system

Abstract

Provide a system. 【Solution means】 Means for automatically collecting information from media, Means for analyzing the collected information and extracting articles based on pre-set criteria, Means for calculating the advertising equivalent amount of the extracted articles, Means for creating a report based on the calculated advertising equivalent amount and analysis results, Means for automatically distributing the report to relevant responsible persons, Means for enabling publicity personnel to check the real-time media exposure situation, Means for measuring the effectiveness of advertising campaigns and providing support for adjusting advertising strategies, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] Calculating the advertising equivalent amount and measuring the effect of media exposure in public relations activities are complicated and time-consuming tasks, and there is a problem that a large number of personnel and external resources are required. As a result, it becomes difficult for public relations staff to allocate time to strategic tasks, and rapid and efficient decision-making is hindered.

Means for Solving the Problems

[0005] To address this challenge, we developed a system that automatically collects data from media outlets and analyzes and filters articles using natural language processing. Furthermore, this system quickly calculates the advertising equivalent value of filtered articles and generates a trend analysis report based on historical data. The generated report is automatically distributed to the relevant personnel, saving time and resources and supporting rapid decision-making. Through this series of measures, it is possible to break away from traditional external reliance and bring processes in-house, thereby significantly improving operational efficiency.

[0006] "Media" refers to various forms of communication used to collect and disseminate information to the public, such as newspapers, magazines, television, radio, and the internet.

[0007] "Data" refers to a collection of information, including numerical values, strings of characters, images, and audio, collected for a specific purpose.

[0008] "Analysis" refers to the process of breaking down data and understanding the meaning and characteristics it contains.

[0009] "Filtering" refers to the process of selecting and extracting data or information based on specific criteria or conditions.

[0010] "Advertising equivalent value" refers to the amount used to evaluate the value and cost-effectiveness of articles and media exposure by converting them into advertising costs.

[0011] "Natural language processing" refers to the technology used to understand, interpret, and generate human natural language using computers.

[0012] A "report" is a document that summarizes the results of an investigation or analysis, and refers to a means of conveying specific information.

[0013] "Person in charge" refers to an individual or team responsible for a specific task or role, and who is responsible for its execution.

[0014] "Distribution" refers to the process of sending information or data to a specific recipient and having them receive it. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine.

Embodiments for Carrying out the Invention

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

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

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

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

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

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

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0036] This invention provides a system that integrates the automatic collection and analysis of media data, as well as the calculation of advertising equivalent value and report generation, into a single process. The system aims to improve the efficiency and reduce costs of public relations activities. Embodiments of this invention are described in detail below.

[0037] First, the server programmatically collects news articles and media coverage information from various media sources on the internet. This allows for real-time data acquisition as soon as articles are published.

[0038] Next, the server uses natural language processing (NLP) techniques to analyze the collected data and filters it against a list of keywords related to specific industries and companies, thereby filtering out relevant articles. This analysis step ensures that only highly relevant articles proceed to the next processing step.

[0039] For filtered articles, the server calculates the advertising equivalent value. This calculation takes into account factors such as the article's word count and the market value of the publishing medium, and references historical databases to provide a more accurate valuation.

[0040] Based on the obtained conversion figures and analysis data, the server automatically generates a report. The report includes an evaluation of current media exposure and the company's position in the market. It also suggests future public relations strategies based on comparisons with past data.

[0041] Finally, the generated report is sent to the user's (public relations officer's) device. The report is editable in PDF or Excel format and can be immediately used as meeting material or a report. This allows the user to quickly share the obtained information both internally and externally, accelerating decision-making.

[0042] By using this embodiment, public relations personnel can reduce the enormous amount of manual work previously required and concentrate their resources on other strategic activities. This is expected to lead to more effective and efficient public relations activities.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server periodically crawls news feeds and various media APIs publicly available on the internet to retrieve newly published article data. It uses RSS feeds and article APIs to update information in real time.

[0046] Step 2:

[0047] The server analyzes the acquired article data using natural language processing (NLP) tools. The analysis extracts keywords based on the article content and matches them against a pre-configured list of related keywords. This process filters the articles to include only those related to specific themes or companies.

[0048] Step 3:

[0049] The server calculates the advertising equivalent value for filtered articles. Specifically, it calculates the value based on the number of characters in the article, the page it appears on, the type of media, and its influence, and then refers to an existing advertising cost database to evaluate its appropriate advertising value.

[0050] Step 4:

[0051] The server generates a report that includes a trend analysis of the article's impact and a comparison with competitors in the market, along with the calculated advertising equivalent value. The generated report is structured to be easy to understand, with data visualizations.

[0052] Step 5:

[0053] The server outputs the generated report in PDF or Excel format and automatically distributes it via email to designated stakeholders (such as public relations personnel). This allows users to easily obtain the information and use it for decision-making and report creation.

[0054] (Example 1)

[0055] 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."

[0056] In today's information society, while a vast amount of information is distributed from diverse sources, it is difficult to extract only the useful information relevant to a specific industry or company. Furthermore, there is a challenge in that it takes considerable effort and time to efficiently analyze this information, evaluate its advertising value, and create concrete and rapid analytical documents for formulating effective strategies.

[0057] 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.

[0058] In this invention, the server includes means for automatically collecting information from information sources, means for applying natural language processing technology to analyze the collected information and filtering articles by keywords based on pre-set criteria, and means for calculating the advertising value of the filtered articles. This makes it possible to quickly and efficiently collect highly relevant information and automatically generate analytical documents for quantifying the value of that information and utilizing it in strategic planning.

[0059] "Information sources" refer to communication infrastructure and platforms that serve as the starting point for acquiring information from various media.

[0060] "Information" refers to news articles and related data published by the media.

[0061] "Natural language processing technology" refers to computational techniques for analyzing texts expressed in human language and identifying and classifying the characteristics of the information.

[0062] "Filtering" refers to the process of selecting information according to specific criteria and extracting only the information that meets the required conditions.

[0063] "Advertising value" refers to a numerical indicator calculated to quantitatively evaluate the advertising effectiveness of information published in the media.

[0064] An "analysis document" refers to a document generated based on collected information and its analysis results, and includes a comprehensive report that includes data evaluation and recommendations.

[0065] "Users" refers to the individuals or organizations that receive the generated analytical documents and use that information to make decisions or develop strategies.

[0066] This invention is a system that automatically collects, analyzes, and evaluates media information, calculates advertising value, and generates a report. The system mainly consists of a server and user terminals. The specific configuration is described below.

[0067] The server connects to various media platforms that serve as information sources via the internet. Data collection from these sources utilizes APIs and web scraping techniques. For example, open-source software like Scrapy is sometimes used. The server uses these techniques to retrieve new article data in real time and record it in a database.

[0068] Next, the server applies natural language processing techniques to the collected information. This is achieved by using specific software libraries (e.g., Python's NLTK or spaCy). This allows the server to extract keywords from the information and filter it according to its relevance. After filtering, only information relevant to a specific industry or company is selected and sent to the next stage.

[0069] The server performs calculations to determine the advertising value of the filtered information. Factors such as the media's influence, the number of words in the article, and the frequency of publication are considered in the calculation. This allows the server to quantitatively evaluate the market value of the collected information.

[0070] The server generates a report based on the analysis results and advertising value. This report generation utilizes data visualization tools such as Matplotlib and Seaborn. The generated report includes graphs and tables, presented in a format that allows for intuitive understanding of the information.

[0071] Finally, the generated report is sent to the user's device. The user then uses the report received via their device to consider advertising campaigns and media strategies. The report is sent in PDF or Excel format, allowing the user to immediately use it as meeting material.

[0072] As a concrete example, when a user's company launches a new product into the market, they can use this system to collect relevant news articles and analyze their media influence. This makes it easier for the user to understand the market penetration of the new product.

[0073] An example of a prompt for a generative AI model is, "Collect news from the fashion industry and create a report that calculates advertising value." This prompt allows users to quickly obtain valuable information relevant to a specific industry.

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

[0075] Step 1:

[0076] The server automatically collects new information from information sources. It uses a pre-configured list of information sources (such as URLs) as input. The server uses APIs and web scraping techniques to visit each information source and extract new article data. The retrieved article data is stored in a database as output. Specifically, a scheduler visits information sources at regular intervals, identifying and retrieving updated content.

[0077] Step 2:

[0078] The server applies natural language processing to the collected data. It uses collected article data stored in a database as input. The server utilizes Python's NLTK and spaCy to perform tokenization, part-of-speech tagging, and extract keywords. The output is a list of articles with evaluated relevance. Specifically, it compares specific keywords with words within articles, quantifies their relevance, and lists them.

[0079] Step 3:

[0080] The server calculates the advertising value of filtered articles. It uses a list of relevant articles as input. The server calculates an evaluation value using a specific formula, taking into account factors such as the article's word count, the reputation of the publication medium, and past advertising value data. The output is the advertising value evaluation result. Specifically, it references past evaluation data from a database and compares it to the current value.

[0081] Step 4:

[0082] The server generates reports based on advertising value and analysis results. It uses evaluation results and analyzed article data as input. The server visually constructs the data using graph libraries (Matplotlib and Seaborn). The final analysis report is generated in PDF or Excel format as output. Specifically, it documents suggestions based on the analysis results, including visualized graphs and charts.

[0083] Step 5:

[0084] The server delivers the generated report to the user's terminal. It uses the generated report file as input. The server sends the report to the user's terminal via the mail system. The output is a report accessible on the user's terminal. Specific operations include executing a send command to the mail server and confirming receipt.

[0085] (Application Example 1)

[0086] 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."

[0087] In modern public relations, it is crucial to properly manage media exposure and quickly evaluate the effectiveness of advertising campaigns. However, manual information gathering and analysis require an enormous amount of time and effort, making it difficult to develop efficient PR strategies. Furthermore, the inability to check information in real time and adjust advertising strategies can lead to delays in responses that require immediate action on the ground. This results in the challenge of not fully realizing the effectiveness of the overall PR activities.

[0088] 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.

[0089] In this invention, the server includes means for automatically collecting information from media, means for analyzing the collected information and extracting articles based on pre-set criteria, means for calculating the advertising equivalent value of the extracted articles, means for enabling public relations personnel to check real-time media exposure status, and means for measuring the effectiveness of advertising campaigns and providing support for adjusting advertising strategies. This enables the rapid and accurate deployment of public relations strategies.

[0090] "Methods for automatically collecting information from the media" refers to the process of quickly obtaining news articles and published information from various sources on the internet.

[0091] "Means for analyzing collected information and extracting articles based on pre-set criteria" refers to a process that uses natural language processing technology to analyze collected information and selects articles that are highly relevant according to specific criteria.

[0092] "Method for calculating the advertising equivalent value of extracted articles" refers to an algorithm that calculates the advertising value of an article based on filtered articles, taking into account factors such as the number of characters in the article and the market value of the publisher.

[0093] "A means for public relations personnel to check real-time media exposure" refers to technology that provides a user interface that allows public relations personnel to instantly check data on current media exposure at any time.

[0094] "Means of measuring the effectiveness of advertising campaigns and providing support for adjusting advertising strategies" refers to a process of analyzing the results of advertising activities and providing support for proposing the optimal public relations strategy based on those results.

[0095] The server first uses internet news APIs to automatically collect news articles and media coverage information from various sources. This collection process allows data to be obtained almost simultaneously with the publication of articles, making it possible to conduct public relations activities based on the latest information.

[0096] The collected information is analyzed on the server using natural language processing (NLP) libraries. Specifically, libraries such as spaCy and NLTK are used to filter the collected articles based on certain criteria. In this way, only highly relevant articles are selected.

[0097] Next, the advertising equivalent value is calculated for the filtered articles. This calculation algorithm considers the number of characters in the article and the market value of its publisher, and evaluates them by referring to a historical database. This process is implemented using a Python program.

[0098] Based on the generated conversion amounts and analysis results, the server automatically creates a report. The report is generated through data organization using the Pandas library and chart creation using Matplotlib. This report is sent to the public relations officer's terminal in PDF or Excel format.

[0099] The reports sent to the terminal allow public relations personnel to check media exposure in real time and immediately measure the effectiveness of advertising campaigns. This enables them to flexibly adjust their strategies.

[0100] For example, when a company conducts a marketing campaign for a new product, this system can be used to understand media exposure immediately after the campaign starts and analyze its effectiveness. This allows the company to quickly develop an effective public relations strategy.

[0101] An example of a prompt to input into the generating AI model would be: "Please generate a report that retrieves media exposure information for the latest product release and analyzes its advertising effectiveness."

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The server uses a news API to collect news articles from various sources on the internet. The input is the news feed URLs provided by each media outlet, and the output is a collection of collected article data. Here, the Python requests library is used to send HTTP requests and retrieve the news article data.

[0105] Step 2:

[0106] The server analyzes the collected data using a natural language processing (NLP) library. The input is the collected article data, and the output is a list of articles filtered according to specific criteria. Specifically, the spaCy library is used to analyze the text within the articles and select relevant articles based on pre-configured keywords.

[0107] Step 3:

[0108] The server calculates the advertising equivalent value for filtered articles. The input is a list of related articles, and the output is a list of the advertising equivalent value for each article. Here, numerical calculations are performed using Python to calculate the advertising value, taking into account the number of characters in the article and the market value of the publishing medium.

[0109] Step 4:

[0110] The server generates a report based on the obtained advertising equivalent value and analysis results. The input is a list of advertising equivalent values ​​and analysis data, and the output is a report in PDF or Excel format. The Pandas library is used to organize the data, Matplotlib is used to create graphs and tables, and finally the report is formatted.

[0111] Step 5:

[0112] The server automatically distributes the generated report to the public relations officer's terminal. The input is the generated report, and the output is the report displayed on the terminal. Here, the report data is distributed via email using the SMTP protocol, allowing the report to be viewed on the terminal.

[0113] Step 6:

[0114] Users can view real-time media exposure information via their devices and measure the effectiveness of advertising campaigns. Input is report data received on the device, and output is information for strategic decision-making. The user interface allows for real-time data viewing and flexible adjustment of advertising strategies.

[0115] 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.

[0116] This invention is a system that combines a sentiment engine, which takes user emotions into consideration, with a series of processes from media data collection and analysis to calculation of advertising equivalent value, report generation, and distribution. This invention was developed to optimize public relations and marketing activities.

[0117] First, the server utilizes existing media data collection systems to automatically acquire data such as news articles and social media posts. Next, the server uses natural language processing (NLP) and machine learning algorithms to analyze the collected data and filter it based on specific criteria and keywords. This filtering process allows the server to proceed to the next stage with only the most relevant information.

[0118] Subsequently, the advertising equivalent value is calculated. The server considers the length of the filtered article, its scope of impact, the type of media, etc., and refers to a database of past advertising costs to accurately assess the advertising value.

[0119] Furthermore, a key aspect of this invention is the use of an emotion engine that recognizes the user's emotions. The emotion engine installed in the terminal determines the user's emotions from their voice tone, facial expressions, input patterns, etc. This emotion information is shared with a server and taken into consideration during the report generation process, allowing for adjustments to the content of the information the user requests and optimization of the presentation method.

[0120] The generated reports are further refined in content and format based on user sentiment and automatically delivered from the server to the user's (or relevant person's) terminal. This process allows users to quickly receive information that better matches their current situation and needs, enabling them to make effective decisions.

[0121] As a concrete example, when a public relations officer evaluates media coverage, the system can capture the officer's stress and satisfaction levels using an emotion engine, and accordingly emphasize positive messages and improvement suggestions within the report. In this way, the present invention is an embodiment that can further improve the user experience by providing information that reflects emotions.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The server collects new article data from pre-configured sources via news APIs and RSS feeds. The collection process is performed regularly to ensure fresh media content.

[0125] Step 2:

[0126] The server analyzes the collected article data using a natural language processing (NLP) engine to check if it matches a pre-configured keyword list. This filtering process selects only the most relevant articles.

[0127] Step 3:

[0128] The server calculates the advertising equivalent value for the filtered data. Specifically, it evaluates the value based on the number of characters in the article, the influence of the media, and the frequency of publication, and obtains reference values ​​from the advertising cost database.

[0129] Step 4:

[0130] The emotion engine built into the device captures user activity, voice, and facial expression data, and analyzes emotions in real time. This emotion data is sent to a server and taken into consideration when generating reports.

[0131] Step 5:

[0132] The server generates customized reports based on analysis results, advertising equivalent value, and user sentiment data. These reports include impact assessments, improvement suggestions, and future strategies, with the amount and presentation of information adjusted to match user sentiment.

[0133] Step 6:

[0134] The server exports the generated report in PDF or Excel format and automatically sends it to the user's specified email address. This allows the user to quickly receive the necessary information and use it as a reference for making work-related decisions.

[0135] (Example 2)

[0136] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0137] In today's information society, a vast amount of media information is generated daily, making it crucial for companies and individuals to quickly select and utilize appropriate information. However, conventional systems struggle to accurately determine the relevance of information and provide information that considers user sentiment, resulting in delays in the development of efficient marketing strategies and decision-making.

[0138] 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.

[0139] In this invention, the server includes means for automatically acquiring information from media, means for analyzing the acquired information and selecting content based on pre-set criteria, and means for calculating advertising value based on the selected content. This makes it possible to provide information tailored to the user's emotions.

[0140] "Media" is a general term for means and platforms used to transmit and share information, such as news articles and social media posts.

[0141] "Information" refers to all data and content that can be obtained through media, and includes formats such as text, audio, images, and video.

[0142] "Acquiring" refers to the process by which a server automatically or semi-automatically gathers information from external media, primarily through web scraping or the use of APIs.

[0143] "Analyzing" means analyzing the content of acquired information using machine learning or natural language processing to determine attributes such as relevance and importance.

[0144] "Selection" refers to the act of selecting information that conforms to pre-established criteria or rules based on analysis results, and eliminating unnecessary information.

[0145] "Advertising value" is an indicator that shows the degree to which selected information has a commercial promotional effect, and is calculated as the potential substitute value of advertising revenue.

[0146] An "analysis device" is a device equipped with the function of determining the emotional state based on the user's actions and reactions, and can utilize voice analysis and facial recognition technology.

[0147] "Electronic document format" refers to one type of digitized document format, such as PDF, which is a file format that is easy to save and transfer electronically.

[0148] "Spreadsheet format" refers to a format that organizes and displays digital data using rows and columns, and is a file format suitable for managing numerical data, such as Excel files.

[0149] The system of this invention integrates multiple platforms and technologies to efficiently acquire and analyze information and generate reports based on advertising effectiveness. The system mainly consists of servers, terminals, and users, with various hardware and software used according to their respective roles.

[0150] The server uses the Python BeautifulSoup library to perform web scraping to automatically retrieve information from media. When obtaining social media posts, data can be collected via APIs. The retrieved information is analyzed using natural language processing (NLP) techniques with Python's NLTK and spaCy. This analysis examines the linguistic features of the information and filters out the most relevant data.

[0151] After the information is filtered, the server uses databases such as MySQL® or PostgreSQL to analyze and compare it with past data in order to calculate its advertising value. This calculation evaluates the commercial value of the information.

[0152] On the other hand, the terminal functions as an analysis device to recognize the user's emotions. Using OpenCV for facial recognition and Librosa for voice analysis, it determines the user's emotions from their voice tone and facial expressions. This emotion information is used when the server generates reports, and is used to adjust the content and format of the reports according to the user's emotions.

[0153] The generated reports are output in electronic document format (such as PDF) or spreadsheet format (such as Excel) and automatically delivered to the user's device. This allows users to quickly receive optimized information tailored to their current situation and needs.

[0154] As a concrete example, when a public relations officer evaluates media coverage, the system can recognize the officer's emotions and emphasize positive messages and improvement suggestions in the report according to their stress levels and satisfaction. In this way, the present invention supports decision-making by providing information that takes user emotions into consideration.

[0155] Example of a prompt:

[0156] "Considering the current stress levels of our public relations staff, please generate a report that highlights improvement suggestions alongside positive news."

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] The server uses the Python BeautifulSoup library to scrape websites and collect news articles and social media posts to retrieve information from media. The input is the URL of the media or an API endpoint, and the output is raw information as text data. This information is stored in a database as external news articles and social media posts.

[0160] Step 2:

[0161] The server analyzes the acquired raw information using natural language processing (NLP) techniques. The input is the text data acquired in step 1, and the output is the analysis data, such as keyword extraction results and sentiment scores. Specifically, it uses the Python libraries NLTK and spaCy to tokenize text, recognize entities, and perform sentiment analysis, filtering out highly relevant information.

[0162] Step 3:

[0163] The server calculates advertising value based on filtered information. The input is the analysis results from step 2, and the output is numerical data as advertising equivalent value. This calculation uses media type, impact, and reference data from a historical advertising database. MySQL or PostgreSQL is used for database management, and calculations are performed based on an advertising value calculation model.

[0164] Step 4:

[0165] The device uses voice tone and facial expression data as input to recognize the user's emotional state. Specifically, it analyzes audio and image data collected using a webcam and microphone using the OpenCV and Librosa libraries to generate an emotion score as output. This information quantifies the user's emotions and is sent to the server.

[0166] Step 5:

[0167] The server generates reports based on sentiment information. Inputs are advertising value data from step 3 and user sentiment data from step 4, and output is a final report in PDF or Excel format. Text generation technology is used to adjust the content according to user sentiment and optimize the report's document format.

[0168] Step 6:

[0169] The server automatically delivers the generated report to the user's terminal. The input is the report generated in step 5, and the output is the file sent to the user's terminal. The report is delivered to the user via email or cloud storage, and the user can receive the information and use it for decision-making.

[0170] (Application Example 2)

[0171] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0172] In modern advertising, media analysis and advertising value assessment are largely manual processes, time-consuming, and difficult to optimize reports through real-time sentiment analysis. This hinders companies and advertising professionals from making quick and accurate decisions.

[0173] 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.

[0174] In this invention, the server includes a device for automatically acquiring information from media, a device for analyzing the acquired information and filtering the content based on pre-set criteria, a device for evaluating the advertising value of the filtered content, a device for analyzing user reactions and adjusting the content and format of the generated document, and a device for automatically providing the adjusted document to the relevant personnel. This makes it possible to grasp the effectiveness of advertising activities in real time and provide optimal reports according to the sentiments of the personnel in charge.

[0175] "Information" refers to data obtained from the media, including news articles and social media posts.

[0176] "Device" refers to a physical or software means for performing a specific function, and in this invention, it refers to a means for acquiring, analyzing, filtering, evaluating, and providing information.

[0177] "Criteria" are pre-set conditions or rules used when filtering collected information, and are used to extract highly relevant information.

[0178] "Advertising value" is an evaluation value calculated based on filtered information, and is a value calculated by referring to a database of past advertising costs.

[0179] "User response" refers to emotional feedback obtained from the recipient's facial expressions, voice, input patterns, etc., and is analyzed by an emotion engine.

[0180] A "document" is a report or notice generated based on the results of information analysis and provided to the relevant personnel.

[0181] The system for implementing this invention is designed to optimize advertising activities in today's information-driven society. The server automatically retrieves information from news APIs, social media APIs, and other sources. Then, using an analysis program built in Python, it analyzes the information using natural language processing technology and filters it based on pre-set criteria.

[0182] The filtered information is then evaluated for its advertising value by a machine learning algorithm based on a database of past advertising costs, which calculates its advertising equivalent value. Tensorflow® and PyTorch are used to improve the accuracy of the model.

[0183] User devices, such as smartphones and personal computers, are equipped with emotion engines that analyze the user's emotions in real time using voice recognition and facial recognition functions. This emotion information is sent to a server and used to adjust the content and presentation method of the information the user requests.

[0184] The generated documents are optimized based on analyzed emotions. For example, if the user is feeling stressed, the document is adjusted to emphasize positive elements. The adjusted documents are then delivered to the relevant personnel through an automated process.

[0185] As a concrete example, consider a scenario where an advertising manager is running a campaign for a new product. The system of this invention makes it easier for the advertising manager to check the recent media effectiveness of the new campaign through the system, and also allows them to receive feedback that takes into account the emotional state of the user on that day. An example of a prompt message would be: "Evaluate the effectiveness of the latest advertising campaign and highlight the positive results. The user is currently feeling a little stressed."

[0186] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0187] Step 1:

[0188] The server automatically retrieves information from media outlets through news APIs and social media APIs. At this stage, it is input with raw text data obtained from the APIs. The server calls the endpoints of these APIs to retrieve and store the latest information.

[0189] Step 2:

[0190] The server analyzes the acquired text data using natural language processing (NLP) techniques. Based on the analysis results, it filters the information according to pre-defined criteria. The input is raw text data, and the output is filtered, highly relevant information. The server uses an NLP library within a Python script to perform keyword extraction and sentiment analysis.

[0191] Step 3:

[0192] The server evaluates the value of an advertisement based on the filtered information. It uses a machine learning algorithm to calculate an appropriate advertising equivalent value from a database of past advertising costs. The input to this process is filtered information, and the output is a numerical value evaluated as the advertising value. The server runs the model and performs the evaluation using TensorFlow or PyTorch.

[0193] Step 4:

[0194] The user's device analyzes the user's emotions using an emotion engine. Voice input and facial expression data are used as input, and the output is the user's subjective emotional state. The device uses its microphone and camera to perform real-time facial recognition and voice tone analysis.

[0195] Step 5:

[0196] The server adjusts the content and presentation of generated documents based on sentiment data obtained from users. Inputs include filtered information, advertising value, and the user's emotional state, while output is an optimized document. The server uses a generative AI model to summarize the content and edit the document format.

[0197] Step 6:

[0198] The server automatically provides the prepared documents to the relevant personnel. The user's email address and cloud storage are used as input, and a ready-to-use document is delivered as output. The server utilizes mail server APIs and storage APIs to handle document transmission.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] [Second Embodiment]

[0203] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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".

[0215] This invention provides a system that integrates the automatic collection and analysis of media data, as well as the calculation of advertising equivalent value and report generation, into a single process. The system aims to improve the efficiency and reduce costs of public relations activities. Embodiments of this invention are described in detail below.

[0216] First, the server programmatically collects news articles and media coverage information from various media sources on the internet. This allows for real-time data acquisition as soon as articles are published.

[0217] Next, the server uses natural language processing (NLP) techniques to analyze the collected data and filters it against a list of keywords related to specific industries and companies, thereby filtering out relevant articles. This analysis step ensures that only highly relevant articles proceed to the next processing step.

[0218] For filtered articles, the server calculates the advertising equivalent value. This calculation takes into account factors such as the article's word count and the market value of the publishing medium, and references historical databases to provide a more accurate valuation.

[0219] Based on the obtained conversion figures and analysis data, the server automatically generates a report. The report includes an evaluation of current media exposure and the company's position in the market. It also suggests future public relations strategies based on comparisons with past data.

[0220] Finally, the generated report is sent to the user's (public relations officer's) device. The report is editable in PDF or Excel format and can be immediately used as meeting material or a report. This allows the user to quickly share the obtained information both internally and externally, accelerating decision-making.

[0221] By using this embodiment, public relations personnel can reduce the enormous amount of manual work previously required and concentrate their resources on other strategic activities. This is expected to lead to more effective and efficient public relations activities.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] The server periodically crawls news feeds and various media APIs publicly available on the internet to retrieve newly published article data. It uses RSS feeds and article APIs to update information in real time.

[0225] Step 2:

[0226] The server analyzes the acquired article data using natural language processing (NLP) tools. The analysis extracts keywords based on the article content and matches them against a pre-configured list of relevant keywords. This process filters the articles to include only those related to specific themes or companies.

[0227] Step 3:

[0228] The server calculates the advertising equivalent value for filtered articles. Specifically, it calculates the value based on the number of characters in the article, the page it appears on, the type of media, and its influence, and then refers to an existing advertising cost database to evaluate its appropriate advertising value.

[0229] Step 4:

[0230] The server generates a report that includes a trend analysis of the article's impact and a comparison with competitors in the market, along with the calculated advertising equivalent value. The generated report is structured to be easy to understand, with data visualizations.

[0231] Step 5:

[0232] The server outputs the generated report in PDF or Excel format and automatically distributes it via email to designated stakeholders (such as public relations personnel). This allows users to easily obtain the information and use it for decision-making and report creation.

[0233] (Example 1)

[0234] 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."

[0235] In today's information society, while a vast amount of information is distributed from diverse sources, it is difficult to extract only the useful information relevant to a specific industry or company. Furthermore, there is a challenge in that it takes considerable effort and time to efficiently analyze this information, evaluate its advertising value, and create concrete and rapid analytical documents for formulating effective strategies.

[0236] 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.

[0237] In this invention, the server includes means for automatically collecting information from information sources, means for applying natural language processing technology to analyze the collected information and filtering articles by keywords based on pre-set criteria, and means for calculating the advertising value of the filtered articles. This makes it possible to quickly and efficiently collect highly relevant information and automatically generate analytical documents for quantifying the value of that information and utilizing it in strategic planning.

[0238] "Information sources" refer to communication infrastructure and platforms that serve as the starting point for acquiring information from various media.

[0239] "Information" refers to news articles and related data published by the media.

[0240] "Natural language processing technology" refers to computational techniques for analyzing texts expressed in human language and identifying and classifying the characteristics of the information.

[0241] "Filtering" refers to the process of selecting information according to specific criteria and extracting only the information that meets the required conditions.

[0242] "Advertising value" refers to a numerical indicator calculated to quantitatively evaluate the advertising effectiveness of information published in the media.

[0243] An "analysis document" refers to a document generated based on collected information and its analysis results, and includes a comprehensive report that includes data evaluation and recommendations.

[0244] "Users" refers to the individuals or organizations that receive the generated analytical documents and use that information to make decisions or develop strategies.

[0245] This invention is a system that automatically collects, analyzes, and evaluates media information, calculates advertising value, and generates a report. The system mainly consists of a server and user terminals. The specific configuration is described below.

[0246] The server connects to various media platforms that serve as information sources via the internet. Data collection from these sources utilizes APIs and web scraping techniques. For example, open-source software like Scrapy is sometimes used. The server uses these techniques to retrieve new article data in real time and record it in a database.

[0247] Next, the server applies natural language processing techniques to the collected information. This is achieved by using specific software libraries (e.g., Python's NLTK or spaCy). This allows the server to extract keywords from the information and filter it according to its relevance. After filtering, only information relevant to a specific industry or company is selected and sent to the next stage.

[0248] The server performs calculations to determine the advertising value of the filtered information. Factors such as the media's influence, the number of words in the article, and the frequency of publication are considered in the calculation. This allows the server to quantitatively evaluate the market value of the collected information.

[0249] The server generates a report based on the analysis results and advertising value. This report generation utilizes data visualization tools such as Matplotlib and Seaborn. The generated report includes graphs and tables, presented in a format that allows for intuitive understanding of the information.

[0250] Finally, the generated report is sent to the user's device. The user then uses the report received via their device to consider advertising campaigns and media strategies. The report is sent in PDF or Excel format, allowing the user to immediately use it as meeting material.

[0251] As a concrete example, when a user's company launches a new product into the market, they can use this system to collect relevant news articles and analyze their media influence. This makes it easier for the user to understand the market penetration of the new product.

[0252] An example of a prompt for a generative AI model is, "Collect news from the fashion industry and create a report that calculates advertising value." This prompt allows users to quickly obtain valuable information relevant to a specific industry.

[0253] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0254] Step 1:

[0255] The server automatically collects new information from information sources. It uses a pre-configured list of information sources (such as URLs) as input. The server uses APIs and web scraping techniques to visit each information source and extract new article data. The retrieved article data is stored in a database as output. Specifically, a scheduler visits information sources at regular intervals, identifying and retrieving updated content.

[0256] Step 2:

[0257] The server applies natural language processing to the collected data. It uses collected article data stored in a database as input. The server utilizes Python's NLTK and spaCy to perform tokenization, part-of-speech tagging, and extract keywords. The output is a list of articles with evaluated relevance. Specifically, it compares specific keywords with words within articles, quantifies their relevance, and lists them.

[0258] Step 3:

[0259] The server calculates the advertising value of filtered articles. It uses a list of relevant articles as input. The server calculates an evaluation value using a specific formula, taking into account factors such as the article's word count, the reputation of the publication medium, and past advertising value data. The output is the advertising value evaluation result. Specifically, it references past evaluation data from a database and compares it to the current value.

[0260] Step 4:

[0261] The server generates reports based on advertising value and analysis results. It uses evaluation results and analyzed article data as input. The server visually constructs the data using graph libraries (Matplotlib and Seaborn). The final analysis report is generated in PDF or Excel format as output. Specifically, it documents suggestions based on the analysis results, including visualized graphs and charts.

[0262] Step 5:

[0263] The server delivers the generated report to the user's terminal. It uses the generated report file as input. The server sends the report to the user's terminal via the mail system. The output is a report accessible on the user's terminal. Specific operations include executing a send command to the mail server and confirming receipt.

[0264] (Application Example 1)

[0265] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0266] In modern public relations, it is crucial to properly manage media exposure and quickly evaluate the effectiveness of advertising campaigns. However, manual information gathering and analysis require an enormous amount of time and effort, making it difficult to develop efficient PR strategies. Furthermore, the inability to check information in real time and adjust advertising strategies can lead to delays in responses that require immediate action on the ground. This results in the challenge of not fully realizing the effectiveness of the overall PR activities.

[0267] 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.

[0268] In this invention, the server includes means for automatically collecting information from media, means for analyzing the collected information and extracting articles based on pre-set criteria, means for calculating the advertising equivalent value of the extracted articles, means for enabling public relations personnel to check real-time media exposure status, and means for measuring the effectiveness of advertising campaigns and providing support for adjusting advertising strategies. This enables the rapid and accurate deployment of public relations strategies.

[0269] "Methods for automatically collecting information from the media" refers to the process of quickly obtaining news articles and published information from various sources on the internet.

[0270] "Means for analyzing collected information and extracting articles based on pre-set criteria" refers to a process that uses natural language processing technology to analyze collected information and selects articles that are highly relevant according to specific criteria.

[0271] "Method for calculating the advertising equivalent value of extracted articles" refers to an algorithm that calculates the advertising value of an article based on filtered articles, taking into account factors such as the number of characters in the article and the market value of the publisher.

[0272] "A means for public relations personnel to check real-time media exposure" refers to technology that provides a user interface that allows public relations personnel to instantly check data on current media exposure at any time.

[0273] "Means of measuring the effectiveness of advertising campaigns and providing support for adjusting advertising strategies" refers to a process of analyzing the results of advertising activities and providing support for proposing the optimal public relations strategy based on those results.

[0274] The server first uses internet news APIs to automatically collect news articles and media coverage information from various sources. This collection process allows data to be obtained almost simultaneously with the publication of articles, making it possible to conduct public relations activities based on the latest information.

[0275] The collected information is analyzed on the server using natural language processing (NLP) libraries. Specifically, libraries such as spaCy and NLTK are used to filter the collected articles based on certain criteria. In this way, only highly relevant articles are selected.

[0276] Next, the advertising equivalent value is calculated for the filtered articles. This calculation algorithm considers the number of characters in the article and the market value of its publisher, and evaluates them by referring to a historical database. This process is implemented using a Python program.

[0277] Based on the generated conversion amounts and analysis results, the server automatically creates a report. The report is generated through data organization using the Pandas library and chart creation using Matplotlib. This report is sent to the public relations officer's terminal in PDF or Excel format.

[0278] The report sent to the terminal enables the publicity staff to check the media exposure status in real time and immediately measure the effectiveness of the advertising campaign. As a result, the publicity staff can flexibly adjust the strategy.

[0279] As a specific example, when a company conducts a marketing campaign for a new product, by using this system, it can grasp the media exposure status immediately after the campaign starts and analyze its effectiveness. As a result, the company can quickly formulate an effective publicity strategy.

[0280] An example of the prompt sentence input into the generative AI model is "Please obtain media exposure information regarding the latest product release and generate a report analyzing its advertising effect."

[0281] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0282] Step 1:

[0283] The server collects news articles from various information sources on the Internet using the News API. The input is the news feed URL provided by each media, and the output is a set of the collected article data. Here, the requests library in Python is used to send an HTTP request to obtain the news article data.

[0284] Step 2:

[0285] The server analyzes the collected data using a natural language processing (NLP) library. The input is the collected article data, and the output is a list of articles filtered by specific criteria. Specifically, the spaCy library is used to analyze the text in the article and perform a process of selecting relevant articles based on pre-set keywords.

[0286] Step 3:

[0287] The server calculates the advertising equivalent amount for the filtered articles. The input is a list of related articles, and the output is a list of the advertising equivalent amounts for each article. Here, numerical calculations are performed in Python, and the advertising value is calculated considering the number of characters in the article and the market value of the publishing medium.

[0288] Step 4:

[0289] The server generates a report based on the obtained advertising equivalent amount and analysis results. The input is a list of advertising equivalent amounts and analysis data, and the output is a report in PDF or Excel format. The data is organized using the Pandas library, graphs and tables are created using Matplotlib, and finally the report is formatted.

[0290] Step 5:

[0291] The server automatically distributes the generated report to the terminals of the publicity staff. The input is the created report, and the output is the report displayed on the terminal. Here, the SMTP protocol is used to distribute the report data via email so that the report can be viewed on the terminal.

[0292] Step 6:

[0293] The user checks the real-time media exposure information via the terminal and measures the effect of the advertising campaign. The input is the report data received on the terminal, and the output is the information for strategic decision-making. Through the user interface, the data is viewed in real time, and the advertising strategy is adjusted flexibly.

[0294] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0295] This invention is a system that combines a sentiment engine, which takes user emotions into consideration, with a series of processes from media data collection and analysis to calculation of advertising equivalent value, report generation, and distribution. This invention was developed to optimize public relations and marketing activities.

[0296] First, the server utilizes existing media data collection systems to automatically acquire data such as news articles and social media posts. Next, the server uses natural language processing (NLP) and machine learning algorithms to analyze the collected data and filter it based on specific criteria and keywords. This filtering process allows the server to proceed to the next stage with only the most relevant information.

[0297] Subsequently, the advertising equivalent value is calculated. The server considers the length of the filtered article, its scope of impact, the type of media, etc., and refers to a database of past advertising costs to accurately assess the advertising value.

[0298] Furthermore, a key aspect of this invention is the use of an emotion engine that recognizes the user's emotions. The emotion engine installed in the terminal determines the user's emotions from their voice tone, facial expressions, input patterns, etc. This emotion information is shared with a server and taken into consideration during the report generation process, allowing for adjustments to the content of the information the user requests and optimization of the presentation method.

[0299] The generated reports are further refined in content and format based on user sentiment and automatically delivered from the server to the user's (or relevant person's) terminal. This process allows users to quickly receive information that better matches their current situation and needs, enabling them to make effective decisions.

[0300] As a specific example, when a publicity staff member evaluates media-published information, the system can capture the stress and satisfaction of the staff member with an emotion engine and accordingly emphasize positive messages and improvement suggestions in the report. Thus, the present invention is an embodiment capable of further improving the user experience through information provision reflecting emotions.

[0301] The following describes the processing flow.

[0302] Step 1:

[0303] The server collects new article data from a pre-set information source through a news API or RSS feed. The collection process is executed periodically to ensure fresh media content.

[0304] Step 2:

[0305] The server analyzes the collected article data with a natural language processing (NLP) engine and checks whether it matches a pre-set keyword list. Through this filtering process, only highly relevant articles are selected.

[0306] Step 3:

[0307] The server calculates an advertising equivalent amount for the filtered data. Specifically, it evaluates the value based on the number of characters in the article, the influence of the media, and the publication frequency, and obtains a reference value from an advertising cost database.

[0308] Step 4:

[0309] The emotion engine installed on the terminal captures the user's operation status, voice, and facial expression data and analyzes emotions in real time. This emotion data is transmitted to the server and considered when generating a report.

[0310] Step 5:

[0311] The server generates customized reports based on analysis results, advertising equivalent value, and user sentiment data. These reports include impact assessments, improvement suggestions, and future strategies, with the amount and presentation of information adjusted to match user sentiment.

[0312] Step 6:

[0313] The server exports the generated report in PDF or Excel format and automatically sends it to the user's specified email address. This allows the user to quickly receive the necessary information and use it as a reference for making work-related decisions.

[0314] (Example 2)

[0315] 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".

[0316] In today's information society, a vast amount of media information is generated daily, making it crucial for companies and individuals to quickly select and utilize appropriate information. However, conventional systems struggle to accurately determine the relevance of information and provide information that considers user sentiment, resulting in delays in the development of efficient marketing strategies and decision-making.

[0317] 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.

[0318] In this invention, the server includes means for automatically acquiring information from media, means for analyzing the acquired information and selecting content based on pre-set criteria, and means for calculating advertising value based on the selected content. This makes it possible to provide information tailored to the user's emotions.

[0319] "Media" is a general term for means and platforms used to transmit and share information, such as news articles and social media posts.

[0320] "Information" refers to all data and content that can be obtained through media, and includes formats such as text, audio, images, and video.

[0321] "Acquiring" refers to the process by which a server automatically or semi-automatically gathers information from external media, primarily through web scraping or the use of APIs.

[0322] "Analyzing" means analyzing the content of acquired information using machine learning or natural language processing to determine attributes such as relevance and importance.

[0323] "Selection" refers to the act of selecting information that conforms to pre-established criteria or rules based on analysis results, and eliminating unnecessary information.

[0324] "Advertising value" is an indicator that shows the degree to which selected information has a commercial promotional effect, and is calculated as the potential substitute value of advertising revenue.

[0325] An "analysis device" is a device equipped with the function of determining the emotional state based on the user's actions and reactions, and can utilize voice analysis and facial recognition technology.

[0326] "Electronic document format" refers to one type of digitized document format, such as PDF, which is a file format that is easy to save and transfer electronically.

[0327] "Spreadsheet format" refers to a format that organizes and displays digital data using rows and columns, and is a file format suitable for managing numerical data, such as Excel files.

[0328] The system of this invention integrates multiple platforms and technologies to efficiently acquire and analyze information and generate reports based on advertising effectiveness. The system mainly consists of servers, terminals, and users, with various hardware and software used according to their respective roles.

[0329] The server uses the Python BeautifulSoup library to perform web scraping to automatically retrieve information from media. When obtaining social media posts, data can be collected via APIs. The retrieved information is analyzed using natural language processing (NLP) techniques with Python's NLTK and spaCy. This analysis examines the linguistic features of the information and filters out the most relevant data.

[0330] After the information is filtered, the server uses databases such as MySQL or PostgreSQL to analyze and compare it with past data in order to calculate its advertising value. This calculation evaluates the commercial value of the information.

[0331] On the other hand, the terminal functions as an analysis device to recognize the user's emotions. Using OpenCV for facial recognition and Librosa for voice analysis, it determines the user's emotions from their voice tone and facial expressions. This emotion information is used when the server generates reports, and is used to adjust the content and format of the reports according to the user's emotions.

[0332] The generated reports are output in electronic document format (such as PDF) or spreadsheet format (such as Excel) and automatically delivered to the user's device. This allows users to quickly receive optimized information tailored to their current situation and needs.

[0333] As a concrete example, when a public relations officer evaluates media coverage, the system can recognize the officer's emotions and emphasize positive messages and improvement suggestions in the report according to their stress levels and satisfaction. In this way, the present invention supports decision-making by providing information that takes user emotions into consideration.

[0334] Example of a prompt:

[0335] "Considering the current stress levels of our public relations staff, please generate a report that highlights improvement suggestions alongside positive news."

[0336] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0337] Step 1:

[0338] The server uses the Python BeautifulSoup library to scrape websites and collect news articles and social media posts to retrieve information from media. The input is the URL of the media or an API endpoint, and the output is raw information as text data. This information is stored in a database as external news articles and social media posts.

[0339] Step 2:

[0340] The server analyzes the acquired raw information using natural language processing (NLP) techniques. The input is the text data acquired in step 1, and the output is the analysis data, such as keyword extraction results and sentiment scores. Specifically, it uses the Python libraries NLTK and spaCy to tokenize text, recognize entities, and perform sentiment analysis, filtering out highly relevant information.

[0341] Step 3:

[0342] The server calculates advertising value based on filtered information. The input is the analysis results from step 2, and the output is numerical data as advertising equivalent value. This calculation uses media type, impact, and reference data from a historical advertising database. MySQL or PostgreSQL is used for database management, and calculations are performed based on an advertising value calculation model.

[0343] Step 4:

[0344] The device uses voice tone and facial expression data as input to recognize the user's emotional state. Specifically, it analyzes audio and image data collected using a webcam and microphone using the OpenCV and Librosa libraries to generate an emotion score as output. This information quantifies the user's emotions and is sent to the server.

[0345] Step 5:

[0346] The server generates reports based on sentiment information. Inputs are advertising value data from step 3 and user sentiment data from step 4, and output is a final report in PDF or Excel format. Text generation technology is used to adjust the content according to user sentiment and optimize the report's document format.

[0347] Step 6:

[0348] The server automatically delivers the generated report to the user's terminal. The input is the report generated in step 5, and the output is the file sent to the user's terminal. The report is delivered to the user via email or cloud storage, and the user can receive the information and use it for decision-making.

[0349] (Application Example 2)

[0350] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0351] In modern advertising, media analysis and advertising value assessment are largely manual processes, time-consuming, and difficult to optimize reports through real-time sentiment analysis. This hinders companies and advertising professionals from making quick and accurate decisions.

[0352] 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.

[0353] In this invention, the server includes a device for automatically acquiring information from media, a device for analyzing the acquired information and filtering the content based on pre-set criteria, a device for evaluating the advertising value of the filtered content, a device for analyzing user reactions and adjusting the content and format of the generated document, and a device for automatically providing the adjusted document to the relevant personnel. This makes it possible to grasp the effectiveness of advertising activities in real time and provide optimal reports according to the sentiments of the personnel in charge.

[0354] "Information" refers to data obtained from the media, including news articles and social media posts.

[0355] "Device" refers to a physical or software means for performing a specific function, and in this invention, it refers to a means for acquiring, analyzing, filtering, evaluating, and providing information.

[0356] "Criteria" are pre-set conditions or rules used when filtering collected information, and are used to extract highly relevant information.

[0357] "Advertising value" is an evaluation value calculated based on filtered information, and is a value calculated by referring to a database of past advertising costs.

[0358] "User response" refers to emotional feedback obtained from the recipient's facial expressions, voice, input patterns, etc., and is analyzed by an emotion engine.

[0359] A "document" is a report or notice generated based on the results of information analysis and provided to the relevant personnel.

[0360] The system for implementing this invention is designed to optimize advertising activities in today's information-driven society. The server automatically retrieves information from news APIs, social media APIs, and other sources. Then, using an analysis program built in Python, it analyzes the information using natural language processing technology and filters it based on pre-set criteria.

[0361] The filtered information is then evaluated for its advertising value by a machine learning algorithm based on a database of past advertising costs, which calculates its advertising equivalent value. TensorFlow and PyTorch are used to improve the accuracy of the model during this process.

[0362] User devices, such as smartphones and personal computers, are equipped with emotion engines that analyze the user's emotions in real time using voice recognition and facial recognition functions. This emotion information is sent to a server and used to adjust the content and presentation method of the information the user requests.

[0363] The generated documents are optimized based on analyzed emotions. For example, if the user is feeling stressed, the document is adjusted to emphasize positive elements. The adjusted documents are then delivered to the relevant personnel through an automated process.

[0364] As a concrete example, consider a scenario where an advertising manager is running a campaign for a new product. The system of this invention makes it easier for the advertising manager to check the recent media effectiveness of the new campaign through the system, and also allows them to receive feedback that takes into account the emotional state of the user on that day. An example of a prompt message would be: "Evaluate the effectiveness of the latest advertising campaign and highlight the positive results. The user is currently feeling a little stressed."

[0365] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0366] Step 1:

[0367] The server automatically retrieves information from media outlets through news APIs and social media APIs. At this stage, it is input with raw text data obtained from the APIs. The server calls the endpoints of these APIs to retrieve and store the latest information.

[0368] Step 2:

[0369] The server analyzes the acquired text data using natural language processing (NLP) techniques. Based on the analysis results, it filters the information according to pre-defined criteria. The input is raw text data, and the output is filtered, highly relevant information. The server uses an NLP library within a Python script to perform keyword extraction and sentiment analysis.

[0370] Step 3:

[0371] The server evaluates the value of an advertisement based on the filtered information. It uses a machine learning algorithm to calculate an appropriate advertising equivalent value from a database of past advertising costs. The input to this process is filtered information, and the output is a numerical value evaluated as the advertising value. The server runs the model and performs the evaluation using TensorFlow or PyTorch.

[0372] Step 4:

[0373] The user's device analyzes the user's emotions using an emotion engine. Voice input and facial expression data are used as input, and the output is the user's subjective emotional state. The device uses its microphone and camera to perform real-time facial recognition and voice tone analysis.

[0374] Step 5:

[0375] The server adjusts the content and presentation of generated documents based on sentiment data obtained from users. Inputs include filtered information, advertising value, and the user's emotional state, while output is an optimized document. The server uses a generative AI model to summarize the content and edit the document format.

[0376] Step 6:

[0377] The server automatically provides the prepared documents to the relevant personnel. The user's email address and cloud storage are used as input, and a ready-to-use document is delivered as output. The server utilizes mail server APIs and storage APIs to handle document transmission.

[0378] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0379] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0380] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0381] [Third Embodiment]

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

[0383] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0384] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0385] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0386] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0387] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0388] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0389] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0390] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0391] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0392] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0393] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0394] This invention provides a system that integrates the automatic collection and analysis of media data, as well as the calculation of advertising equivalent value and report generation, into a single process. The system aims to improve the efficiency and reduce costs of public relations activities. Embodiments of this invention are described in detail below.

[0395] First, the server programmatically collects news articles and media coverage information from various media sources on the internet. This allows for real-time data acquisition as soon as articles are published.

[0396] Next, the server uses natural language processing (NLP) techniques to analyze the collected data and filters it against a list of keywords related to specific industries and companies, thereby filtering out relevant articles. This analysis step ensures that only highly relevant articles proceed to the next processing step.

[0397] For filtered articles, the server calculates the advertising equivalent value. This calculation takes into account factors such as the article's word count and the market value of the publishing medium, and references historical databases to provide a more accurate valuation.

[0398] Based on the obtained conversion figures and analysis data, the server automatically generates a report. The report includes an evaluation of current media exposure and the company's position in the market. It also suggests future public relations strategies based on comparisons with past data.

[0399] Finally, the generated report is sent to the user's (public relations officer's) device. The report is editable in PDF or Excel format and can be immediately used as meeting material or a report. This allows the user to quickly share the obtained information both internally and externally, accelerating decision-making.

[0400] By using this embodiment, public relations personnel can reduce the enormous amount of manual work previously required and concentrate their resources on other strategic activities. This is expected to lead to more effective and efficient public relations activities.

[0401] The following describes the processing flow.

[0402] Step 1:

[0403] The server periodically crawls news feeds and various media APIs publicly available on the internet to retrieve newly published article data. It uses RSS feeds and article APIs to update information in real time.

[0404] Step 2:

[0405] The server analyzes the acquired article data using natural language processing (NLP) tools. The analysis extracts keywords based on the article content and matches them against a pre-configured list of relevant keywords. This process filters the articles to include only those related to specific themes or companies.

[0406] Step 3:

[0407] The server calculates the advertising equivalent value for filtered articles. Specifically, it calculates the value based on the number of characters in the article, the page it appears on, the type of media, and its influence, and then refers to an existing advertising cost database to evaluate its appropriate advertising value.

[0408] Step 4:

[0409] The server generates a report that includes a trend analysis of the article's impact and a comparison with competitors in the market, along with the calculated advertising equivalent value. The generated report is structured to be easy to understand, with data visualizations.

[0410] Step 5:

[0411] The server outputs the generated report in PDF or Excel format and automatically distributes it via email to designated stakeholders (such as public relations personnel). This allows users to easily obtain the information and use it for decision-making and report creation.

[0412] (Example 1)

[0413] 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."

[0414] In today's information society, while a vast amount of information is distributed from diverse sources, it is difficult to extract only the useful information relevant to a specific industry or company. Furthermore, there is a challenge in that it takes considerable effort and time to efficiently analyze this information, evaluate its advertising value, and create concrete and rapid analytical documents for formulating effective strategies.

[0415] 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.

[0416] In this invention, the server includes means for automatically collecting information from information sources, means for applying natural language processing technology to analyze the collected information and filtering articles by keywords based on pre-set criteria, and means for calculating the advertising value of the filtered articles. This makes it possible to quickly and efficiently collect highly relevant information and automatically generate analytical documents for quantifying the value of that information and utilizing it in strategic planning.

[0417] "Information sources" refer to communication infrastructure and platforms that serve as the starting point for acquiring information from various media.

[0418] "Information" refers to news articles and related data published by the media.

[0419] "Natural language processing technology" refers to computational techniques for analyzing texts expressed in human language and identifying and classifying the characteristics of the information.

[0420] "Filtering" refers to the process of selecting information according to specific criteria and extracting only the information that meets the required conditions.

[0421] "Advertising value" refers to a numerical indicator calculated to quantitatively evaluate the advertising effectiveness of information published in the media.

[0422] An "analysis document" refers to a document generated based on collected information and its analysis results, and includes a comprehensive report that includes data evaluation and recommendations.

[0423] "Users" refers to the individuals or organizations that receive the generated analytical documents and use that information to make decisions or develop strategies.

[0424] This invention is a system that automatically collects, analyzes, and evaluates media information, calculates advertising value, and generates a report. The system mainly consists of a server and user terminals. The specific configuration is described below.

[0425] The server connects to various media platforms that serve as information sources via the internet. Data collection from these sources utilizes APIs and web scraping techniques. For example, open-source software like Scrapy is sometimes used. The server uses these techniques to retrieve new article data in real time and record it in a database.

[0426] Next, the server applies natural language processing techniques to the collected information. This is achieved by using specific software libraries (e.g., Python's NLTK or spaCy). This allows the server to extract keywords from the information and filter it according to its relevance. After filtering, only information relevant to a specific industry or company is selected and sent to the next stage.

[0427] The server performs calculations to determine the advertising value of the filtered information. Factors such as the media's influence, the number of words in the article, and the frequency of publication are considered in the calculation. This allows the server to quantitatively evaluate the market value of the collected information.

[0428] The server generates a report based on the analysis results and advertising value. This report generation utilizes data visualization tools such as Matplotlib and Seaborn. The generated report includes graphs and tables, presented in a format that allows for intuitive understanding of the information.

[0429] Finally, the generated report is sent to the user's device. The user then uses the report received via their device to consider advertising campaigns and media strategies. The report is sent in PDF or Excel format, allowing the user to immediately use it as meeting material.

[0430] As a concrete example, when a user's company launches a new product into the market, they can use this system to collect relevant news articles and analyze their media influence. This makes it easier for the user to understand the market penetration of the new product.

[0431] An example of a prompt for a generative AI model is, "Collect news from the fashion industry and create a report that calculates advertising value." This prompt allows users to quickly obtain valuable information relevant to a specific industry.

[0432] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0433] Step 1:

[0434] The server automatically collects new information from information sources. It uses a pre-configured list of information sources (such as URLs) as input. The server uses APIs and web scraping techniques to visit each information source and extract new article data. The retrieved article data is stored in a database as output. Specifically, a scheduler visits information sources at regular intervals, identifying and retrieving updated content.

[0435] Step 2:

[0436] The server applies natural language processing to the collected data. It uses collected article data stored in a database as input. The server utilizes Python's NLTK and spaCy to perform tokenization, part-of-speech tagging, and extract keywords. The output is a list of articles with evaluated relevance. Specifically, it compares specific keywords with words within articles, quantifies their relevance, and lists them.

[0437] Step 3:

[0438] The server calculates the advertising value of filtered articles. It uses a list of relevant articles as input. The server calculates an evaluation value using a specific formula, taking into account factors such as the article's word count, the reputation of the publication medium, and past advertising value data. The output is the advertising value evaluation result. Specifically, it references past evaluation data from a database and compares it to the current value.

[0439] Step 4:

[0440] The server generates reports based on advertising value and analysis results. It uses evaluation results and analyzed article data as input. The server visually constructs the data using graph libraries (Matplotlib and Seaborn). The final analysis report is generated in PDF or Excel format as output. Specifically, it documents suggestions based on the analysis results, including visualized graphs and charts.

[0441] Step 5:

[0442] The server delivers the generated report to the user's terminal. It uses the generated report file as input. The server sends the report to the user's terminal via the mail system. The output is a report accessible on the user's terminal. Specific operations include executing a send command to the mail server and confirming receipt.

[0443] (Application Example 1)

[0444] 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."

[0445] In modern public relations, it is crucial to properly manage media exposure and quickly evaluate the effectiveness of advertising campaigns. However, manual information gathering and analysis require an enormous amount of time and effort, making it difficult to develop efficient PR strategies. Furthermore, the inability to check information in real time and adjust advertising strategies can lead to delays in responses that require immediate action on the ground. This results in the challenge of not fully realizing the effectiveness of the overall PR activities.

[0446] 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.

[0447] In this invention, the server includes means for automatically collecting information from media, means for analyzing the collected information and extracting articles based on pre-set criteria, means for calculating the advertising equivalent value of the extracted articles, means for enabling public relations personnel to check real-time media exposure status, and means for measuring the effectiveness of advertising campaigns and providing support for adjusting advertising strategies. This enables the rapid and accurate deployment of public relations strategies.

[0448] "Methods for automatically collecting information from the media" refers to the process of quickly obtaining news articles and published information from various sources on the internet.

[0449] "Means for analyzing collected information and extracting articles based on pre-set criteria" refers to a process that uses natural language processing technology to analyze collected information and selects articles that are highly relevant according to specific criteria.

[0450] "Method for calculating the advertising equivalent value of extracted articles" refers to an algorithm that calculates the advertising value of an article based on filtered articles, taking into account factors such as the number of characters in the article and the market value of the publisher.

[0451] "A means for public relations personnel to check real-time media exposure" refers to technology that provides a user interface that allows public relations personnel to instantly check data on current media exposure at any time.

[0452] "Means of measuring the effectiveness of advertising campaigns and providing support for adjusting advertising strategies" refers to a process of analyzing the results of advertising activities and providing support for proposing the optimal public relations strategy based on those results.

[0453] The server first uses internet news APIs to automatically collect news articles and media coverage information from various sources. This collection process allows data to be obtained almost simultaneously with the publication of articles, making it possible to conduct public relations activities based on the latest information.

[0454] The collected information is analyzed on the server using natural language processing (NLP) libraries. Specifically, libraries such as spaCy and NLTK are used to filter the collected articles based on certain criteria. In this way, only highly relevant articles are selected.

[0455] Next, the advertising equivalent value is calculated for the filtered articles. This calculation algorithm considers the number of characters in the article and the market value of its publisher, and evaluates them by referring to a historical database. This process is implemented using a Python program.

[0456] Based on the generated conversion amounts and analysis results, the server automatically creates a report. The report is generated through data organization using the Pandas library and chart creation using Matplotlib. This report is sent to the public relations officer's terminal in PDF or Excel format.

[0457] The reports sent to the terminal allow public relations personnel to check media exposure in real time and immediately measure the effectiveness of advertising campaigns. This enables them to flexibly adjust their strategies.

[0458] For example, when a company conducts a marketing campaign for a new product, this system can be used to understand media exposure immediately after the campaign starts and analyze its effectiveness. This allows the company to quickly develop an effective public relations strategy.

[0459] An example of a prompt to input into the generating AI model would be: "Please generate a report that retrieves media exposure information for the latest product release and analyzes its advertising effectiveness."

[0460] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0461] Step 1:

[0462] The server uses a news API to collect news articles from various sources on the internet. The input is the news feed URLs provided by each media outlet, and the output is a collection of collected article data. Here, the Python requests library is used to send HTTP requests and retrieve the news article data.

[0463] Step 2:

[0464] The server analyzes the collected data using a natural language processing (NLP) library. The input is the collected article data, and the output is a list of articles filtered according to specific criteria. Specifically, the spaCy library is used to analyze the text within the articles and select relevant articles based on pre-configured keywords.

[0465] Step 3:

[0466] The server calculates the advertising equivalent value for filtered articles. The input is a list of related articles, and the output is a list of the advertising equivalent value for each article. Here, numerical calculations are performed using Python to calculate the advertising value, taking into account the number of characters in the article and the market value of the publishing medium.

[0467] Step 4:

[0468] The server generates a report based on the obtained advertising equivalent value and analysis results. The input is a list of advertising equivalent values ​​and analysis data, and the output is a report in PDF or Excel format. The Pandas library is used to organize the data, Matplotlib is used to create graphs and tables, and finally the report is formatted.

[0469] Step 5:

[0470] The server automatically distributes the generated report to the public relations officer's terminal. The input is the generated report, and the output is the report displayed on the terminal. Here, the report data is distributed via email using the SMTP protocol, allowing the report to be viewed on the terminal.

[0471] Step 6:

[0472] Users can view real-time media exposure information via their devices and measure the effectiveness of advertising campaigns. Input is report data received on the device, and output is information for strategic decision-making. The user interface allows for real-time data viewing and flexible adjustment of advertising strategies.

[0473] 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.

[0474] This invention is a system that combines a sentiment engine, which takes user emotions into consideration, with a series of processes from media data collection and analysis to calculation of advertising equivalent value, report generation, and distribution. This invention was developed to optimize public relations and marketing activities.

[0475] First, the server utilizes existing media data collection systems to automatically acquire data such as news articles and social media posts. Next, the server uses natural language processing (NLP) and machine learning algorithms to analyze the collected data and filter it based on specific criteria and keywords. This filtering process allows the server to proceed to the next stage with only the most relevant information.

[0476] Subsequently, the advertising equivalent value is calculated. The server considers the length of the filtered article, its scope of impact, the type of media, etc., and refers to a database of past advertising costs to accurately assess the advertising value.

[0477] Furthermore, a key aspect of this invention is the use of an emotion engine that recognizes the user's emotions. The emotion engine installed in the terminal determines the user's emotions from their voice tone, facial expressions, input patterns, etc. This emotion information is shared with a server and taken into consideration during the report generation process, allowing for adjustments to the content of the information the user requests and optimization of the presentation method.

[0478] The generated reports are further refined in content and format based on user sentiment and automatically delivered from the server to the user's (or relevant person's) terminal. This process allows users to quickly receive information that better matches their current situation and needs, enabling them to make effective decisions.

[0479] As a concrete example, when a public relations officer evaluates media coverage, the system can capture the officer's stress and satisfaction levels using an emotion engine, and accordingly emphasize positive messages and improvement suggestions within the report. In this way, the present invention is an embodiment that can further improve the user experience by providing information that reflects emotions.

[0480] The following describes the processing flow.

[0481] Step 1:

[0482] The server collects new article data from pre-configured sources via news APIs and RSS feeds. The collection process is performed regularly to ensure fresh media content.

[0483] Step 2:

[0484] The server analyzes the collected article data using a natural language processing (NLP) engine to check if it matches a pre-configured keyword list. This filtering process selects only the most relevant articles.

[0485] Step 3:

[0486] The server calculates the advertising equivalent value for the filtered data. Specifically, it evaluates the value based on the number of characters in the article, the influence of the media, and the frequency of publication, and obtains reference values ​​from the advertising cost database.

[0487] Step 4:

[0488] The emotion engine built into the device captures user activity, voice, and facial expression data, and analyzes emotions in real time. This emotion data is sent to a server and taken into consideration when generating reports.

[0489] Step 5:

[0490] The server generates customized reports based on analysis results, advertising equivalent value, and user sentiment data. These reports include impact assessments, improvement suggestions, and future strategies, with the amount and presentation of information adjusted to match user sentiment.

[0491] Step 6:

[0492] The server exports the generated report in PDF or Excel format and automatically sends it to the user's specified email address. This allows the user to quickly receive the necessary information and use it as a reference for making work-related decisions.

[0493] (Example 2)

[0494] 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."

[0495] In today's information society, a vast amount of media information is generated daily, making it crucial for companies and individuals to quickly select and utilize appropriate information. However, conventional systems struggle to accurately determine the relevance of information and provide information that considers user sentiment, resulting in delays in the development of efficient marketing strategies and decision-making.

[0496] 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.

[0497] In this invention, the server includes means for automatically acquiring information from media, means for analyzing the acquired information and selecting content based on pre-set criteria, and means for calculating advertising value based on the selected content. This makes it possible to provide information tailored to the user's emotions.

[0498] "Media" is a general term for means and platforms used to transmit and share information, such as news articles and social media posts.

[0499] "Information" refers to all data and content that can be obtained through media, and includes formats such as text, audio, images, and video.

[0500] "Acquiring" refers to the process by which a server automatically or semi-automatically gathers information from external media, primarily through web scraping or the use of APIs.

[0501] "Analyzing" means analyzing the content of acquired information using machine learning or natural language processing to determine attributes such as relevance and importance.

[0502] "Selection" refers to the act of selecting information that conforms to pre-established criteria or rules based on analysis results, and eliminating unnecessary information.

[0503] "Advertising value" is an indicator that shows the degree to which selected information has a commercial promotional effect, and is calculated as the potential substitute value of advertising revenue.

[0504] An "analysis device" is a device equipped with the function of determining the emotional state based on the user's actions and reactions, and can utilize voice analysis and facial recognition technology.

[0505] "Electronic document format" refers to one type of digitized document format, such as PDF, which is a file format that is easy to save and transfer electronically.

[0506] "Spreadsheet format" refers to a format that organizes and displays digital data using rows and columns, and is a file format suitable for managing numerical data, such as Excel files.

[0507] The system of this invention integrates multiple platforms and technologies to efficiently acquire and analyze information and generate reports based on advertising effectiveness. The system mainly consists of servers, terminals, and users, with various hardware and software used according to their respective roles.

[0508] The server uses the Python BeautifulSoup library to perform web scraping to automatically retrieve information from media. When obtaining social media posts, data can be collected via APIs. The retrieved information is analyzed using natural language processing (NLP) techniques with Python's NLTK and spaCy. This analysis examines the linguistic features of the information and filters out the most relevant data.

[0509] After the information is filtered, the server uses databases such as MySQL or PostgreSQL to analyze and compare it with past data in order to calculate its advertising value. This calculation evaluates the commercial value of the information.

[0510] On the other hand, the terminal functions as an analysis device to recognize the user's emotions. Using OpenCV for facial recognition and Librosa for voice analysis, it determines the user's emotions from their voice tone and facial expressions. This emotion information is used when the server generates reports, and is used to adjust the content and format of the reports according to the user's emotions.

[0511] The generated reports are output in electronic document format (such as PDF) or spreadsheet format (such as Excel) and automatically delivered to the user's device. This allows users to quickly receive optimized information tailored to their current situation and needs.

[0512] As a concrete example, when a public relations officer evaluates media coverage, the system can recognize the officer's emotions and emphasize positive messages and improvement suggestions in the report according to their stress levels and satisfaction. In this way, the present invention supports decision-making by providing information that takes user emotions into consideration.

[0513] Example of a prompt:

[0514] "Considering the current stress levels of our public relations staff, please generate a report that highlights improvement suggestions alongside positive news."

[0515] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0516] Step 1:

[0517] The server uses the Python BeautifulSoup library to scrape websites and collect news articles and social media posts to retrieve information from media. The input is the URL of the media or an API endpoint, and the output is raw information as text data. This information is stored in a database as external news articles and social media posts.

[0518] Step 2:

[0519] The server analyzes the acquired raw information using natural language processing (NLP) techniques. The input is the text data acquired in step 1, and the output is the analysis data, such as keyword extraction results and sentiment scores. Specifically, it uses the Python libraries NLTK and spaCy to tokenize text, recognize entities, and perform sentiment analysis, filtering out highly relevant information.

[0520] Step 3:

[0521] The server calculates advertising value based on filtered information. The input is the analysis results from step 2, and the output is numerical data as advertising equivalent value. This calculation uses media type, impact, and reference data from a historical advertising database. MySQL or PostgreSQL is used for database management, and calculations are performed based on an advertising value calculation model.

[0522] Step 4:

[0523] The device uses voice tone and facial expression data as input to recognize the user's emotional state. Specifically, it analyzes audio and image data collected using a webcam and microphone using the OpenCV and Librosa libraries to generate an emotion score as output. This information quantifies the user's emotions and is sent to the server.

[0524] Step 5:

[0525] The server generates reports based on sentiment information. Inputs are advertising value data from step 3 and user sentiment data from step 4, and output is a final report in PDF or Excel format. Text generation technology is used to adjust the content according to user sentiment and optimize the report's document format.

[0526] Step 6:

[0527] The server automatically delivers the generated report to the user's terminal. The input is the report generated in step 5, and the output is the file sent to the user's terminal. The report is delivered to the user via email or cloud storage, and the user can receive the information and use it for decision-making.

[0528] (Application Example 2)

[0529] 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."

[0530] In modern advertising, media analysis and advertising value assessment are largely manual processes, time-consuming, and difficult to optimize reports through real-time sentiment analysis. This hinders companies and advertising professionals from making quick and accurate decisions.

[0531] 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.

[0532] In this invention, the server includes a device for automatically acquiring information from media, a device for analyzing the acquired information and filtering the content based on pre-set criteria, a device for evaluating the advertising value of the filtered content, a device for analyzing user reactions and adjusting the content and format of the generated document, and a device for automatically providing the adjusted document to the relevant personnel. This makes it possible to grasp the effectiveness of advertising activities in real time and provide optimal reports according to the sentiments of the personnel in charge.

[0533] "Information" refers to data obtained from the media, including news articles and social media posts.

[0534] "Device" refers to a physical or software means for performing a specific function, and in this invention, it refers to a means for acquiring, analyzing, filtering, evaluating, and providing information.

[0535] "Criteria" are pre-set conditions or rules used when filtering collected information, and are used to extract highly relevant information.

[0536] "Advertising value" is an evaluation value calculated based on filtered information, and is a value calculated by referring to a database of past advertising costs.

[0537] "User response" refers to emotional feedback obtained from the recipient's facial expressions, voice, input patterns, etc., and is analyzed by an emotion engine.

[0538] A "document" is a report or notice generated based on the results of information analysis and provided to the relevant personnel.

[0539] The system for implementing this invention is designed to optimize advertising activities in today's information-driven society. The server automatically retrieves information from news APIs, social media APIs, and other sources. Then, using an analysis program built in Python, it analyzes the information using natural language processing technology and filters it based on pre-set criteria.

[0540] The filtered information is then evaluated for its advertising value by a machine learning algorithm based on a database of past advertising costs, which calculates its advertising equivalent value. TensorFlow and PyTorch are used to improve the accuracy of the model during this process.

[0541] User devices, such as smartphones and personal computers, are equipped with emotion engines that analyze the user's emotions in real time using voice recognition and facial recognition functions. This emotion information is sent to a server and used to adjust the content and presentation method of the information the user requests.

[0542] The generated documents are optimized based on analyzed emotions. For example, if the user is feeling stressed, the document is adjusted to emphasize positive elements. The adjusted documents are then delivered to the relevant personnel through an automated process.

[0543] As a concrete example, consider a scenario where an advertising manager is running a campaign for a new product. The system of this invention makes it easier for the advertising manager to check the recent media effectiveness of the new campaign through the system, and also allows them to receive feedback that takes into account the emotional state of the user on that day. An example of a prompt message would be: "Evaluate the effectiveness of the latest advertising campaign and highlight the positive results. The user is currently feeling a little stressed."

[0544] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0545] Step 1:

[0546] The server automatically retrieves information from media outlets through news APIs and social media APIs. At this stage, it is input with raw text data obtained from the APIs. The server calls the endpoints of these APIs to retrieve and store the latest information.

[0547] Step 2:

[0548] The server analyzes the acquired text data using natural language processing (NLP) techniques. Based on the analysis results, it filters the information according to pre-defined criteria. The input is raw text data, and the output is filtered, highly relevant information. The server uses an NLP library within a Python script to perform keyword extraction and sentiment analysis.

[0549] Step 3:

[0550] The server evaluates the value of an advertisement based on the filtered information. It uses a machine learning algorithm to calculate an appropriate advertising equivalent value from a database of past advertising costs. The input to this process is filtered information, and the output is a numerical value evaluated as the advertising value. The server runs the model and performs the evaluation using TensorFlow or PyTorch.

[0551] Step 4:

[0552] The user's device analyzes the user's emotions using an emotion engine. Voice input and facial expression data are used as input, and the output is the user's subjective emotional state. The device uses its microphone and camera to perform real-time facial recognition and voice tone analysis.

[0553] Step 5:

[0554] The server adjusts the content and presentation of generated documents based on sentiment data obtained from users. Inputs include filtered information, advertising value, and the user's emotional state, while output is an optimized document. The server uses a generative AI model to summarize the content and edit the document format.

[0555] Step 6:

[0556] The server automatically provides the prepared documents to the relevant personnel. The user's email address and cloud storage are used as input, and a ready-to-use document is delivered as output. The server utilizes mail server APIs and storage APIs to handle document transmission.

[0557] 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.

[0558] 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.

[0559] 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.

[0560] [Fourth Embodiment]

[0561] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0562] 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.

[0563] 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).

[0564] 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.

[0565] 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.

[0566] 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).

[0567] 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.

[0568] 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.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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.

[0573] 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".

[0574] This invention provides a system that integrates the automatic collection and analysis of media data, as well as the calculation of advertising equivalent value and report generation, into a single process. The system aims to improve the efficiency and reduce costs of public relations activities. Embodiments of this invention are described in detail below.

[0575] First, the server programmatically collects news articles and media coverage information from various media sources on the internet. This allows for real-time data acquisition as soon as articles are published.

[0576] Next, the server uses natural language processing (NLP) techniques to analyze the collected data and filters it against a list of keywords related to specific industries and companies, thereby filtering out relevant articles. This analysis step ensures that only highly relevant articles proceed to the next processing step.

[0577] For filtered articles, the server calculates the advertising equivalent value. This calculation takes into account factors such as the article's word count and the market value of the publishing medium, and references historical databases to provide a more accurate valuation.

[0578] Based on the obtained conversion figures and analysis data, the server automatically generates a report. The report includes an evaluation of current media exposure and the company's position in the market. It also suggests future public relations strategies based on comparisons with past data.

[0579] Finally, the generated report is sent to the user's (public relations officer's) device. The report is editable in PDF or Excel format and can be immediately used as meeting material or a report. This allows the user to quickly share the obtained information both internally and externally, accelerating decision-making.

[0580] By using this embodiment, public relations personnel can reduce the enormous amount of manual work previously required and concentrate their resources on other strategic activities. This is expected to lead to more effective and efficient public relations activities.

[0581] The following describes the processing flow.

[0582] Step 1:

[0583] The server periodically crawls news feeds and various media APIs publicly available on the internet to retrieve newly published article data. It uses RSS feeds and article APIs to update information in real time.

[0584] Step 2:

[0585] The server analyzes the acquired article data using natural language processing (NLP) tools. The analysis extracts keywords based on the article content and matches them against a pre-configured list of relevant keywords. This process filters the articles to include only those related to specific themes or companies.

[0586] Step 3:

[0587] The server calculates the advertising equivalent value for filtered articles. Specifically, it calculates the value based on the number of characters in the article, the page it appears on, the type of media, and its influence, and then refers to an existing advertising cost database to evaluate its appropriate advertising value.

[0588] Step 4:

[0589] The server generates a report that includes a trend analysis of the article's impact and a comparison with competitors in the market, along with the calculated advertising equivalent value. The generated report is structured to be easy to understand, with data visualizations.

[0590] Step 5:

[0591] The server outputs the generated report in PDF or Excel format and automatically distributes it via email to designated stakeholders (such as public relations personnel). This allows users to easily obtain the information and use it for decision-making and report creation.

[0592] (Example 1)

[0593] 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".

[0594] In today's information society, while a vast amount of information is distributed from diverse sources, it is difficult to extract only the useful information relevant to a specific industry or company. Furthermore, there is a challenge in that it takes considerable effort and time to efficiently analyze this information, evaluate its advertising value, and create concrete and rapid analytical documents for formulating effective strategies.

[0595] 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.

[0596] In this invention, the server includes means for automatically collecting information from information sources, means for applying natural language processing technology to analyze the collected information and filtering articles by keywords based on pre-set criteria, and means for calculating the advertising value of the filtered articles. This makes it possible to quickly and efficiently collect highly relevant information and automatically generate analytical documents for quantifying the value of that information and utilizing it in strategic planning.

[0597] "Information sources" refer to communication infrastructure and platforms that serve as the starting point for acquiring information from various media.

[0598] "Information" refers to news articles and related data published by the media.

[0599] "Natural language processing technology" refers to computational techniques for analyzing texts expressed in human language and identifying and classifying the characteristics of the information.

[0600] "Filtering" refers to the process of selecting information according to specific criteria and extracting only the information that meets the required conditions.

[0601] "Advertising value" refers to a numerical indicator calculated to quantitatively evaluate the advertising effectiveness of information published in the media.

[0602] An "analysis document" refers to a document generated based on collected information and its analysis results, and includes a comprehensive report that includes data evaluation and recommendations.

[0603] "Users" refers to the individuals or organizations that receive the generated analytical documents and use that information to make decisions or develop strategies.

[0604] This invention is a system that automatically collects, analyzes, and evaluates media information, calculates advertising value, and generates a report. The system mainly consists of a server and user terminals. The specific configuration is described below.

[0605] The server connects to various media platforms that serve as information sources via the internet. Data collection from these sources utilizes APIs and web scraping techniques. For example, open-source software like Scrapy is sometimes used. The server uses these techniques to retrieve new article data in real time and record it in a database.

[0606] Next, the server applies natural language processing techniques to the collected information. This is achieved by using specific software libraries (e.g., Python's NLTK or spaCy). This allows the server to extract keywords from the information and filter it according to its relevance. After filtering, only information relevant to a specific industry or company is selected and sent to the next stage.

[0607] The server performs calculations to determine the advertising value of the filtered information. Factors such as the media's influence, the number of words in the article, and the frequency of publication are considered in the calculation. This allows the server to quantitatively evaluate the market value of the collected information.

[0608] The server generates a report based on the analysis results and advertising value. This report generation utilizes data visualization tools such as Matplotlib and Seaborn. The generated report includes graphs and tables, presented in a format that allows for intuitive understanding of the information.

[0609] Finally, the generated report is sent to the user's device. The user then uses the report received via their device to consider advertising campaigns and media strategies. The report is sent in PDF or Excel format, allowing the user to immediately use it as meeting material.

[0610] As a concrete example, when a user's company launches a new product into the market, they can use this system to collect relevant news articles and analyze their media influence. This makes it easier for the user to understand the market penetration of the new product.

[0611] An example of a prompt for a generative AI model is, "Collect news from the fashion industry and create a report that calculates advertising value." This prompt allows users to quickly obtain valuable information relevant to a specific industry.

[0612] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0613] Step 1:

[0614] The server automatically collects new information from information sources. It uses a pre-configured list of information sources (such as URLs) as input. The server uses APIs and web scraping techniques to visit each information source and extract new article data. The retrieved article data is stored in a database as output. Specifically, a scheduler visits information sources at regular intervals, identifying and retrieving updated content.

[0615] Step 2:

[0616] The server applies natural language processing to the collected data. It uses collected article data stored in a database as input. The server utilizes Python's NLTK and spaCy to perform tokenization, part-of-speech tagging, and extract keywords. The output is a list of articles with evaluated relevance. Specifically, it compares specific keywords with words within articles, quantifies their relevance, and lists them.

[0617] Step 3:

[0618] The server calculates the advertising value of filtered articles. It uses a list of relevant articles as input. The server calculates an evaluation value using a specific formula, taking into account factors such as the article's word count, the reputation of the publication medium, and past advertising value data. The output is the advertising value evaluation result. Specifically, it references past evaluation data from a database and compares it to the current value.

[0619] Step 4:

[0620] The server generates reports based on advertising value and analysis results. It uses evaluation results and analyzed article data as input. The server visually constructs the data using graph libraries (Matplotlib and Seaborn). The final analysis report is generated in PDF or Excel format as output. Specifically, it documents suggestions based on the analysis results, including visualized graphs and charts.

[0621] Step 5:

[0622] The server delivers the generated report to the user's terminal. It uses the generated report file as input. The server sends the report to the user's terminal via the mail system. The output is a report accessible on the user's terminal. Specific operations include executing a send command to the mail server and confirming receipt.

[0623] (Application Example 1)

[0624] 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".

[0625] In modern public relations, it is crucial to properly manage media exposure and quickly evaluate the effectiveness of advertising campaigns. However, manual information gathering and analysis require an enormous amount of time and effort, making it difficult to develop efficient PR strategies. Furthermore, the inability to check information in real time and adjust advertising strategies can lead to delays in responses that require immediate action on the ground. This results in the challenge of not fully realizing the effectiveness of the overall PR activities.

[0626] 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.

[0627] In this invention, the server includes means for automatically collecting information from media, means for analyzing the collected information and extracting articles based on pre-set criteria, means for calculating the advertising equivalent value of the extracted articles, means for enabling public relations personnel to check real-time media exposure status, and means for measuring the effectiveness of advertising campaigns and providing support for adjusting advertising strategies. This enables the rapid and accurate deployment of public relations strategies.

[0628] "Methods for automatically collecting information from the media" refers to the process of quickly obtaining news articles and published information from various sources on the internet.

[0629] "Means for analyzing collected information and extracting articles based on pre-set criteria" refers to a process that uses natural language processing technology to analyze collected information and selects articles that are highly relevant according to specific criteria.

[0630] "Method for calculating the advertising equivalent value of extracted articles" refers to an algorithm that calculates the advertising value of an article based on filtered articles, taking into account factors such as the number of characters in the article and the market value of the publisher.

[0631] "A means for public relations personnel to check real-time media exposure" refers to technology that provides a user interface that allows public relations personnel to instantly check data on current media exposure at any time.

[0632] "Means of measuring the effectiveness of advertising campaigns and providing support for adjusting advertising strategies" refers to a process of analyzing the results of advertising activities and providing support for proposing the optimal public relations strategy based on those results.

[0633] The server first uses internet news APIs to automatically collect news articles and media coverage information from various sources. This collection process allows data to be obtained almost simultaneously with the publication of articles, making it possible to conduct public relations activities based on the latest information.

[0634] The collected information is analyzed on the server using natural language processing (NLP) libraries. Specifically, libraries such as spaCy and NLTK are used to filter the collected articles based on certain criteria. In this way, only highly relevant articles are selected.

[0635] Next, the advertising equivalent value is calculated for the filtered articles. This calculation algorithm considers the number of characters in the article and the market value of its publisher, and evaluates them by referring to a historical database. This process is implemented using a Python program.

[0636] Based on the generated conversion amounts and analysis results, the server automatically creates a report. The report is generated through data organization using the Pandas library and chart creation using Matplotlib. This report is sent to the public relations officer's terminal in PDF or Excel format.

[0637] The reports sent to the terminal allow public relations personnel to check media exposure in real time and immediately measure the effectiveness of advertising campaigns. This enables them to flexibly adjust their strategies.

[0638] For example, when a company conducts a marketing campaign for a new product, this system can be used to understand media exposure immediately after the campaign starts and analyze its effectiveness. This allows the company to quickly develop an effective public relations strategy.

[0639] An example of a prompt to input into the generating AI model would be: "Please generate a report that retrieves media exposure information for the latest product release and analyzes its advertising effectiveness."

[0640] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0641] Step 1:

[0642] The server uses a news API to collect news articles from various sources on the internet. The input is the news feed URLs provided by each media outlet, and the output is a collection of collected article data. Here, the Python requests library is used to send HTTP requests and retrieve the news article data.

[0643] Step 2:

[0644] The server analyzes the collected data using a natural language processing (NLP) library. The input is the collected article data, and the output is a list of articles filtered according to specific criteria. Specifically, the spaCy library is used to analyze the text within the articles and select relevant articles based on pre-configured keywords.

[0645] Step 3:

[0646] The server calculates the advertising equivalent value for filtered articles. The input is a list of related articles, and the output is a list of the advertising equivalent value for each article. Here, numerical calculations are performed using Python to calculate the advertising value, taking into account the number of characters in the article and the market value of the publishing medium.

[0647] Step 4:

[0648] The server generates a report based on the obtained advertising equivalent value and analysis results. The input is a list of advertising equivalent values ​​and analysis data, and the output is a report in PDF or Excel format. The Pandas library is used to organize the data, Matplotlib is used to create graphs and tables, and finally the report is formatted.

[0649] Step 5:

[0650] The server automatically distributes the generated report to the public relations officer's terminal. The input is the generated report, and the output is the report displayed on the terminal. Here, the report data is distributed via email using the SMTP protocol, allowing the report to be viewed on the terminal.

[0651] Step 6:

[0652] Users can view real-time media exposure information via their devices and measure the effectiveness of advertising campaigns. Input is report data received on the device, and output is information for strategic decision-making. The user interface allows for real-time data viewing and flexible adjustment of advertising strategies.

[0653] 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.

[0654] This invention is a system that combines a sentiment engine, which takes user emotions into consideration, with a series of processes from media data collection and analysis to calculation of advertising equivalent value, report generation, and distribution. This invention was developed to optimize public relations and marketing activities.

[0655] First, the server utilizes existing media data collection systems to automatically acquire data such as news articles and social media posts. Next, the server uses natural language processing (NLP) and machine learning algorithms to analyze the collected data and filter it based on specific criteria and keywords. This filtering process allows the server to proceed to the next stage with only the most relevant information.

[0656] Subsequently, the advertising equivalent value is calculated. The server considers the length of the filtered article, its scope of impact, the type of media, etc., and refers to a database of past advertising costs to accurately assess the advertising value.

[0657] Furthermore, a key aspect of this invention is the use of an emotion engine that recognizes the user's emotions. The emotion engine installed in the terminal determines the user's emotions from their voice tone, facial expressions, input patterns, etc. This emotion information is shared with a server and taken into consideration during the report generation process, allowing for adjustments to the content of the information the user requests and optimization of the presentation method.

[0658] The generated reports are further refined in content and format based on user sentiment and automatically delivered from the server to the user's (or relevant person's) terminal. This process allows users to quickly receive information that better matches their current situation and needs, enabling them to make effective decisions.

[0659] As a concrete example, when a public relations officer evaluates media coverage, the system can capture the officer's stress and satisfaction levels using an emotion engine, and accordingly emphasize positive messages and improvement suggestions within the report. In this way, the present invention is an embodiment that can further improve the user experience by providing information that reflects emotions.

[0660] The following describes the processing flow.

[0661] Step 1:

[0662] The server collects new article data from pre-configured sources via news APIs and RSS feeds. The collection process is performed regularly to ensure fresh media content.

[0663] Step 2:

[0664] The server analyzes the collected article data using a natural language processing (NLP) engine to check if it matches a pre-configured keyword list. This filtering process selects only the most relevant articles.

[0665] Step 3:

[0666] The server calculates the advertising equivalent value for the filtered data. Specifically, it evaluates the value based on the number of characters in the article, the influence of the media, and the frequency of publication, and obtains reference values ​​from the advertising cost database.

[0667] Step 4:

[0668] The emotion engine built into the device captures user activity, voice, and facial expression data, and analyzes emotions in real time. This emotion data is sent to a server and taken into consideration when generating reports.

[0669] Step 5:

[0670] The server generates customized reports based on analysis results, advertising equivalent value, and user sentiment data. These reports include impact assessments, improvement suggestions, and future strategies, with the amount and presentation of information adjusted to match user sentiment.

[0671] Step 6:

[0672] The server exports the generated report in PDF or Excel format and automatically sends it to the user's specified email address. This allows the user to quickly receive the necessary information and use it as a reference for making work-related decisions.

[0673] (Example 2)

[0674] 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".

[0675] In today's information society, a vast amount of media information is generated daily, making it crucial for companies and individuals to quickly select and utilize appropriate information. However, conventional systems struggle to accurately determine the relevance of information and provide information that considers user sentiment, resulting in delays in the development of efficient marketing strategies and decision-making.

[0676] 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.

[0677] In this invention, the server includes means for automatically acquiring information from media, means for analyzing the acquired information and selecting content based on pre-set criteria, and means for calculating advertising value based on the selected content. This makes it possible to provide information tailored to the user's emotions.

[0678] "Media" is a general term for means and platforms used to transmit and share information, such as news articles and social media posts.

[0679] "Information" refers to all data and content that can be obtained through media, and includes formats such as text, audio, images, and video.

[0680] "Acquiring" refers to the process by which a server automatically or semi-automatically gathers information from external media, primarily through web scraping or the use of APIs.

[0681] "Analyzing" means analyzing the content of acquired information using machine learning or natural language processing to determine attributes such as relevance and importance.

[0682] "Selection" refers to the act of selecting information that conforms to pre-established criteria or rules based on analysis results, and eliminating unnecessary information.

[0683] "Advertising value" is an indicator that shows the degree to which selected information has a commercial promotional effect, and is calculated as the potential substitute value of advertising revenue.

[0684] An "analysis device" is a device equipped with the function of determining the emotional state based on the user's actions and reactions, and can utilize voice analysis and facial recognition technology.

[0685] "Electronic document format" refers to one type of digitized document format, such as PDF, which is a file format that is easy to save and transfer electronically.

[0686] "Spreadsheet format" refers to a format that organizes and displays digital data using rows and columns, and is a file format suitable for managing numerical data, such as Excel files.

[0687] The system of this invention integrates multiple platforms and technologies to efficiently acquire and analyze information and generate reports based on advertising effectiveness. The system mainly consists of servers, terminals, and users, with various hardware and software used according to their respective roles.

[0688] The server uses the Python BeautifulSoup library to perform web scraping to automatically retrieve information from media. When obtaining social media posts, data can be collected via APIs. The retrieved information is analyzed using natural language processing (NLP) techniques with Python's NLTK and spaCy. This analysis examines the linguistic features of the information and filters out the most relevant data.

[0689] After the information is filtered, the server uses databases such as MySQL or PostgreSQL to analyze and compare it with past data in order to calculate its advertising value. This calculation evaluates the commercial value of the information.

[0690] On the other hand, the terminal functions as an analysis device to recognize the user's emotions. Using OpenCV for facial recognition and Librosa for voice analysis, it determines the user's emotions from their voice tone and facial expressions. This emotion information is used when the server generates reports, and is used to adjust the content and format of the reports according to the user's emotions.

[0691] The generated reports are output in electronic document format (such as PDF) or spreadsheet format (such as Excel) and automatically delivered to the user's device. This allows users to quickly receive optimized information tailored to their current situation and needs.

[0692] As a concrete example, when a public relations officer evaluates media coverage, the system can recognize the officer's emotions and emphasize positive messages and improvement suggestions in the report according to their stress levels and satisfaction. In this way, the present invention supports decision-making by providing information that takes user emotions into consideration.

[0693] Example of a prompt:

[0694] "Considering the current stress levels of our public relations staff, please generate a report that highlights improvement suggestions alongside positive news."

[0695] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0696] Step 1:

[0697] The server uses the Python BeautifulSoup library to scrape websites and collect news articles and social media posts to retrieve information from media. The input is the URL of the media or an API endpoint, and the output is raw information as text data. This information is stored in a database as external news articles and social media posts.

[0698] Step 2:

[0699] The server analyzes the acquired raw information using natural language processing (NLP) techniques. The input is the text data acquired in step 1, and the output is the analysis data, such as keyword extraction results and sentiment scores. Specifically, it uses the Python libraries NLTK and spaCy to tokenize text, recognize entities, and perform sentiment analysis, filtering out highly relevant information.

[0700] Step 3:

[0701] The server calculates advertising value based on filtered information. The input is the analysis results from step 2, and the output is numerical data as advertising equivalent value. This calculation uses media type, impact, and reference data from a historical advertising database. MySQL or PostgreSQL is used for database management, and calculations are performed based on an advertising value calculation model.

[0702] Step 4:

[0703] The device uses voice tone and facial expression data as input to recognize the user's emotional state. Specifically, it analyzes audio and image data collected using a webcam and microphone using the OpenCV and Librosa libraries to generate an emotion score as output. This information quantifies the user's emotions and is sent to the server.

[0704] Step 5:

[0705] The server generates reports based on sentiment information. Inputs are advertising value data from step 3 and user sentiment data from step 4, and output is a final report in PDF or Excel format. Text generation technology is used to adjust the content according to user sentiment and optimize the report's document format.

[0706] Step 6:

[0707] The server automatically delivers the generated report to the user's terminal. The input is the report generated in step 5, and the output is the file sent to the user's terminal. The report is delivered to the user via email or cloud storage, and the user can receive the information and use it for decision-making.

[0708] (Application Example 2)

[0709] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0710] In modern advertising, media analysis and advertising value assessment are largely manual processes, time-consuming, and difficult to optimize reports through real-time sentiment analysis. This hinders companies and advertising professionals from making quick and accurate decisions.

[0711] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0712] In this invention, the server includes a device for automatically acquiring information from media, a device for analyzing the acquired information and filtering the content based on pre-set criteria, a device for evaluating the advertising value of the filtered content, a device for analyzing user reactions and adjusting the content and format of the generated document, and a device for automatically providing the adjusted document to the relevant personnel. This makes it possible to grasp the effectiveness of advertising activities in real time and provide optimal reports according to the sentiments of the personnel in charge.

[0713] "Information" refers to data obtained from the media, including news articles and social media posts.

[0714] "Device" refers to a physical or software means for performing a specific function, and in this invention, it refers to a means for acquiring, analyzing, filtering, evaluating, and providing information.

[0715] "Criteria" are pre-set conditions or rules used when filtering collected information, and are used to extract highly relevant information.

[0716] "Advertising value" is an evaluation value calculated based on filtered information, and is a value calculated by referring to a database of past advertising costs.

[0717] "User response" refers to emotional feedback obtained from the recipient's facial expressions, voice, input patterns, etc., and is analyzed by an emotion engine.

[0718] A "document" is a report or notice generated based on the results of information analysis and provided to the relevant personnel.

[0719] The system for implementing this invention is designed to optimize advertising activities in today's information-driven society. The server automatically retrieves information from news APIs, social media APIs, and other sources. Then, using an analysis program built in Python, it analyzes the information using natural language processing technology and filters it based on pre-set criteria.

[0720] The filtered information is then evaluated for its advertising value by a machine learning algorithm based on a database of past advertising costs, which calculates its advertising equivalent value. TensorFlow and PyTorch are used to improve the accuracy of the model during this process.

[0721] User devices, such as smartphones and personal computers, are equipped with emotion engines that analyze the user's emotions in real time using voice recognition and facial recognition functions. This emotion information is sent to a server and used to adjust the content and presentation method of the information the user requests.

[0722] The generated documents are optimized based on analyzed emotions. For example, if the user is feeling stressed, the document is adjusted to emphasize positive elements. The adjusted documents are then delivered to the relevant personnel through an automated process.

[0723] As a concrete example, consider a scenario where an advertising manager is running a campaign for a new product. The system of this invention makes it easier for the advertising manager to check the recent media effectiveness of the new campaign through the system, and also allows them to receive feedback that takes into account the emotional state of the user on that day. An example of a prompt message would be: "Evaluate the effectiveness of the latest advertising campaign and highlight the positive results. The user is currently feeling a little stressed."

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

[0725] Step 1:

[0726] The server automatically retrieves information from media outlets through news APIs and social media APIs. At this stage, it is input with raw text data obtained from the APIs. The server calls the endpoints of these APIs to retrieve and store the latest information.

[0727] Step 2:

[0728] The server analyzes the acquired text data using natural language processing (NLP) techniques. Based on the analysis results, it filters the information according to pre-defined criteria. The input is raw text data, and the output is filtered, highly relevant information. The server uses an NLP library within a Python script to perform keyword extraction and sentiment analysis.

[0729] Step 3:

[0730] The server evaluates the value of an advertisement based on the filtered information. It uses a machine learning algorithm to calculate an appropriate advertising equivalent value from a database of past advertising costs. The input to this process is filtered information, and the output is a numerical value evaluated as the advertising value. The server runs the model and performs the evaluation using TensorFlow or PyTorch.

[0731] Step 4:

[0732] The user's device analyzes the user's emotions using an emotion engine. Voice input and facial expression data are used as input, and the output is the user's subjective emotional state. The device uses its microphone and camera to perform real-time facial recognition and voice tone analysis.

[0733] Step 5:

[0734] The server adjusts the content and presentation of generated documents based on sentiment data obtained from users. Inputs include filtered information, advertising value, and the user's emotional state, while output is an optimized document. The server uses a generative AI model to summarize the content and edit the document format.

[0735] Step 6:

[0736] The server automatically provides the prepared documents to the relevant personnel. The user's email address and cloud storage are used as input, and a ready-to-use document is delivered as output. The server utilizes mail server APIs and storage APIs to handle document transmission.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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."

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0758] The following is further disclosed regarding the embodiments described above.

[0759] (Claim 1)

[0760] A means of automatically collecting data from media,

[0761] A means of analyzing the collected data and filtering articles based on pre-set criteria,

[0762] A method for calculating the advertising equivalent value of filtered articles,

[0763] A means of generating a report based on the calculated advertising equivalent value and analysis results,

[0764] A means of automatically distributing the report to the relevant personnel,

[0765] A system that includes this.

[0766] (Claim 2)

[0767] The system according to claim 1, further comprising means for performing trend analysis based on past data in report generation.

[0768] (Claim 3)

[0769] The system according to claim 1, wherein the report is output in PDF or Excel format.

[0770] "Example 1"

[0771] (Claim 1)

[0772] Means for automatically collecting information from sources,

[0773] A method for analyzing collected information by applying natural language processing technology and filtering articles by keywords based on pre-set criteria,

[0774] A method for calculating the advertising value of filtered articles,

[0775] A means for generating an analytical document based on the calculated advertising value and analysis results,

[0776] Means for electronically transmitting the analysis document to the relevant users,

[0777] A system that includes this.

[0778] (Claim 2)

[0779] The system according to claim 1, further comprising means for performing trend analysis based on past information in generating an analytical document.

[0780] (Claim 3)

[0781] The system according to claim 1, wherein the analysis document is output in electronic document format.

[0782] "Application Example 1"

[0783] (Claim 1)

[0784] A means of automatically collecting information from the media,

[0785] A means of analyzing collected information and extracting articles based on pre-set criteria,

[0786] A method for calculating the advertising equivalent value of the extracted articles,

[0787] A means of creating a report based on the calculated advertising equivalent value and analysis results,

[0788] A means of automatically distributing the report to the relevant personnel,

[0789] A means for public relations personnel to check real-time media exposure status,

[0790] A means to measure the effectiveness of advertising campaigns and provide support for adjusting advertising strategies,

[0791] A system that includes this.

[0792] (Claim 2)

[0793] The system according to claim 1, further comprising means for performing trend analysis based on past information in preparing a report.

[0794] (Claim 3)

[0795] The system according to claim 1, wherein the report is output in electronic file format.

[0796] "Example 2 of combining an emotion engine"

[0797] (Claim 1)

[0798] A means of automatically obtaining information from the media,

[0799] A means of analyzing acquired information and selecting content based on pre-set criteria,

[0800] A means of calculating advertising value based on selected content,

[0801] A means of creating a report based on calculated advertising value and analysis results,

[0802] A means equipped with an analysis device for detecting the emotional state of a user,

[0803] A means to adjust the report based on the user's sentiment and automatically send it to the relevant personnel,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, further comprising means for performing trend analysis based on past information in preparing a report.

[0807] (Claim 3)

[0808] The system according to claim 1, wherein reports are output in electronic document format or spreadsheet format.

[0809] "Application example 2 when combining with an emotional engine"

[0810] (Claim 1)

[0811] A device that automatically acquires information from the media,

[0812] A device that analyzes acquired information and filters the content based on pre-set criteria,

[0813] A device for evaluating the advertising value of filtered content,

[0814] A device that generates documents based on the evaluated advertising value and analysis results,

[0815] A device that analyzes user responses and adjusts the content and format of the generated document,

[0816] A device that automatically provides the prepared document to the relevant person in charge,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, further comprising a device that performs pattern analysis based on historical information in document generation.

[0820] (Claim 3)

[0821] The system according to claim 1, wherein a document is output in image format or spreadsheet format. [Explanation of symbols]

[0822] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of automatically collecting information from the media, A means of analyzing collected information and extracting articles based on pre-set criteria, A method for calculating the advertising equivalent value of the extracted articles, A means of creating a report based on the calculated advertising equivalent value and analysis results, A means of automatically distributing the report to the relevant personnel, A means for public relations personnel to check real-time media exposure status, A means to measure the effectiveness of advertising campaigns and provide support for adjusting advertising strategies, A system that includes this.

2. The system according to claim 1, further comprising means for performing trend analysis based on past information in preparing a report.

3. The system according to claim 1, wherein the report is output in electronic file format.

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