system

Abstract

We provide the system. [Solution] An input device that acquires user input, An information collection device that collects information based on acquired user input, A data analysis device that analyzes the collected information, A generation device that automatically generates content based on analysis results, An evaluation device that evaluates the generated content and verifies its quality, A transmission device that sends the generated content to the user, A content generation 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the field of content creation, the burden of manual work in terms of time and labor is large, and it is a problem to consistently provide high-quality content efficiently. In addition, customization of content according to various styles and formats is also required. It is necessary to automate professional-level data analysis, report creation, article writing, generation of advertising copy, and further production of videos and images. Thereby, it is required to reduce the problems faced by creators and marketing personnel and speed up content production without sacrificing quality.

Means for Solving the Problems

[0005] This invention provides a content generation system that accurately grasps user needs by incorporating an input device that acquires user input. An information gathering device efficiently collects relevant information based on a specified theme, and a data analysis device scientifically and professionally analyzes that information. Furthermore, the generation device is designed to automatically generate high-quality content in various formats, such as articles, reports, advertising messages, videos, or images, using the analysis results. An evaluation device checks the quality of the generated content and provides rapid feedback through a transmission device that delivers the content to the user in an optimal state. This significantly improves the efficiency and consistency of content creation.

[0006] "User input" refers to information provided by the user, such as themes, formats, and target audiences necessary for content creation.

[0007] An "input device" is an interface or hardware used to obtain information from a user.

[0008] An "information gathering device" is a device that collects information related to a specified theme from the internet or an internal database.

[0009] A "data analysis device" is a device that analyzes collected information from a professional perspective and extracts useful insights and results.

[0010] A "generator" is a device that automatically creates content based on analysis results, and generates articles, reports, advertising messages, videos, or images.

[0011] An "evaluation device" is a device that checks the quality of generated content and evaluates whether it meets the standards.

[0012] A "transmission device" is a device that transmits data to a terminal in order to provide the generated content to the user.

[0013] "Content" refers to the format of information that is generated, and includes articles, reports, advertising messages, videos, images, and so on. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for carrying out the invention

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention provides a system that allows users to efficiently generate and customize content. Specific embodiments of this system are described below.

[0036] The process begins when the user operates a device and inputs the type of content, theme, and target audience they wish to generate. The device retrieves this information and sends it to the server. Upon receiving a request from the user, the server uses an information gathering device to collect data related to the specified theme from the internet and internal databases. The collected information is processed to filter out irrelevant data and extract only the necessary data. Next, the server uses a data analysis device to analyze the collected information. Through this analysis, data trends and patterns are identified, and useful insights are extracted.

[0037] Next, the server uses a generation device to automatically generate content based on the analysis results. For example, if a user requests an article, the server organizes the information and constructs the text. In the case of reports, it generates content incorporating advanced analysis results based on the data. For advertising messages, it creates catchy slogans that appeal to the target audience, and for videos and images, it generates dynamic visual content.

[0038] After the content is generated, the server uses an evaluation device to assess its quality. This evaluation is based on criteria such as grammatical accuracy, content consistency, and visual appeal. If the evaluation determines that the quality does not meet the standards, the content is regenerated or modified. The generated high-quality content is then sent to the terminal and provided to the user.

[0039] For example, if a user specifies that they want to create an article about next-generation AI technology, the server can gather relevant information and automatically generate an article that includes the latest technological advancements in that field. Similarly, if an advertising agency inputs that they want to create an advertising message for a new product, the server will provide creative copy tailored to the target market. In this way, the system can meet a wide variety of content generation needs.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user requests content generation via their device. Here, the user enters details about the type of content they want to generate (e.g., article, report), theme, target audience, and required format. The device receives this information and forms the request data.

[0043] Step 2:

[0044] The terminal sends the formed request data to the server. The server analyzes the received data and understands the user's request. Based on this, it prepares for the next information gathering step.

[0045] Step 3:

[0046] The server uses information gathering devices to collect relevant information based on specified themes and keywords from the internet and internal databases. In this process, the server prioritizes obtaining information from reliable sources.

[0047] Step 4:

[0048] The collected information is organized on a server, and irrelevant data is removed. The information is formatted for data analysis and processed as needed. At this stage, the information is filtered, and highly useful data is extracted.

[0049] Step 5:

[0050] The server uses data analysis equipment to analyze the organized information. This analysis includes trend discovery, relevance assessment, and statistical analysis. Ultimately, key data points for content generation are identified.

[0051] Step 6:

[0052] The server activates the generation device and automatically generates the specified type of content (articles, reports, advertising messages, videos, or images) based on the data analysis results. The generation process is customized according to user instructions.

[0053] Step 7:

[0054] The server uses evaluation equipment to check the content to ensure its quality. This process evaluates grammar, content consistency, visual quality, and suitability for the target audience.

[0055] Step 8:

[0056] The server modifies or regenerates the content if necessary based on the evaluation results. If the criteria are met, the content is ultimately sent to the terminal.

[0057] Step 9:

[0058] The device provides the received content to the user. The user makes adjustments as needed, and the content is finally ready to be used.

[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] Conventional digital content generation systems struggle to respond quickly and flexibly to the diverse content requests of users. Furthermore, the consistency of generated content quality and the provision of optimized formats are insufficient, leading to a need for improved user satisfaction. To address these issues, the development of a system that enables more accurate information gathering, analysis, and quality evaluation is crucial.

[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 acquiring user input, means for collecting data based on the acquired user input, and means for analyzing the collected data. This enables the automatic generation of high-quality digital content that meets the diverse needs of users, and the rapid provision of such content.

[0064] "User input" refers to instructions and data that the system receives from the user, and includes content type, theme, target audience, etc.

[0065] "Means" refers to methods or technical components used to achieve a specific objective.

[0066] "Data" refers to the collection of information that a system gathers using information gathering devices, and this includes information obtained from the internet and internal databases.

[0067] "Digital content" refers to information expressed in electronic form, and includes documents, reports, promotional messages, visual content, or images.

[0068] "Quality standards" refer to the criteria that generated content must meet, and include grammatical accuracy, content consistency, and visual appeal.

[0069] This invention is a system that efficiently generates diverse digital content based on user input. This makes it possible to quickly provide high-quality content that meets user needs.

[0070] The user inputs the type, theme, and target audience of the digital content they wish to generate into the system using their device. The device organizes the input information as packet data and sends it to the server using a secure communication protocol. Personal computers and mobile devices are commonly used as this device.

[0071] The server activates an information gathering device based on data sent by the user. This device uses, for example, web crawlers and scraping software to collect necessary information from the internet and a dedicated internal database. The collected data is then analyzed by a data analysis device. This analysis utilizes data analysis libraries such as Pandas and NumPy to identify data trends and relationships.

[0072] By using a generative AI model, the server can automatically generate optimal digital content from the analysis results. For example, if a user requests a news article, it will organize the information and generate a coherent text. The generated content is checked by an algorithm that evaluates quality, and is only provided to the user if it meets the criteria.

[0073] For example, if a user inputs "I want to create an article about next-generation AI technology," the server will gather relevant information and automatically generate an article that reflects the latest technological trends. Similarly, if an advertising agency requests "I want to create an advertising message for a new product," the server will generate creative copy tailored to the target market.

[0074] Examples of prompt statements include:

[0075] "Requesting article generation on next-generation AI technology, including the latest technological advancements."

[0076] "We request the creation of creative advertising messages for a new product. The target market is millennials."

[0077] Thus, the system of the present invention can quickly and accurately generate and evaluate the quality of digital content that meets the diverse needs of users.

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

[0079] Step 1:

[0080] The user inputs the type, theme, and target audience of the digital content they wish to generate through the device. The device receives this user input and formats it as a data packet. The input contains detailed content information, and the output is the formatted data packet.

[0081] Step 2:

[0082] The terminal sends a formatted data packet to the server. For security reasons, the HTTPS protocol is used to ensure secure communication during transmission. The input is the data packet on the terminal, and the output is the data transferred to the server.

[0083] Step 3:

[0084] The server activates an information gathering device based on the data it receives. Using automated web crawlers and scraping tools, it collects data related to a specified theme from the internet and internal databases. The input is the user's request data, and the output is the associated dataset.

[0085] Step 4:

[0086] The server analyzes the collected data using a data analysis tool. Data analysis libraries such as Pandas and NumPy are used to analyze data trends and patterns. The input is the collected dataset, and the output is trends and key insights.

[0087] Step 5:

[0088] The server uses an AI model to automatically generate digital content based on the analysis results. Natural language processing technology is utilized to create text and advertising messages tailored to user specifications. The input is the data analysis results, and the output is the generated digital content.

[0089] Step 6:

[0090] The server evaluates the quality of the generated content using an evaluation device. Evaluation criteria include grammatical accuracy, content consistency, and visual appeal, and regeneration or modification is performed as needed. The input is the generated content, and the output is the evaluation result.

[0091] Step 7:

[0092] The server sends evaluated, high-quality content to the terminal. Security is ensured again during transmission using the HTTPS protocol. The input is the evaluated content, and the output is the content displayed on the user's terminal.

[0093] Step 8:

[0094] The device provides the received content to the user, who then reviews the content. The user can provide feedback and take further action as needed based on the received content. The input is the content from the server, and the output is the content information presented to the user.

[0095] (Application Example 1)

[0096] 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."

[0097] Traditional content generation systems required significant time and effort to meet diverse user demands, making it difficult to respond quickly to individual needs. Furthermore, the generated content often failed to meet user expectations, requiring optimization. Additionally, there was a lack of efficient means to deliver content based on specific categories.

[0098] 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.

[0099] In this invention, the server includes input means for acquiring user input, information gathering means for collecting information, and data analysis means for analyzing the collected information. This enables the automatic generation and distribution of highly optimized content based on information selected by the user.

[0100] An "input means for acquiring user input" is a device that has the function of allowing users to input information through a device.

[0101] An "information gathering means for collecting information" is a device that collects relevant data from external databases or networks based on user input.

[0102] A "data analysis tool for analyzing collected information" is a device that analyzes collected data and extracts meaningful patterns and trends.

[0103] An "automatically generated generation method" is a device that automatically constructs content based on analysis results.

[0104] An "evaluation means for evaluating and confirming quality" is a device that determines the quality of generated content and confirms its quality by comparing it with predetermined standards.

[0105] A "transmission means" is a device that transmits generated content in order to deliver it to the user.

[0106] A "distribution method" is a device equipped with the function to provide optimized content based on categories and information selected by the user.

[0107] To implement this invention, the server introduces an input device for acquiring user input. The user selects categories and keywords of interest using a smartphone or tablet, and information based on these is collected. The collection is performed using a cloud platform (e.g., Amazon Web Services or Google Cloud Platform) and is obtained from a wide range of databases and the internet.

[0108] The server is based on Python and uses libraries such as Pandas and Numpy to filter and integrate data. Furthermore, it utilizes NLTK and Transformers (Hugging Face) for natural language processing to perform data analysis and semantic analysis. This allows it to identify trends and patterns that meet demand and prepare information for generative AI model units.

[0109] By utilizing generative AI models (such as GPT-4®), the server automatically generates content that meets user needs. This generation method emphasizes information structuring and writing fluency, providing valuable content for users. Examples include news articles, visual content, and audio content. The completed content is checked to ensure it meets requirements through quality evaluation. If it does not meet the standards, the server regenerates or modifies the content.

[0110] The generated content is then delivered to the user via a transmission device. Based on the information category selected by the user, the most relevant content is delivered preferentially. This process allows users to receive information curated based on their specific interests.

[0111] For example, if a user selects the theme "Environmental Protection and Technology," this system will collect and organize articles and interviews on the latest technologies and deliver them to the user. Another example of a prompt used when inputting instructions for writing an article into the AI ​​model is, "Create a detailed article on the latest environmental protection technologies, including recent discoveries, developments, and social impacts." This prompt allows the model to accurately create content containing the desired information.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The user uses a terminal to input categories and topics of interest. The terminal sends this information to the server as input data. The main output at this stage is the category data selected by the user.

[0115] Step 2:

[0116] The server uses information gathering tools to collect relevant data from external databases and the internet based on the received category data. The collected data is stored on the server as raw data, such as articles and materials related to that category. The output is this raw data.

[0117] Step 3:

[0118] The server uses data analysis tools to filter and integrate the collected raw data. It formats the data using tools like Pandas and NumPy, removes unnecessary data, extracts trends and patterns, and stores them on the server as analytical data. This is the main output of this step.

[0119] Step 4:

[0120] The server inputs the analysis data into a generation AI model and generates content based on a specified prompt (e.g., "Create an article about the latest environmental protection technologies."). The generated content can be in the form of a document, visual, or audio-based output.

[0121] Step 5:

[0122] The generated content is evaluated to verify its quality. It is reviewed based on evaluation criteria such as AI-powered grammar checks, content consistency, and visual appeal. If the evaluation results do not meet the criteria, the content is regenerated or revised. The output is the evaluated content.

[0123] Step 6:

[0124] Content that ultimately passes the evaluation is sent to the device via a transmission method and provided to the user. The user can then view optimized content of interest on their device. The output of this step is high-quality content accessible to the user.

[0125] 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.

[0126] The system based on this invention automatically generates content that is more in tune with the user's emotions by incorporating an emotion engine into the content generation process for content requested by the user. This system mainly consists of a terminal, a server, and an emotion engine.

[0127] First, the user provides input about the content they want to generate through their device. This input includes the type of content, theme, format, target audience, and information indicating the user's sentiment. After the device receives this information, it sends it to the server as request data.

[0128] The server analyzes the received data to understand information based on the user's requests and emotions. At this time, the emotion engine analyzes the user's emotional information and estimates their emotional state. This emotional state is used as an important parameter during content generation.

[0129] Next, the server uses an information gathering device to collect information related to the specified theme from the internet and internal databases. The collected data is analyzed by a data analysis device to extract insights and trends necessary for content generation. Subsequently, the generation device automatically generates content, taking into account this information and the user's emotional state obtained by the emotion engine. At this time, the tone and style of the content are adjusted to correspond to the user's emotions.

[0130] The generated content is evaluated for quality by an evaluation device. The evaluation process assesses the content not only for grammatical accuracy, consistency, and visual impact, but also from an emotional perspective. Based on the evaluation results, necessary adjustments are made, and only content that meets the standards is ultimately sent to the device.

[0131] For example, if a user requests to create an article to read when they want to relax, and simultaneously inputs a relaxed emotion, the server will collect and analyze relevant information and automatically generate an article containing text and visuals in a relaxed tone. This system, incorporating an emotion engine, enables the provision of personalized content that is more attuned to user needs.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user inputs their emotional state along with a content generation request via their device. Input fields include the type of content to be generated (article, report, etc.), theme, format, target audience, and emotional state (e.g., relaxed, excited, etc.). The device compiles this information to form the request data.

[0135] Step 2:

[0136] The terminal sends the formed request data to the server. The server analyzes the received data and interprets the user's requests and sentiment information.

[0137] Step 3:

[0138] The server uses an emotion engine to analyze the user's input emotional state. This analysis identifies emotional elements that are appropriate for the content the user desires.

[0139] Step 4:

[0140] The server operates an information gathering device to collect information related to specified themes and keywords from online sources and internal databases. The server then filters the information to prioritize data that matches the emotional state.

[0141] Step 5:

[0142] The server analyzes the collected information using a data analysis device, identifying trends and important data points from the collected data. This analysis then forms the foundational data necessary for content generation.

[0143] Step 6:

[0144] The server operates the generation device and automatically generates content in the specified format based on data analysis results and the evaluation of the emotion engine. In particular, the tone and style are adjusted to match the user's emotions.

[0145] Step 7:

[0146] The server uses an evaluation device to perform a quality assessment on the generated content. This assessment includes checking for grammatical accuracy, consistency, and information validity, as well as whether the content is appropriate for the user's emotional state.

[0147] Step 8:

[0148] The server regenerates or modifies the content as needed. Content that meets the criteria is ultimately sent to the device.

[0149] Step 9:

[0150] The device provides the transmitted content to the user. The user reviews the content, makes any necessary final adjustments, and then uses it.

[0151] (Example 2)

[0152] 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".

[0153] Traditional content generation systems fail to adequately address user emotions, resulting in a lack of complete user satisfaction. Furthermore, the tone and style of generated content may not align with the user's desired emotions, leading to an inconsistent user experience.

[0154] 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.

[0155] In this invention, the server includes means for acquiring user input and sending requests containing emotional information; means for providing an emotional analysis engine for analyzing information based on the acquired input and emotional information and estimating the emotional state; and means for collecting and analyzing information based on the analyzed emotional state. This enables the automatic generation of content that is sensitive to the user's emotions.

[0156] "User input" refers to data that includes the type, theme, format, target audience, and user sentiment information necessary for content creation.

[0157] "Emotional information" refers to data that indicates a user's current emotional state and serves as the basis for determining the tone and style of content.

[0158] An "emotion analysis engine" is a program or device that analyzes emotional information contained in user input and estimates the user's emotional state.

[0159] "The Internet and internal databases" are data sources for collecting relevant information necessary for content generation.

[0160] "Generation means" refers to a device or program for automatically generating content based on analysis results and emotional states.

[0161] The "emotional perspective" is a viewpoint used in evaluating content to confirm its relevance to the user's emotions.

[0162] A "prompt" is a series of instructions or input data used during content generation, and is information supplied to the generating AI model.

[0163] This system automatically generates content that resonates with the user's emotions. It primarily consists of a terminal, a server, and an emotion engine. Users input content generation information via the terminal. This user input includes content type, theme, format, target audience, and emotional information. The terminal receives this information and sends it to the server as request data.

[0164] The server analyzes the received data, uses an emotion engine to analyze emotional information in detail, and estimates the user's emotional state. This emotional state becomes a crucial parameter in subsequent content generation. The server uses the internet and internal databases to collect information related to the specified theme. This involves using web scraping techniques and API access.

[0165] Next, the server uses a data analysis device to analyze the collected data. The main analyses include trend analysis and in-depth theme exploration, extracting insights necessary for content generation. The generation device then automatically generates content, taking these analysis results and estimated emotional states into consideration. A generation AI model is used to provide content that is tailored to the user's emotional state in terms of tone and style.

[0166] The generated content undergoes quality checks using an evaluation device, where it is assessed for grammatical accuracy, consistency, visual impact, and emotional appeal. Only content that meets the criteria is edited and then sent to the terminal.

[0167] For example, if a user requests "I want to create an article to read when I want to relax" and enters "relax" as emotion information, the server will gather data related to that theme. For instance, it can collect the latest trends in relaxation methods and ways of spending time, and use a generative AI model to create content with a relaxed tone. One example of a prompt would be, "Please generate an article introducing a list of music suitable for relaxation. Please make the content focus on creating a relaxed mood."

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] Users input content generation information via their devices. This input includes content type, theme, format, target audience, and user sentiment information. The device aggregates this information and sends it to the server as request data. The server receives the input information directly, preparing it for analysis.

[0171] Step 2:

[0172] The server analyzes the received request data. First, it uses an emotion analysis engine to analyze the user's emotional information in detail and estimate their emotional state based on that analysis. At this time, the emotional information is cross-referenced with a database, and relevant emotional parameters are selected based on an emotion such as "relaxed." This emotional state is then used as output in the next information gathering phase.

[0173] Step 3:

[0174] The server uses information gathering devices to collect information related to a specified theme from the internet and internal databases. For example, using API access and web scraping techniques, it might collect the latest travel destination information if the theme is travel. The collected data is structured and supplied as input to a data analysis device.

[0175] Step 4:

[0176] The server analyzes the data collected by the data analysis device. This analysis includes trend analysis, keyword extraction, and relevance evaluation. As a result, insights and key topics necessary for content generation are extracted. These analysis results are then passed as input to the generation device.

[0177] Step 5:

[0178] The generation device automatically generates content using a generation AI model based on the analysis results and estimated emotional state. During this process, the content is adjusted to match the user's emotions in terms of tone and style. The generated content is output and supplied to the evaluation device.

[0179] Step 6:

[0180] The generated content undergoes quality evaluation using an evaluation system. Evaluation is conducted based on grammatical accuracy, consistency, visual impact, and emotional appeal. Only content that meets the evaluation criteria is deemed to require revision, and adjustments are made as needed.

[0181] Step 7:

[0182] Content that passes the evaluation is sent to the device as the final version. Users can then review the final content received on their device and use it according to their purpose.

[0183] (Application Example 2)

[0184] 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."

[0185] In today's information-saturated society, users find it difficult to find content that suits their emotional state. Furthermore, existing content generation systems often fail to adequately adjust tone and style to match user emotions, highlighting the need for improvements to enhance user satisfaction.

[0186] 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.

[0187] In this invention, the server includes an input means for acquiring user emotional information, an information gathering means for collecting information based on the acquired user input and emotional information, and a data analysis means for analyzing the collected information and estimating the emotional state based on the user's emotions. This enables the automatic generation of content with a tone and style adjusted according to the user's emotional state.

[0188] "User emotional information" refers to data that users input indicating their own emotional state, and is reflected when the emotional engine generates content.

[0189] An "input device" is a device that allows a user to provide the system with the information and emotional information necessary for content creation.

[0190] An "information gathering device" is a device that obtains relevant information from the internet or databases based on a specified theme or user request.

[0191] A "data analysis tool" is a device that analyzes collected information and user sentiment data to extract important trends and insights and estimate emotional states.

[0192] A "generation method" is a device for automatically creating content while adjusting the tone and style based on analysis results and the user's emotional state.

[0193] An "evaluation tool" is a device used to verify the quality and emotional relevance of generated content and to confirm whether it meets the necessary evaluation criteria.

[0194] "Transmission means" refers to communication devices used to deliver the final evaluated content to the user.

[0195] The system used to implement this application has the functionality to automatically generate optimal content based on the user's emotional information. Specifically, it starts by receiving emotional information and content requests entered by the user from a device such as a smartphone. The device then sends this information to the server as request data.

[0196] The server uses analysis software such as IBM Watson® and Google Cloud Natural Language API as its emotion engine to analyze user sentiment information. This analysis estimates the user's emotional state and reflects it in the next content generation. At this stage, technologies such as Elasticsearch® and Apache® Kafka are used as information gathering tools to collect relevant information from databases and the internet. The collected data is analyzed to extract insights and trends.

[0197] Next, content generation takes place using a generative AI model. Natural language generation software such as OpenAI® GPT-3® can automatically generate content with adjusted tone and style while reflecting the user's emotional state. In this generation process, the context and style of the text are adjusted to best suit the user's emotional state.

[0198] The generated content is quality-checked using evaluation tools such as the Grammarly API and Hemingway Editor. This evaluation verifies that the content is appropriate for the user's emotional state and that grammar and consistency are maintained. Finally, the evaluated and revised content is delivered to the user via their device.

[0199] This system allows users to easily enjoy personalized content tailored to their emotional state. For example, when a user feels like relaxing, it can generate and suggest a music playlist or article that matches that feeling. An example of a prompt to the generation AI model would be, "Please create a music playlist that matches my relaxed mood. Please provide a list of song titles and artists, along with genre suggestions."

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The user uses their smartphone's input method to enter the desired content type and their emotional information. This input includes an interface for selecting emotions. The entered data is sent from the device to the server as request data for the next processing step.

[0203] Step 2:

[0204] The server analyzes the received request data and interprets the user's content request and sentiment information. At this stage, the server activates the sentiment engine and uses analysis software such as IBM Watson or Google Cloud Natural Language API to estimate the user's sentiment state and retrieve it as data.

[0205] Step 3:

[0206] The server uses information gathering tools to collect relevant information based on user requests from databases and the internet. This information includes news articles, music lists, image data, and other materials for content generation. The collected data is organized and integrated using Elasticsearch or Apache Kafka.

[0207] Step 4:

[0208] The server uses data analysis tools to analyze the collected information and extract trends and insights. Here, data calculations are performed to evaluate how the collected data matches the user's emotional state. These analysis results are then used as parameters for content generation.

[0209] Step 5:

[0210] The server launches the OpenAI GPT-3 generative AI model and automatically generates content based on acquired emotional states and analysis data. During generation, prompts are used to adjust the tone and style, creating content that matches the user's emotional state. An example of a prompt is, "Please create a music playlist that matches my relaxed mood. Please provide a list of song titles and artists, along with genre suggestions."

[0211] Step 6:

[0212] The generated content undergoes quality checks using server-side evaluation tools. This process involves evaluation of grammatical accuracy, content consistency, and emotional relevance using tools such as the Grammarly API and Hemingway Editor, with corrections made if necessary.

[0213] Step 7:

[0214] Finally, the evaluated and revised content is sent from the server to the device. Through the device, the user receives, views, or uses personalized content tailored to their emotions.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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".

[0231] This invention provides a system that allows users to efficiently generate and customize content. Specific embodiments of this system are described below.

[0232] The process begins when the user operates a device and inputs the type of content, theme, and target audience they wish to generate. The device retrieves this information and sends it to the server. Upon receiving a request from the user, the server uses an information gathering device to collect data related to the specified theme from the internet and internal databases. The collected information is processed to filter out irrelevant data and extract only the necessary data. Next, the server uses a data analysis device to analyze the collected information. Through this analysis, data trends and patterns are identified, and useful insights are extracted.

[0233] Next, the server uses a generation device to automatically generate content based on the analysis results. For example, if a user requests an article, the server organizes the information and constructs the text. In the case of reports, it generates content incorporating advanced analysis results based on the data. For advertising messages, it creates catchy slogans that appeal to the target audience, and for videos and images, it generates dynamic visual content.

[0234] After the content is generated, the server uses an evaluation device to assess its quality. This evaluation is based on criteria such as grammatical accuracy, content consistency, and visual appeal. If the evaluation determines that the quality does not meet the standards, the content is regenerated or modified. The generated high-quality content is then sent to the terminal and provided to the user.

[0235] For example, if a user specifies that they want to create an article about next-generation AI technology, the server can gather relevant information and automatically generate an article that includes the latest technological advancements in that field. Similarly, if an advertising agency inputs that they want to create an advertising message for a new product, the server will provide creative copy tailored to the target market. In this way, the system can meet a wide variety of content generation needs.

[0236] The following describes the processing flow.

[0237] Step 1:

[0238] The user requests content generation via their device. Here, the user enters details about the type of content they want to generate (e.g., article, report), theme, target audience, and required format. The device receives this information and forms the request data.

[0239] Step 2:

[0240] The terminal sends the formed request data to the server. The server analyzes the received data and understands the user's request. Based on this, it prepares for the next information gathering step.

[0241] Step 3:

[0242] The server uses information gathering devices to collect relevant information based on specified themes and keywords from the internet and internal databases. In this process, the server prioritizes obtaining information from reliable sources.

[0243] Step 4:

[0244] The collected information is organized on a server, and irrelevant data is removed. The information is formatted for data analysis and processed as needed. At this stage, the information is filtered, and highly useful data is extracted.

[0245] Step 5:

[0246] The server uses data analysis equipment to analyze the organized information. This analysis includes trend discovery, relevance assessment, and statistical analysis. Ultimately, key data points for content generation are identified.

[0247] Step 6:

[0248] The server activates the generation device and automatically generates the specified type of content (articles, reports, advertising messages, videos, or images) based on the data analysis results. The generation process is customized according to user instructions.

[0249] Step 7:

[0250] The server uses evaluation equipment to check the content to ensure its quality. This process evaluates grammar, content consistency, visual quality, and suitability for the target audience.

[0251] Step 8:

[0252] The server modifies or regenerates the content if necessary based on the evaluation results. If the criteria are met, the content is ultimately sent to the terminal.

[0253] Step 9:

[0254] The device provides the received content to the user. The user makes adjustments as needed, and the content is finally ready to be used.

[0255] (Example 1)

[0256] 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."

[0257] Conventional digital content generation systems struggle to respond quickly and flexibly to the diverse content requests of users. Furthermore, the consistency of generated content quality and the provision of optimized formats are insufficient, leading to a need for improved user satisfaction. To address these issues, the development of a system that enables more accurate information gathering, analysis, and quality evaluation is crucial.

[0258] 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.

[0259] In this invention, the server includes means for acquiring user input, means for collecting data based on the acquired user input, and means for analyzing the collected data. This enables the automatic generation of high-quality digital content that meets the diverse needs of users, and the rapid provision of such content.

[0260] "User input" refers to instructions and data that the system receives from the user, and includes content type, theme, target audience, etc.

[0261] "Means" refers to methods or technical components used to achieve a specific objective.

[0262] "Data" refers to the collection of information that a system gathers using information gathering devices, and this includes information obtained from the internet and internal databases.

[0263] "Digital content" refers to information expressed in electronic form, and includes documents, reports, promotional messages, visual content, or images.

[0264] "Quality standards" refer to the criteria that generated content must meet, and include grammatical accuracy, content consistency, and visual appeal.

[0265] This invention is a system that efficiently generates diverse digital content based on user input. This makes it possible to quickly provide high-quality content that meets user needs.

[0266] The user inputs the type, theme, and target audience of the digital content they wish to generate into the system using their device. The device organizes the input information as packet data and sends it to the server using a secure communication protocol. Personal computers and mobile devices are commonly used as this device.

[0267] The server activates an information gathering device based on data sent by the user. This device uses, for example, web crawlers and scraping software to collect necessary information from the internet and a dedicated internal database. The collected data is then analyzed by a data analysis device. This analysis utilizes data analysis libraries such as Pandas and NumPy to identify data trends and relationships.

[0268] By using a generative AI model, the server can automatically generate optimal digital content from the analysis results. For example, if a user requests a news article, it will organize the information and generate a coherent text. The generated content is checked by an algorithm that evaluates quality, and is only provided to the user if it meets the criteria.

[0269] For example, if a user inputs "I want to create an article about next-generation AI technology," the server will gather relevant information and automatically generate an article that reflects the latest technological trends. Similarly, if an advertising agency requests "I want to create an advertising message for a new product," the server will generate creative copy tailored to the target market.

[0270] Examples of prompt statements include:

[0271] "Requesting article generation on next-generation AI technology, including the latest technological advancements."

[0272] "We request the creation of creative advertising messages for a new product. The target market is millennials."

[0273] Thus, the system of the present invention can quickly and accurately generate and evaluate the quality of digital content that meets the diverse needs of users.

[0274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0275] Step 1:

[0276] The user inputs the type, theme, and target audience of the digital content they wish to generate through the device. The device receives this user input and formats it as a data packet. The input contains detailed content information, and the output is the formatted data packet.

[0277] Step 2:

[0278] The terminal sends a formatted data packet to the server. For security reasons, the HTTPS protocol is used to ensure secure communication during transmission. The input is the data packet on the terminal, and the output is the data transferred to the server.

[0279] Step 3:

[0280] The server activates an information gathering device based on the data it receives. Using automated web crawlers and scraping tools, it collects data related to a specified theme from the internet and internal databases. The input is the user's request data, and the output is the associated dataset.

[0281] Step 4:

[0282] The server analyzes the collected data using a data analysis tool. Data analysis libraries such as Pandas and NumPy are used to analyze data trends and patterns. The input is the collected dataset, and the output is trends and key insights.

[0283] Step 5:

[0284] The server uses the generated AI model to automatically generate digital content based on the analysis results. For the generation, natural language processing technology is utilized to create texts and advertising messages according to the content specified by the user. The input is the result of data analysis, and the output is the generated digital content.

[0285] Step 6:

[0286] The server evaluates the quality of the generated content with an evaluation device. The evaluation criteria include grammatical accuracy, content consistency, and visual appeal, and regeneration and correction are performed as necessary. The input is the generated content, and the output is the evaluation result.

[0287] Step 7:

[0288] The server transmits the evaluated high-quality content to the terminal. During transmission, the HTTPS protocol is used again to ensure security. The input is the evaluated content, and the output is the content displayed on the user's terminal.

[0289] Step 8:

[0290] The terminal provides the received content to the user, and the user checks the content. The user can provide feedback and perform further operations as necessary based on the received content. The input is the content from the server, and the output is the content information presented to the user.

[0291] (Application Example 1)

[0292] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0293] Traditional content generation systems required significant time and effort to meet diverse user demands, making it difficult to respond quickly to individual needs. Furthermore, the generated content often failed to meet user expectations, requiring optimization. Additionally, there was a lack of efficient means to deliver content based on specific categories.

[0294] 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.

[0295] In this invention, the server includes input means for acquiring user input, information gathering means for collecting information, and data analysis means for analyzing the collected information. This enables the automatic generation and distribution of highly optimized content based on information selected by the user.

[0296] An "input means for acquiring user input" is a device that has the function of allowing users to input information through a device.

[0297] An "information gathering means for collecting information" is a device that collects relevant data from external databases or networks based on user input.

[0298] A "data analysis tool for analyzing collected information" is a device that analyzes collected data and extracts meaningful patterns and trends.

[0299] An "automatically generated generation method" is a device that automatically constructs content based on analysis results.

[0300] An "evaluation means for evaluating and confirming quality" is a device that determines the quality of generated content and confirms its quality by comparing it with predetermined standards.

[0301] A "transmission means" is a device that transmits generated content in order to deliver it to the user.

[0302] A "distribution method" is a device equipped with the function to provide optimized content based on categories and information selected by the user.

[0303] To implement this invention, the server introduces an input device for acquiring user input. The user selects categories and keywords of interest using a smartphone or tablet, and information based on these is collected. The collection is performed using a cloud platform (e.g., Amazon Web Services or Google Cloud Platform) and is obtained from a wide range of databases and the internet.

[0304] The server is based on Python and uses libraries such as Pandas and Numpy to filter and integrate data. Furthermore, it utilizes NLTK and Transformers (Hugging Face) for natural language processing to perform data analysis and semantic analysis. This allows it to identify trends and patterns that meet demand and prepare information for generative AI model units.

[0305] By utilizing generative AI models (such as GPT-4), the server automatically generates content that meets user needs. This generation method emphasizes information structuring and writing fluency, providing valuable content for users. Examples include news articles, visual content, and audio content. The completed content is checked to ensure it meets requirements through quality evaluation mechanisms. If it does not meet the standards, the server regenerates or modifies the content.

[0306] The generated content is then delivered to the user via a transmission device. Based on the information category selected by the user, the most relevant content is delivered preferentially. This process allows users to receive information curated based on their specific interests.

[0307] As a specific example, when a user selects the theme of "environmental protection and technology", this system collects and organizes articles and interviews related to the latest technologies and delivers them to the user. Also, as an example of a prompt sentence when inputting instructions to write an article into the generation AI model, there is "Please create a detailed article about the latest environmental protection technologies. Please include recent discoveries, progress, social impacts, etc." With this prompt, the model can accurately create content containing the targeted information.

[0308] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0309] Step 1:

[0310] The user uses the terminal to input categories or topics of interest. The terminal sends this information as input data to the server. The main output at this stage is the category data selected by the user.

[0311] Step 2:

[0312] Based on the received category data, the server uses information collection means to collect relevant data from external databases or the Internet. The collected data is accumulated on the server as raw data such as articles and materials related to that category. The output is this raw data.

[0313] Step 3:

[0314] The server uses the collected raw data to perform filtering and integration with data analysis means. By arranging the data format using Pandas and Numpy and eliminating unnecessary data, trends and patterns are extracted and saved on the server as analysis data. This is the main output of this step.

[0315] Step 4:

[0316] The server inputs the analysis data into a generation AI model and generates content based on a specified prompt (e.g., "Create an article about the latest environmental protection technologies."). The generated content can be in the form of a document, visual, or audio-based output.

[0317] Step 5:

[0318] The generated content is evaluated to verify its quality. It is reviewed based on evaluation criteria such as AI-powered grammar checks, content consistency, and visual appeal. If the evaluation results do not meet the criteria, the content is regenerated or revised. The output is the evaluated content.

[0319] Step 6:

[0320] Content that ultimately passes the evaluation is sent to the device via a transmission method and provided to the user. The user can then view optimized content of interest on their device. The output of this step is high-quality content accessible to the user.

[0321] 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.

[0322] The system based on this invention automatically generates content that is more in tune with the user's emotions by incorporating an emotion engine into the content generation process for content requested by the user. This system mainly consists of a terminal, a server, and an emotion engine.

[0323] First, the user provides input about the content they want to generate through their device. This input includes the type of content, theme, format, target audience, and information indicating the user's sentiment. After the device receives this information, it sends it to the server as request data.

[0324] The server analyzes the received data to understand information based on the user's requests and emotions. At this time, the emotion engine analyzes the user's emotional information and estimates their emotional state. This emotional state is used as an important parameter during content generation.

[0325] Next, the server uses an information gathering device to collect information related to the specified theme from the internet and internal databases. The collected data is analyzed by a data analysis device to extract insights and trends necessary for content generation. Subsequently, the generation device automatically generates content, taking into account this information and the user's emotional state obtained by the emotion engine. At this time, the tone and style of the content are adjusted to correspond to the user's emotions.

[0326] The generated content is evaluated for quality by an evaluation device. The evaluation process assesses the content not only for grammatical accuracy, consistency, and visual impact, but also from an emotional perspective. Based on the evaluation results, necessary adjustments are made, and only content that meets the standards is ultimately sent to the device.

[0327] For example, if a user requests to create an article to read when they want to relax, and simultaneously inputs a relaxed emotion, the server will collect and analyze relevant information and automatically generate an article containing text and visuals in a relaxed tone. This system, incorporating an emotion engine, enables the provision of personalized content that is more attuned to user needs.

[0328] The following describes the processing flow.

[0329] Step 1:

[0330] The user inputs their emotional state along with a content generation request via their device. Input fields include the type of content to be generated (article, report, etc.), theme, format, target audience, and emotional state (e.g., relaxed, excited, etc.). The device compiles this information to form the request data.

[0331] Step 2:

[0332] The terminal sends the formed request data to the server. The server analyzes the received data and interprets the user's requests and sentiment information.

[0333] Step 3:

[0334] The server uses an emotion engine to analyze the user's input emotional state. This analysis identifies emotional elements that are appropriate for the content the user desires.

[0335] Step 4:

[0336] The server operates an information gathering device to collect information related to specified themes and keywords from online sources and internal databases. The server then filters the information to prioritize data that matches the emotional state.

[0337] Step 5:

[0338] The server analyzes the collected information using a data analysis device, identifying trends and important data points from the collected data. This analysis then forms the foundational data necessary for content generation.

[0339] Step 6:

[0340] The server operates the generation device and automatically generates content in the specified format based on data analysis results and the evaluation of the emotion engine. In particular, the tone and style are adjusted to match the user's emotions.

[0341] Step 7:

[0342] The server uses an evaluation device to perform a quality assessment on the generated content. This assessment includes checking for grammatical accuracy, consistency, and information validity, as well as whether the content is appropriate for the user's emotional state.

[0343] Step 8:

[0344] The server regenerates or modifies the content as needed. Content that meets the criteria is ultimately sent to the device.

[0345] Step 9:

[0346] The device provides the transmitted content to the user. The user reviews the content, makes any necessary final adjustments, and then uses it.

[0347] (Example 2)

[0348] 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".

[0349] Traditional content generation systems fail to adequately address user emotions, resulting in a lack of complete user satisfaction. Furthermore, the tone and style of generated content may not align with the user's desired emotions, leading to an inconsistent user experience.

[0350] 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.

[0351] In this invention, the server includes means for acquiring user input and sending requests containing emotional information; means for providing an emotional analysis engine for analyzing information based on the acquired input and emotional information and estimating the emotional state; and means for collecting and analyzing information based on the analyzed emotional state. This enables the automatic generation of content that is sensitive to the user's emotions.

[0352] "User input" refers to data that includes the type, theme, format, target audience, and user sentiment information necessary for content creation.

[0353] "Emotional information" refers to data that indicates a user's current emotional state and serves as the basis for determining the tone and style of content.

[0354] An "emotion analysis engine" is a program or device that analyzes emotional information contained in user input and estimates the user's emotional state.

[0355] "The Internet and internal databases" are data sources for collecting relevant information necessary for content generation.

[0356] "Generation means" refers to a device or program for automatically generating content based on analysis results and emotional states.

[0357] The "emotional perspective" is a viewpoint used in evaluating content to confirm its relevance to the user's emotions.

[0358] A "prompt" is a series of instructions or input data used during content generation, and is information supplied to the generating AI model.

[0359] This system automatically generates content that resonates with the user's emotions. It primarily consists of a terminal, a server, and an emotion engine. Users input content generation information via the terminal. This user input includes content type, theme, format, target audience, and emotional information. The terminal receives this information and sends it to the server as request data.

[0360] The server analyzes the received data, uses an emotion engine to analyze emotional information in detail, and estimates the user's emotional state. This emotional state becomes a crucial parameter in subsequent content generation. The server uses the internet and internal databases to collect information related to the specified theme. This involves using web scraping techniques and API access.

[0361] Next, the server uses a data analysis device to analyze the collected data. The main analyses include trend analysis and in-depth theme exploration, extracting insights necessary for content generation. The generation device then automatically generates content, taking these analysis results and estimated emotional states into consideration. A generation AI model is used to provide content that is tailored to the user's emotional state in terms of tone and style.

[0362] The generated content undergoes quality checks using an evaluation device, where it is assessed for grammatical accuracy, consistency, visual impact, and emotional appeal. Only content that meets the criteria is edited and then sent to the terminal.

[0363] For example, if a user requests "I want to create an article to read when I want to relax" and enters "relax" as emotion information, the server will gather data related to that theme. For instance, it can collect the latest trends in relaxation methods and ways of spending time, and use a generative AI model to create content with a relaxed tone. One example of a prompt would be, "Please generate an article introducing a list of music suitable for relaxation. Please make the content focus on creating a relaxed mood."

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] Users input content generation information via their devices. This input includes content type, theme, format, target audience, and user sentiment information. The device aggregates this information and sends it to the server as request data. The server receives the input information directly, preparing it for analysis.

[0367] Step 2:

[0368] The server analyzes the received request data. First, it uses an emotion analysis engine to analyze the user's emotional information in detail and estimate their emotional state based on that analysis. At this time, the emotional information is cross-referenced with a database, and relevant emotional parameters are selected based on an emotion such as "relaxed." This emotional state is then used as output in the next information gathering phase.

[0369] Step 3:

[0370] The server uses information gathering devices to collect information related to a specified theme from the internet and internal databases. For example, using API access and web scraping techniques, it might collect the latest travel destination information if the theme is travel. The collected data is structured and supplied as input to a data analysis device.

[0371] Step 4:

[0372] The server analyzes the data collected by the data analysis device. This analysis includes trend analysis, keyword extraction, and relevance evaluation. As a result, insights and key topics necessary for content generation are extracted. These analysis results are then passed as input to the generation device.

[0373] Step 5:

[0374] The generation device automatically generates content using a generation AI model based on the analysis results and estimated emotional state. During this process, the content is adjusted to match the user's emotions in terms of tone and style. The generated content is output and supplied to the evaluation device.

[0375] Step 6:

[0376] The generated content undergoes quality evaluation using an evaluation system. Evaluation is conducted based on grammatical accuracy, consistency, visual impact, and emotional appeal. Only content that meets the evaluation criteria is deemed to require revision, and adjustments are made as needed.

[0377] Step 7:

[0378] Content that passes the evaluation is sent to the device as the final version. Users can then review the final content received on their device and use it according to their purpose.

[0379] (Application Example 2)

[0380] 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 as the "terminal".

[0381] In today's information-saturated society, users find it difficult to find content that suits their emotional state. Furthermore, existing content generation systems often fail to adequately adjust tone and style to match user emotions, highlighting the need for improvements to enhance user satisfaction.

[0382] 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.

[0383] In this invention, the server includes an input means for acquiring user emotional information, an information gathering means for collecting information based on the acquired user input and emotional information, and a data analysis means for analyzing the collected information and estimating the emotional state based on the user's emotions. This enables the automatic generation of content with a tone and style adjusted according to the user's emotional state.

[0384] "User emotional information" refers to data that users input indicating their own emotional state, and is reflected when the emotional engine generates content.

[0385] An "input device" is a device that allows a user to provide the system with the information and emotional information necessary for content creation.

[0386] An "information gathering device" is a device that obtains relevant information from the internet or databases based on a specified theme or user request.

[0387] A "data analysis tool" is a device that analyzes collected information and user sentiment data to extract important trends and insights and estimate emotional states.

[0388] A "generation method" is a device for automatically creating content while adjusting the tone and style based on analysis results and the user's emotional state.

[0389] An "evaluation tool" is a device used to verify the quality and emotional relevance of generated content and to confirm whether it meets the necessary evaluation criteria.

[0390] "Transmission means" refers to communication devices used to deliver the final evaluated content to the user.

[0391] The system used to implement this application has the functionality to automatically generate optimal content based on the user's emotional information. Specifically, it starts by receiving emotional information and content requests entered by the user from a device such as a smartphone. The device then sends this information to the server as request data.

[0392] The server uses analysis software such as IBM Watson or Google Cloud Natural Language API as its emotion engine to analyze user sentiment information. This analysis estimates the user's emotional state and reflects it in the subsequent content generation. At this stage, technologies such as Elasticsearch and Apache Kafka are used as information gathering tools to collect relevant information from databases and the internet. The collected data is analyzed to extract insights and trends.

[0393] Next, content generation takes place using a generative AI model. Natural language generation software such as OpenAI GPT-3 can automatically generate content with adjusted tone and style while reflecting the user's emotional state. In this generation process, the context and style of the text are adjusted to best suit the user's emotional state.

[0394] The generated content is quality-checked using evaluation tools such as the Grammarly API and Hemingway Editor. This evaluation verifies that the content is appropriate for the user's emotional state and that grammar and consistency are maintained. Finally, the evaluated and revised content is delivered to the user via their device.

[0395] This system allows users to easily enjoy personalized content tailored to their emotional state. For example, when a user feels like relaxing, it can generate and suggest a music playlist or article that matches that feeling. An example of a prompt to the generation AI model would be, "Please create a music playlist that matches my relaxed mood. Please provide a list of song titles and artists, along with genre suggestions."

[0396] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0397] Step 1:

[0398] The user uses their smartphone's input method to enter the desired content type and their emotional information. This input includes an interface for selecting emotions. The entered data is sent from the device to the server as request data for the next processing step.

[0399] Step 2:

[0400] The server analyzes the received request data and interprets the user's content request and sentiment information. At this stage, the server activates the sentiment engine and uses analysis software such as IBM Watson or Google Cloud Natural Language API to estimate the user's sentiment state and retrieve it as data.

[0401] Step 3:

[0402] The server uses information gathering tools to collect relevant information based on user requests from databases and the internet. This information includes news articles, music lists, image data, and other materials for content generation. The collected data is organized and integrated using Elasticsearch or Apache Kafka.

[0403] Step 4:

[0404] The server uses data analysis tools to analyze the collected information and extract trends and insights. Here, data calculations are performed to evaluate how the collected data matches the user's emotional state. These analysis results are then used as parameters for content generation.

[0405] Step 5:

[0406] The server launches the OpenAI GPT-3 generative AI model and automatically generates content based on acquired emotional states and analysis data. During generation, prompts are used to adjust the tone and style, creating content that matches the user's emotional state. An example of a prompt is, "Please create a music playlist that matches my relaxed mood. Please provide a list of song titles and artists, along with genre suggestions."

[0407] Step 6:

[0408] The generated content undergoes quality checks using server-side evaluation tools. This process involves evaluation of grammatical accuracy, content consistency, and emotional relevance using tools such as the Grammarly API and Hemingway Editor, with corrections made if necessary.

[0409] Step 7:

[0410] Finally, the evaluated and revised content is sent from the server to the device. Through the device, the user receives, views, or uses personalized content tailored to their emotions.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] [Third Embodiment]

[0415] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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".

[0427] This invention provides a system that allows users to efficiently generate and customize content. Specific embodiments of this system are described below.

[0428] The process begins when the user operates a device and inputs the type of content, theme, and target audience they wish to generate. The device retrieves this information and sends it to the server. Upon receiving a request from the user, the server uses an information gathering device to collect data related to the specified theme from the internet and internal databases. The collected information is processed to filter out irrelevant data and extract only the necessary data. Next, the server uses a data analysis device to analyze the collected information. Through this analysis, data trends and patterns are identified, and useful insights are extracted.

[0429] Next, the server uses a generation device to automatically generate content based on the analysis results. For example, if a user requests an article, the server organizes the information and constructs the text. In the case of reports, it generates content incorporating advanced analysis results based on the data. For advertising messages, it creates catchy slogans that appeal to the target audience, and for videos and images, it generates dynamic visual content.

[0430] After the content is generated, the server uses an evaluation device to assess its quality. This evaluation is based on criteria such as grammatical accuracy, content consistency, and visual appeal. If the evaluation determines that the quality does not meet the standards, the content is regenerated or modified. The generated high-quality content is then sent to the terminal and provided to the user.

[0431] For example, if a user specifies that they want to create an article about next-generation AI technology, the server can gather relevant information and automatically generate an article that includes the latest technological advancements in that field. Similarly, if an advertising agency inputs that they want to create an advertising message for a new product, the server will provide creative copy tailored to the target market. In this way, the system can meet a wide variety of content generation needs.

[0432] The following describes the processing flow.

[0433] Step 1:

[0434] The user requests content generation via their device. Here, the user enters details about the type of content they want to generate (e.g., article, report), theme, target audience, and required format. The device receives this information and forms the request data.

[0435] Step 2:

[0436] The terminal sends the formed request data to the server. The server analyzes the received data and understands the user's request. Based on this, it prepares for the next information gathering step.

[0437] Step 3:

[0438] The server uses information gathering devices to collect relevant information based on specified themes and keywords from the internet and internal databases. In this process, the server prioritizes obtaining information from reliable sources.

[0439] Step 4:

[0440] The collected information is organized on a server, and irrelevant data is removed. The information is formatted for data analysis and processed as needed. At this stage, the information is filtered, and highly useful data is extracted.

[0441] Step 5:

[0442] The server uses data analysis equipment to analyze the organized information. This analysis includes trend discovery, relevance assessment, and statistical analysis. Ultimately, key data points for content generation are identified.

[0443] Step 6:

[0444] The server activates the generation device and automatically generates the specified type of content (articles, reports, advertising messages, videos, or images) based on the data analysis results. The generation process is customized according to user instructions.

[0445] Step 7:

[0446] The server uses evaluation equipment to check the content to ensure its quality. This process evaluates grammar, content consistency, visual quality, and suitability for the target audience.

[0447] Step 8:

[0448] The server modifies or regenerates the content if necessary based on the evaluation results. If the criteria are met, the content is ultimately sent to the terminal.

[0449] Step 9:

[0450] The device provides the received content to the user. The user makes adjustments as needed, and the content is finally ready to be used.

[0451] (Example 1)

[0452] 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."

[0453] Conventional digital content generation systems struggle to respond quickly and flexibly to the diverse content requests of users. Furthermore, the consistency of generated content quality and the provision of optimized formats are insufficient, leading to a need for improved user satisfaction. To address these issues, the development of a system that enables more accurate information gathering, analysis, and quality evaluation is crucial.

[0454] 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.

[0455] In this invention, the server includes means for acquiring user input, means for collecting data based on the acquired user input, and means for analyzing the collected data. This enables the automatic generation of high-quality digital content that meets the diverse needs of users, and the rapid provision of such content.

[0456] "User input" refers to instructions and data that the system receives from the user, and includes content type, theme, target audience, etc.

[0457] "Means" refers to methods or technical components used to achieve a specific objective.

[0458] "Data" refers to the collection of information that a system gathers using information gathering devices, and this includes information obtained from the internet and internal databases.

[0459] "Digital content" refers to information expressed in electronic form, and includes documents, reports, promotional messages, visual content, or images.

[0460] "Quality standards" refer to the criteria that generated content must meet, and include grammatical accuracy, content consistency, and visual appeal.

[0461] This invention is a system that efficiently generates diverse digital content based on user input. This makes it possible to quickly provide high-quality content that meets user needs.

[0462] The user inputs the type, theme, and target audience of the digital content they wish to generate into the system using their device. The device organizes the input information as packet data and sends it to the server using a secure communication protocol. Personal computers and mobile devices are commonly used as this device.

[0463] The server activates an information gathering device based on data sent by the user. This device uses, for example, web crawlers and scraping software to collect necessary information from the internet and a dedicated internal database. The collected data is then analyzed by a data analysis device. This analysis utilizes data analysis libraries such as Pandas and NumPy to identify data trends and relationships.

[0464] By using a generative AI model, the server can automatically generate optimal digital content from the analysis results. For example, if a user requests a news article, it will organize the information and generate a coherent text. The generated content is checked by an algorithm that evaluates quality, and is only provided to the user if it meets the criteria.

[0465] For example, if a user inputs "I want to create an article about next-generation AI technology," the server will gather relevant information and automatically generate an article that reflects the latest technological trends. Similarly, if an advertising agency requests "I want to create an advertising message for a new product," the server will generate creative copy tailored to the target market.

[0466] Examples of prompt statements include:

[0467] "Requesting article generation on next-generation AI technology, including the latest technological advancements."

[0468] "We request the creation of creative advertising messages for a new product. The target market is millennials."

[0469] Thus, the system of the present invention can quickly and accurately generate and evaluate the quality of digital content that meets the diverse needs of users.

[0470] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0471] Step 1:

[0472] The user inputs the type, theme, and target audience of the digital content they wish to generate through the device. The device receives this user input and formats it as a data packet. The input contains detailed content information, and the output is the formatted data packet.

[0473] Step 2:

[0474] The terminal sends a formatted data packet to the server. For security reasons, the HTTPS protocol is used to ensure secure communication during transmission. The input is the data packet on the terminal, and the output is the data transferred to the server.

[0475] Step 3:

[0476] The server activates an information gathering device based on the data it receives. Using automated web crawlers and scraping tools, it collects data related to a specified theme from the internet and internal databases. The input is the user's request data, and the output is the associated dataset.

[0477] Step 4:

[0478] The server analyzes the collected data using a data analysis tool. Data analysis libraries such as Pandas and NumPy are used to analyze data trends and patterns. The input is the collected dataset, and the output is trends and key insights.

[0479] Step 5:

[0480] The server uses an AI model to automatically generate digital content based on the analysis results. Natural language processing technology is utilized to create text and advertising messages tailored to user specifications. The input is the data analysis results, and the output is the generated digital content.

[0481] Step 6:

[0482] The server evaluates the quality of the generated content using an evaluation device. Evaluation criteria include grammatical accuracy, content consistency, and visual appeal, and regeneration or modification is performed as needed. The input is the generated content, and the output is the evaluation result.

[0483] Step 7:

[0484] The server sends evaluated, high-quality content to the terminal. Security is ensured again during transmission using the HTTPS protocol. The input is the evaluated content, and the output is the content displayed on the user's terminal.

[0485] Step 8:

[0486] The device provides the received content to the user, who then reviews the content. The user can provide feedback and take further action as needed based on the received content. The input is the content from the server, and the output is the content information presented to the user.

[0487] (Application Example 1)

[0488] 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."

[0489] Traditional content generation systems required significant time and effort to meet diverse user demands, making it difficult to respond quickly to individual needs. Furthermore, the generated content often failed to meet user expectations, requiring optimization. Additionally, there was a lack of efficient means to deliver content based on specific categories.

[0490] 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.

[0491] In this invention, the server includes input means for acquiring user input, information gathering means for collecting information, and data analysis means for analyzing the collected information. This enables the automatic generation and distribution of highly optimized content based on information selected by the user.

[0492] An "input means for acquiring user input" is a device that has the function of allowing users to input information through a device.

[0493] An "information gathering means for collecting information" is a device that collects relevant data from external databases or networks based on user input.

[0494] A "data analysis tool for analyzing collected information" is a device that analyzes collected data and extracts meaningful patterns and trends.

[0495] An "automatically generated generation method" is a device that automatically constructs content based on analysis results.

[0496] An "evaluation means for evaluating and confirming quality" is a device that determines the quality of generated content and confirms its quality by comparing it with predetermined standards.

[0497] A "transmission means" is a device that transmits generated content in order to deliver it to the user.

[0498] A "distribution method" is a device equipped with the function to provide optimized content based on categories and information selected by the user.

[0499] To implement this invention, the server introduces an input device for acquiring user input. The user selects categories and keywords of interest using a smartphone or tablet, and information based on these is collected. The collection is performed using a cloud platform (e.g., Amazon Web Services or Google Cloud Platform) and is obtained from a wide range of databases and the internet.

[0500] The server is based on Python and uses libraries such as Pandas and Numpy to filter and integrate data. Furthermore, it utilizes NLTK and Transformers (Hugging Face) for natural language processing to perform data analysis and semantic analysis. This allows it to identify trends and patterns that meet demand and prepare information for generative AI model units.

[0501] By utilizing generative AI models (such as GPT-4), the server automatically generates content that meets user needs. This generation method emphasizes information structuring and writing fluency, providing valuable content for users. Examples include news articles, visual content, and audio content. The completed content is checked to ensure it meets requirements through quality evaluation mechanisms. If it does not meet the standards, the server regenerates or modifies the content.

[0502] The generated content is then delivered to the user via a transmission device. Based on the information category selected by the user, the most relevant content is delivered preferentially. This process allows users to receive information curated based on their specific interests.

[0503] For example, if a user selects the theme "Environmental Protection and Technology," this system will collect and organize articles and interviews on the latest technologies and deliver them to the user. Another example of a prompt used when inputting instructions for writing an article into the AI ​​model is, "Create a detailed article on the latest environmental protection technologies, including recent discoveries, developments, and social impacts." This prompt allows the model to accurately create content containing the desired information.

[0504] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0505] Step 1:

[0506] The user uses a terminal to input categories and topics of interest. The terminal sends this information to the server as input data. The main output at this stage is the category data selected by the user.

[0507] Step 2:

[0508] The server uses information gathering tools to collect relevant data from external databases and the internet based on the received category data. The collected data is stored on the server as raw data, such as articles and materials related to that category. The output is this raw data.

[0509] Step 3:

[0510] The server uses data analysis tools to filter and integrate the collected raw data. It formats the data using tools like Pandas and NumPy, removes unnecessary data, extracts trends and patterns, and stores them on the server as analytical data. This is the main output of this step.

[0511] Step 4:

[0512] The server inputs the analysis data into a generation AI model and generates content based on a specified prompt (e.g., "Create an article about the latest environmental protection technologies."). The generated content can be in the form of a document, visual, or audio-based output.

[0513] Step 5:

[0514] The generated content is evaluated to verify its quality. It is reviewed based on evaluation criteria such as AI-powered grammar checks, content consistency, and visual appeal. If the evaluation results do not meet the criteria, the content is regenerated or revised. The output is the evaluated content.

[0515] Step 6:

[0516] Content that ultimately passes the evaluation is sent to the device via a transmission method and provided to the user. The user can then view optimized content of interest on their device. The output of this step is high-quality content accessible to the user.

[0517] 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.

[0518] The system based on this invention automatically generates content that is more in tune with the user's emotions by incorporating an emotion engine into the content generation process for content requested by the user. This system mainly consists of a terminal, a server, and an emotion engine.

[0519] First, the user provides input about the content they want to generate through their device. This input includes the type of content, theme, format, target audience, and information indicating the user's sentiment. After the device receives this information, it sends it to the server as request data.

[0520] The server analyzes the received data to understand information based on the user's requests and emotions. At this time, the emotion engine analyzes the user's emotional information and estimates their emotional state. This emotional state is used as an important parameter during content generation.

[0521] Next, the server uses an information gathering device to collect information related to the specified theme from the internet and internal databases. The collected data is analyzed by a data analysis device to extract insights and trends necessary for content generation. Subsequently, the generation device automatically generates content, taking into account this information and the user's emotional state obtained by the emotion engine. At this time, the tone and style of the content are adjusted to correspond to the user's emotions.

[0522] The generated content is evaluated for quality by an evaluation device. The evaluation process assesses the content not only for grammatical accuracy, consistency, and visual impact, but also from an emotional perspective. Based on the evaluation results, necessary adjustments are made, and only content that meets the standards is ultimately sent to the device.

[0523] For example, if a user requests to create an article to read when they want to relax, and simultaneously inputs a relaxed emotion, the server will collect and analyze relevant information and automatically generate an article containing text and visuals in a relaxed tone. This system, incorporating an emotion engine, enables the provision of personalized content that is more attuned to user needs.

[0524] The following describes the processing flow.

[0525] Step 1:

[0526] The user inputs their emotional state along with a content generation request via their device. Input fields include the type of content to be generated (article, report, etc.), theme, format, target audience, and emotional state (e.g., relaxed, excited, etc.). The device compiles this information to form the request data.

[0527] Step 2:

[0528] The terminal sends the formed request data to the server. The server analyzes the received data and interprets the user's requests and sentiment information.

[0529] Step 3:

[0530] The server uses an emotion engine to analyze the user's input emotional state. This analysis identifies emotional elements that are appropriate for the content the user desires.

[0531] Step 4:

[0532] The server operates an information gathering device to collect information related to specified themes and keywords from online sources and internal databases. The server then filters the information to prioritize data that matches the emotional state.

[0533] Step 5:

[0534] The server analyzes the collected information using a data analysis device, identifying trends and important data points from the collected data. This analysis then forms the foundational data necessary for content generation.

[0535] Step 6:

[0536] The server operates the generation device and automatically generates content in the specified format based on data analysis results and the evaluation of the emotion engine. In particular, the tone and style are adjusted to match the user's emotions.

[0537] Step 7:

[0538] The server uses an evaluation device to perform a quality assessment on the generated content. This assessment includes checking for grammatical accuracy, consistency, and information validity, as well as whether the content is appropriate for the user's emotional state.

[0539] Step 8:

[0540] The server regenerates or modifies the content as needed. Content that meets the criteria is ultimately sent to the device.

[0541] Step 9:

[0542] The device provides the transmitted content to the user. The user reviews the content, makes any necessary final adjustments, and then uses it.

[0543] (Example 2)

[0544] 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."

[0545] Traditional content generation systems fail to adequately address user emotions, resulting in a lack of complete user satisfaction. Furthermore, the tone and style of generated content may not align with the user's desired emotions, leading to an inconsistent user experience.

[0546] 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.

[0547] In this invention, the server includes means for acquiring user input and sending requests containing emotional information; means for providing an emotional analysis engine for analyzing information based on the acquired input and emotional information and estimating the emotional state; and means for collecting and analyzing information based on the analyzed emotional state. This enables the automatic generation of content that is sensitive to the user's emotions.

[0548] "User input" refers to data that includes the type, theme, format, target audience, and user sentiment information necessary for content creation.

[0549] "Emotional information" refers to data that indicates a user's current emotional state and serves as the basis for determining the tone and style of content.

[0550] An "emotion analysis engine" is a program or device that analyzes emotional information contained in user input and estimates the user's emotional state.

[0551] "The Internet and internal databases" are data sources for collecting relevant information necessary for content generation.

[0552] "Generation means" refers to a device or program for automatically generating content based on analysis results and emotional states.

[0553] The "emotional perspective" is a viewpoint used in evaluating content to confirm its relevance to the user's emotions.

[0554] A "prompt" is a series of instructions or input data used during content generation, and is information supplied to the generating AI model.

[0555] This system automatically generates content that resonates with the user's emotions. It primarily consists of a terminal, a server, and an emotion engine. Users input content generation information via the terminal. This user input includes content type, theme, format, target audience, and emotional information. The terminal receives this information and sends it to the server as request data.

[0556] The server analyzes the received data, uses an emotion engine to analyze emotional information in detail, and estimates the user's emotional state. This emotional state becomes a crucial parameter in subsequent content generation. The server uses the internet and internal databases to collect information related to the specified theme. This involves using web scraping techniques and API access.

[0557] Next, the server uses a data analysis device to analyze the collected data. The main analyses include trend analysis and in-depth theme exploration, extracting insights necessary for content generation. The generation device then automatically generates content, taking these analysis results and estimated emotional states into consideration. A generation AI model is used to provide content that is tailored to the user's emotional state in terms of tone and style.

[0558] The generated content undergoes quality checks using an evaluation device, where it is assessed for grammatical accuracy, consistency, visual impact, and emotional appeal. Only content that meets the criteria is edited and then sent to the terminal.

[0559] For example, if a user requests "I want to create an article to read when I want to relax" and enters "relax" as emotion information, the server will gather data related to that theme. For instance, it can collect the latest trends in relaxation methods and ways of spending time, and use a generative AI model to create content with a relaxed tone. One example of a prompt would be, "Please generate an article introducing a list of music suitable for relaxation. Please make the content focus on creating a relaxed mood."

[0560] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0561] Step 1:

[0562] Users input content generation information via their devices. This input includes content type, theme, format, target audience, and user sentiment information. The device aggregates this information and sends it to the server as request data. The server receives the input information directly, preparing it for analysis.

[0563] Step 2:

[0564] The server analyzes the received request data. First, it uses an emotion analysis engine to analyze the user's emotional information in detail and estimate their emotional state based on that analysis. At this time, the emotional information is cross-referenced with a database, and relevant emotional parameters are selected based on an emotion such as "relaxed." This emotional state is then used as output in the next information gathering phase.

[0565] Step 3:

[0566] The server uses information gathering devices to collect information related to a specified theme from the internet and internal databases. For example, using API access and web scraping techniques, it might collect the latest travel destination information if the theme is travel. The collected data is structured and supplied as input to a data analysis device.

[0567] Step 4:

[0568] The server analyzes the data collected by the data analysis device. This analysis includes trend analysis, keyword extraction, and relevance evaluation. As a result, insights and key topics necessary for content generation are extracted. These analysis results are then passed as input to the generation device.

[0569] Step 5:

[0570] The generation device automatically generates content using a generation AI model based on the analysis results and estimated emotional state. During this process, the content is adjusted to match the user's emotions in terms of tone and style. The generated content is output and supplied to the evaluation device.

[0571] Step 6:

[0572] The generated content undergoes quality evaluation using an evaluation system. Evaluation is conducted based on grammatical accuracy, consistency, visual impact, and emotional appeal. Only content that meets the evaluation criteria is deemed to require revision, and adjustments are made as needed.

[0573] Step 7:

[0574] Content that passes the evaluation is sent to the device as the final version. Users can then review the final content received on their device and use it according to their purpose.

[0575] (Application Example 2)

[0576] 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."

[0577] In today's information-saturated society, users find it difficult to find content that suits their emotional state. Furthermore, existing content generation systems often fail to adequately adjust tone and style to match user emotions, highlighting the need for improvements to enhance user satisfaction.

[0578] 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.

[0579] In this invention, the server includes an input means for acquiring user emotional information, an information gathering means for collecting information based on the acquired user input and emotional information, and a data analysis means for analyzing the collected information and estimating the emotional state based on the user's emotions. This enables the automatic generation of content with a tone and style adjusted according to the user's emotional state.

[0580] "User emotional information" refers to data that users input indicating their own emotional state, and is reflected when the emotional engine generates content.

[0581] An "input device" is a device that allows a user to provide the system with the information and emotional information necessary for content creation.

[0582] An "information gathering device" is a device that obtains relevant information from the internet or databases based on a specified theme or user request.

[0583] A "data analysis tool" is a device that analyzes collected information and user sentiment data to extract important trends and insights and estimate emotional states.

[0584] A "generation method" is a device for automatically creating content while adjusting the tone and style based on analysis results and the user's emotional state.

[0585] An "evaluation tool" is a device used to verify the quality and emotional relevance of generated content and to confirm whether it meets the necessary evaluation criteria.

[0586] "Transmission means" refers to communication devices used to deliver the final evaluated content to the user.

[0587] The system used to implement this application has the functionality to automatically generate optimal content based on the user's emotional information. Specifically, it starts by receiving emotional information and content requests entered by the user from a device such as a smartphone. The device then sends this information to the server as request data.

[0588] The server uses analysis software such as IBM Watson or Google Cloud Natural Language API as its emotion engine to analyze user sentiment information. This analysis estimates the user's emotional state and reflects it in the subsequent content generation. At this stage, technologies such as Elasticsearch and Apache Kafka are used as information gathering tools to collect relevant information from databases and the internet. The collected data is analyzed to extract insights and trends.

[0589] Next, content generation takes place using a generative AI model. Natural language generation software such as OpenAI GPT-3 can automatically generate content with adjusted tone and style while reflecting the user's emotional state. In this generation process, the context and style of the text are adjusted to best suit the user's emotional state.

[0590] The generated content is quality-checked using evaluation tools such as the Grammarly API and Hemingway Editor. This evaluation verifies that the content is appropriate for the user's emotional state and that grammar and consistency are maintained. Finally, the evaluated and revised content is delivered to the user via their device.

[0591] This system allows users to easily enjoy personalized content tailored to their emotional state. For example, when a user feels like relaxing, it can generate and suggest a music playlist or article that matches that feeling. An example of a prompt to the generation AI model would be, "Please create a music playlist that matches my relaxed mood. Please provide a list of song titles and artists, along with genre suggestions."

[0592] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0593] Step 1:

[0594] The user uses their smartphone's input method to enter the desired content type and their emotional information. This input includes an interface for selecting emotions. The entered data is sent from the device to the server as request data for the next processing step.

[0595] Step 2:

[0596] The server analyzes the received request data and interprets the user's content request and sentiment information. At this stage, the server activates the sentiment engine and uses analysis software such as IBM Watson or Google Cloud Natural Language API to estimate the user's sentiment state and retrieve it as data.

[0597] Step 3:

[0598] The server uses information gathering tools to collect relevant information based on user requests from databases and the internet. This information includes news articles, music lists, image data, and other materials for content generation. The collected data is organized and integrated using Elasticsearch or Apache Kafka.

[0599] Step 4:

[0600] The server uses data analysis tools to analyze the collected information and extract trends and insights. Here, data calculations are performed to evaluate how the collected data matches the user's emotional state. These analysis results are then used as parameters for content generation.

[0601] Step 5:

[0602] The server launches the OpenAI GPT-3 generative AI model and automatically generates content based on acquired emotional states and analysis data. During generation, prompts are used to adjust the tone and style, creating content that matches the user's emotional state. An example of a prompt is, "Please create a music playlist that matches my relaxed mood. Please provide a list of song titles and artists, along with genre suggestions."

[0603] Step 6:

[0604] The generated content undergoes quality checks using server-side evaluation tools. This process involves evaluation of grammatical accuracy, content consistency, and emotional relevance using tools such as the Grammarly API and Hemingway Editor, with corrections made if necessary.

[0605] Step 7:

[0606] Finally, the evaluated and revised content is sent from the server to the device. Through the device, the user receives, views, or uses personalized content tailored to their emotions.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] [Fourth Embodiment]

[0611] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0612] 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.

[0613] 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).

[0614] 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.

[0615] 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.

[0616] 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).

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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".

[0624] This invention provides a system that allows users to efficiently generate and customize content. Specific embodiments of this system are described below.

[0625] The process begins when the user operates a device and inputs the type of content, theme, and target audience they wish to generate. The device retrieves this information and sends it to the server. Upon receiving a request from the user, the server uses an information gathering device to collect data related to the specified theme from the internet and internal databases. The collected information is processed to filter out irrelevant data and extract only the necessary data. Next, the server uses a data analysis device to analyze the collected information. Through this analysis, data trends and patterns are identified, and useful insights are extracted.

[0626] Next, the server uses a generation device to automatically generate content based on the analysis results. For example, if a user requests an article, the server organizes the information and constructs the text. In the case of reports, it generates content incorporating advanced analysis results based on the data. For advertising messages, it creates catchy slogans that appeal to the target audience, and for videos and images, it generates dynamic visual content.

[0627] After the content is generated, the server uses an evaluation device to assess its quality. This evaluation is based on criteria such as grammatical accuracy, content consistency, and visual appeal. If the evaluation determines that the quality does not meet the standards, the content is regenerated or modified. The generated high-quality content is then sent to the terminal and provided to the user.

[0628] For example, if a user specifies that they want to create an article about next-generation AI technology, the server can gather relevant information and automatically generate an article that includes the latest technological advancements in that field. Similarly, if an advertising agency inputs that they want to create an advertising message for a new product, the server will provide creative copy tailored to the target market. In this way, the system can meet a wide variety of content generation needs.

[0629] The following describes the processing flow.

[0630] Step 1:

[0631] The user requests content generation via their device. Here, the user enters details about the type of content they want to generate (e.g., article, report), theme, target audience, and required format. The device receives this information and forms the request data.

[0632] Step 2:

[0633] The terminal sends the formed request data to the server. The server analyzes the received data and understands the user's request. Based on this, it prepares for the next information gathering step.

[0634] Step 3:

[0635] The server uses information gathering devices to collect relevant information based on specified themes and keywords from the internet and internal databases. In this process, the server prioritizes obtaining information from reliable sources.

[0636] Step 4:

[0637] The collected information is organized on a server, and irrelevant data is removed. The information is formatted for data analysis and processed as needed. At this stage, the information is filtered, and highly useful data is extracted.

[0638] Step 5:

[0639] The server uses data analysis equipment to analyze the organized information. This analysis includes trend discovery, relevance assessment, and statistical analysis. Ultimately, key data points for content generation are identified.

[0640] Step 6:

[0641] The server activates the generation device and automatically generates the specified type of content (articles, reports, advertising messages, videos, or images) based on the data analysis results. The generation process is customized according to user instructions.

[0642] Step 7:

[0643] The server uses evaluation equipment to check the content to ensure its quality. This process evaluates grammar, content consistency, visual quality, and suitability for the target audience.

[0644] Step 8:

[0645] The server modifies or regenerates the content if necessary based on the evaluation results. If the criteria are met, the content is ultimately sent to the terminal.

[0646] Step 9:

[0647] The device provides the received content to the user. The user makes adjustments as needed, and the content is finally ready to be used.

[0648] (Example 1)

[0649] 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".

[0650] Conventional digital content generation systems struggle to respond quickly and flexibly to the diverse content requests of users. Furthermore, the consistency of generated content quality and the provision of optimized formats are insufficient, leading to a need for improved user satisfaction. To address these issues, the development of a system that enables more accurate information gathering, analysis, and quality evaluation is crucial.

[0651] 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.

[0652] In this invention, the server includes means for acquiring user input, means for collecting data based on the acquired user input, and means for analyzing the collected data. This enables the automatic generation of high-quality digital content that meets the diverse needs of users, and the rapid provision of such content.

[0653] "User input" refers to instructions and data that the system receives from the user, and includes content type, theme, target audience, etc.

[0654] "Means" refers to methods or technical components used to achieve a specific objective.

[0655] "Data" refers to the collection of information that a system gathers using information gathering devices, and this includes information obtained from the internet and internal databases.

[0656] "Digital content" refers to information expressed in electronic form, and includes documents, reports, promotional messages, visual content, or images.

[0657] "Quality standards" refer to the criteria that generated content must meet, and include grammatical accuracy, content consistency, and visual appeal.

[0658] This invention is a system that efficiently generates diverse digital content based on user input. This makes it possible to quickly provide high-quality content that meets user needs.

[0659] The user inputs the type, theme, and target audience of the digital content they wish to generate into the system using their device. The device organizes the input information as packet data and sends it to the server using a secure communication protocol. Personal computers and mobile devices are commonly used as this device.

[0660] The server activates an information gathering device based on data sent by the user. This device uses, for example, web crawlers and scraping software to collect necessary information from the internet and a dedicated internal database. The collected data is then analyzed by a data analysis device. This analysis utilizes data analysis libraries such as Pandas and NumPy to identify data trends and relationships.

[0661] By using a generative AI model, the server can automatically generate optimal digital content from the analysis results. For example, if a user requests a news article, it will organize the information and generate a coherent text. The generated content is checked by an algorithm that evaluates quality, and is only provided to the user if it meets the criteria.

[0662] For example, if a user inputs "I want to create an article about next-generation AI technology," the server will gather relevant information and automatically generate an article that reflects the latest technological trends. Similarly, if an advertising agency requests "I want to create an advertising message for a new product," the server will generate creative copy tailored to the target market.

[0663] Examples of prompt statements include:

[0664] "Requesting article generation on next-generation AI technology, including the latest technological advancements."

[0665] "We request the creation of creative advertising messages for a new product. The target market is millennials."

[0666] Thus, the system of the present invention can quickly and accurately generate and evaluate the quality of digital content that meets the diverse needs of users.

[0667] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0668] Step 1:

[0669] The user inputs the type, theme, and target audience of the digital content they wish to generate through the device. The device receives this user input and formats it as a data packet. The input contains detailed content information, and the output is the formatted data packet.

[0670] Step 2:

[0671] The terminal sends a formatted data packet to the server. For security reasons, the HTTPS protocol is used to ensure secure communication during transmission. The input is the data packet on the terminal, and the output is the data transferred to the server.

[0672] Step 3:

[0673] The server activates an information gathering device based on the data it receives. Using automated web crawlers and scraping tools, it collects data related to a specified theme from the internet and internal databases. The input is the user's request data, and the output is the associated dataset.

[0674] Step 4:

[0675] The server analyzes the collected data using a data analysis tool. Data analysis libraries such as Pandas and NumPy are used to analyze data trends and patterns. The input is the collected dataset, and the output is trends and key insights.

[0676] Step 5:

[0677] The server uses an AI model to automatically generate digital content based on the analysis results. Natural language processing technology is utilized to create text and advertising messages tailored to user specifications. The input is the data analysis results, and the output is the generated digital content.

[0678] Step 6:

[0679] The server evaluates the quality of the generated content using an evaluation device. Evaluation criteria include grammatical accuracy, content consistency, and visual appeal, and regeneration or modification is performed as needed. The input is the generated content, and the output is the evaluation result.

[0680] Step 7:

[0681] The server sends evaluated, high-quality content to the terminal. Security is ensured again during transmission using the HTTPS protocol. The input is the evaluated content, and the output is the content displayed on the user's terminal.

[0682] Step 8:

[0683] The device provides the received content to the user, who then reviews the content. The user can provide feedback and take further action as needed based on the received content. The input is the content from the server, and the output is the content information presented to the user.

[0684] (Application Example 1)

[0685] 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".

[0686] Traditional content generation systems required significant time and effort to meet diverse user demands, making it difficult to respond quickly to individual needs. Furthermore, the generated content often failed to meet user expectations, requiring optimization. Additionally, there was a lack of efficient means to deliver content based on specific categories.

[0687] 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.

[0688] In this invention, the server includes input means for acquiring user input, information gathering means for collecting information, and data analysis means for analyzing the collected information. This enables the automatic generation and distribution of highly optimized content based on information selected by the user.

[0689] An "input means for acquiring user input" is a device that has the function of allowing users to input information through a device.

[0690] An "information gathering means for collecting information" is a device that collects relevant data from external databases or networks based on user input.

[0691] A "data analysis tool for analyzing collected information" is a device that analyzes collected data and extracts meaningful patterns and trends.

[0692] An "automatically generated generation method" is a device that automatically constructs content based on analysis results.

[0693] An "evaluation means for evaluating and confirming quality" is a device that determines the quality of generated content and confirms its quality by comparing it with predetermined standards.

[0694] A "transmission means" is a device that transmits generated content in order to deliver it to the user.

[0695] A "distribution method" is a device equipped with the function to provide optimized content based on categories and information selected by the user.

[0696] To implement this invention, the server introduces an input device for acquiring user input. The user selects categories and keywords of interest using a smartphone or tablet, and information based on these is collected. The collection is performed using a cloud platform (e.g., Amazon Web Services or Google Cloud Platform) and is obtained from a wide range of databases and the internet.

[0697] The server is based on Python and uses libraries such as Pandas and Numpy to filter and integrate data. Furthermore, it utilizes NLTK and Transformers (Hugging Face) for natural language processing to perform data analysis and semantic analysis. This allows it to identify trends and patterns that meet demand and prepare information for generative AI model units.

[0698] By utilizing generative AI models (such as GPT-4), the server automatically generates content that meets user needs. This generation method emphasizes information structuring and writing fluency, providing valuable content for users. Examples include news articles, visual content, and audio content. The completed content is checked to ensure it meets requirements through quality evaluation mechanisms. If it does not meet the standards, the server regenerates or modifies the content.

[0699] The generated content is then delivered to the user via a transmission device. Based on the information category selected by the user, the most relevant content is delivered preferentially. This process allows users to receive information curated based on their specific interests.

[0700] For example, if a user selects the theme "Environmental Protection and Technology," this system will collect and organize articles and interviews on the latest technologies and deliver them to the user. Another example of a prompt used when inputting instructions for writing an article into the AI ​​model is, "Create a detailed article on the latest environmental protection technologies, including recent discoveries, developments, and social impacts." This prompt allows the model to accurately create content containing the desired information.

[0701] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0702] Step 1:

[0703] The user uses a terminal to input categories and topics of interest. The terminal sends this information to the server as input data. The main output at this stage is the category data selected by the user.

[0704] Step 2:

[0705] The server uses information gathering tools to collect relevant data from external databases and the internet based on the received category data. The collected data is stored on the server as raw data, such as articles and materials related to that category. The output is this raw data.

[0706] Step 3:

[0707] The server uses data analysis tools to filter and integrate the collected raw data. It formats the data using tools like Pandas and NumPy, removes unnecessary data, extracts trends and patterns, and stores them on the server as analytical data. This is the main output of this step.

[0708] Step 4:

[0709] The server inputs the analysis data into a generation AI model and generates content based on a specified prompt (e.g., "Create an article about the latest environmental protection technologies."). The generated content can be in the form of a document, visual, or audio-based output.

[0710] Step 5:

[0711] The generated content is evaluated to verify its quality. It is reviewed based on evaluation criteria such as AI-powered grammar checks, content consistency, and visual appeal. If the evaluation results do not meet the criteria, the content is regenerated or revised. The output is the evaluated content.

[0712] Step 6:

[0713] Content that ultimately passes the evaluation is sent to the device via a transmission method and provided to the user. The user can then view optimized content of interest on their device. The output of this step is high-quality content accessible to the user.

[0714] 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.

[0715] The system based on this invention automatically generates content that is more in tune with the user's emotions by incorporating an emotion engine into the content generation process for content requested by the user. This system mainly consists of a terminal, a server, and an emotion engine.

[0716] First, the user provides input about the content they want to generate through their device. This input includes the type of content, theme, format, target audience, and information indicating the user's sentiment. After the device receives this information, it sends it to the server as request data.

[0717] The server analyzes the received data to understand information based on the user's requests and emotions. At this time, the emotion engine analyzes the user's emotional information and estimates their emotional state. This emotional state is used as an important parameter during content generation.

[0718] Next, the server uses an information gathering device to collect information related to the specified theme from the internet and internal databases. The collected data is analyzed by a data analysis device to extract insights and trends necessary for content generation. Subsequently, the generation device automatically generates content, taking into account this information and the user's emotional state obtained by the emotion engine. At this time, the tone and style of the content are adjusted to correspond to the user's emotions.

[0719] The generated content is evaluated for quality by an evaluation device. The evaluation process assesses the content not only for grammatical accuracy, consistency, and visual impact, but also from an emotional perspective. Based on the evaluation results, necessary adjustments are made, and only content that meets the standards is ultimately sent to the device.

[0720] For example, if a user requests to create an article to read when they want to relax, and simultaneously inputs a relaxed emotion, the server will collect and analyze relevant information and automatically generate an article containing text and visuals in a relaxed tone. This system, incorporating an emotion engine, enables the provision of personalized content that is more attuned to user needs.

[0721] The following describes the processing flow.

[0722] Step 1:

[0723] The user inputs their emotional state along with a content generation request via their device. Input fields include the type of content to be generated (article, report, etc.), theme, format, target audience, and emotional state (e.g., relaxed, excited, etc.). The device compiles this information to form the request data.

[0724] Step 2:

[0725] The terminal sends the formed request data to the server. The server analyzes the received data and interprets the user's requests and sentiment information.

[0726] Step 3:

[0727] The server uses an emotion engine to analyze the user's input emotional state. This analysis identifies emotional elements that are appropriate for the content the user desires.

[0728] Step 4:

[0729] The server operates an information gathering device to collect information related to specified themes and keywords from online sources and internal databases. The server then filters the information to prioritize data that matches the emotional state.

[0730] Step 5:

[0731] The server analyzes the collected information using a data analysis device, identifying trends and important data points from the collected data. This analysis then forms the foundational data necessary for content generation.

[0732] Step 6:

[0733] The server operates the generation device and automatically generates content in the specified format based on data analysis results and the evaluation of the emotion engine. In particular, the tone and style are adjusted to match the user's emotions.

[0734] Step 7:

[0735] The server uses an evaluation device to perform a quality assessment on the generated content. This assessment includes checking for grammatical accuracy, consistency, and information validity, as well as whether the content is appropriate for the user's emotional state.

[0736] Step 8:

[0737] The server regenerates or modifies the content as needed. Content that meets the criteria is ultimately sent to the device.

[0738] Step 9:

[0739] The device provides the transmitted content to the user. The user reviews the content, makes any necessary final adjustments, and then uses it.

[0740] (Example 2)

[0741] 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".

[0742] Traditional content generation systems fail to adequately address user emotions, resulting in a lack of complete user satisfaction. Furthermore, the tone and style of generated content may not align with the user's desired emotions, leading to an inconsistent user experience.

[0743] 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.

[0744] In this invention, the server includes means for acquiring user input and sending requests containing emotional information; means for providing an emotional analysis engine for analyzing information based on the acquired input and emotional information and estimating the emotional state; and means for collecting and analyzing information based on the analyzed emotional state. This enables the automatic generation of content that is sensitive to the user's emotions.

[0745] "User input" refers to data that includes the type, theme, format, target audience, and user sentiment information necessary for content creation.

[0746] "Emotional information" refers to data that indicates a user's current emotional state and serves as the basis for determining the tone and style of content.

[0747] An "emotion analysis engine" is a program or device that analyzes emotional information contained in user input and estimates the user's emotional state.

[0748] "The Internet and internal databases" are data sources for collecting relevant information necessary for content generation.

[0749] "Generation means" refers to a device or program for automatically generating content based on analysis results and emotional states.

[0750] The "emotional perspective" is a viewpoint used in evaluating content to confirm its relevance to the user's emotions.

[0751] A "prompt" is a series of instructions or input data used during content generation, and is information supplied to the generating AI model.

[0752] This system automatically generates content that resonates with the user's emotions. It primarily consists of a terminal, a server, and an emotion engine. Users input content generation information via the terminal. This user input includes content type, theme, format, target audience, and emotional information. The terminal receives this information and sends it to the server as request data.

[0753] The server analyzes the received data, uses an emotion engine to analyze emotional information in detail, and estimates the user's emotional state. This emotional state becomes a crucial parameter in subsequent content generation. The server uses the internet and internal databases to collect information related to the specified theme. This involves using web scraping techniques and API access.

[0754] Next, the server uses a data analysis device to analyze the collected data. The main analyses include trend analysis and in-depth theme exploration, extracting insights necessary for content generation. The generation device then automatically generates content, taking these analysis results and estimated emotional states into consideration. A generation AI model is used to provide content that is tailored to the user's emotional state in terms of tone and style.

[0755] The generated content undergoes quality checks using an evaluation device, where it is assessed for grammatical accuracy, consistency, visual impact, and emotional appeal. Only content that meets the criteria is edited and then sent to the terminal.

[0756] For example, if a user requests "I want to create an article to read when I want to relax" and enters "relax" as emotion information, the server will gather data related to that theme. For instance, it can collect the latest trends in relaxation methods and ways of spending time, and use a generative AI model to create content with a relaxed tone. One example of a prompt would be, "Please generate an article introducing a list of music suitable for relaxation. Please make the content focus on creating a relaxed mood."

[0757] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0758] Step 1:

[0759] Users input content generation information via their devices. This input includes content type, theme, format, target audience, and user sentiment information. The device aggregates this information and sends it to the server as request data. The server receives the input information directly, preparing it for analysis.

[0760] Step 2:

[0761] The server analyzes the received request data. First, it uses an emotion analysis engine to analyze the user's emotional information in detail and estimate their emotional state based on that analysis. At this time, the emotional information is cross-referenced with a database, and relevant emotional parameters are selected based on an emotion such as "relaxed." This emotional state is then used as output in the next information gathering phase.

[0762] Step 3:

[0763] The server uses information gathering devices to collect information related to a specified theme from the internet and internal databases. For example, using API access and web scraping techniques, it might collect the latest travel destination information if the theme is travel. The collected data is structured and supplied as input to a data analysis device.

[0764] Step 4:

[0765] The server analyzes the data collected by the data analysis device. This analysis includes trend analysis, keyword extraction, and relevance evaluation. As a result, insights and key topics necessary for content generation are extracted. These analysis results are then passed as input to the generation device.

[0766] Step 5:

[0767] The generation device automatically generates content using a generation AI model based on the analysis results and estimated emotional state. During this process, the content is adjusted to match the user's emotions in terms of tone and style. The generated content is output and supplied to the evaluation device.

[0768] Step 6:

[0769] The generated content undergoes quality evaluation using an evaluation system. Evaluation is conducted based on grammatical accuracy, consistency, visual impact, and emotional appeal. Only content that meets the evaluation criteria is deemed to require revision, and adjustments are made as needed.

[0770] Step 7:

[0771] Content that passes the evaluation is sent to the device as the final version. Users can then review the final content received on their device and use it according to their purpose.

[0772] (Application Example 2)

[0773] 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".

[0774] In today's information-saturated society, users find it difficult to find content that suits their emotional state. Furthermore, existing content generation systems often fail to adequately adjust tone and style to match user emotions, highlighting the need for improvements to enhance user satisfaction.

[0775] 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.

[0776] In this invention, the server includes an input means for acquiring user emotional information, an information gathering means for collecting information based on the acquired user input and emotional information, and a data analysis means for analyzing the collected information and estimating the emotional state based on the user's emotions. This enables the automatic generation of content with a tone and style adjusted according to the user's emotional state.

[0777] "User emotional information" refers to data that users input indicating their own emotional state, and is reflected when the emotional engine generates content.

[0778] An "input device" is a device that allows a user to provide the system with the information and emotional information necessary for content creation.

[0779] An "information gathering device" is a device that obtains relevant information from the internet or databases based on a specified theme or user request.

[0780] A "data analysis tool" is a device that analyzes collected information and user sentiment data to extract important trends and insights and estimate emotional states.

[0781] A "generation method" is a device for automatically creating content while adjusting the tone and style based on analysis results and the user's emotional state.

[0782] An "evaluation tool" is a device used to verify the quality and emotional relevance of generated content and to confirm whether it meets the necessary evaluation criteria.

[0783] "Transmission means" refers to communication devices used to deliver the final evaluated content to the user.

[0784] The system used to implement this application has the functionality to automatically generate optimal content based on the user's emotional information. Specifically, it starts by receiving emotional information and content requests entered by the user from a device such as a smartphone. The device then sends this information to the server as request data.

[0785] The server uses analysis software such as IBM Watson or Google Cloud Natural Language API as its emotion engine to analyze user sentiment information. This analysis estimates the user's emotional state and reflects it in the subsequent content generation. At this stage, technologies such as Elasticsearch and Apache Kafka are used as information gathering tools to collect relevant information from databases and the internet. The collected data is analyzed to extract insights and trends.

[0786] Next, content generation takes place using a generative AI model. Natural language generation software such as OpenAI GPT-3 can automatically generate content with adjusted tone and style while reflecting the user's emotional state. In this generation process, the context and style of the text are adjusted to best suit the user's emotional state.

[0787] The generated content is quality-checked using evaluation tools such as the Grammarly API and Hemingway Editor. This evaluation verifies that the content is appropriate for the user's emotional state and that grammar and consistency are maintained. Finally, the evaluated and revised content is delivered to the user via their device.

[0788] This system allows users to easily enjoy personalized content tailored to their emotional state. For example, when a user feels like relaxing, it can generate and suggest a music playlist or article that matches that feeling. An example of a prompt to the generation AI model would be, "Please create a music playlist that matches my relaxed mood. Please provide a list of song titles and artists, along with genre suggestions."

[0789] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0790] Step 1:

[0791] The user uses their smartphone's input method to enter the desired content type and their emotional information. This input includes an interface for selecting emotions. The entered data is sent from the device to the server as request data for the next processing step.

[0792] Step 2:

[0793] The server analyzes the received request data and interprets the user's content request and sentiment information. At this stage, the server activates the sentiment engine and uses analysis software such as IBM Watson or Google Cloud Natural Language API to estimate the user's sentiment state and retrieve it as data.

[0794] Step 3:

[0795] The server uses information gathering tools to collect relevant information based on user requests from databases and the internet. This information includes news articles, music lists, image data, and other materials for content generation. The collected data is organized and integrated using Elasticsearch or Apache Kafka.

[0796] Step 4:

[0797] The server uses data analysis tools to analyze the collected information and extract trends and insights. Here, data calculations are performed to evaluate how the collected data matches the user's emotional state. These analysis results are then used as parameters for content generation.

[0798] Step 5:

[0799] The server launches the OpenAI GPT-3 generative AI model and automatically generates content based on acquired emotional states and analysis data. During generation, prompts are used to adjust the tone and style, creating content that matches the user's emotional state. An example of a prompt is, "Please create a music playlist that matches my relaxed mood. Please provide a list of song titles and artists, along with genre suggestions."

[0800] Step 6:

[0801] The generated content undergoes quality checks using server-side evaluation tools. This process involves evaluation of grammatical accuracy, content consistency, and emotional relevance using tools such as the Grammarly API and Hemingway Editor, with corrections made if necessary.

[0802] Step 7:

[0803] Finally, the evaluated and revised content is sent from the server to the device. Through the device, the user receives, views, or uses personalized content tailored to their emotions.

[0804] 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.

[0805] 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.

[0806] 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 robot 414.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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."

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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 as being incorporated by reference.

[0825] The following is further disclosed regarding the embodiments described above.

[0826] (Claim 1)

[0827] An input device that acquires user input,

[0828] An information collection device that collects information based on acquired user input,

[0829] A data analysis device that analyzes the collected information,

[0830] A generation device that automatically generates content based on analysis results,

[0831] An evaluation device that evaluates the generated content and verifies its quality,

[0832] A transmission device that sends the generated content to the user,

[0833] A content generation system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, comprising optimization means for optimizing content according to a specific format and quality standard in the generation and evaluation of content.

[0836] (Claim 3)

[0837] The system according to claim 1, wherein the type of content generated based on user specifications is an article, report, advertising message, video, or image.

[0838] "Example 1"

[0839] (Claim 1)

[0840] A means of obtaining user input,

[0841] Means for collecting data based on acquired user input,

[0842] Means for analyzing the collected data,

[0843] A means of automatically generating digital content based on analysis results,

[0844] A means of evaluating generated digital content and verifying it based on quality standards,

[0845] A means of sending evaluated digital content to users,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, comprising means for optimizing the generated digital content according to specific structure and quality standards.

[0849] (Claim 3)

[0850] The system according to claim 1, wherein the type of digital content selected based on user specifications is a document, report, promotional message, visual content, or image.

[0851] "Application Example 1"

[0852] (Claim 1)

[0853] An input means for obtaining user input,

[0854] Information gathering means that collect information based on acquired user input,

[0855] Data analysis methods for analyzing collected information,

[0856] A generation method that automatically generates content based on analysis results,

[0857] An evaluation method for evaluating the generated content and confirming its quality,

[0858] A means of sending the generated content to the user,

[0859] A system that includes a means for delivering content based on information selected by the user.

[0860] (Claim 2)

[0861] The system according to claim 1, which optimizes content according to specific format and quality standards in the generation and evaluation of content, and delivers content based on user-specified categories.

[0862] (Claim 3)

[0863] The system according to claim 1, wherein the type of content generated based on user specifications is a document, visual content, audio content, or multimedia.

[0864] "Example 2 of combining an emotion engine"

[0865] (Claim 1)

[0866] A means of obtaining user input and sending a request containing emotional information through a terminal,

[0867] A means comprising an emotion analysis engine for analyzing information based on acquired user input and emotion information and estimating the emotional state,

[0868] Based on the analyzed emotional state, a means of collecting and analyzing information from the internet and internal databases,

[0869] A generation method that automatically generates content based on analysis results and emotional state, and adjusts its tone and style,

[0870] A means of evaluating the generated content and confirming its quality, including emotional perspective,

[0871] A means of sending the evaluated and adjusted content to the user,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, which optimizes content using specific prompts that correspond to the user's emotional state in the generation and evaluation of content.

[0875] (Claim 3)

[0876] The system according to claim 1, wherein the type of content generated based on user specifications is a document, report, informational message, video, or diagram.

[0877] "Application example 2 of combining emotional engines"

[0878] (Claim 1)

[0879] An input method for acquiring user emotional information,

[0880] Information gathering means for collecting information based on acquired user input and sentiment information,

[0881] A data analysis method that analyzes collected information and estimates the emotional state based on the user's feelings,

[0882] A generation method that automatically generates content based on analysis results and emotional state, and adjusts its tone and style,

[0883] An evaluation method for assessing the generated content and confirming its quality and emotional relevance,

[0884] A means of sending evaluated content to users,

[0885] A system that includes this.

[0886] (Claim 2)

[0887] The system according to claim 1, comprising optimization means for optimizing content that takes emotional states into account, in accordance with specific format and quality standards.

[0888] (Claim 3)

[0889] The system according to claim 1, wherein the type of content generated based on user specifications is a music list, news articles, or visual content. [Explanation of Symbols]

[0890] 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 input device that acquires user input, An information collection device that collects information based on acquired user input, A data analysis device that analyzes the collected information, A generation device that automatically generates content based on analysis results, An evaluation device that evaluates the generated content and verifies its quality, A transmission device that sends the generated content to the user, A content generation system that includes this.

2. The system according to claim 1, comprising optimization means for optimizing content according to a specific format and quality standard in the generation and evaluation of content.

3. The system according to claim 1, wherein the type of content generated based on user specifications is an article, report, advertising message, video, or image.

Images