that can input, process, and output data, and performs specific tasks or calculations according to the user's requests.

[0687] "Natural language" refers to the language that humans use on a daily basis, which develops naturally without being influenced by specific programs or algorithms, and is a linguistic form used for communication.

[0688] "Natural language processing means" refers to techniques or methods for analyzing natural language data such as text and speech, and for extracting its structure and semantic information.

[0689] "Sentiment analysis means" refers to a technology or method for recognizing emotions from user text or other input data and for evaluating or classifying information based on these emotions.

[0690] A "template engine" is a software component that uses reusable templates to dynamically generate content, producing standardized output based on different data.

[0691] This invention is a system that analyzes emotions using data entered by a user in natural language via an information processing device, and automatically generates presentation materials based on that analysis. This system makes it possible to efficiently create engaging materials that reflect the user's intentions and emotions.

[0692] First, the user uses a terminal to input their presentation ideas and content in natural language. For example, they might input, "I want to create a presentation about the economic effects of introducing a new product." This input data is then sent to the server via the network.

[0693] The server analyzes the received data using natural language processing techniques. Specifically, for example, it uses the natural language processing library SpaCy to extract important keywords from the text. The server also utilizes an emotion analysis engine to evaluate the user's emotions. This emotion analysis can utilize technologies such as emotion analysis APIs.

[0694] Based on the analysis results, the server determines the structure of the presentation materials. The design and visual style of the materials are optimized using the Jinja2 template engine. Because the materials are automatically generated based on the prompts entered by the user, the content and tone can accurately reflect the user's intent.

[0695] Through this invention, users can quickly obtain professional presentation materials that perfectly match the emotions and intentions they express, saving them considerable time and effort. Furthermore, by applying various generative AI models, it is expected that user satisfaction will be enhanced and its use in business settings will be further promoted.

[0696] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0697] Step 1:

[0698] The user inputs an idea using natural language on their device. For example, they might input, "I want to create a presentation about the economic effects of introducing a new product." The input data is sent to the server in its original format. Here, the input data is in text form, and the output is the data sent to the server.

[0699] Step 2:

[0700] The server receives natural language data from the terminal. The received data is then analyzed using natural language processing techniques. Specifically, a natural language processing library (e.g., SpaCy) is used to analyze important keywords and topics from the data. The input for this step is text data, and the output is the analyzed information (keywords and topics).

[0701] Step 3:

[0702] The server performs sentiment analysis based on the analyzed information. For example, it might use a sentiment analysis API. Using the already analyzed information as input, it recognizes emotions and outputs the results as data. This output is contextual information that reflects the user's emotions and intentions.

[0703] Step 4:

[0704] The server determines the structure of the presentation materials based on the results of sentiment analysis. A template engine (e.g., Jinja2) is used to structure the materials, optimizing the design according to the sentiment. The input for this step is sentiment-based contextual information, and the output is the presentation materials to which the template has been applied.

[0705] Step 5:

[0706] The server sends the final generated presentation materials to the user's terminal. The user receives the materials on their terminal, reviews the content as needed, and makes any necessary adjustments. Here, the input is the generated materials, and the output is the finalized materials provided to the user.

[0707] Through this processing flow, users can easily obtain presentation materials that reflect emotions and intentions, and effectively utilize them in business and other settings.

[0708] (Application Example 2)

[0709] 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".

[0710] A challenge in generating presentation materials is the inability to adequately reflect the user's intentions and emotions. Furthermore, the generated materials tend to be uniform, making it difficult to leave a strong impression on the audience. Therefore, there is a need to automatically generate individually optimized materials that take into account the emotional characteristics of each user.

[0711] 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.

[0712] In this invention, the server includes means for receiving information input from a user in natural language, natural language processing means for analyzing the received information and extracting key points and important information fragments, and plan generation means for determining the structure of presentation materials based on the extracted information fragments and the user's emotional characteristics. This enables the automatic generation of presentation materials that take the user's emotions into consideration.

[0713] "Information entered in natural language" refers to data in the language format used by users when speaking or writing, and is the subject of processing by the system.

[0714] "Natural language processing means" refers to technologies or devices used by computers to understand and analyze the language that humans use on a daily basis.

[0715] "Emotional characteristics" are data obtained by analyzing the nuances of emotions expressed through a user's language and expressions.

[0716] A "plan generation means" is a function or device that designs how to structure data and present it based on the analyzed information.

[0717] "Material generation means" refers to a function or device that creates specific visual materials according to a predetermined configuration.

[0718] A "template engine" is software or a device that efficiently generates visual materials using pre-prepared design templates.

[0719] The system implementing this invention consists of a user terminal, a server operating on the cloud, and various software components for processing data. The user inputs information in natural language using a smartphone or smart glasses. This information is then transmitted from the user terminal to the server.

[0720] The server uses natural language processing technology developed with Python (e.g., spaCy) to extract important keywords and topics from the received information. At the same time, it analyzes sentiment features using deep learning frameworks such as TensorFlow to understand the user's emotional state.

[0721] Next, the server uses a template engine (e.g., Jinja2) to determine an optimized visual design based on emotion and automatically generate presentation materials. These materials can then be presented to the user, for example, as a virtual tour of a virtual store.

[0722] The generated materials are returned to the user's terminal, where they can visually review and make further adjustments. These materials, generated with the user's emotions in mind, support more engaging and effective presentations.

[0723] As a concrete example, when a user passionately introduces a new eco-bag, this system generates a trendy and energetic tour. An example of a prompt given to the generating AI model might be: "While the user is excitedly introducing the new eco-bag, analyze their emotions and generate a trendy and energetic tour."

[0724] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0725] Step 1:

[0726] The user uses a device to input information in natural language. This input is either in the form of speech recognition or text input and is sent from the device to the server. During this process, the user's spoken content is captured as digital data.

[0727] Step 2:

[0728] The server uses spaCy to analyze the received natural language data. It extracts important keywords and topics from the input data. This data processing extracts the key points of the information.

[0729] Step 3:

[0730] The server analyzes emotional characteristics from the information extracted using TensorFlow. This process detects the intensity and type of emotion based on the user's statements. The output is the emotional state expressed by the user.

[0731] Step 4:

[0732] The server uses Jinja2 based on the analysis results to determine the visual design of the presentation materials. By applying templates, it generates visual content with a tone and style that matches the desired emotion. The output is a visually optimized document.

[0733] Step 5:

[0734] The server sends the generated presentation materials to the user's terminal. The user can view these materials and make adjustments as needed. The content displayed on the terminal is the final output for the user.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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."

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] The following is further disclosed regarding the embodiments described above.

[0757] (Claim 1)

[0758] An information processing device includes means for receiving information input from a user in natural language,

[0759] A natural language processing means for analyzing the received information and extracting key points and important information fragments,

[0760] A plan generation means that determines the structure of the presentation materials based on the extracted information fragments,

[0761] A document generation means that automatically generates charts, slides, and related visual information according to the document structure plan,

[0762] Means for providing the generated materials to the user terminal,

[0763] A system that includes this.

[0764] (Claim 2)

[0765] The system according to claim 1, characterized in that the material generation means includes a speech script generation function for supporting the user's presentation practice.

[0766] (Claim 3)

[0767] The system according to claim 1, characterized in that the material generation means uses a template engine for automatically optimizing visual effects and design.

[0768] "Example 1"

[0769] (Claim 1)

[0770] A means of receiving information entered by the user in natural language,

[0771] Language processing means for analyzing the received information and extracting key points and important information elements,

[0772] A plan generation means for determining the structure of audiovisual materials based on the extracted information elements,

[0773] A document generation means that automatically generates visual elements and related visual information in accordance with the document structure plan,

[0774] Means for providing the generated material to a receiving device,

[0775] A system that includes this.

[0776] (Claim 2)

[0777] The system according to claim 1, characterized in that the material generation means includes a speech script generation function for supporting the user's audiovisual material presentation practice.

[0778] (Claim 3)

[0779] The system according to claim 1, characterized in that the data generation means uses a configuration engine for automatically optimizing visual effects and design.

[0780] "Application Example 1"

[0781] (Claim 1)

[0782] An information processing device includes means for receiving information input from a user in natural language,

[0783] A natural language processing means for analyzing the received information and extracting key points and important information fragments,

[0784] A plan generation means that determines the structure of the presentation materials based on the extracted information fragments,

[0785] A document generation means that automatically generates charts, slides, and related visual information in accordance with the document structure plan,

[0786] Means for providing the generated materials to the user terminal,

[0787] An advertising creation method that automatically generates advertising slides and scripts,

[0788] A system that includes this.

[0789] (Claim 2)

[0790] The system according to claim 1, characterized in that the material generation means includes a function for generating voice guidance text to assist users in practicing advertising campaigns.

[0791] (Claim 3)

[0792] The system according to claim 1, characterized in that the material generation means uses a style engine for automatically optimizing visual effects and design.

[0793] "Example 2 of combining an emotion engine"

[0794] (Claim 1)

[0795] An information processing device includes a means for receiving data input from a user in natural language,

[0796] A natural language processing means for analyzing the received data and extracting key points and important information,

[0797] Based on the extracted information, an emotion analysis means analyzes the user's emotions and generates an emotion-based context.

[0798] A plan generation means that determines the structure of presentation materials and automatically generates visual information using the aforementioned emotion-based context,

[0799] A means for optimizing the visual design using a template engine, in accordance with the aforementioned document structure plan,

[0800] Means for providing the generated materials to the user terminal,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, characterized in that the material generation means includes a function to adjust visual effects and style that reflect the user's intent.

[0804] (Claim 3)

[0805] The system according to claim 1, characterized in that the material generation means includes a function to generate prompt sentences for dynamically optimizing presentation materials based on the results of user sentiment analysis.

[0806] "Application example 2 when combining with an emotional engine"

[0807] (Claim 1)

[0808] A means of receiving information entered by the user in natural language,

[0809] A natural language processing means for analyzing the received information and extracting key points and important information fragments,

[0810] A plan generation means that determines the structure of the presentation materials based on the extracted information fragments and the user's emotional characteristics,

[0811] A document generation means that automatically generates charts, slides, and related visual information in accordance with the document structure plan, while performing tone adjustments based on emotion.

[0812] Means for providing the generated materials to the user terminal,

[0813] A system that includes this.

[0814] (Claim 2)

[0815] The system according to claim 1, characterized in that the material generation means includes a function for generating virtual experience materials based on user sentiment analysis.

[0816] (Claim 3)

[0817] The system according to claim 1, characterized in that the material generation means uses a template engine for automatically optimizing visual effects and design based on the user's emotional state. [Explanation of Symbols]

[0818] 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. An information processing device includes means for receiving information input from a user in natural language, A natural language processing means for analyzing the received information and extracting key points and important information fragments, A plan generation means that determines the structure of the presentation materials based on the extracted information fragments, A document generation means that automatically generates charts, slides, and related visual information according to the document structure plan, Means for providing the generated materials to the user terminal, A system that includes this.

2. The system according to claim 1, characterized in that the material generation means includes a speech script generation function for supporting the user's presentation practice.

3. The system according to claim 1, characterized in that the material generation means uses a template engine for automatically optimizing visual effects and design.

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