system

Abstract

Provide a system. The method comprises: means for automatically collecting data from designated internet sources; A means for preprocessing the collected data and analyzing it using natural language processing techniques; A means of detecting industry trends and generating reports based on the analysis results; a means for displaying the generated report on a user interface and presenting specific action ideas to the user; A system including:

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In today's business environment, it is extremely important to quickly and accurately grasp trends in each industry, allowing for the development of appropriate strategies and rapid response. However, manually collecting and analyzing vast amounts of information from the Internet is extremely difficult and consumes a great deal of time and resources. Furthermore, there is a lack of effective systems for properly analyzing information and linking it to specific actions. Therefore, a system is needed that automatically collects and analyzes industry trends and provides users with accurate information and suggested actions. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. This system includes: means for automatically collecting data from specified Internet information sources; means for preprocessing the collected data and analyzing it using natural language processing technology; means for detecting industry trends based on the analysis results and generating a report; and means for displaying the generated report on a user interface and presenting specific action ideas to the user. The collection means includes crawler means for launching a crawler according to a specified schedule and collecting text data from specified websites and social networking services. The analysis means includes analysis means for extracting major keywords and topics from the text data and analyzing fluctuations in their frequency of appearance. This makes it possible to automatically grasp industry trends and suggest specific actions.

[0006] "Designated Internet sources" refers to information collection sources such as websites, news sites, and social networking services related to specific industries or topics of interest to users.

[0007] "Data collection methods" refers to technical methods, including crawlers and scrapers, used to automatically collect relevant information from sources on the Internet.

[0008] "Data pre-processing means" refers to technical means for filtering, cleaning, and normalizing collected data to convert it into a form suitable for analysis.

[0009] "Natural language processing technology" refers to methods by which computers understand and analyze human language, and includes keyword extraction, sentiment analysis, time series analysis, etc.

[0010] "Means of analysis" refers to the technical means of applying natural language processing technology to preprocessed data to extract important keywords and topics and analyze trends.

[0011] "Industry trend detection measures" refers to technological measures that, based on analytical results, identify significant changes or emerging trends in a particular industry.

[0012] "Means for generating reports" refers to technical means for automatically generating reports in a format that is easy for users to understand based on detected industry trends.

[0013] "User interface" refers to an interface that visually presents generated reports and action suggestions to a user.

[0014] "Means for presenting action ideas" refers to technical means for presenting specific action plans and strategic proposals to users based on the data and analysis results submitted as a report.

[0015] A "crawler" is a program or bot that crawls websites and social networking services according to a specified schedule to collect information.

[0016] "Extracting key keywords and topics" refers to the process of identifying important words and themes from collected text data.

[0017] "Means for analyzing fluctuations in frequency of occurrence" refers to technical means for analyzing changes in the frequency of occurrence of keywords or topics over a specific period of time and identifying trends or anomalies. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a 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.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0032] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention provides a specific embodiment of a system for automatically collecting and analyzing data from information sources on the Internet to understand industry trends and then suggesting actions to users. To achieve this, the following processes are performed.

[0040] This system is divided into the main roles of the server, the terminal, and the user.

[0041] First, the server collects data from sources on the Internet according to a specified schedule. The server launches a crawler using a pre-defined list of URLs and search keywords. For example, to obtain the latest information on a specific industry (e.g., the IT industry), the server collects text data from related news sites and social networking services.

[0042] The server then preprocesses the collected data, which includes removing HTML tags, eliminating duplicate data, and normalizing text (e.g., converting to lowercase, removing stop words) to prepare a dataset suitable for analysis.

[0043] The server then analyzes the preprocessed data using natural language processing techniques. Specifically, it uses NLP libraries to extract important keywords and topics from the text data. For example, it uses techniques such as TF-IDF and Word2Vec to identify frequently occurring keywords. It also performs time series analysis to generate graphs showing how specific keywords increase or decrease.

[0044] The server automatically generates reports based on the analysis results. The generated reports include extracted keywords, trend fluctuations, and predictive analysis results. The reports are generated periodically and saved in PDF or HTML format.

[0045] The generated report is provided to the user via the device. The device displays the report on the user interface, allowing the user to easily check industry trends. In addition, the device suggests specific action ideas to the user based on the report. For example, if it is found that AI technology is rapidly increasing, the device will suggest actions such as "conduct AI-related training" or "start a new project using AI technology."

[0046] Finally, users can consider specific actions based on the reports and recommendations displayed on their devices, such as launching new projects or revising their market strategies, to ensure prompt and appropriate responses.

[0047] As a specific example, to collect the latest trends in the IT industry, the server collects data from tech news sites and related social media (e.g., IT news) every morning at 8:00 and extracts key technology keywords using NLP technology. If the results show that there has been a sharp increase in "cloud computing" or "AI" in a particular week, the server generates a report based on that information, and the device suggests actions to the user, such as "hiring cloud engineers" or "launching an AI project."

[0048] As described above, by using the system of the present invention, it is possible to automatically grasp trends in each industry and take appropriate action in a timely manner.

[0049] The processing flow will be explained below.

[0050] Step 1:

[0051] The server launches a crawler to collect information according to a specified schedule. The crawler reads a pre-defined list of URLs and search keywords and collects information from target websites and social networking services.

[0052] Step 2:

[0053] The server preprocesses the collected data by removing HTML tags, removing duplicate data, and normalizing the text (e.g., converting to lowercase, removing stop words) to create a dataset suitable for analysis.

[0054] Step 3:

[0055] The server applies natural language processing techniques to the preprocessed data to extract keywords and topics. Specifically, it uses NLP libraries (e.g., spaCy, NLTK) to extract key keywords and topics from the text data.

[0056] Step 4:

[0057] The server analyzes the frequency of occurrence of the extracted keywords over time, which allows the fluctuations in the frequency of occurrence of specific keywords to be graphed and trends to be identified.

[0058] Step 5:

[0059] The server automatically generates periodic reports based on the analysis results, including key keywords, trend fluctuations, and predictive analysis results. The generated reports are saved in PDF or HTML format.

[0060] Step 6:

[0061] The server sends the generated report to the terminal, which updates the report periodically according to a schedule.

[0062] Step 7:

[0063] The terminal displays the received report on the user interface, allowing the user to check industry trends and important keywords through the report.

[0064] Step 8:

[0065] Based on the analysis results and the generated report, the device will suggest specific action ideas to the user, such as "conduct training in related technologies" or "launch a new project" based on rapidly increasing keywords.

[0066] Step 9:

[0067] Users can review the reports and proposals displayed on their devices and take action, such as launching a new project or revising their strategy, if necessary.

[0068] By repeating the above steps, users can always grasp the latest industry trends and respond quickly and appropriately.

[0069] Example 1

[0070] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0071] Conventional data collection and analysis systems have made it difficult to quickly and accurately grasp specific industry trends. Furthermore, preprocessing and analyzing the collected data takes time, making it difficult to provide timely information that allows users to take action. Furthermore, there is a lack of action suggestions based on the analysis results, meaning there is insufficient support for users to take specific actions.

[0072] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0073] In this invention, the server includes means for automatically collecting data from specified Internet information sources, means for preprocessing the collected data and analyzing it using natural language processing technology, means for detecting industry trends based on the analysis results and generating reports, means for collecting data using a crawler or according to a specified schedule, means for removing HTML tags, eliminating duplicate data, and normalizing text in the preprocessing, means for extracting important keywords using TF-IDF or Word2Vec and creating graphs in the analysis, and means for saving the generated reports in PDF or HTML format. This enables fast and accurate data collection and analysis, and enables timely and specific actions to be proposed to users.

[0074] "Designated Internet Sources" refers to websites and social networking services from which data is collected according to a designated schedule.

[0075] "Automatic data collection means" refers to a mechanism that obtains data from a programmatically designated source without requiring manual operation.

[0076] "Preprocessing" refers to various preparatory tasks performed on collected data before analysis, such as removing HTML tags, eliminating duplicate data, and normalizing text.

[0077] "Natural language processing technology" or "NLP technology" refers to technology that enables computers to understand, analyze, and generate human language.

[0078] "Analysis means" refers to the function of using preprocessed data to extract important keywords, analyze topics, and analyze fluctuations in frequency of occurrence.

[0079] "Means for generating reports" refers to a mechanism for automatically creating documents summarizing industry trends based on the analysis results.

[0080] "User interface" refers to an interface that has a screen and input form that allows a user to interact with a system.

[0081] "Crawler" refers to a program that automatically collects data from designated Internet sources.

[0082] "TF-IDF" stands for "Term Frequency-Inverse Document Frequency" and refers to a statistical method for extracting important keywords from text data.

[0083] "Word2Vec" refers to a machine learning model that converts words into a vector space and analyzes their semantic relationships.

[0084] "HTML tag removal" refers to the process of removing unnecessary HTML formatting tags from collected text data.

[0085] "Duplicate data elimination" refers to the process of removing identical or very similar data.

[0086] "Text normalization" refers to the process of standardizing text data into a format that is easier to analyze, such as converting it to lowercase or removing stop words.

[0087] "Means for saving in PDF or HTML format" refers to the function for outputting and saving the generated report in a file format that can be displayed visually.

[0088] This invention is a system that automatically collects and analyzes data from online sources to understand industry trends and then suggests actions to users. This system is divided into three main parts: the server, the terminal, and the user.

[0089] First, the server collects data according to a specified schedule. The server launches a crawler using a pre-defined list of URLs and search keywords. For example, to obtain the latest information in a specific industry (e.g., the IT industry), the server collects text data from related news sites and social networking services. The crawler uses a library such as BeautifulSoup to extract text from HTML data.

[0090] The server then pre-processes the collected data, which includes:

[0091] Strip HTML tags: Use the BeautifulSoup library to strip HTML tags from text data.

[0092] Eliminate duplicate data: Use the Pandas library to eliminate duplicate data.

[0093] Text normalization: Use the NLTK library to convert the text to lower case and remove stop words.

[0094] The server then analyzes the preprocessed data using natural language processing techniques, including:

[0095] TF-IDF (Term Frequency-Inverse Document Frequency): Uses the scikit-learn library to extract important keywords.

[0096] Word2Vec: Uses the Gensim library to analyze relationships between keywords.

[0097] Time series analysis: Use Matplotlib and Seaborn libraries to graph the increase or decrease of specific keywords.

[0098] The server then generates a report based on the analysis results, which includes extracted important keywords, trend change graphs, and predictive analysis results. Using the Sphinx library, the report is generated and saved in PDF and HTML formats.

[0099] The generated report is provided to the user via the terminal. The terminal displays the report on a web interface, allowing the user to easily check industry trends. In addition, the terminal suggests specific action ideas to the user based on the analysis results. For example, it may suggest, "If cloud technology is rapidly increasing, consider hiring cloud engineers."

[0100] Finally, users can consider specific actions based on the reports and recommendations displayed on their devices, such as launching a new project or reviewing their market strategy, allowing them to take prompt and appropriate action.

[0101] To give a specific example, to understand the latest trends in the IT industry, a server collects data from specific news sites and social media every morning at 8:00 a.m. The collected data is preprocessed and key keywords such as "cloud computing" and "AI" are extracted using NLP technology. Based on the results, a report is automatically generated, and the device suggests specific actions to the user, such as "hiring cloud engineers" or "launching an AI project."

[0102] An example of a prompt sentence would be, "Please generate a report that will grasp the latest trends in the IT industry and suggest appropriate actions," and the generative AI model would then provide an appropriate report and suggest actions.

[0103] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0104] Step 1:

[0105] Data collection

[0106] The server launches the data collection task based on a specified schedule. For example, if the schedule is set to run once a day, the server will launch the crawler at a specific time every day. The crawler collects relevant data from news sites and social networking services on the Internet based on a pre-defined URL list and search keywords. It sends an HTTP request and retrieves the HTML data of the web page.

[0107] Input: URL list, search keywords

[0108] Output: HTML data

[0109] Step 2:

[0110] Data Preprocessing

[0111] The server preprocesses the collected HTML data. Specific actions include:

[0112] Strip HTML tags: Use the BeautifulSoup library to strip HTML tags from text data.

[0113] Eliminate duplicate data: Use the Pandas library to eliminate duplicate data.

[0114] Text normalization: Use the NLTK library to convert the text to lower case and remove stop words.

[0115] Input: HTML data

[0116] Output: Preprocessed text data

[0117] Step 3:

[0118] Data analysis

[0119] The server performs natural language processing on the preprocessed text data, using techniques such as:

[0120] TF-IDF (Term Frequency-Inverse Document Frequency): Uses the scikit-learn library to extract important keywords.

[0121] Word2Vec: Uses the Gensim library to analyze the relationships between keywords.

[0122] Time series analysis: Use Matplotlib and Seaborn libraries to graph the increase or decrease in frequency of specific keywords.

[0123] Input: Preprocessed text data

[0124] Output: Keyword list, trend graph

[0125] Step 4:

[0126] Report Generation

[0127] The server automatically generates industry trend reports based on the analysis results. The reports are generated and saved in PDF and HTML formats using the Sphinx library. The reports include extracted important keywords, trend fluctuations, and predictive analysis results.

[0128] Input: Keyword list, trend graph

[0129] Output: PDF report, HTML report

[0130] Step 5:

[0131] Reporting and action recommendations

[0132] The device displays the generated report on the user interface. The user can view the report through a web browser. Furthermore, the device suggests specific action ideas to the user based on the report. For example, the device may suggest, "Consider hiring cloud engineers as cloud technology is rapidly increasing."

[0133] Input: PDF report, HTML report

[0134] Output: Display on the user interface, action suggestions

[0135] Step 6:

[0136] User action consideration

[0137] Based on the report and proposals displayed on the device, the user considers specific actions, such as launching a new project or reviewing market strategies. Specific actions include holding a planning meeting based on the report and issuing specific instructions to the relevant department.

[0138] Input: Display on the user interface, action suggestions

[0139] Output: Implementation of specific actions (e.g., launching a new project, formulating a recruitment plan)

[0140] (Application example 1)

[0141] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0142] Systems already exist that automatically collect industry trends from online sources and suggest specific actions to users based on the analysis results, but these systems are rarely used in real time on actual factory floors. In particular, there is a need for a system that allows factory workers to easily grasp industry trends and immediately take specific actions regarding manufacturing processes and the introduction of new technologies. To meet these needs, a system is needed that provides a real-time process from information collection to analysis and specific action suggestions.

[0143] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0144] In this invention, the server includes means for automatically collecting data from specified internet information sources, means for preprocessing the collected data and analyzing it using natural language processing technology, means for detecting industry trends based on the analysis results and generating a report, and means for displaying the generated report on smart glasses worn by factory workers, allowing them to grasp industry trends in real time and propose specific actions regarding manufacturing processes and the introduction of new technologies, thereby enabling factory workers to grasp industry trends in real time and take specific actions immediately.

[0145] "Internet sources" refers to text data and multimedia content provided by online platforms such as websites, social networking services, and news portals.

[0146] "Means of collecting data" refers to technical means, such as programs or crawlers, for automatically collecting data from designated Internet sources.

[0147] "Data preprocessing" refers to the process of removing unnecessary information from collected data and converting it into a format suitable for analysis, including removing HTML tags, eliminating duplicate data, and normalizing text.

[0148] "Natural language processing technology" refers to technology for analyzing text data, and refers to techniques such as keyword extraction, topic modeling, and sentiment analysis.

[0149] "Industry trends" refers to movements and trends such as technological advances, market fluctuations, and the activities of major companies in a particular industry field.

[0150] The means for generating a "report" refers to a program or algorithm that automatically creates a report summarizing industry trends based on the analysis results.

[0151] "User interface" refers to the interactive display on a screen or device that allows a user to view information and take action.

[0152] "Smart glasses" are a type of wearable device that has the ability to display information through a built-in display and is used to provide information in real time.

[0153] "Real-time" refers to data acquisition, analysis, display, etc. occurring immediately and without delay.

[0154] "Manufacturing process" refers to the series of steps that create a product from raw materials, including production activities within a factory.

[0155] "New technology introduction" refers to incorporating the latest technologies and methods into existing systems and processes, and is an activity aimed at improving factory efficiency and product quality.

[0156] System Overview

[0157] This invention is a system that uses smart glasses used in factories to enable workers to grasp industry trends in real time and receive specific action proposals for production processes and the introduction of new technologies.The system is mainly composed of three entities: a server, a terminal, and a user.

[0158] Data collection and analysis by the server

[0159] The server first automatically collects data from designated Internet sources. This involves launching a crawler on a scheduled basis to collect text data from designated websites and social networking services. The collected data is then preprocessed to remove HTML tags, eliminate duplicate data, and normalize the text.

[0160] The server then analyzes the preprocessed data using natural language processing techniques, extracting key keywords and topics and analyzing fluctuations in their frequency of occurrence, using techniques such as TF-IDF and Word2Vec. This uncovers important trends and industry developments.

[0161] Generated reports include extracted keywords, trend changes, and predictive analysis results and are generated periodically in PDF and HTML formats.

[0162] Display reports and action suggestions on your device

[0163] The reports generated by the server are displayed via smart glasses worn by factory workers. The smart glasses have a built-in display that allows users to understand industry trends in real time. Specific action suggestions are also provided to workers based on the analysis results. For example, specific actions such as "consider introducing 3D printing technology" or "hiring cloud engineers" are suggested.

[0164] User Behavior

[0165] The users, or workers, can quickly take specific actions within the factory based on the reports and action suggestions presented through the smart glasses, allowing them to grasp trends in real time and take appropriate measures.

[0166] Hardware and Software Details

[0167] Server: We use requests and BeautifulSoup for data collection, and nltk and sklearn libraries for NLP and analysis.

[0168] Smart glasses: A wearable device with a display function that allows factory workers to check information in real time.

[0169] Crawler: An automated program that collects data from designated internet sources.

[0170] For example, factory workers working on conveyor lines can wear smart glasses and receive real-time action suggestions based on the latest technology keywords and industry trends. For example, specific suggestions such as "consider introducing 3D printing technology" can be viewed on the glasses' display.

[0171] Prompt Sentence Examples

[0172] "Please design a system that uses smart glasses to grasp the latest industrial technology trends in real time and provide specific action suggestions for the manufacturing process in a factory. Please also tell us the detailed steps for data collection, analysis, and report generation, as well as the libraries used."

[0173] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0174] Step 1:

[0175] Data collection

[0176] The server automatically collects data from specified sources on the Internet. During this process, it launches a crawler according to a set schedule to retrieve text data from websites and social networking services based on a list of URLs and search keywords. It uses the specified URLs and search keywords as input and generates the retrieved text data as output. The crawler analyzes the HTML of web pages and extracts the body text.

[0177] Step 2:

[0178] Data Preprocessing

[0179] The server preprocesses the collected text data. This process involves removing HTML tags, eliminating duplicate data, and normalizing the text (converting to lowercase and removing stop words). It uses the collected text data as input and generates preprocessed text data as output. Specifically, it uses BeautifulSoup to remove HTML tags and regular expressions to eliminate unnecessary strings.

[0180] Step 3:

[0181] Data analysis

[0182] The server analyzes the preprocessed data using natural language processing technology. This process uses TF-IDF, Word2Vec, etc. to extract key keywords and topics and analyzes fluctuations in their frequency of appearance. It uses the preprocessed text data as input and generates analysis results (lists of key keywords and topics) as output. Specifically, it uses an NLP library to vectorize the text and extract keywords.

[0183] Step 4:

[0184] Generate reports

[0185] The server automatically generates a report based on the analysis results. This process creates a report that includes industry trends, trend fluctuations, and predictive analysis results based on the extracted keywords and topics. It uses the analysis results as input and generates a report in PDF or HTML format as output. Specifically, it embeds the analysis results into a template to create a report and converts it to PDF or HTML format.

[0186] Step 5:

[0187] Viewing Reports

[0188] The terminal displays the generated report on a user interface. In this process, the report is displayed through smart glasses worn by workers in the factory. The generated report is used as input to generate information to be displayed on the smart glasses as output. Specifically, the report is displayed in real time in HTML or PDF format on a display device inside the smart glasses.

[0189] Step 6:

[0190] Action proposals

[0191] The device then suggests specific action ideas to the user based on the report. This process suggests actions derived from the analysis results and provides information to factory workers in real time. The generated report is used as input and action suggestions are generated as output. Specific actions include suggesting actions such as "consider introducing 3D printing technology" or "hiring cloud engineers" and displaying them on the smart glasses' display.

[0192] Step 7:

[0193] User Actions

[0194] The user, a factory worker, takes specific actions based on the report and action suggestions presented through the smart glasses. This process involves improving the manufacturing process or introducing new technologies in accordance with the suggestions. The action suggestions displayed on the smart glasses are used as input, and actual actions are generated as output. Specific actions involve the worker adjusting equipment and processes based on the suggested actions.

[0195] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0196] This invention provides a concrete form of a system that automatically collects and analyzes data from sources on the Internet to understand industry trends, and further recognizes the user's emotional state and suggests actions. This system is divided into the main players: a server, a terminal, and a user.

[0197] First, the server launches a crawler based on a specified schedule to collect information from target websites and social networking services. For example, to collect trend information for a specific industry (e.g., the IT industry), it retrieves text data from related news sites and social media feeds.

[0198] The server then preprocesses the collected data by removing HTML tags, removing duplicate data, and normalizing the text (such as converting to lowercase and removing stop words) to create a dataset suitable for analysis.

[0199] The server then applies natural language processing techniques to the preprocessed data to extract key keywords and topics. Specifically, it uses NLP libraries (e.g., spaCy, NLTK) to extract important keywords from the text data. It also analyzes how specific keywords increase or decrease over time and graphs the trend fluctuations.

[0200] Furthermore, the server uses an emotion engine to analyze the collected text data and user feedback. The emotion engine identifies the emotional tendencies of the text and classifies them into categories such as positive, negative, and neutral. The emotion engine also estimates the user's emotional state from their past actions and feedback, and optimizes the content of reports and action ideas based on this.

[0201] The generated reports are periodically saved in PDF or HTML format and sent to the terminal, which displays the received reports on the user interface, allowing users to easily check industry trends and sentiment.

[0202] Furthermore, the device can suggest specific action ideas to the user based on the information obtained from the emotion engine. For example, if the user's emotional state is positive, it can recommend proposing a new project or promoting team activities. If the emotional state is negative, it can also suggest taking a break to refresh themselves or taking psychological care.

[0203] Users can consider specific actions based on the reports and recommendations displayed on their devices, and if necessary, launch new projects or revise their market strategies, enabling them to respond quickly and appropriately.

[0204] As a concrete example, to collect the latest trends in the IT industry and take user emotions into consideration, the server collects data from tech news sites and related social media feeds every morning at 8:00 and extracts key technology keywords using NLP technology. The analysis results and the emotion engine also take into account the user's emotional trends. For example, if it is determined that "cloud computing" or "AI" are rapidly increasing in popularity, the system will suggest appropriate actions taking into account the user's emotional state.

[0205] As described above, by using the system of the present invention, it is possible to automatically grasp trends in each industry and further recognize the emotional state of the user to suggest appropriate actions.

[0206] The processing flow will be explained below.

[0207] Step 1:

[0208] The server launches a crawler to collect information according to a specified schedule. The crawler reads a pre-defined list of URLs and search keywords and collects information from target websites and social networking services.

[0209] Step 2:

[0210] The server preprocesses the collected data by removing HTML tags, deleting duplicate data, and normalizing the text (e.g., converting to lowercase, removing stop words) to create a dataset suitable for analysis.

[0211] Step 3:

[0212] The server applies natural language processing techniques to the preprocessed data to extract keywords and topics. It uses NLP libraries (e.g., spaCy, NLTK) to extract key keywords and topics from the text data.

[0213] Step 4:

[0214] The server analyzes the frequency of occurrence of the extracted keywords over time, which allows the fluctuations in the frequency of occurrence of specific keywords to be graphed and trends to be identified.

[0215] Step 5:

[0216] The server analyzes the collected text data and user feedback using an emotion engine. The emotion engine identifies the emotional tendencies of the text and categorizes them into positive, negative, and neutral categories. It also infers the user's emotional state from their past actions and feedback.

[0217] Step 6:

[0218] The server automatically generates periodic reports based on the analysis results, including key keywords, trend fluctuations, sentiment trends, and predictive analysis results. The generated reports are saved in PDF or HTML format.

[0219] Step 7:

[0220] The server sends the generated report to the terminal, which updates the report periodically according to a schedule.

[0221] Step 8:

[0222] The terminal displays the received reports on a user interface, allowing users to check industry trends, important keywords, and sentiment trends.

[0223] Step 9:

[0224] Based on the analysis results and the generated report, the device will suggest specific action ideas to the user. For example, if the user's emotional state is positive, it will recommend proposing a new project or promoting team activities. If the emotional state is negative, it will suggest rest or psychological care.

[0225] Step 10:

[0226] Users can review the reports and proposals displayed on their devices and take action, such as launching a new project or revising their strategy, if necessary.

[0227] By repeating the above steps, users can always keep up with the latest industry trends and can take prompt and appropriate action taking into account their emotional state.

[0228] Example 2

[0229] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0230] In today's information society, there is a demand for efficient and effective collection and analysis of the overwhelming amount of information available on the Internet. It is also important for businesses to understand industry trends from collected information and propose appropriate actions that take into account the user's emotional state. However, conventional systems rely on manual data collection and analysis, which is time-consuming and labor-intensive, and it is difficult to propose actions that take into account the user's emotional state.

[0231] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0232] In this invention, the server includes means for automatically collecting data from specified information sources on the Internet, means for preprocessing the collected data and analyzing it using natural language processing technology, means for detecting industry trends based on the analysis results and generating a report, means for displaying the generated report on a user interface and presenting specific action ideas to the user, means for analyzing the collected text data and feedback from the user and identifying the emotional state, and means for optimizing the content of the report taking the emotional state into consideration and suggesting action ideas.This makes it possible to automatically collect and analyze information on the Internet, grasp industry trends, and suggest actions that are suited to the user's emotional state.

[0233] "Designated internet sources" are sources for collecting data from pre-set websites, social networking services, etc.

[0234] An "automatic data collection means" is a method or apparatus for launching a crawler according to a schedule and automatically collecting data from designated sources.

[0235] "Data preprocessing means" refers to methods or techniques for removing unnecessary information (e.g., HTML tags, duplicate data) from collected data and converting it into a format suitable for analysis.

[0236] "Natural language processing technology" is a technology that analyzes text data, extracts important keywords and topics, and analyzes fluctuations in their frequency of appearance.

[0237] "Means for identifying emotional state" refers to methods or techniques that analyze collected text data and user feedback and determine the user's emotional tendency (positive, negative, neutral, etc.) from that data.

[0238] "Means for generating reports" refers to methods or devices for organizing information such as industry trends and compiling it into report format based on natural language processing technology and sentiment analysis results.

[0239] A "user interface" refers to a display device or software that displays the generated report to the user, allowing the user to easily check the information.

[0240] "Means for presenting action ideas" refers to methods or techniques for suggesting specific actions to the user, taking into account the emotional state.

[0241] This invention is a system that automatically collects and analyzes data from online sources to understand industry trends, and also recognizes the user's emotional state and suggests actions. The system is divided into three entities: a server, a terminal, and a user.

[0242] The server automatically launches the crawler based on a specified schedule. The crawler collects data from tech news sites and social networking services. For example, it uses news APIs and RSS feeds to gather trend information for specific industries. The collected data is first preprocessed, such as removing HTML tags and duplicate data. Text normalization (e.g., converting uppercase to lowercase, removing stop words, etc.) is also performed at this stage.

[0243] The server then applies natural language processing techniques to the preprocessed data. Specifically, it uses natural language processing libraries (e.g., spaCy, NLTK) to extract key keywords and topics. It also analyzes whether specific keywords are increasing or decreasing over time, and can graph trend fluctuations.

[0244] The server then uses a sentiment analysis engine to analyze the collected text data and user feedback. The sentiment analysis engine identifies the emotional tendencies of the text and classifies them into categories such as positive, negative, and neutral. It can also estimate the user's emotional state based on the user's past actions and feedback. Based on the analysis results, the report is optimized.

[0245] The generated reports are periodically sent from the server to the terminal in PDF or HTML format, and the terminal displays the received reports on the user interface, allowing users to easily check industry trends and sentiment.

[0246] The device also uses the information obtained from the emotion analysis engine to suggest specific action ideas to the user. For example, if the user's emotional state is positive, it may recommend starting a new project or promoting team activities. If the emotional state is negative, it may suggest taking a break to refresh yourself or taking psychological care.

[0247] Users can consider and implement specific actions based on the reports and proposals displayed on their devices, enabling them to launch new projects and revise market strategies quickly and appropriately.

[0248] As a concrete example, consider a system that collects the latest trends in the IT industry and takes user emotions into account. Every morning at 8:00, the server collects data from tech news sites and related social media feeds, and uses NLP technology to extract key technology keywords. For example, if it finds that "cloud computing" or "artificial intelligence" are rapidly increasing in popularity, the server compiles that information into a report. The emotion engine also takes the user's emotional tendencies into account and suggests appropriate actions to the user.

[0249] Examples of prompts for a generative AI model might include:

[0250] "Summarize the following passage and identify its emotional tone (positive, negative, neutral):

[0251] "Recently, technologies related to cloud computing and AI have been developing rapidly. Many companies are adopting these technologies and building new business models."

[0252] In this way, by using the system of the present invention, it is possible to effectively collect and analyze information on the Internet and propose actions according to the user's emotional state.

[0253] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0254] Step 1: Data collection

[0255] The server automatically launches the crawler based on a specified schedule. For example, if it is set to run every morning at 8:00, it will collect data from tech news sites and social networking services. The input is a list of specific URLs, and the output is raw HTML data. This data is obtained using news APIs and RSS feeds.

[0256] Specific behavior:

[0257] The crawler downloads the RSS feed from https: / / technews.example.com / rss and retrieves the latest news articles.

[0258] Step 2: Data Preprocessing

[0259] The server preprocesses the collected HTML data, which includes removing HTML tags, removing duplicate data, and normalizing the text (e.g., converting uppercase to lowercase, removing stop words, etc.). The input is the collected raw HTML data, and the output is the preprocessed, clean text data.

[0260] Specific behavior:

[0261] Convert the raw HTML data "AI is transforming the industry" to "ai is transforming industry".

[0262] Step 3: Keyword extraction and trend analysis

[0263] The server applies natural language processing techniques to the preprocessed text data. It uses an NLP library (e.g., spaCy, NLTK) to extract important keywords and topics. It also analyzes the frequency of keyword occurrence over a certain period of time and graphs trend fluctuations. The input is the preprocessed text data, and the output is time series data of important keywords and their frequency of occurrence.

[0264] Specific behavior:

[0265] Apply NLP to the text "ai is transforming industry" to extract "ai" and "industry" as important keywords.

[0266] Using data from the past seven days, generate a time series graph showing the increasing frequency of the keyword "ai."

[0267] Step 4: Sentiment Analysis

[0268] The server uses a sentiment analysis engine to analyze the preprocessed text data and user feedback. The sentiment analysis engine identifies the emotional tendencies of the input text and classifies it into positive, negative, or neutral categories. The input is the preprocessed text data and user feedback, and the output is an emotional category label.

[0269] Specific behavior:

[0270] Enter the text "The new AI technology is amazing!" into the sentiment engine and classify this text as positive.

[0271] Step 5: Generate reports

[0272] The server generates an industry trend report based on the extracted keywords and sentiment analysis results. This report is saved in PDF or HTML format. The input is the important keyword data and sentiment analysis results, and the output is the generated report.

[0273] Specific behavior:

[0274] Generate and save a report in PDF format stating "AI is on the rise."

[0275] Step 6: Send and view the report

[0276] The server periodically sends the generated report to the terminal, and the terminal displays the received report on the user interface. The input is the generated report, and the output is the report sent to the terminal and the displayed content.

[0277] Specific behavior:

[0278] Email a PDF report to your device.

[0279] The device displays a dashboard with a report titled "Industry Trends: AI is on the Rise."

[0280] Step 7: Present your ideas for action

[0281] The terminal proposes specific action ideas to the user based on the sentiment analysis results. The input is the sentiment analysis results and the generated report, and the output is the action ideas displayed on the user interface.

[0282] Specific behavior:

[0283] Display the action idea "Consider launching a new AI project" in the user interface.

[0284] Step 8: User takes action

[0285] The user considers and executes specific actions based on the reports and suggestions displayed on the terminal. The input is the proposed action ideas, and the output is the specific actions executed by the user.

[0286] Specific behavior:

[0287] A user schedules a meeting to launch a "new AI project."

[0288] (Application example 2)

[0289] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0290] Conventional systems for grasping industry trends do not suggest specific actions that take into account the user's emotional state. As a result, even if users can obtain materials to understand industry trends, it is difficult for them to take appropriate actions based on their individual emotional state. Furthermore, when dealing with customers in physical stores, it is difficult to provide appropriate customer service that matches the customer's emotional state. A system that solves these problems and suggests specific actions based on the user's emotional state is needed.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for automatically collecting data from specified information sources on the Internet, means for preprocessing the collected data and analyzing it using natural language processing technology, means for detecting industry trends based on the analysis results and generating a report, means for displaying the generated report on a user interface and presenting specific action ideas to the user, and means for recognizing the user's emotional state and suggesting appropriate actions. This makes it possible not only to grasp industry trends but also to suggest optimal actions based on the user's emotional state. This enables optimal customer service tailored to the emotional state of customers, especially in physical stores.

[0292] "Designated internet sources" refers to specific data collection targets, such as pre-defined websites or social networking services.

[0293] "Means for automatically collecting data" refers to the function of launching crawlers or other machines according to a specified schedule to collect text data from sources on the Internet.

[0294] "Preprocessing" refers to the process of removing unnecessary information from collected data and preparing it in a format suitable for analysis, such as removing HTML tags, deleting duplicate data, and normalizing text.

[0295] "Natural language processing technology" refers to a set of techniques for understanding and analyzing text data, including means for extracting key keywords and topics.

[0296] "Industry trends" refers to fluctuations and trends in the frequency of occurrence of key keywords and topics within a particular industry.

[0297] "Report" refers to a written report generated based on collected and analyzed data, which includes industry trends and action ideas.

[0298] "User interface" refers to the screens and applications that users can interact with directly and check and manipulate information.

[0299] "User's emotional state" refers to the psychological tendency, such as positive, negative, or neutral, displayed by the user.

[0300] "Means to suggest appropriate actions" refers to a function that suggests specific actions based on the user's emotional state and industry trends.

[0301] The present invention is embodied in a specific form as a system that automatically collects and analyzes data from designated internet sources to understand industry trends, and further recognizes the user's emotional state and suggests actions.

[0302] System Program Overview

[0303] The server first launches a crawler based on a specified schedule to collect text data from relevant websites and social networking services. The collected data is preprocessed to remove HTML tags, delete duplicate data, and normalize the text. Natural language processing techniques (e.g., spaCy and TextBlob) are then used to extract key keywords and topics, analyze their frequency over time, and graph industry trends.

[0304] The server then analyzes the collected text data and user feedback using an emotion engine, which identifies emotional trends such as positive, negative, and neutral, and estimates the user's emotional state. Based on this information, the content of reports and action ideas is optimized.

[0305] The generated report is saved in PDF or HTML format and sent to the device, where it is displayed on the user interface, allowing users to easily check industry trends and emotional tendencies. The device also suggests specific action ideas to users based on the information obtained from the emotion engine.

[0306] Hardware and Software Examples

[0307] Hardware: Server, smart glasses

[0308] Software: Python, spaCy, TextBlob, Requests library

[0309] Specific examples

[0310] For example, to gather the latest trends in the IT industry, the server collects data from tech news sites and related social media feeds at 8:00 every morning. NLP technology is used to extract key technology keywords and analyze trends. Using the analysis results and an emotion engine, the system takes into account the user's emotional tendencies and suggests new projects or breaks to refresh them.

[0311] Store staff wearing smart glasses are dynamically suggested appropriate ways to serve customers based on the emotional state of the customer. For example, if a customer says, "I was really looking forward to visiting this store today!", the smart glasses' display will suggest, "Proactively introduce new products."

[0312] Explicit prompt example

[0313] "Analyze your customers' emotional state in real time and suggest the best way to serve them based on the latest trend information. Latest customer feedback: {Customer feedback}"

[0314] This will enable brick-and-mortar stores to provide optimal responses according to the emotional state of customers. Furthermore, understanding industry trends and proposing specific actions based on those trends will support user decision-making.

[0315] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0316] Step 1:

[0317] The server launches a crawler according to a specified schedule. The crawler collects text data from specified Internet sources (such as news sites and social network feeds). The inputs to this step are the specified URLs and schedule information, and the output is the collected text data.

[0318] Step 2:

[0319] The server preprocesses the collected text data. Preprocessing includes removing HTML tags, deleting duplicate data, and normalizing the text (converting to lowercase and removing stop words). The input of this step is the collected text data, and the output is the preprocessed, clean text data.

[0320] Step 3:

[0321] The server applies natural language processing (NLP) technology to the preprocessed data. Specifically, it uses tools such as spaCy and TextBlob to extract key keywords and topics from the text data. It also analyzes fluctuations in their frequency of appearance over time. The input for this step is the preprocessed text data, and the output is key keywords and topics and information on fluctuations in their frequency of appearance.

[0322] Step 4:

[0323] The server analyzes the collected text data and user feedback using an emotion engine, which identifies the emotional tendencies of the text and classifies them into categories such as positive, negative, and neutral. The inputs of this step are the preprocessed text data and user feedback, and the output is the sentiment analysis result of the text.

[0324] Step 5:

[0325] The server detects industry trends based on the analysis results and generates a report that includes analysis results of key keywords and topics, sentiment analysis results, and insights into industry trends. The inputs to this step are the sentiment analysis results and NLP analysis results, and the output is an industry trend report.

[0326] Step 6:

[0327] The server periodically saves the generated reports in PDF or HTML format and sends them to the terminal. The input of this step is the generated report, and the output is the saved and sent report.

[0328] Step 7:

[0329] The terminal displays the received report on the user interface, allowing the user to check industry trends and sentiment. The input of this step is the report sent from the server, and the output is the report displayed on the user interface.

[0330] Step 8:

[0331] The terminal proposes specific action ideas to the user based on the information obtained from the emotion engine. The input of this step is the emotion analysis result and the generated report, and the output is the action ideas presented to the user.

[0332] Step 9:

[0333] The user considers and takes specific actions based on the report and proposals displayed on the device. If necessary, the user may launch a new project or revise their market strategy. The input for this step is the report and proposed actions displayed on the device, and the output is the specific action taken by the user.

[0334] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0335] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0336] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0337] [Second embodiment]

[0338] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0339] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0340] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0341] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0342] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0343] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0344] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0345] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0346] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0347] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0348] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0349] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0350] The present invention provides a specific embodiment of a system for automatically collecting and analyzing data from information sources on the Internet to understand industry trends and then suggesting actions to users. To achieve this, the following processes are performed.

[0351] This system is divided into the main roles of the server, the terminal, and the user.

[0352] First, the server collects data from sources on the Internet according to a specified schedule. The server launches a crawler using a pre-defined list of URLs and search keywords. For example, to obtain the latest information on a specific industry (e.g., the IT industry), the server collects text data from related news sites and social networking services.

[0353] The server then preprocesses the collected data, which includes removing HTML tags, eliminating duplicate data, and normalizing text (e.g., converting to lowercase, removing stop words) to prepare a dataset suitable for analysis.

[0354] The server then analyzes the preprocessed data using natural language processing techniques. Specifically, it uses NLP libraries to extract important keywords and topics from the text data. For example, it uses techniques such as TF-IDF and Word2Vec to identify frequently occurring keywords. It also performs time series analysis to generate graphs showing how specific keywords increase or decrease.

[0355] The server automatically generates reports based on the analysis results. The generated reports include extracted keywords, trend fluctuations, and predictive analysis results. The reports are generated periodically and saved in PDF or HTML format.

[0356] The generated report is provided to the user via the device. The device displays the report on the user interface, allowing the user to easily check industry trends. In addition, the device suggests specific action ideas to the user based on the report. For example, if it is found that AI technology is rapidly increasing, the device will suggest actions such as "conduct AI-related training" or "start a new project using AI technology."

[0357] Finally, users can consider specific actions based on the reports and recommendations displayed on their devices, such as launching new projects or revising their market strategies, to ensure prompt and appropriate responses.

[0358] As a specific example, to collect the latest trends in the IT industry, the server collects data from tech news sites and related social media (e.g., IT news) every morning at 8:00 and extracts key technology keywords using NLP technology. If the results show that there has been a sharp increase in "cloud computing" or "AI" in a particular week, the server generates a report based on that information, and the device suggests actions to the user, such as "hiring cloud engineers" or "launching an AI project."

[0359] As described above, by using the system of the present invention, it is possible to automatically grasp trends in each industry and take appropriate action in a timely manner.

[0360] The processing flow will be explained below.

[0361] Step 1:

[0362] The server launches a crawler to collect information according to a specified schedule. The crawler reads a pre-defined list of URLs and search keywords and collects information from target websites and social networking services.

[0363] Step 2:

[0364] The server preprocesses the collected data by removing HTML tags, removing duplicate data, and normalizing the text (e.g., converting to lowercase, removing stop words) to create a dataset suitable for analysis.

[0365] Step 3:

[0366] The server applies natural language processing techniques to the preprocessed data to extract keywords and topics. Specifically, it uses NLP libraries (e.g., spaCy, NLTK) to extract key keywords and topics from the text data.

[0367] Step 4:

[0368] The server analyzes the frequency of occurrence of the extracted keywords over time, which allows the fluctuations in the frequency of occurrence of specific keywords to be graphed and trends to be identified.

[0369] Step 5:

[0370] The server automatically generates periodic reports based on the analysis results, including key keywords, trend fluctuations, and predictive analysis results. The generated reports are saved in PDF or HTML format.

[0371] Step 6:

[0372] The server sends the generated report to the terminal, which updates the report periodically according to a schedule.

[0373] Step 7:

[0374] The terminal displays the received report on the user interface, allowing the user to check industry trends and important keywords through the report.

[0375] Step 8:

[0376] Based on the analysis results and the generated report, the device will suggest specific action ideas to the user, such as "conduct training in related technologies" or "launch a new project" based on rapidly increasing keywords.

[0377] Step 9:

[0378] Users can review the reports and proposals displayed on their devices and take action, such as launching a new project or revising their strategy, if necessary.

[0379] By repeating the above steps, users can always grasp the latest industry trends and respond quickly and appropriately.

[0380] Example 1

[0381] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0382] Conventional data collection and analysis systems have made it difficult to quickly and accurately grasp specific industry trends. Furthermore, preprocessing and analyzing the collected data takes time, making it difficult to provide timely information that allows users to take action. Furthermore, there is a lack of action suggestions based on the analysis results, meaning there is insufficient support for users to take specific actions.

[0383] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0384] In this invention, the server includes means for automatically collecting data from specified Internet information sources, means for preprocessing the collected data and analyzing it using natural language processing technology, means for detecting industry trends based on the analysis results and generating reports, means for collecting data using a crawler or according to a specified schedule, means for removing HTML tags, eliminating duplicate data, and normalizing text in the preprocessing, means for extracting important keywords using TF-IDF or Word2Vec and creating graphs in the analysis, and means for saving the generated reports in PDF or HTML format. This enables fast and accurate data collection and analysis, and enables timely and specific actions to be proposed to users.

[0385] "Designated Internet Sources" refers to websites and social networking services from which data is collected according to a designated schedule.

[0386] "Automatic data collection means" refers to a mechanism that obtains data from a programmatically designated source without requiring manual operation.

[0387] "Preprocessing" refers to various preparatory tasks performed on collected data before analysis, such as removing HTML tags, eliminating duplicate data, and normalizing text.

[0388] "Natural language processing technology" or "NLP technology" refers to technology that enables computers to understand, analyze, and generate human language.

[0389] "Analysis means" refers to the function of using preprocessed data to extract important keywords, analyze topics, and analyze fluctuations in frequency of occurrence.

[0390] "Means for generating reports" refers to a mechanism for automatically creating documents summarizing industry trends based on the analysis results.

[0391] "User interface" refers to an interface that has a screen and input form that allows a user to interact with a system.

[0392] "Crawler" refers to a program that automatically collects data from designated Internet sources.

[0393] "TF-IDF" stands for "Term Frequency-Inverse Document Frequency" and refers to a statistical method for extracting important keywords from text data.

[0394] "Word2Vec" refers to a machine learning model that converts words into a vector space and analyzes their semantic relationships.

[0395] "HTML tag removal" refers to the process of removing unnecessary HTML formatting tags from collected text data.

[0396] "Duplicate data elimination" refers to the process of removing identical or very similar data.

[0397] "Text normalization" refers to the process of standardizing text data into a format that is easier to analyze, such as converting it to lowercase or removing stop words.

[0398] "Means for saving in PDF or HTML format" refers to the function for outputting and saving the generated report in a file format that can be displayed visually.

[0399] This invention is a system that automatically collects and analyzes data from online sources to understand industry trends and then suggests actions to users. This system is divided into three main parts: the server, the terminal, and the user.

[0400] First, the server collects data according to a specified schedule. The server launches a crawler using a pre-defined list of URLs and search keywords. For example, to obtain the latest information in a specific industry (e.g., the IT industry), the server collects text data from related news sites and social networking services. The crawler uses a library such as BeautifulSoup to extract text from HTML data.

[0401] The server then pre-processes the collected data, which includes:

[0402] Strip HTML tags: Use the BeautifulSoup library to strip HTML tags from text data.

[0403] Eliminate duplicate data: Use the Pandas library to eliminate duplicate data.

[0404] Text normalization: Use the NLTK library to convert the text to lower case and remove stop words.

[0405] The server then analyzes the preprocessed data using natural language processing techniques, including:

[0406] TF-IDF (Term Frequency-Inverse Document Frequency): Uses the scikit-learn library to extract important keywords.

[0407] Word2Vec: Uses the Gensim library to analyze relationships between keywords.

[0408] Time series analysis: Use Matplotlib and Seaborn libraries to graph the increase or decrease of specific keywords.

[0409] The server then generates a report based on the analysis results, which includes extracted important keywords, trend change graphs, and predictive analysis results. Using the Sphinx library, the report is generated and saved in PDF and HTML formats.

[0410] The generated report is provided to the user via the terminal. The terminal displays the report on a web interface, allowing the user to easily check industry trends. In addition, the terminal suggests specific action ideas to the user based on the analysis results. For example, it may suggest, "If cloud technology is rapidly increasing, consider hiring cloud engineers."

[0411] Finally, users can consider specific actions based on the reports and recommendations displayed on their devices, such as launching a new project or reviewing their market strategy, allowing them to take prompt and appropriate action.

[0412] To give a specific example, to understand the latest trends in the IT industry, a server collects data from specific news sites and social media every morning at 8:00 a.m. The collected data is preprocessed and key keywords such as "cloud computing" and "AI" are extracted using NLP technology. Based on the results, a report is automatically generated, and the device suggests specific actions to the user, such as "hiring cloud engineers" or "launching an AI project."

[0413] An example of a prompt sentence would be, "Please generate a report that will grasp the latest trends in the IT industry and suggest appropriate actions," and the generative AI model would then provide an appropriate report and suggest actions.

[0414] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0415] Step 1:

[0416] Data collection

[0417] The server launches the data collection task based on a specified schedule. For example, if the schedule is set to run once a day, the server will launch the crawler at a specific time every day. The crawler collects relevant data from news sites and social networking services on the Internet based on a pre-defined URL list and search keywords. It sends an HTTP request and retrieves the HTML data of the web page.

[0418] Input: URL list, search keywords

[0419] Output: HTML data

[0420] Step 2:

[0421] Data Preprocessing

[0422] The server preprocesses the collected HTML data. Specific actions include:

[0423] Strip HTML tags: Use the BeautifulSoup library to strip HTML tags from text data.

[0424] Eliminate duplicate data: Use the Pandas library to eliminate duplicate data.

[0425] Text normalization: Use the NLTK library to convert the text to lower case and remove stop words.

[0426] Input: HTML data

[0427] Output: Preprocessed text data

[0428] Step 3:

[0429] Data analysis

[0430] The server performs natural language processing on the preprocessed text data, using techniques such as:

[0431] TF-IDF (Term Frequency-Inverse Document Frequency): Uses the scikit-learn library to extract important keywords.

[0432] Word2Vec: Uses the Gensim library to analyze the relationships between keywords.

[0433] Time series analysis: Use Matplotlib and Seaborn libraries to graph the increase or decrease in frequency of specific keywords.

[0434] Input: Preprocessed text data

[0435] Output: Keyword list, trend graph

[0436] Step 4:

[0437] Report Generation

[0438] The server automatically generates industry trend reports based on the analysis results. The reports are generated and saved in PDF and HTML formats using the Sphinx library. The reports include extracted important keywords, trend fluctuations, and predictive analysis results.

[0439] Input: Keyword list, trend graph

[0440] Output: PDF report, HTML report

[0441] Step 5:

[0442] Reporting and action recommendations

[0443] The device displays the generated report on the user interface. The user can view the report through a web browser. Furthermore, the device suggests specific action ideas to the user based on the report. For example, the device may suggest, "Consider hiring cloud engineers as cloud technology is rapidly increasing."

[0444] Input: PDF report, HTML report

[0445] Output: Display on the user interface, action suggestions

[0446] Step 6:

[0447] User action consideration

[0448] Based on the report and proposals displayed on the device, the user considers specific actions, such as launching a new project or reviewing market strategies. Specific actions include holding a planning meeting based on the report and issuing specific instructions to the relevant department.

[0449] Input: Display on the user interface, action suggestions

[0450] Output: Implementation of specific actions (e.g., launching a new project, formulating a recruitment plan)

[0451] (Application example 1)

[0452] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0453] Systems already exist that automatically collect industry trends from online sources and suggest specific actions to users based on the analysis results, but these systems are rarely used in real time on actual factory floors. In particular, there is a need for a system that allows factory workers to easily grasp industry trends and immediately take specific actions regarding manufacturing processes and the introduction of new technologies. To meet these needs, a system is needed that provides a real-time process from information collection to analysis and specific action suggestions.

[0454] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0455] In this invention, the server includes means for automatically collecting data from specified internet information sources, means for preprocessing the collected data and analyzing it using natural language processing technology, means for detecting industry trends based on the analysis results and generating a report, and means for displaying the generated report on smart glasses worn by factory workers, allowing them to grasp industry trends in real time and propose specific actions regarding manufacturing processes and the introduction of new technologies, thereby enabling factory workers to grasp industry trends in real time and take specific actions immediately.

[0456] "Internet sources" refers to text data and multimedia content provided by online platforms such as websites, social networking services, and news portals.

[0457] "Means of collecting data" refers to technical means, such as programs or crawlers, for automatically collecting data from designated Internet sources.

[0458] "Data preprocessing" refers to the process of removing unnecessary information from collected data and converting it into a format suitable for analysis, including removing HTML tags, eliminating duplicate data, and normalizing text.

[0459] "Natural language processing technology" refers to technology for analyzing text data, and refers to techniques such as keyword extraction, topic modeling, and sentiment analysis.

[0460] "Industry trends" refers to movements and trends such as technological advances, market fluctuations, and the activities of major companies in a particular industry field.

[0461] The means for generating a "report" refers to a program or algorithm that automatically creates a report summarizing industry trends based on the analysis results.

[0462] "User interface" refers to the interactive display on a screen or device that allows a user to view information and take action.

[0463] "Smart glasses" are a type of wearable device that has the ability to display information through a built-in display and is used to provide information in real time.

[0464] "Real-time" refers to data acquisition, analysis, display, etc. occurring immediately and without delay.

[0465] "Manufacturing process" refers to the series of steps that create a product from raw materials, including production activities within a factory.

[0466] "New technology introduction" refers to incorporating the latest technologies and methods into existing systems and processes, and is an activity aimed at improving factory efficiency and product quality.

[0467] System Overview

[0468] This invention is a system that uses smart glasses used in factories to enable workers to grasp industry trends in real time and receive specific action proposals for production processes and the introduction of new technologies.The system is mainly composed of three entities: a server, a terminal, and a user.

[0469] Data collection and analysis by the server

[0470] The server first automatically collects data from designated Internet sources. This involves launching a crawler on a scheduled basis to collect text data from designated websites and social networking services. The collected data is then preprocessed to remove HTML tags, eliminate duplicate data, and normalize the text.

[0471] The server then analyzes the preprocessed data using natural language processing techniques, extracting key keywords and topics and analyzing fluctuations in their frequency of occurrence, using techniques such as TF-IDF and Word2Vec. This uncovers important trends and industry developments.

[0472] Generated reports include extracted keywords, trend changes, and predictive analysis results and are generated periodically in PDF and HTML formats.

[0473] Display reports and action suggestions on your device

[0474] The reports generated by the server are displayed via smart glasses worn by factory workers. The smart glasses have a built-in display that allows users to understand industry trends in real time. Specific action suggestions are also provided to workers based on the analysis results. For example, specific actions such as "consider introducing 3D printing technology" or "hiring cloud engineers" are suggested.

[0475] User Behavior

[0476] The users, or workers, can quickly take specific actions within the factory based on the reports and action suggestions presented through the smart glasses, allowing them to grasp trends in real time and take appropriate measures.

[0477] Hardware and Software Details

[0478] Server: We use requests and BeautifulSoup for data collection, and nltk and sklearn libraries for NLP and analysis.

[0479] Smart glasses: A wearable device with a display function that allows factory workers to check information in real time.

[0480] Crawler: An automated program that collects data from designated internet sources.

[0481] For example, factory workers working on conveyor lines can wear smart glasses and receive real-time action suggestions based on the latest technology keywords and industry trends. For example, specific suggestions such as "consider introducing 3D printing technology" can be viewed on the glasses' display.

[0482] Prompt Sentence Examples

[0483] "Please design a system that uses smart glasses to grasp the latest industrial technology trends in real time and provide specific action suggestions for the manufacturing process in a factory. Please also tell us the detailed steps for data collection, analysis, and report generation, as well as the libraries used."

[0484] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0485] Step 1:

[0486] Data collection

[0487] The server automatically collects data from specified sources on the Internet. During this process, it launches a crawler according to a set schedule to retrieve text data from websites and social networking services based on a list of URLs and search keywords. It uses the specified URLs and search keywords as input and generates the retrieved text data as output. The crawler analyzes the HTML of web pages and extracts the body text.

[0488] Step 2:

[0489] Data Preprocessing

[0490] The server preprocesses the collected text data. This process involves removing HTML tags, eliminating duplicate data, and normalizing the text (converting to lowercase and removing stop words). It uses the collected text data as input and generates preprocessed text data as output. Specifically, it uses BeautifulSoup to remove HTML tags and regular expressions to eliminate unnecessary strings.

[0491] Step 3:

[0492] Data analysis

[0493] The server analyzes the preprocessed data using natural language processing technology. This process uses TF-IDF, Word2Vec, etc. to extract key keywords and topics and analyzes fluctuations in their frequency of appearance. It uses the preprocessed text data as input and generates analysis results (lists of key keywords and topics) as output. Specifically, it uses an NLP library to vectorize the text and extract keywords.

[0494] Step 4:

[0495] Generate reports

[0496] The server automatically generates a report based on the analysis results. This process creates a report that includes industry trends, trend fluctuations, and predictive analysis results based on the extracted keywords and topics. It uses the analysis results as input and generates a report in PDF or HTML format as output. Specifically, it embeds the analysis results into a template to create a report and converts it to PDF or HTML format.

[0497] Step 5:

[0498] Viewing Reports

[0499] The terminal displays the generated report on a user interface. In this process, the report is displayed through smart glasses worn by workers in the factory. The generated report is used as input to generate information to be displayed on the smart glasses as output. Specifically, the report is displayed in real time in HTML or PDF format on a display device inside the smart glasses.

[0500] Step 6:

[0501] Action proposals

[0502] The device then suggests specific action ideas to the user based on the report. This process suggests actions derived from the analysis results and provides information to factory workers in real time. The generated report is used as input and action suggestions are generated as output. Specific actions include suggesting actions such as "consider introducing 3D printing technology" or "hiring cloud engineers" and displaying them on the smart glasses' display.

[0503] Step 7:

[0504] User Actions

[0505] The user, a factory worker, takes specific actions based on the report and action suggestions presented through the smart glasses. This process involves improving the manufacturing process or introducing new technologies in accordance with the suggestions. The action suggestions displayed on the smart glasses are used as input, and actual actions are generated as output. Specific actions involve the worker adjusting equipment and processes based on the suggested actions.

[0506] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0507] This invention provides a concrete form of a system that automatically collects and analyzes data from sources on the Internet to understand industry trends, and further recognizes the user's emotional state and suggests actions. This system is divided into the main players: a server, a terminal, and a user.

[0508] First, the server launches a crawler based on a specified schedule to collect information from target websites and social networking services. For example, to collect trend information for a specific industry (e.g., the IT industry), it retrieves text data from related news sites and social media feeds.

[0509] The server then preprocesses the collected data by removing HTML tags, removing duplicate data, and normalizing the text (such as converting to lowercase and removing stop words) to create a dataset suitable for analysis.

[0510] The server then applies natural language processing techniques to the preprocessed data to extract key keywords and topics. Specifically, it uses NLP libraries (e.g., spaCy, NLTK) to extract important keywords from the text data. It also analyzes how specific keywords increase or decrease over time and graphs the trend fluctuations.

[0511] Furthermore, the server uses an emotion engine to analyze the collected text data and user feedback. The emotion engine identifies the emotional tendencies of the text and classifies them into categories such as positive, negative, and neutral. The emotion engine also estimates the user's emotional state from their past actions and feedback, and optimizes the content of reports and action ideas based on this.

[0512] The generated reports are periodically saved in PDF or HTML format and sent to the terminal, which displays the received reports on the user interface, allowing users to easily check industry trends and sentiment.

[0513] Furthermore, the device can suggest specific action ideas to the user based on the information obtained from the emotion engine. For example, if the user's emotional state is positive, it can recommend proposing a new project or promoting team activities. If the emotional state is negative, it can also suggest taking a break to refresh themselves or taking psychological care.

[0514] Users can consider specific actions based on the reports and recommendations displayed on their devices, and if necessary, launch new projects or revise their market strategies, enabling them to respond quickly and appropriately.

[0515] As a concrete example, to collect the latest trends in the IT industry and take user emotions into consideration, the server collects data from tech news sites and related social media feeds every morning at 8:00 and extracts key technology keywords using NLP technology. The analysis results and the emotion engine also take into account the user's emotional trends. For example, if it is determined that "cloud computing" or "AI" are rapidly increasing in popularity, the system will suggest appropriate actions taking into account the user's emotional state.

[0516] As described above, by using the system of the present invention, it is possible to automatically grasp trends in each industry and further recognize the emotional state of the user to suggest appropriate actions.

[0517] The processing flow will be explained below.

[0518] Step 1:

[0519] The server launches a crawler to collect information according to a specified schedule. The crawler reads a pre-defined list of URLs and search keywords and collects information from target websites and social networking services.

[0520] Step 2:

[0521] The server preprocesses the collected data by removing HTML tags, deleting duplicate data, and normalizing the text (e.g., converting to lowercase, removing stop words) to create a dataset suitable for analysis.

[0522] Step 3:

[0523] The server applies natural language processing techniques to the preprocessed data to extract keywords and topics. It uses NLP libraries (e.g., spaCy, NLTK) to extract key keywords and topics from the text data.

[0524] Step 4:

[0525] The server analyzes the frequency of occurrence of the extracted keywords over time, which allows the fluctuations in the frequency of occurrence of specific keywords to be graphed and trends to be identified.

[0526] Step 5:

[0527] The server analyzes the collected text data and user feedback using an emotion engine. The emotion engine identifies the emotional tendencies of the text and categorizes them into positive, negative, and neutral categories. It also infers the user's emotional state from their past actions and feedback.

[0528] Step 6:

[0529] The server automatically generates periodic reports based on the analysis results, including key keywords, trend fluctuations, sentiment trends, and predictive analysis results. The generated reports are saved in PDF or HTML format.

[0530] Step 7:

[0531] The server sends the generated report to the terminal, which updates the report periodically according to a schedule.

[0532] Step 8:

[0533] The terminal displays the received reports on a user interface, allowing users to check industry trends, important keywords, and sentiment trends.

[0534] Step 9:

[0535] Based on the analysis results and the generated report, the device will suggest specific action ideas to the user. For example, if the user's emotional state is positive, it will recommend proposing a new project or promoting team activities. If the emotional state is negative, it will suggest rest or psychological care.

[0536] Step 10:

[0537] Users can review the reports and proposals displayed on their devices and take action, such as launching a new project or revising their strategy, if necessary.

[0538] By repeating the above steps, users can always keep up with the latest industry trends and can take prompt and appropriate action taking into account their emotional state.

[0539] Example 2

[0540] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0541] In today's information society, there is a demand for efficient and effective collection and analysis of the overwhelming amount of information available on the Internet. It is also important for businesses to understand industry trends from collected information and propose appropriate actions that take into account the user's emotional state. However, conventional systems rely on manual data collection and analysis, which is time-consuming and labor-intensive, and it is difficult to propose actions that take into account the user's emotional state.

[0542] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0543] In this invention, the server includes means for automatically collecting data from specified information sources on the Internet, means for preprocessing the collected data and analyzing it using natural language processing technology, means for detecting industry trends based on the analysis results and generating a report, means for displaying the generated report on a user interface and presenting specific action ideas to the user, means for analyzing the collected text data and feedback from the user and identifying the emotional state, and means for optimizing the content of the report taking the emotional state into consideration and suggesting action ideas.This makes it possible to automatically collect and analyze information on the Internet, grasp industry trends, and suggest actions that are suited to the user's emotional state.

[0544] "Designated internet sources" are sources for collecting data from pre-set websites, social networking services, etc.

[0545] An "automatic data collection means" is a method or apparatus for launching a crawler according to a schedule and automatically collecting data from designated sources.

[0546] "Data preprocessing means" refers to methods or techniques for removing unnecessary information (e.g., HTML tags, duplicate data) from collected data and converting it into a format suitable for analysis.

[0547] "Natural language processing technology" is a technology that analyzes text data, extracts important keywords and topics, and analyzes fluctuations in their frequency of appearance.

[0548] "Means for identifying emotional state" refers to methods or techniques that analyze collected text data and user feedback and determine the user's emotional tendency (positive, negative, neutral, etc.) from that data.

[0549] "Means for generating reports" refers to methods or devices for organizing information such as industry trends and compiling it into report format based on natural language processing technology and sentiment analysis results.

[0550] A "user interface" refers to a display device or software that displays the generated report to the user, allowing the user to easily check the information.

[0551] "Means for presenting action ideas" refers to methods or techniques for suggesting specific actions to the user, taking into account the emotional state.

[0552] This invention is a system that automatically collects and analyzes data from online sources to understand industry trends, and also recognizes the user's emotional state and suggests actions. The system is divided into three entities: a server, a terminal, and a user.

[0553] The server automatically launches the crawler based on a specified schedule. The crawler collects data from tech news sites and social networking services. For example, it uses news APIs and RSS feeds to gather trend information for specific industries. The collected data is first preprocessed, such as removing HTML tags and duplicate data. Text normalization (e.g., converting uppercase to lowercase, removing stop words, etc.) is also performed at this stage.

[0554] The server then applies natural language processing techniques to the preprocessed data. Specifically, it uses natural language processing libraries (e.g., spaCy, NLTK) to extract key keywords and topics. It also analyzes whether specific keywords are increasing or decreasing over time, and can graph trend fluctuations.

[0555] The server then uses a sentiment analysis engine to analyze the collected text data and user feedback. The sentiment analysis engine identifies the emotional tendencies of the text and classifies them into categories such as positive, negative, and neutral. It can also estimate the user's emotional state based on the user's past actions and feedback. Based on the analysis results, the report is optimized.

[0556] The generated reports are periodically sent from the server to the terminal in PDF or HTML format, and the terminal displays the received reports on the user interface, allowing users to easily check industry trends and sentiment.

[0557] The device also uses the information obtained from the emotion analysis engine to suggest specific action ideas to the user. For example, if the user's emotional state is positive, it may recommend starting a new project or promoting team activities. If the emotional state is negative, it may suggest taking a break to refresh yourself or taking psychological care.

[0558] Users can consider and implement specific actions based on the reports and proposals displayed on their devices, enabling them to launch new projects and revise market strategies quickly and appropriately.

[0559] As a concrete example, consider a system that collects the latest trends in the IT industry and takes user emotions into account. Every morning at 8:00, the server collects data from tech news sites and related social media feeds, and uses NLP technology to extract key technology keywords. For example, if it finds that "cloud computing" or "artificial intelligence" are rapidly increasing in popularity, the server compiles that information into a report. The emotion engine also takes the user's emotional tendencies into account and suggests appropriate actions to the user.

[0560] Examples of prompts for a generative AI model might include:

[0561] "Summarize the following passage and identify its emotional tone (positive, negative, neutral):

[0562] "Recently, technologies related to cloud computing and AI have been developing rapidly. Many companies are adopting these technologies and building new business models."

[0563] In this way, by using the system of the present invention, it is possible to effectively collect and analyze information on the Internet and propose actions according to the user's emotional state.

[0564] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0565] Step 1: Data collection

[0566] The server automatically launches the crawler based on a specified schedule. For example, if it is set to run every morning at 8:00, it will collect data from tech news sites and social networking services. The input is a list of specific URLs, and the output is raw HTML data. This data is obtained using news APIs and RSS feeds.

[0567] Specific behavior:

[0568] The crawler downloads the RSS feed from https: / / technews.example.com / rss and retrieves the latest news articles.

[0569] Step 2: Data Preprocessing

[0570] The server preprocesses the collected HTML data, which includes removing HTML tags, removing duplicate data, and normalizing the text (e.g., converting uppercase to lowercase, removing stop words, etc.). The input is the collected raw HTML data, and the output is the preprocessed, clean text data.

[0571] Specific behavior:

[0572] Convert the raw HTML data "AI is transforming the industry" to "ai is transforming industry".

[0573] Step 3: Keyword extraction and trend analysis

[0574] The server applies natural language processing techniques to the preprocessed text data. It uses an NLP library (e.g., spaCy, NLTK) to extract important keywords and topics. It also analyzes the frequency of keyword occurrence over a certain period of time and graphs trend fluctuations. The input is the preprocessed text data, and the output is time series data of important keywords and their frequency of occurrence.

[0575] Specific behavior:

[0576] Apply NLP to the text "ai is transforming industry" to extract "ai" and "industry" as important keywords.

[0577] Using data from the past seven days, generate a time series graph showing the increasing frequency of the keyword "ai."

[0578] Step 4: Sentiment Analysis

[0579] The server uses a sentiment analysis engine to analyze the preprocessed text data and user feedback. The sentiment analysis engine identifies the emotional tendencies of the input text and classifies it into positive, negative, or neutral categories. The input is the preprocessed text data and user feedback, and the output is an emotional category label.

[0580] Specific behavior:

[0581] Enter the text "The new AI technology is amazing!" into the sentiment engine and classify this text as positive.

[0582] Step 5: Generate reports

[0583] The server generates an industry trend report based on the extracted keywords and sentiment analysis results. This report is saved in PDF or HTML format. The input is the important keyword data and sentiment analysis results, and the output is the generated report.

[0584] Specific behavior:

[0585] Generate and save a report in PDF format stating "AI is on the rise."

[0586] Step 6: Send and view the report

[0587] The server periodically sends the generated report to the terminal, and the terminal displays the received report on the user interface. The input is the generated report, and the output is the report sent to the terminal and the displayed content.

[0588] Specific behavior:

[0589] Email a PDF report to your device.

[0590] The device displays a dashboard with a report titled "Industry Trends: AI is on the Rise."

[0591] Step 7: Present your ideas for action

[0592] The terminal proposes specific action ideas to the user based on the sentiment analysis results. The input is the sentiment analysis results and the generated report, and the output is the action ideas displayed on the user interface.

[0593] Specific behavior:

[0594] Display the action idea "Consider launching a new AI project" in the user interface.

[0595] Step 8: User takes action

[0596] The user considers and executes specific actions based on the reports and suggestions displayed on the terminal. The input is the proposed action ideas, and the output is the specific actions executed by the user.

[0597] Specific behavior:

[0598] A user schedules a meeting to launch a "new AI project."

[0599] (Application example 2)

[0600] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0601] Conventional systems for grasping industry trends do not suggest specific actions that take into account the user's emotional state. As a result, even if users can obtain materials to understand industry trends, it is difficult for them to take appropriate actions based on their individual emotional state. Furthermore, when dealing with customers in physical stores, it is difficult to provide appropriate customer service that matches the customer's emotional state. A system that solves these problems and suggests specific actions based on the user's emotional state is needed.

[0602] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for automatically collecting data from specified information sources on the Internet, means for preprocessing the collected data and analyzing it using natural language processing technology, means for detecting industry trends based on the analysis results and generating a report, means for displaying the generated report on a user interface and presenting specific action ideas to the user, and means for recognizing the user's emotional state and suggesting appropriate actions. This makes it possible not only to grasp industry trends but also to suggest optimal actions based on the user's emotional state. This enables optimal customer service tailored to the emotional state of customers, especially in physical stores.

[0603] "Designated internet sources" refers to specific data collection targets, such as pre-defined websites or social networking services.

[0604] "Means for automatically collecting data" refers to the function of launching crawlers or other machines according to a specified schedule to collect text data from sources on the Internet.

[0605] "Preprocessing" refers to the process of removing unnecessary information from collected data and preparing it in a format suitable for analysis, such as removing HTML tags, deleting duplicate data, and normalizing text.

[0606] "Natural language processing technology" refers to a set of techniques for understanding and analyzing text data, including means for extracting key keywords and topics.

[0607] "Industry trends" refers to fluctuations and trends in the frequency of occurrence of key keywords and topics within a particular industry.

[0608] "Report" refers to a written report generated based on collected and analyzed data, which includes industry trends and action ideas.

[0609] "User interface" refers to the screens and applications that users can interact with directly and check and manipulate information.

[0610] "User's emotional state" refers to the psychological tendency, such as positive, negative, or neutral, displayed by the user.

[0611] "Means to suggest appropriate actions" refers to a function that suggests specific actions based on the user's emotional state and industry trends.

[0612] The present invention is embodied in a specific form as a system that automatically collects and analyzes data from designated internet sources to understand industry trends, and further recognizes the user's emotional state and suggests actions.

[0613] System Program Overview

[0614] The server first launches a crawler based on a specified schedule to collect text data from relevant websites and social networking services. The collected data is preprocessed to remove HTML tags, delete duplicate data, and normalize the text. Natural language processing techniques (e.g., spaCy and TextBlob) are then used to extract key keywords and topics, analyze their frequency over time, and graph industry trends.

[0615] The server then analyzes the collected text data and user feedback using an emotion engine, which identifies emotional trends such as positive, negative, and neutral, and estimates the user's emotional state. Based on this information, the content of reports and action ideas is optimized.

[0616] The generated report is saved in PDF or HTML format and sent to the device, where it is displayed on the user interface, allowing users to easily check industry trends and emotional tendencies. The device also suggests specific action ideas to users based on the information obtained from the emotion engine.

[0617] Hardware and Software Examples

[0618] Hardware: Server, smart glasses

[0619] Software: Python, spaCy, TextBlob, Requests library

[0620] Specific examples

[0621] For example, to gather the latest trends in the IT industry, the server collects data from tech news sites and related social media feeds at 8:00 every morning. NLP technology is used to extract key technology keywords and analyze trends. Using the analysis results and an emotion engine, the system takes into account the user's emotional tendencies and suggests new projects or breaks to refresh them.

[0622] Store staff wearing smart glasses are dynamically suggested appropriate ways to serve customers based on the emotional state of the customer. For example, if a customer says, "I was really looking forward to visiting this store today!", the smart glasses' display will suggest, "Proactively introduce new products."

[0623] Explicit prompt example

[0624] "Analyze your customers' emotional state in real time and suggest the best way to serve them based on the latest trend information. Latest customer feedback: {Customer feedback}"

[0625] This will enable brick-and-mortar stores to provide optimal responses according to the emotional state of customers. Furthermore, understanding industry trends and proposing specific actions based on those trends will support user decision-making.

[0626] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0627] Step 1:

[0628] The server launches a crawler according to a specified schedule. The crawler collects text data from specified Internet sources (such as news sites and social network feeds). The inputs to this step are the specified URLs and schedule information, and the output is the collected text data.

[0629] Step 2:

[0630] The server preprocesses the collected text data. Preprocessing includes removing HTML tags, deleting duplicate data, and normalizing the text (converting to lowercase and removing stop words). The input of this step is the collected text data, and the output is the preprocessed, clean text data.

[0631] Step 3:

[0632] The server applies natural language processing (NLP) technology to the preprocessed data. Specifically, it uses tools such as spaCy and TextBlob to extract key keywords and topics from the text data. It also analyzes fluctuations in their frequency of appearance over time. The input for this step is the preprocessed text data, and the output is key keywords and topics and information on fluctuations in their frequency of appearance.

[0633] Step 4:

[0634] The server analyzes the collected text data and user feedback using an emotion engine, which identifies the emotional tendencies of the text and classifies them into categories such as positive, negative, and neutral. The inputs of this step are the preprocessed text data and user feedback, and the output is the sentiment analysis result of the text.

[0635] Step 5:

[0636] The server detects industry trends based on the analysis results and generates a report that includes analysis results of key keywords and topics, sentiment analysis results, and insights into industry trends. The inputs to this step are the sentiment analysis results and NLP analysis results, and the output is an industry trend report.

[0637] Step 6:

[0638] The server periodically saves the generated reports in PDF or HTML format and sends them to the terminal. The input of this step is the generated report, and the output is the saved and sent report.

[0639] Step 7:

[0640] The terminal displays the received report on the user interface, allowing the user to check industry trends and sentiment. The input of this step is the report sent from the server, and the output is the report displayed on the user interface.

[0641] Step 8:

[0642] The terminal proposes specific action ideas to the user based on the information obtained from the emotion engine. The input of this step is the emotion analysis result and the generated report, and the output is the action ideas presented to the user.

[0643] Step 9:

[0644] The user considers and takes specific actions based on the report and proposals displayed on the device. If necessary, the user may launch a new project or revise their market strategy. The input for this step is the report and proposed actions displayed on the device, and the output is the specific action taken by the user.

[0645] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0646] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0647] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0648] [Third embodiment]

[0649] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0650] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0651] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0652] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0653] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0654] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0655] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0656] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0657] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0658] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0659] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0660] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0661] The present invention provides a specific embodiment of a system for automatically collecting and analyzing data from information sources on the Internet to understand industry trends and then suggesting actions to users. To achieve this, the following processes are performed.

[0662] This system is divided into the main roles of the server, the terminal, and the user.

[0663] First, the server collects data from sources on the Internet according to a specified schedule. The server launches a crawler using a pre-defined list of URLs and search keywords. For example, to obtain the latest information on a specific industry (e.g., the IT industry), the server collects text data from related news sites and social networking services.

[0664] The server then preprocesses the collected data, which includes removing HTML tags, eliminating duplicate data, and normalizing text (e.g., converting to lowercase, removing stop words) to prepare a dataset suitable for analysis.

[0665] The server then analyzes the preprocessed data using natural language processing techniques. Specifically, it uses NLP libraries to extract important keywords and topics from the text data. For example, it uses techniques such as TF-IDF and Word2Vec to identify frequently occurring keywords. It also performs time series analysis to generate graphs showing how specific keywords increase or decrease.

[0666] The server automatically generates reports based on the analysis results. The generated reports include extracted keywords, trend fluctuations, and predictive analysis results. The reports are generated periodically and saved in PDF or HTML format.

[0667] The generated report is provided to the user via the device. The device displays the report on the user interface, allowing the user to easily check industry trends. In addition, the device suggests specific action ideas to the user based on the report. For example, if it is found that AI technology is rapidly increasing, the device will suggest actions such as "conduct AI-related training" or "start a new project using AI technology."

[0668] Finally, users can consider specific actions based on the reports and recommendations displayed on their devices, such as launching new projects or revising their market strategies, to ensure prompt and appropriate responses.

[0669] As a specific example, to collect the latest trends in the IT industry, the server collects data from tech news sites and related social media (e.g., IT news) every morning at 8:00 and extracts key technology keywords using NLP technology. If the results show that there has been a sharp increase in "cloud computing" or "AI" in a particular week, the server generates a report based on that information, and the device suggests actions to the user, such as "hiring cloud engineers" or "launching an AI project."

[0670] As described above, by using the system of the present invention, it is possible to automatically grasp trends in each industry and take appropriate action in a timely manner.

[0671] The processing flow will be explained below.

[0672] Step 1:

[0673] The server launches a crawler to collect information according to a specified schedule. The crawler reads a pre-defined list of URLs and search keywords and collects information from target websites and social networking services.

[0674] Step 2:

[0675] The server preprocesses the collected data by removing HTML tags, removing duplicate data, and normalizing the text (e.g., converting to lowercase, removing stop words) to create a dataset suitable for analysis.

[0676] Step 3:

[0677] The server applies natural language processing techniques to the preprocessed data to extract keywords and topics. Specifically, it uses NLP libraries (e.g., spaCy, NLTK) to extract key keywords and topics from the text data.

[0678] Step 4:

[0679] The server analyzes the frequency of occurrence of the extracted keywords over time, which allows the fluctuations in the frequency of occurrence of specific keywords to be graphed and trends to be identified.

[0680] Step 5:

[0681] The server automatically generates periodic reports based on the analysis results, including key keywords, trend fluctuations, and predictive analysis results. The generated reports are saved in PDF or HTML format.

[0682] Step 6:

[0683] The server sends the generated report to the terminal, which updates the report periodically according to a schedule.

[0684] Step 7:

[0685] The terminal displays the received report on the user interface, allowing the user to check industry trends and important keywords through the report.

[0686] Step 8:

[0687] Based on the analysis results and the generated report, the device will suggest specific action ideas to the user, such as "conduct training in related technologies" or "launch a new project" based on rapidly increasing keywords.

[0688] Step 9:

[0689] Users can review the reports and proposals displayed on their devices and take action, such as launching a new project or revising their strategy, if necessary.

[0690] By repeating the above steps, users can always grasp the latest industry trends and respond quickly and appropriately.

[0691] Example 1

[0692] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0693] Conventional data collection and analysis systems have made it difficult to quickly and accurately grasp specific industry trends. Furthermore, preprocessing and analyzing the collected data takes time, making it difficult to provide timely information that allows users to take action. Furthermore, there is a lack of action suggestions based on the analysis results, meaning there is insufficient support for users to take specific actions.

[0694] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0695] In this invention, the server includes means for automatically collecting data from specified Internet information sources, means for preprocessing the collected data and analyzing it using natural language processing technology, means for detecting industry trends based on the analysis results and generating reports, means for collecting data using a crawler or according to a specified schedule, means for removing HTML tags, eliminating duplicate data, and normalizing text in the preprocessing, means for extracting important keywords using TF-IDF or Word2Vec and creating graphs in the analysis, and means for saving the generated reports in PDF or HTML format. This enables fast and accurate data collection and analysis, and enables timely and specific actions to be proposed to users.

[0696] "Designated Internet Sources" refers to websites and social networking services from which data is collected according to a designated schedule.

[0697] "Automatic data collection means" refers to a mechanism that obtains data from a programmatically designated source without requiring manual operation.

[0698] "Preprocessing" refers to various preparatory tasks performed on collected data before analysis, such as removing HTML tags, eliminating duplicate data, and normalizing text.

[0699] "Natural language processing technology" or "NLP technology" refers to technology that enables computers to understand, analyze, and generate human language.

[0700] "Analysis means" refers to the function of using preprocessed data to extract important keywords, analyze topics, and analyze fluctuations in frequency of occurrence.

[0701] "Means for generating reports" refers to a mechanism for automatically creating documents summarizing industry trends based on the analysis results.

[0702] "User interface" refers to an interface that has a screen and input form that allows a user to interact with a system.

[0703] "Crawler" refers to a program that automatically collects data from designated Internet sources.

[0704] "TF-IDF" stands for "Term Frequency-Inverse Document Frequency" and refers to a statistical method for extracting important keywords from text data.

[0705] "Word2Vec" refers to a machine learning model that converts words into a vector space and analyzes their semantic relationships.

[0706] "HTML tag removal" refers to the process of removing unnecessary HTML formatting tags from collected text data.

[0707] "Duplicate data elimination" refers to the process of removing identical or very similar data.

[0708] "Text normalization" refers to the process of standardizing text data into a format that is easier to analyze, such as converting it to lowercase or removing stop words.

[0709] "Means for saving in PDF or HTML format" refers to the function for outputting and saving the generated report in a file format that can be displayed visually.

[0710] This invention is a system that automatically collects and analyzes data from online sources to understand industry trends and then suggests actions to users. This system is divided into three main parts: the server, the terminal, and the user.

[0711] First, the server collects data according to a specified schedule. The server launches a crawler using a pre-defined list of URLs and search keywords. For example, to obtain the latest information in a specific industry (e.g., the IT industry), the server collects text data from related news sites and social networking services. The crawler uses a library such as BeautifulSoup to extract text from HTML data.

[0712] The server then pre-processes the collected data, which includes:

[0713] Strip HTML tags: Use the BeautifulSoup library to strip HTML tags from text data.

[0714] Eliminate duplicate data: Use the Pandas library to eliminate duplicate data.

[0715] Text normalization: Use the NLTK library to convert the text to lower case and remove stop words.

[0716] The server then analyzes the preprocessed data using natural language processing techniques, including:

[0717] TF-IDF (Term Frequency-Inverse Document Frequency): Uses the scikit-learn library to extract important keywords.

[0718] Word2Vec: Uses the Gensim library to analyze relationships between keywords.

[0719] Time series analysis: Use Matplotlib and Seaborn libraries to graph the increase or decrease of specific keywords.

[0720] The server then generates a report based on the analysis results, which includes extracted important keywords, trend change graphs, and predictive analysis results. Using the Sphinx library, the report is generated and saved in PDF and HTML formats.

[0721] The generated report is provided to the user via the terminal. The terminal displays the report on a web interface, allowing the user to easily check industry trends. In addition, the terminal suggests specific action ideas to the user based on the analysis results. For example, it may suggest, "If cloud technology is rapidly increasing, consider hiring cloud engineers."

[0722] Finally, users can consider specific actions based on the reports and recommendations displayed on their devices, such as launching a new project or reviewing their market strategy, allowing them to take prompt and appropriate action.

[0723] To give a specific example, to understand the latest trends in the IT industry, a server collects data from specific news sites and social media every morning at 8:00 a.m. The collected data is preprocessed and key keywords such as "cloud computing" and "AI" are extracted using NLP technology. Based on the results, a report is automatically generated, and the device suggests specific actions to the user, such as "hiring cloud engineers" or "launching an AI project."

[0724] An example of a prompt sentence would be, "Please generate a report that will grasp the latest trends in the IT industry and suggest appropriate actions," and the generative AI model would then provide an appropriate report and suggest actions.

[0725] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0726] Step 1:

[0727] Data collection

[0728] The server launches the data collection task based on a specified schedule. For example, if the schedule is set to run once a day, the server will launch the crawler at a specific time every day. The crawler collects relevant data from news sites and social networking services on the Internet based on a pre-defined URL list and search keywords. It sends an HTTP request and retrieves the HTML data of the web page.

[0729] Input: URL list, search keywords

[0730] Output: HTML data

[0731] Step 2:

[0732] Data Preprocessing

[0733] The server preprocesses the collected HTML data. Specific actions include:

[0734] Strip HTML tags: Use the BeautifulSoup library to strip HTML tags from text data.

[0735] Eliminate duplicate data: Use the Pandas library to eliminate duplicate data.

[0736] Text normalization: Use the NLTK library to convert the text to lower case and remove stop words.

[0737] Input: HTML data

[0738] Output: Preprocessed text data

[0739] Step 3:

[0740] Data analysis

[0741] The server performs natural language processing on the preprocessed text data, using techniques such as:

[0742] TF-IDF (Term Frequency-Inverse Document Frequency): Uses the scikit-learn library to extract important keywords.

[0743] Word2Vec: Uses the Gensim library to analyze the relationships between keywords.

[0744] Time series analysis: Use Matplotlib and Seaborn libraries to graph the increase or decrease in frequency of specific keywords.

[0745] Input: Preprocessed text data

[0746] Output: Keyword list, trend graph

[0747] Step 4:

[0748] Report Generation

[0749] The server automatically generates industry trend reports based on the analysis results. The reports are generated and saved in PDF and HTML formats using the Sphinx library. The reports include extracted important keywords, trend fluctuations, and predictive analysis results.

[0750] Input: Keyword list, trend graph

[0751] Output: PDF report, HTML report

[0752] Step 5:

[0753] Reporting and action recommendations

[0754] The device displays the generated report on the user interface. The user can view the report through a web browser. Furthermore, the device suggests specific action ideas to the user based on the report. For example, the device may suggest, "Consider hiring cloud engineers as cloud technology is rapidly increasing."

[0755] Input: PDF report, HTML report

[0756] Output: Display on the user interface, action suggestions

[0757] Step 6:

[0758] User action consideration

[0759] Based on the report and proposals displayed on the device, the user considers specific actions, such as launching a new project or reviewing market strategies. Specific actions include holding a planning meeting based on the report and issuing specific instructions to the relevant department.

[0760] Input: Display on the user interface, action suggestions

[0761] Output: Implementation of specific actions (e.g., launching a new project, formulating a recruitment plan)

[0762] (Application example 1)

[0763] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0764] Systems already exist that automatically collect industry trends from online sources and suggest specific actions to users based on the analysis results, but these systems are rarely used in real time on actual factory floors. In particular, there is a need for a system that allows factory workers to easily grasp industry trends and immediately take specific actions regarding manufacturing processes and the introduction of new technologies. To meet these needs, a system is needed that provides a real-time process from information collection to analysis and specific action suggestions.

[0765] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0766] In this invention, the server includes means for automatically collecting data from specified internet information sources, means for preprocessing the collected data and analyzing it using natural language processing technology, means for detecting industry trends based on the analysis results and generating a report, and means for displaying the generated report on smart glasses worn by factory workers, allowing them to grasp industry trends in real time and propose specific actions regarding manufacturing processes and the introduction of new technologies, thereby enabling factory workers to grasp industry trends in real time and take specific actions immediately.

[0767] "Internet sources" refers to text data and multimedia content provided by online platforms such as websites, social networking services, and news portals.

[0768] "Means of collecting data" refers to technical means, such as programs or crawlers, for automatically collecting data from designated Internet sources.

[0769] "Data preprocessing" refers to the process of removing unnecessary information from collected data and converting it into a format suitable for analysis, including removing HTML tags, eliminating duplicate data, and normalizing text.

[0770] "Natural language processing technology" refers to technology for analyzing text data, and refers to techniques such as keyword extraction, topic modeling, and sentiment analysis.

[0771] "Industry trends" refers to movements and trends such as technological advances, market fluctuations, and the activities of major companies in a particular industry field.

[0772] The means for generating a "report" refers to a program or algorithm that automatically creates a report summarizing industry trends based on the analysis results.

[0773] "User interface" refers to the interactive display on a screen or device that allows a user to view information and take action.

[0774] "Smart glasses" are a type of wearable device that has the ability to display information through a built-in display and is used to provide information in real time.

[0775] "Real-time" refers to data acquisition, analysis, display, etc. occurring immediately and without delay.

[0776] "Manufacturing process" refers to the series of steps that create a product from raw materials, including production activities within a factory.

[0777] "New technology introduction" refers to incorporating the latest technologies and methods into existing systems and processes, and is an activity aimed at improving factory efficiency and product quality.

[0778] System Overview

[0779] This invention is a system that uses smart glasses used in factories to enable workers to grasp industry trends in real time and receive specific action proposals for production processes and the introduction of new technologies.The system is mainly composed of three entities: a server, a terminal, and a user.

[0780] Data collection and analysis by the server

[0781] The server first automatically collects data from designated Internet sources. This involves launching a crawler on a scheduled basis to collect text data from designated websites and social networking services. The collected data is then preprocessed to remove HTML tags, eliminate duplicate data, and normalize the text.

[0782] The server then analyzes the preprocessed data using natural language processing techniques, extracting key keywords and topics and analyzing fluctuations in their frequency of occurrence, using techniques such as TF-IDF and Word2Vec. This uncovers important trends and industry developments.

[0783] Generated reports include extracted keywords, trend changes, and predictive analysis results and are generated periodically in PDF and HTML formats.

[0784] Display reports and action suggestions on your device

[0785] The reports generated by the server are displayed via smart glasses worn by factory workers. The smart glasses have a built-in display that allows users to understand industry trends in real time. Specific action suggestions are also provided to workers based on the analysis results. For example, specific actions such as "consider introducing 3D printing technology" or "hiring cloud engineers" are suggested.

[0786] User Behavior

[0787] The users, or workers, can quickly take specific actions within the factory based on the reports and action suggestions presented through the smart glasses, allowing them to grasp trends in real time and take appropriate measures.

[0788] Hardware and Software Details

[0789] Server: We use requests and BeautifulSoup for data collection, and nltk and sklearn libraries for NLP and analysis.

[0790] Smart glasses: A wearable device with a display function that allows factory workers to check information in real time.

[0791] Crawler: An automated program that collects data from designated internet sources.

[0792] For example, factory workers working on conveyor lines can wear smart glasses and receive real-time action suggestions based on the latest technology keywords and industry trends. For example, specific suggestions such as "consider introducing 3D printing technology" can be viewed on the glasses' display.

[0793] Prompt Sentence Examples

[0794] "Please design a system that uses smart glasses to grasp the latest industrial technology trends in real time and provide specific action suggestions for the manufacturing process in a factory. Please also tell us the detailed steps for data collection, analysis, and report generation, as well as the libraries used."

[0795] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0796] Step 1:

[0797] Data collection

[0798] The server automatically collects data from specified sources on the Internet. During this process, it launches a crawler according to a set schedule to retrieve text data from websites and social networking services based on a list of URLs and search keywords. It uses the specified URLs and search keywords as input and generates the retrieved text data as output. The crawler analyzes the HTML of web pages and extracts the body text.

[0799] Step 2:

[0800] Data Preprocessing

[0801] The server preprocesses the collected text data. This process involves removing HTML tags, eliminating duplicate data, and normalizing the text (converting to lowercase and removing stop words). It uses the collected text data as input and generates preprocessed text data as output. Specifically, it uses BeautifulSoup to remove HTML tags and regular expressions to eliminate unnecessary strings.

[0802] Step 3:

[0803] Data analysis

[0804] The server analyzes the preprocessed data using natural language processing technology. This process uses TF-IDF, Word2Vec, etc. to extract key keywords and topics and analyzes fluctuations in their frequency of appearance. It uses the preprocessed text data as input and generates analysis results (lists of key keywords and topics) as output. Specifically, it uses an NLP library to vectorize the text and extract keywords.

[0805] Step 4:

[0806] Generate reports

[0807] The server automatically generates a report based on the analysis results. This process creates a report that includes industry trends, trend fluctuations, and predictive analysis results based on the extracted keywords and topics. It uses the analysis results as input and generates a report in PDF or HTML format as output. Specifically, it embeds the analysis results into a template to create a report and converts it to PDF or HTML format.

[0808] Step 5:

[0809] Viewing Reports

[0810] The terminal displays the generated report on a user interface. In this process, the report is displayed through smart glasses worn by workers in the factory. The generated report is used as input to generate information to be displayed on the smart glasses as output. Specifically, the report is displayed in real time in HTML or PDF format on a display device inside the smart glasses.

[0811] Step 6:

[0812] Action proposals

[0813] The device then suggests specific action ideas to the user based on the report. This process suggests actions derived from the analysis results and provides information to factory workers in real time. The generated report is used as input and action suggestions are generated as output. Specific actions include suggesting actions such as "consider introducing 3D printing technology" or "hiring cloud engineers" and displaying them on the smart glasses' display.

[0814] Step 7:

[0815] User Actions

[0816] The user, a factory worker, takes specific actions based on the report and action suggestions presented through the smart glasses. This process involves improving the manufacturing process or introducing new technologies in accordance with the suggestions. The action suggestions displayed on the smart glasses are used as input, and actual actions are generated as output. Specific actions involve the worker adjusting equipment and processes based on the suggested actions.

[0817] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0818] This invention provides a concrete form of a system that automatically collects and analyzes data from sources on the Internet to understand industry trends, and further recognizes the user's emotional state and suggests actions. This system is divided into the main players: a server, a terminal, and a user.

[0819] First, the server launches a crawler based on a specified schedule to collect information from target websites and social networking services. For example, to collect trend information for a specific industry (e.g., the IT industry), it retrieves text data from related news sites and social media feeds.

[0820] The server then preprocesses the collected data by removing HTML tags, removing duplicate data, and normalizing the text (such as converting to lowercase and removing stop words) to create a dataset suitable for analysis.

[0821] The server then applies natural language processing techniques to the preprocessed data to extract key keywords and topics. Specifically, it uses NLP libraries (e.g., spaCy, NLTK) to extract important keywords from the text data. It also analyzes how specific keywords increase or decrease over time and graphs the trend fluctuations.

[0822] Furthermore, the server uses an emotion engine to analyze the collected text data and user feedback. The emotion engine identifies the emotional tendencies of the text and classifies them into categories such as positive, negative, and neutral. The emotion engine also estimates the user's emotional state from their past actions and feedback, and optimizes the content of reports and action ideas based on this.

[0823] The generated reports are periodically saved in PDF or HTML format and sent to the terminal, which displays the received reports on the user interface, allowing users to easily check industry trends and sentiment.

[0824] Furthermore, the device can suggest specific action ideas to the user based on the information obtained from the emotion engine. For example, if the user's emotional state is positive, it can recommend proposing a new project or promoting team activities. If the emotional state is negative, it can also suggest taking a break to refresh themselves or taking psychological care.

[0825] Users can consider specific actions based on the reports and recommendations displayed on their devices, and if necessary, launch new projects or revise their market strategies, enabling them to respond quickly and appropriately.

[0826] As a concrete example, to collect the latest trends in the IT industry and take user emotions into consideration, the server collects data from tech news sites and related social media feeds every morning at 8:00 and extracts key technology keywords using NLP technology. The analysis results and the emotion engine also take into account the user's emotional trends. For example, if it is determined that "cloud computing" or "AI" are rapidly increasing in popularity, the system will suggest appropriate actions taking into account the user's emotional state.

[0827] As described above, by using the system of the present invention, it is possible to automatically grasp trends in each industry and further recognize the emotional state of the user to suggest appropriate actions.

[0828] The processing flow will be explained below.

[0829] Step 1:

[0830] The server launches a crawler to collect information according to a specified schedule. The crawler reads a pre-defined list of URLs and search keywords and collects information from target websites and social networking services.

[0831] Step 2:

[0832] The server preprocesses the collected data by removing HTML tags, deleting duplicate data, and normalizing the text (e.g., converting to lowercase, removing stop words) to create a dataset suitable for analysis.

[0833] Step 3:

[0834] The server applies natural language processing techniques to the preprocessed data to extract keywords and topics. It uses NLP libraries (e.g., spaCy, NLTK) to extract key keywords and topics from the text data.

[0835] Step 4:

[0836] The server analyzes the frequency of occurrence of the extracted keywords over time, which allows the fluctuations in the frequency of occurrence of specific keywords to be graphed and trends to be identified.

[0837] Step 5:

[0838] The server analyzes the collected text data and user feedback using an emotion engine. The emotion engine identifies the emotional tendencies of the text and categorizes them into positive, negative, and neutral categories. It also infers the user's emotional state from their past actions and feedback.

[0839] Step 6:

[0840] The server automatically generates periodic reports based on the analysis results, including key keywords, trend fluctuations, sentiment trends, and predictive analysis results. The generated reports are saved in PDF or HTML format.

[0841] Step 7:

[0842] The server sends the generated report to the terminal, which updates the report periodically according to a schedule.

[0843] Step 8:

[0844] The terminal displays the received reports on a user interface, allowing users to check industry trends, important keywords, and sentiment trends.

[0845] Step 9:

[0846] Based on the analysis results and the generated report, the device will suggest specific action ideas to the user. For example, if the user's emotional state is positive, it will recommend proposing a new project or promoting team activities. If the emotional state is negative, it will suggest rest or psychological care.

[0847] Step 10:

[0848] Users can review the reports and proposals displayed on their devices and take action, such as launching a new project or revising their strategy, if necessary.

[0849] By repeating the above steps, users can always keep up with the latest industry trends and can take prompt and appropriate action taking into account their emotional state.

[0850] Example 2

[0851] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0852] In today's information society, there is a demand for efficient and effective collection and analysis of the overwhelming amount of information available on the Internet. It is also important for businesses to understand industry trends from collected information and propose appropriate actions that take into account the user's emotional state. However, conventional systems rely on manual data collection and analysis, which is time-consuming and labor-intensive, and it is difficult to propose actions that take into account the user's emotional state.

[0853] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0854] In this invention, the server includes means for automatically collecting data from specified information sources on the Internet, means for preprocessing the collected data and analyzing it using natural language processing technology, means for detecting industry trends based on the analysis results and generating a report, means for displaying the generated report on a user interface and presenting specific action ideas to the user, means for analyzing the collected text data and feedback from the user and identifying the emotional state, and means for optimizing the content of the report taking the emotional state into consideration and suggesting action ideas.This makes it possible to automatically collect and analyze information on the Internet, grasp industry trends, and suggest actions that are suited to the user's emotional state.

[0855] "Designated internet sources" are sources for collecting data from pre-set websites, social networking services, etc.

[0856] An "automatic data collection means" is a method or apparatus for launching a crawler according to a schedule and automatically collecting data from designated sources.

[0857] "Data preprocessing means" refers to methods or techniques for removing unnecessary information (e.g., HTML tags, duplicate data) from collected data and converting it into a format suitable for analysis.

[0858] "Natural language processing technology" is a technology that analyzes text data, extracts important keywords and topics, and analyzes fluctuations in their frequency of appearance.

[0859] "Means for identifying emotional state" refers to methods or techniques that analyze collected text data and user feedback and determine the user's emotional tendency (positive, negative, neutral, etc.) from that data.

[0860] "Means for generating reports" refers to methods or devices for organizing information such as industry trends and compiling it into report format based on natural language processing technology and sentiment analysis results.

[0861] A "user interface" refers to a display device or software that displays the generated report to the user, allowing the user to easily check the information.

[0862] "Means for presenting action ideas" refers to methods or techniques for suggesting specific actions to the user, taking into account the emotional state.

[0863] This invention is a system that automatically collects and analyzes data from online sources to understand industry trends, and also recognizes the user's emotional state and suggests actions. The system is divided into three entities: a server, a terminal, and a user.

[0864] The server automatically launches the crawler based on a specified schedule. The crawler collects data from tech news sites and social networking services. For example, it uses news APIs and RSS feeds to gather trend information for specific industries. The collected data is first preprocessed, such as removing HTML tags and duplicate data. Text normalization (e.g., converting uppercase to lowercase, removing stop words, etc.) is also performed at this stage.

[0865] The server then applies natural language processing techniques to the preprocessed data. Specifically, it uses natural language processing libraries (e.g., spaCy, NLTK) to extract key keywords and topics. It also analyzes whether specific keywords are increasing or decreasing over time, and can graph trend fluctuations.

[0866] The server then uses a sentiment analysis engine to analyze the collected text data and user feedback. The sentiment analysis engine identifies the emotional tendencies of the text and classifies them into categories such as positive, negative, and neutral. It can also estimate the user's emotional state based on the user's past actions and feedback. Based on the analysis results, the report is optimized.

[0867] The generated reports are periodically sent from the server to the terminal in PDF or HTML format, and the terminal displays the received reports on the user interface, allowing users to easily check industry trends and sentiment.

[0868] The device also uses the information obtained from the emotion analysis engine to suggest specific action ideas to the user. For example, if the user's emotional state is positive, it may recommend starting a new project or promoting team activities. If the emotional state is negative, it may suggest taking a break to refresh yourself or taking psychological care.

[0869] Users can consider and implement specific actions based on the reports and proposals displayed on their devices, enabling them to launch new projects and revise market strategies quickly and appropriately.

[0870] As a concrete example, consider a system that collects the latest trends in the IT industry and takes user emotions into account. Every morning at 8:00, the server collects data from tech news sites and related social media feeds, and uses NLP technology to extract key technology keywords. For example, if it finds that "cloud computing" or "artificial intelligence" are rapidly increasing in popularity, the server compiles that information into a report. The emotion engine also takes the user's emotional tendencies into account and suggests appropriate actions to the user.

[0871] Examples of prompts for a generative AI model might include:

[0872] "Summarize the following passage and identify its emotional tone (positive, negative, neutral):

[0873] "Recently, technologies related to cloud computing and AI have been developing rapidly. Many companies are adopting these technologies and building new business models."

[0874] In this way, by using the system of the present invention, it is possible to effectively collect and analyze information on the Internet and propose actions according to the user's emotional state.

[0875] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0876] Step 1: Data collection

[0877] The server automatically launches the crawler based on a specified schedule. For example, if it is set to run every morning at 8:00, it will collect data from tech news sites and social networking services. The input is a list of specific URLs, and the output is raw HTML data. This data is obtained using news APIs and RSS feeds.

[0878] Specific behavior:

[0879] The crawler downloads the RSS feed from https: / / technews.example.com / rss and retrieves the latest news articles.

[0880] Step 2: Data Preprocessing

[0881] The server preprocesses the collected HTML data, which includes removing HTML tags, removing duplicate data, and normalizing the text (e.g., converting uppercase to lowercase, removing stop words, etc.). The input is the collected raw HTML data, and the output is the preprocessed, clean text data.

[0882] Specific behavior:

[0883] Convert the raw HTML data "AI is transforming the industry" to "ai is transforming industry".

[0884] Step 3: Keyword extraction and trend analysis

[0885] The server applies natural language processing techniques to the preprocessed text data. It uses an NLP library (e.g., spaCy, NLTK) to extract important keywords and topics. It also analyzes the frequency of keyword occurrence over a certain period of time and graphs trend fluctuations. The input is the preprocessed text data, and the output is time series data of important keywords and their frequency of occurrence.

[0886] Specific behavior:

[0887] Apply NLP to the text "ai is transforming industry" to extract "ai" and "industry" as important keywords.

[0888] Using data from the past seven days, generate a time series graph showing the increasing frequency of the keyword "ai."

[0889] Step 4: Sentiment Analysis

[0890] The server uses a sentiment analysis engine to analyze the preprocessed text data and user feedback. The sentiment analysis engine identifies the emotional tendencies of the input text and classifies it into positive, negative, or neutral categories. The input is the preprocessed text data and user feedback, and the output is an emotional category label.

[0891] Specific behavior:

[0892] Enter the text "The new AI technology is amazing!" into the sentiment engine and classify this text as positive.

[0893] Step 5: Generate reports

[0894] The server generates an industry trend report based on the extracted keywords and sentiment analysis results. This report is saved in PDF or HTML format. The input is the important keyword data and sentiment analysis results, and the output is the generated report.

[0895] Specific behavior:

[0896] Generate and save a report in PDF format stating "AI is on the rise."

[0897] Step 6: Send and view the report

[0898] The server periodically sends the generated report to the terminal, and the terminal displays the received report on the user interface. The input is the generated report, and the output is the report sent to the terminal and the displayed content.

[0899] Specific behavior:

[0900] Email a PDF report to your device.

[0901] The device displays a dashboard with a report titled "Industry Trends: AI is on the Rise."

[0902] Step 7: Present your ideas for action

[0903] The terminal proposes specific action ideas to the user based on the sentiment analysis results. The input is the sentiment analysis results and the generated report, and the output is the action ideas displayed on the user interface.

[0904] Specific behavior:

[0905] Display the action idea "Consider launching a new AI project" in the user interface.

[0906] Step 8: User takes action

[0907] The user considers and executes specific actions based on the reports and suggestions displayed on the terminal. The input is the proposed action ideas, and the output is the specific actions executed by the user.

[0908] Specific behavior:

[0909] A user schedules a meeting to launch a "new AI project."

[0910] (Application example 2)

[0911] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0912] Conventional systems for grasping industry trends do not suggest specific actions that take into account the user's emotional state. As a result, even if users can obtain materials to understand industry trends, it is difficult for them to take appropriate actions based on their individual emotional state. Furthermore, when dealing with customers in physical stores, it is difficult to provide appropriate customer service that matches the customer's emotional state. A system that solves these problems and suggests specific actions based on the user's emotional state is needed.

[0913] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for automatically collecting data from specified information sources on the Internet, means for preprocessing the collected data and analyzing it using natural language processing technology, means for detecting industry trends based on the analysis results and generating a report, means for displaying the generated report on a user interface and presenting specific action ideas to the user, and means for recognizing the user's emotional state and suggesting appropriate actions. This makes it possible not only to grasp industry trends but also to suggest optimal actions based on the user's emotional state. This enables optimal customer service tailored to the emotional state of customers, especially in physical stores.

[0914] "Designated internet sources" refers to specific data collection targets, such as pre-defined websites or social networking services.

[0915] "Means for automatically collecting data" refers to the function of launching crawlers or other machines according to a specified schedule to collect text data from sources on the Internet.

[0916] "Preprocessing" refers to the process of removing unnecessary information from collected data and preparing it in a format suitable for analysis, such as removing HTML tags, deleting duplicate data, and normalizing text.

[0917] "Natural language processing technology" refers to a set of techniques for understanding and analyzing text data, including means for extracting key keywords and topics.

[0918] "Industry trends" refers to fluctuations and trends in the frequency of occurrence of key keywords and topics within a particular industry.

[0919] "Report" refers to a written report generated based on collected and analyzed data, which includes industry trends and action ideas.

[0920] "User interface" refers to the screens and applications that users can interact with directly and check and manipulate information.

[0921] "User's emotional state" refers to the psychological tendency, such as positive, negative, or neutral, displayed by the user.

[0922] "Means to suggest appropriate actions" refers to a function that suggests specific actions based on the user's emotional state and industry trends.

[0923] The present invention is embodied in a specific form as a system that automatically collects and analyzes data from designated internet sources to understand industry trends, and further recognizes the user's emotional state and suggests actions.

[0924] System Program Overview

[0925] The server first launches a crawler based on a specified schedule to collect text data from relevant websites and social networking services. The collected data is preprocessed to remove HTML tags, delete duplicate data, and normalize the text. Natural language processing techniques (e.g., spaCy and TextBlob) are then used to extract key keywords and topics, analyze their frequency over time, and graph industry trends.

[0926] The server then analyzes the collected text data and user feedback using an emotion engine, which identifies emotional trends such as positive, negative, and neutral, and estimates the user's emotional state. Based on this information, the content of reports and action ideas is optimized.

[0927] The generated report is saved in PDF or HTML format and sent to the device, where it is displayed on the user interface, allowing users to easily check industry trends and emotional tendencies. The device also suggests specific action ideas to users based on the information obtained from the emotion engine.

[0928] Hardware and Software Examples

[0929] Hardware: Server, smart glasses

[0930] Software: Python, spaCy, TextBlob, Requests library

[0931] Specific examples

[0932] For example, to gather the latest trends in the IT industry, the server collects data from tech news sites and related social media feeds at 8:00 every morning. NLP technology is used to extract key technology keywords and analyze trends. Using the analysis results and an emotion engine, the system takes into account the user's emotional tendencies and suggests new projects or breaks to refresh them.

[0933] Store staff wearing smart glasses are dynamically suggested appropriate ways to serve customers based on the emotional state of the customer. For example, if a customer says, "I was really looking forward to visiting this store today!", the smart glasses' display will suggest, "Proactively introduce new products."

[0934] Explicit prompt example

[0935] "Analyze your customers' emotional state in real time and suggest the best way to serve them based on the latest trend information. Latest customer feedback: {Customer feedback}"

[0936] This will enable brick-and-mortar stores to provide optimal responses according to the emotional state of customers. Furthermore, understanding industry trends and proposing specific actions based on those trends will support user decision-making.

[0937] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0938] Step 1:

[0939] The server launches a crawler according to a specified schedule. The crawler collects text data from specified Internet sources (such as news sites and social network feeds). The inputs to this step are the specified URLs and schedule information, and the output is the collected text data.

[0940] Step 2:

[0941] The server preprocesses the collected text data. Preprocessing includes removing HTML tags, deleting duplicate data, and normalizing the text (converting to lowercase and removing stop words). The input of this step is the collected text data, and the output is the preprocessed, clean text data.

[0942] Step 3:

[0943] The server applies natural language processing (NLP) technology to the preprocessed data. Specifically, it uses tools such as spaCy and TextBlob to extract key keywords and topics from the text data. It also analyzes fluctuations in their frequency of appearance over time. The input for this step is the preprocessed text data, and the output is key keywords and topics and information on fluctuations in their frequency of appearance.

[0944] Step 4:

[0945] The server analyzes the collected text data and user feedback using an emotion engine, which identifies the emotional tendencies of the text and classifies them into categories such as positive, negative, and neutral. The inputs of this step are the preprocessed text data and user feedback, and the output is the sentiment analysis result of the text.

[0946] Step 5:

[0947] The server detects industry trends based on the analysis results and generates a report that includes analysis results of key keywords and topics, sentiment analysis results, and insights into industry trends. The inputs to this step are the sentiment analysis results and NLP analysis results, and the output is an industry trend report.

[0948] Step 6:

[0949] The server periodically saves the generated reports in PDF or HTML format and sends them to the terminal. The input of this step is the generated report, and the output is the saved and sent report.

[0950] Step 7:

[0951] The terminal displays the received report on the user interface, allowing the user to check industry trends and sentiment. The input of this step is the report sent from the server, and the output is the report displayed on the user interface.

[0952] Step 8:

[0953] The terminal proposes specific action ideas to the user based on the information obtained from the emotion engine. The input of this step is the emotion analysis result and the generated report, and the output is the action ideas presented to the user.

[0954] Step 9:

[0955] The user considers and takes specific actions based on the report and proposals displayed on the device. If necessary, the user may launch a new project or revise their market strategy. The input for this step is the report and proposed actions displayed on the device, and the output is the specific action taken by the user.

[0956] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0957] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0958] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0959] [Fourth embodiment]

[0960] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0961] 7, a 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.

[0962] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0963] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0964] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0965] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0966] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0967] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0968] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0969] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0970] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0971] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0972] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0973] The present invention provides a specific embodiment of a system for automatically collecting and analyzing data from information sources on the Internet to understand industry trends and then suggesting actions to users. To achieve this, the following processes are performed.

[0974] This system is divided into the main roles of the server, the terminal, and the user.

[0975] First, the server collects data from sources on the Internet according to a specified schedule. The server launches a crawler using a pre-defined list of URLs and search keywords. For example, to obtain the latest information on a specific industry (e.g., the IT industry), the server collects text data from related news sites and social networking services.

[0976] The server then preprocesses the collected data, which includes removing HTML tags, eliminating duplicate data, and normalizing text (e.g., converting to lowercase, removing stop words) to prepare a dataset suitable for analysis.

[0977] The server then analyzes the preprocessed data using natural language processing techniques. Specifically, it uses NLP libraries to extract important keywords and topics from the text data. For example, it uses techniques such as TF-IDF and Word2Vec to identify frequently occurring keywords. It also performs time series analysis to generate graphs showing how specific keywords increase or decrease.

[0978] The server automatically generates reports based on the analysis results. The generated reports include extracted keywords, trend fluctuations, and predictive analysis results. The reports are generated periodically and saved in PDF or HTML format.

[0979] The generated report is provided to the user via the device. The device displays the report on the user interface, allowing the user to easily check industry trends. In addition, the device suggests specific action ideas to the user based on the report. For example, if it is found that AI technology is rapidly increasing, the device will suggest actions such as "conduct AI-related training" or "start a new project using AI technology."

[0980] Finally, users can consider specific actions based on the reports and recommendations displayed on their devices, such as launching new projects or revising their market strategies, to ensure prompt and appropriate responses.

[0981] As a specific example, to collect the latest trends in the IT industry, the server collects data from tech news sites and related social media (e.g., IT news) every morning at 8:00 and extracts key technology keywords using NLP technology. If the results show that there has been a sharp increase in "cloud computing" or "AI" in a particular week, the server generates a report based on that information, and the device suggests actions to the user, such as "hiring cloud engineers" or "launching an AI project."

[0982] As described above, by using the system of the present invention, it is possible to automatically grasp trends in each industry and take appropriate action in a timely manner.

[0983] The processing flow will be explained below.

[0984] Step 1:

[0985] The server launches a crawler to collect information according to a specified schedule. The crawler reads a pre-defined list of URLs and search keywords and collects information from target websites and social networking services.

[0986] Step 2:

[0987] The server preprocesses the collected data by removing HTML tags, removing duplicate data, and normalizing the text (e.g., converting to lowercase, removing stop words) to create a dataset suitable for analysis.

[0988] Step 3:

[0989] The server applies natural language processing techniques to the preprocessed data to extract keywords and topics. Specifically, it uses NLP libraries (e.g., spaCy, NLTK) to extract key keywords and topics from the text data.

[0990] Step 4:

[0991] The server analyzes the frequency of occurrence of the extracted keywords over time, which allows the fluctuations in the frequency of occurrence of specific keywords to be graphed and trends to be identified.

[0992] Step 5:

[0993] The server automatically generates periodic reports based on the analysis results, including key keywords, trend fluctuations, and predictive analysis results. The generated reports are saved in PDF or HTML format.

[0994] Step 6:

[0995] The server sends the generated report to the terminal, which updates the report periodically according to a schedule.

[0996] Step 7:

[0997] The terminal displays the received report on the user interface, allowing the user to check industry trends and important keywords through the report.

[0998] Step 8:

[0999] Based on the analysis results and the generated report, the device will suggest specific action ideas to the user, such as "conduct training in related technologies" or "launch a new project" based on rapidly increasing keywords.

[1000] Step 9:

[1001] Users can review the reports and proposals displayed on their devices and take action, such as launching a new project or revising their strategy, if necessary.

[1002] By repeating the above steps, users can always grasp the latest industry trends and respond quickly and appropriately.

[1003] Example 1

[1004] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1005] Conventional data collection and analysis systems have made it difficult to quickly and accurately grasp specific industry trends. Furthermore, preprocessing and analyzing the collected data takes time, making it difficult to provide timely information that allows users to take action. Furthermore, there is a lack of action suggestions based on the analysis results, meaning there is insufficient support for users to take specific actions.

[1006] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1007] In this invention, the server includes means for automatically collecting data from specified Internet information sources, means for preprocessing the collected data and analyzing it using natural language processing technology, means for detecting industry trends based on the analysis results and generating reports, means for collecting data using a crawler or according to a specified schedule, means for removing HTML tags, eliminating duplicate data, and normalizing text in the preprocessing, means for extracting important keywords using TF-IDF or Word2Vec and creating graphs in the analysis, and means for saving the generated reports in PDF or HTML format. This enables fast and accurate data collection and analysis, and enables timely and specific actions to be proposed to users.

[1008] "Designated Internet Sources" refers to websites and social networking services from which data is collected according to a designated schedule.

[1009] "Automatic data collection means" refers to a mechanism that obtains data from a programmatically designated source without requiring manual operation.

[1010] "Preprocessing" refers to various preparatory tasks performed on collected data before analysis, such as removing HTML tags, eliminating duplicate data, and normalizing text.

[1011] "Natural language processing technology" or "NLP technology" refers to technology that enables computers to understand, analyze, and generate human language.

[1012] "Analysis means" refers to the function of using preprocessed data to extract important keywords, analyze topics, and analyze fluctuations in frequency of occurrence.

[1013] "Means for generating reports" refers to a mechanism for automatically creating documents summarizing industry trends based on the analysis results.

[1014] "User interface" refers to an interface that has a screen and input form that allows a user to interact with a system.

[1015] "Crawler" refers to a program that automatically collects data from designated Internet sources.

[1016] "TF-IDF" stands for "Term Frequency-Inverse Document Frequency" and refers to a statistical method for extracting important keywords from text data.

[1017] "Word2Vec" refers to a machine learning model that converts words into a vector space and analyzes their semantic relationships.

[1018] "HTML tag removal" refers to the process of removing unnecessary HTML formatting tags from collected text data.

[1019] "Duplicate data elimination" refers to the process of removing identical or very similar data.

[1020] "Text normalization" refers to the process of standardizing text data into a format that is easier to analyze, such as converting it to lowercase or removing stop words.

[1021] "Means for saving in PDF or HTML format" refers to the function for outputting and saving the generated report in a file format that can be displayed visually.

[1022] This invention is a system that automatically collects and analyzes data from online sources to understand industry trends and then suggests actions to users. This system is divided into three main parts: the server, the terminal, and the user.

[1023] First, the server collects data according to a specified schedule. The server launches a crawler using a pre-defined list of URLs and search keywords. For example, to obtain the latest information in a specific industry (e.g., the IT industry), the server collects text data from related news sites and social networking services. The crawler uses a library such as BeautifulSoup to extract text from HTML data.

[1024] The server then pre-processes the collected data, which includes:

[1025] Strip HTML tags: Use the BeautifulSoup library to strip HTML tags from text data.

[1026] Eliminate duplicate data: Use the Pandas library to eliminate duplicate data.

[1027] Text normalization: Use the NLTK library to convert the text to lower case and remove stop words.

[1028] The server then analyzes the preprocessed data using natural language processing techniques, including:

[1029] TF-IDF (Term Frequency-Inverse Document Frequency): Uses the scikit-learn library to extract important keywords.

[1030] Word2Vec: Uses the Gensim library to analyze relationships between keywords.

[1031] Time series analysis: Use Matplotlib and Seaborn libraries to graph the increase or decrease of specific keywords.

[1032] The server then generates a report based on the analysis results, which includes extracted important keywords, trend change graphs, and predictive analysis results. Using the Sphinx library, the report is generated and saved in PDF and HTML formats.

[1033] The generated report is provided to the user via the terminal. The terminal displays the report on a web interface, allowing the user to easily check industry trends. In addition, the terminal suggests specific action ideas to the user based on the analysis results. For example, it may suggest, "If cloud technology is rapidly increasing, consider hiring cloud engineers."

[1034] Finally, users can consider specific actions based on the reports and recommendations displayed on their devices, such as launching a new project or reviewing their market strategy, allowing them to take prompt and appropriate action.

[1035] To give a specific example, to understand the latest trends in the IT industry, a server collects data from specific news sites and social media every morning at 8:00 a.m. The collected data is preprocessed and key keywords such as "cloud computing" and "AI" are extracted using NLP technology. Based on the results, a report is automatically generated, and the device suggests specific actions to the user, such as "hiring cloud engineers" or "launching an AI project."

[1036] An example of a prompt sentence would be, "Please generate a report that will grasp the latest trends in the IT industry and suggest appropriate actions," and the generative AI model would then provide an appropriate report and suggest actions.

[1037] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1038] Step 1:

[1039] Data collection

[1040] The server launches the data collection task based on a specified schedule. For example, if the schedule is set to run once a day, the server will launch the crawler at a specific time every day. The crawler collects relevant data from news sites and social networking services on the Internet based on a pre-defined URL list and search keywords. It sends an HTTP request and retrieves the HTML data of the web page.

[1041] Input: URL list, search keywords

[1042] Output: HTML data

[1043] Step 2:

[1044] Data Preprocessing

[1045] The server preprocesses the collected HTML data. Specific actions include:

[1046] Strip HTML tags: Use the BeautifulSoup library to strip HTML tags from text data.

[1047] Eliminate duplicate data: Use the Pandas library to eliminate duplicate data.

[1048] Text normalization: Use the NLTK library to convert the text to lower case and remove stop words.

[1049] Input: HTML data

[1050] Output: Preprocessed text data

[1051] Step 3:

[1052] Data analysis

[1053] The server performs natural language processing on the preprocessed text data, using techniques such as:

[1054] TF-IDF (Term Frequency-Inverse Document Frequency): Uses the scikit-learn library to extract important keywords.

[1055] Word2Vec: Uses the Gensim library to analyze the relationships between keywords.

[1056] Time series analysis: Use Matplotlib and Seaborn libraries to graph the increase or decrease in frequency of specific keywords.

[1057] Input: Preprocessed text data

[1058] Output: Keyword list, trend graph

[1059] Step 4:

[1060] Report Generation

[1061] The server automatically generates industry trend reports based on the analysis results. The reports are generated and saved in PDF and HTML formats using the Sphinx library. The reports include extracted important keywords, trend fluctuations, and predictive analysis results.

[1062] Input: Keyword list, trend graph

[1063] Output: PDF report, HTML report

[1064] Step 5:

[1065] Reporting and action recommendations

[1066] The device displays the generated report on the user interface. The user can view the report through a web browser. Furthermore, the device suggests specific action ideas to the user based on the report. For example, the device may suggest, "Consider hiring cloud engineers as cloud technology is rapidly increasing."

[1067] Input: PDF report, HTML report

[1068] Output: Display on the user interface, action suggestions

[1069] Step 6:

[1070] User action consideration

[1071] Based on the report and proposals displayed on the device, the user considers specific actions, such as launching a new project or reviewing market strategies. Specific actions include holding a planning meeting based on the report and issuing specific instructions to the relevant department.

[1072] Input: Display on the user interface, action suggestions

[1073] Output: Implementation of specific actions (e.g., launching a new project, formulating a recruitment plan)

[1074] (Application example 1)

[1075] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1076] Systems already exist that automatically collect industry trends from online sources and suggest specific actions to users based on the analysis results, but these systems are rarely used in real time on actual factory floors. In particular, there is a need for a system that allows factory workers to easily grasp industry trends and immediately take specific actions regarding manufacturing processes and the introduction of new technologies. To meet these needs, a system is needed that provides a real-time process from information collection to analysis and specific action suggestions.

[1077] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1078] In this invention, the server includes means for automatically collecting data from specified internet information sources, means for preprocessing the collected data and analyzing it using natural language processing technology, means for detecting industry trends based on the analysis results and generating a report, and means for displaying the generated report on smart glasses worn by factory workers, allowing them to grasp industry trends in real time and propose specific actions regarding manufacturing processes and the introduction of new technologies, thereby enabling factory workers to grasp industry trends in real time and take specific actions immediately.

[1079] "Internet sources" refers to text data and multimedia content provided by online platforms such as websites, social networking services, and news portals.

[1080] "Means of collecting data" refers to technical means, such as programs or crawlers, for automatically collecting data from designated Internet sources.

[1081] "Data preprocessing" refers to the process of removing unnecessary information from collected data and converting it into a format suitable for analysis, including removing HTML tags, eliminating duplicate data, and normalizing text.

[1082] "Natural language processing technology" refers to technology for analyzing text data, and refers to techniques such as keyword extraction, topic modeling, and sentiment analysis.

[1083] "Industry trends" refers to movements and trends such as technological advances, market fluctuations, and the activities of major companies in a particular industry field.

[1084] The means for generating a "report" refers to a program or algorithm that automatically creates a report summarizing industry trends based on the analysis results.

[1085] "User interface" refers to the interactive display on a screen or device that allows a user to view information and take action.

[1086] "Smart glasses" are a type of wearable device that has the ability to display information through a built-in display and is used to provide information in real time.

[1087] "Real-time" refers to data acquisition, analysis, display, etc. occurring immediately and without delay.

[1088] "Manufacturing process" refers to the series of steps that create a product from raw materials, including production activities within a factory.

[1089] "New technology introduction" refers to incorporating the latest technologies and methods into existing systems and processes, and is an activity aimed at improving factory efficiency and product quality.

[1090] System Overview

[1091] This invention is a system that uses smart glasses used in factories to enable workers to grasp industry trends in real time and receive specific action proposals for production processes and the introduction of new technologies.The system is mainly composed of three entities: a server, a terminal, and a user.

[1092] Data collection and analysis by the server

[1093] The server first automatically collects data from designated Internet sources. This involves launching a crawler on a scheduled basis to collect text data from designated websites and social networking services. The collected data is then preprocessed to remove HTML tags, eliminate duplicate data, and normalize the text.

[1094] The server then analyzes the preprocessed data using natural language processing techniques, extracting key keywords and topics and analyzing fluctuations in their frequency of occurrence, using techniques such as TF-IDF and Word2Vec. This uncovers important trends and industry developments.

[1095] Generated reports include extracted keywords, trend changes, and predictive analysis results and are generated periodically in PDF and HTML formats.

[1096] Display reports and action suggestions on your device

[1097] The reports generated by the server are displayed via smart glasses worn by factory workers. The smart glasses have a built-in display that allows users to understand industry trends in real time. Specific action suggestions are also provided to workers based on the analysis results. For example, specific actions such as "consider introducing 3D printing technology" or "hiring cloud engineers" are suggested.

[1098] User Behavior

[1099] The users, or workers, can quickly take specific actions within the factory based on the reports and action suggestions presented through the smart glasses, allowing them to grasp trends in real time and take appropriate measures.

[1100] Hardware and Software Details

[1101] Server: We use requests and BeautifulSoup for data collection, and nltk and sklearn libraries for NLP and analysis.

[1102] Smart glasses: A wearable device with a display function that allows factory workers to check information in real time.

[1103] Crawler: An automated program that collects data from designated internet sources.

[1104] For example, factory workers working on conveyor lines can wear smart glasses and receive real-time action suggestions based on the latest technology keywords and industry trends. For example, specific suggestions such as "consider introducing 3D printing technology" can be viewed on the glasses' display.

[1105] Prompt Sentence Examples

[1106] "Please design a system that uses smart glasses to grasp the latest industrial technology trends in real time and provide specific action suggestions for the manufacturing process in a factory. Please also tell us the detailed steps for data collection, analysis, and report generation, as well as the libraries used."

[1107] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1108] Step 1:

[1109] Data collection

[1110] The server automatically collects data from specified sources on the Internet. During this process, it launches a crawler according to a set schedule to retrieve text data from websites and social networking services based on a list of URLs and search keywords. It uses the specified URLs and search keywords as input and generates the retrieved text data as output. The crawler analyzes the HTML of web pages and extracts the body text.

[1111] Step 2:

[1112] Data Preprocessing

[1113] The server preprocesses the collected text data. This process involves removing HTML tags, eliminating duplicate data, and normalizing the text (converting to lowercase and removing stop words). It uses the collected text data as input and generates preprocessed text data as output. Specifically, it uses BeautifulSoup to remove HTML tags and regular expressions to eliminate unnecessary strings.

[1114] Step 3:

[1115] Data analysis

[1116] The server analyzes the preprocessed data using natural language processing technology. This process uses TF-IDF, Word2Vec, etc. to extract key keywords and topics and analyzes fluctuations in their frequency of appearance. It uses the preprocessed text data as input and generates analysis results (lists of key keywords and topics) as output. Specifically, it uses an NLP library to vectorize the text and extract keywords.

[1117] Step 4:

[1118] Generate reports

[1119] The server automatically generates a report based on the analysis results. This process creates a report that includes industry trends, trend fluctuations, and predictive analysis results based on the extracted keywords and topics. It uses the analysis results as input and generates a report in PDF or HTML format as output. Specifically, it embeds the analysis results into a template to create a report and converts it to PDF or HTML format.

[1120] Step 5:

[1121] Viewing Reports

[1122] The terminal displays the generated report on a user interface. In this process, the report is displayed through smart glasses worn by workers in the factory. The generated report is used as input to generate information to be displayed on the smart glasses as output. Specifically, the report is displayed in real time in HTML or PDF format on a display device inside the smart glasses.

[1123] Step 6:

[1124] Action proposals

[1125] The device then suggests specific action ideas to the user based on the report. This process suggests actions derived from the analysis results and provides information to factory workers in real time. The generated report is used as input and action suggestions are generated as output. Specific actions include suggesting actions such as "consider introducing 3D printing technology" or "hiring cloud engineers" and displaying them on the smart glasses' display.

[1126] Step 7:

[1127] User Actions

[1128] The user, a factory worker, takes specific actions based on the report and action suggestions presented through the smart glasses. This process involves improving the manufacturing process or introducing new technologies in accordance with the suggestions. The action suggestions displayed on the smart glasses are used as input, and actual actions are generated as output. Specific actions involve the worker adjusting equipment and processes based on the suggested actions.

[1129] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1130] This invention provides a concrete form of a system that automatically collects and analyzes data from sources on the Internet to understand industry trends, and further recognizes the user's emotional state and suggests actions. This system is divided into the main players: a server, a terminal, and a user.

[1131] First, the server launches a crawler based on a specified schedule to collect information from target websites and social networking services. For example, to collect trend information for a specific industry (e.g., the IT industry), it retrieves text data from related news sites and social media feeds.

[1132] The server then preprocesses the collected data by removing HTML tags, removing duplicate data, and normalizing the text (such as converting to lowercase and removing stop words) to create a dataset suitable for analysis.

[1133] The server then applies natural language processing techniques to the preprocessed data to extract key keywords and topics. Specifically, it uses NLP libraries (e.g., spaCy, NLTK) to extract important keywords from the text data. It also analyzes how specific keywords increase or decrease over time and graphs the trend fluctuations.

[1134] Furthermore, the server uses an emotion engine to analyze the collected text data and user feedback. The emotion engine identifies the emotional tendencies of the text and classifies them into categories such as positive, negative, and neutral. The emotion engine also estimates the user's emotional state from their past actions and feedback, and optimizes the content of reports and action ideas based on this.

[1135] The generated reports are periodically saved in PDF or HTML format and sent to the terminal, which displays the received reports on the user interface, allowing users to easily check industry trends and sentiment.

[1136] Furthermore, the device can suggest specific action ideas to the user based on the information obtained from the emotion engine. For example, if the user's emotional state is positive, it can recommend proposing a new project or promoting team activities. If the emotional state is negative, it can also suggest taking a break to refresh themselves or taking psychological care.

[1137] Users can consider specific actions based on the reports and recommendations displayed on their devices, and if necessary, launch new projects or revise their market strategies, enabling them to respond quickly and appropriately.

[1138] As a concrete example, to collect the latest trends in the IT industry and take user emotions into consideration, the server collects data from tech news sites and related social media feeds every morning at 8:00 and extracts key technology keywords using NLP technology. The analysis results and the emotion engine also take into account the user's emotional trends. For example, if it is determined that "cloud computing" or "AI" are rapidly increasing in popularity, the system will suggest appropriate actions taking into account the user's emotional state.

[1139] As described above, by using the system of the present invention, it is possible to automatically grasp trends in each industry and further recognize the emotional state of the user to suggest appropriate actions.

[1140] The processing flow will be explained below.

[1141] Step 1:

[1142] The server launches a crawler to collect information according to a specified schedule. The crawler reads a pre-defined list of URLs and search keywords and collects information from target websites and social networking services.

[1143] Step 2:

[1144] The server preprocesses the collected data by removing HTML tags, deleting duplicate data, and normalizing the text (e.g., converting to lowercase, removing stop words) to create a dataset suitable for analysis.

[1145] Step 3:

[1146] The server applies natural language processing techniques to the preprocessed data to extract keywords and topics. It uses NLP libraries (e.g., spaCy, NLTK) to extract key keywords and topics from the text data.

[1147] Step 4:

[1148] The server analyzes the frequency of occurrence of the extracted keywords over time, which allows the fluctuations in the frequency of occurrence of specific keywords to be graphed and trends to be identified.

[1149] Step 5:

[1150] The server analyzes the collected text data and user feedback using an emotion engine. The emotion engine identifies the emotional tendencies of the text and categorizes them into positive, negative, and neutral categories. It also infers the user's emotional state from their past actions and feedback.

[1151] Step 6:

[1152] The server automatically generates periodic reports based on the analysis results, including key keywords, trend fluctuations, sentiment trends, and predictive analysis results. The generated reports are saved in PDF or HTML format.

[1153] Step 7:

[1154] The server sends the generated report to the terminal, which updates the report periodically according to a schedule.

[1155] Step 8:

[1156] The terminal displays the received reports on a user interface, allowing users to check industry trends, important keywords, and sentiment trends.

[1157] Step 9:

[1158] Based on the analysis results and the generated report, the device will suggest specific action ideas to the user. For example, if the user's emotional state is positive, it will recommend proposing a new project or promoting team activities. If the emotional state is negative, it will suggest rest or psychological care.

[1159] Step 10:

[1160] Users can review the reports and proposals displayed on their devices and take action, such as launching a new project or revising their strategy, if necessary.

[1161] By repeating the above steps, users can always keep up with the latest industry trends and can take prompt and appropriate action taking into account their emotional state.

[1162] Example 2

[1163] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1164] In today's information society, there is a demand for efficient and effective collection and analysis of the overwhelming amount of information available on the Internet. It is also important for businesses to understand industry trends from collected information and propose appropriate actions that take into account the user's emotional state. However, conventional systems rely on manual data collection and analysis, which is time-consuming and labor-intensive, and it is difficult to propose actions that take into account the user's emotional state.

[1165] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1166] In this invention, the server includes means for automatically collecting data from specified information sources on the Internet, means for preprocessing the collected data and analyzing it using natural language processing technology, means for detecting industry trends based on the analysis results and generating a report, means for displaying the generated report on a user interface and presenting specific action ideas to the user, means for analyzing the collected text data and feedback from the user and identifying the emotional state, and means for optimizing the content of the report taking the emotional state into consideration and suggesting action ideas.This makes it possible to automatically collect and analyze information on the Internet, grasp industry trends, and suggest actions that are suited to the user's emotional state.

[1167] "Designated internet sources" are sources for collecting data from pre-set websites, social networking services, etc.

[1168] An "automatic data collection means" is a method or apparatus for launching a crawler according to a schedule and automatically collecting data from designated sources.

[1169] "Data preprocessing means" refers to methods or techniques for removing unnecessary information (e.g., HTML tags, duplicate data) from collected data and converting it into a format suitable for analysis.

[1170] "Natural language processing technology" is a technology that analyzes text data, extracts important keywords and topics, and analyzes fluctuations in their frequency of appearance.

[1171] "Means for identifying emotional state" refers to methods or techniques that analyze collected text data and user feedback and determine the user's emotional tendency (positive, negative, neutral, etc.) from that data.

[1172] "Means for generating reports" refers to methods or devices for organizing information such as industry trends and compiling it into report format based on natural language processing technology and sentiment analysis results.

[1173] A "user interface" refers to a display device or software that displays the generated report to the user, allowing the user to easily check the information.

[1174] "Means for presenting action ideas" refers to methods or techniques for suggesting specific actions to the user, taking into account the emotional state.

[1175] This invention is a system that automatically collects and analyzes data from online sources to understand industry trends, and also recognizes the user's emotional state and suggests actions. The system is divided into three entities: a server, a terminal, and a user.

[1176] The server automatically launches the crawler based on a specified schedule. The crawler collects data from tech news sites and social networking services. For example, it uses news APIs and RSS feeds to gather trend information for specific industries. The collected data is first preprocessed, such as removing HTML tags and duplicate data. Text normalization (e.g., converting uppercase to lowercase, removing stop words, etc.) is also performed at this stage.

[1177] The server then applies natural language processing techniques to the preprocessed data. Specifically, it uses natural language processing libraries (e.g., spaCy, NLTK) to extract key keywords and topics. It also analyzes whether specific keywords are increasing or decreasing over time, and can graph trend fluctuations.

[1178] The server then uses a sentiment analysis engine to analyze the collected text data and user feedback. The sentiment analysis engine identifies the emotional tendencies of the text and classifies them into categories such as positive, negative, and neutral. It can also estimate the user's emotional state based on the user's past actions and feedback. Based on the analysis results, the report is optimized.

[1179] The generated reports are periodically sent from the server to the terminal in PDF or HTML format, and the terminal displays the received reports on the user interface, allowing users to easily check industry trends and sentiment.

[1180] The device also uses the information obtained from the emotion analysis engine to suggest specific action ideas to the user. For example, if the user's emotional state is positive, it may recommend starting a new project or promoting team activities. If the emotional state is negative, it may suggest taking a break to refresh yourself or taking psychological care.

[1181] Users can consider and implement specific actions based on the reports and proposals displayed on their devices, enabling them to launch new projects and revise market strategies quickly and appropriately.

[1182] As a concrete example, consider a system that collects the latest trends in the IT industry and takes user emotions into account. Every morning at 8:00, the server collects data from tech news sites and related social media feeds, and uses NLP technology to extract key technology keywords. For example, if it finds that "cloud computing" or "artificial intelligence" are rapidly increasing in popularity, the server compiles that information into a report. The emotion engine also takes the user's emotional tendencies into account and suggests appropriate actions to the user.

[1183] Examples of prompts for a generative AI model might include:

[1184] "Summarize the following passage and identify its emotional tone (positive, negative, neutral):

[1185] "Recently, technologies related to cloud computing and AI have been developing rapidly. Many companies are adopting these technologies and building new business models."

[1186] In this way, by using the system of the present invention, it is possible to effectively collect and analyze information on the Internet and propose actions according to the user's emotional state.

[1187] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1188] Step 1: Data collection

[1189] The server automatically launches the crawler based on a specified schedule. For example, if it is set to run every morning at 8:00, it will collect data from tech news sites and social networking services. The input is a list of specific URLs, and the output is raw HTML data. This data is obtained using news APIs and RSS feeds.

[1190] Specific behavior:

[1191] The crawler downloads the RSS feed from https: / / technews.example.com / rss and retrieves the latest news articles.

[1192] Step 2: Data Preprocessing

[1193] The server preprocesses the collected HTML data, which includes removing HTML tags, removing duplicate data, and normalizing the text (e.g., converting uppercase to lowercase, removing stop words, etc.). The input is the collected raw HTML data, and the output is the preprocessed, clean text data.

[1194] Specific behavior:

[1195] Convert the raw HTML data "AI is transforming the industry" to "ai is transforming industry".

[1196] Step 3: Keyword extraction and trend analysis

[1197] The server applies natural language processing techniques to the preprocessed text data. It uses an NLP library (e.g., spaCy, NLTK) to extract important keywords and topics. It also analyzes the frequency of keyword occurrence over a certain period of time and graphs trend fluctuations. The input is the preprocessed text data, and the output is time series data of important keywords and their frequency of occurrence.

[1198] Specific behavior:

[1199] Apply NLP to the text "ai is transforming industry" to extract "ai" and "industry" as important keywords.

[1200] Using data from the past seven days, generate a time series graph showing the increasing frequency of the keyword "ai."

[1201] Step 4: Sentiment Analysis

[1202] The server uses a sentiment analysis engine to analyze the preprocessed text data and user feedback. The sentiment analysis engine identifies the emotional tendencies of the input text and classifies it into positive, negative, or neutral categories. The input is the preprocessed text data and user feedback, and the output is an emotional category label.

[1203] Specific behavior:

[1204] Enter the text "The new AI technology is amazing!" into the sentiment engine and classify this text as positive.

[1205] Step 5: Generate reports

[1206] The server generates an industry trend report based on the extracted keywords and sentiment analysis results. This report is saved in PDF or HTML format. The input is the important keyword data and sentiment analysis results, and the output is the generated report.

[1207] Specific behavior:

[1208] Generate and save a report in PDF format stating "AI is on the rise."

[1209] Step 6: Send and view the report

[1210] The server periodically sends the generated report to the terminal, and the terminal displays the received report on the user interface. The input is the generated report, and the output is the report sent to the terminal and the displayed content.

[1211] Specific behavior:

[1212] Email a PDF report to your device.

[1213] The device displays a dashboard with a report titled "Industry Trends: AI is on the Rise."

[1214] Step 7: Present your ideas for action

[1215] The terminal proposes specific action ideas to the user based on the sentiment analysis results. The input is the sentiment analysis results and the generated report, and the output is the action ideas displayed on the user interface.

[1216] Specific behavior:

[1217] Display the action idea "Consider launching a new AI project" in the user interface.

[1218] Step 8: User takes action

[1219] The user considers and executes specific actions based on the reports and suggestions displayed on the terminal. The input is the proposed action ideas, and the output is the specific actions executed by the user.

[1220] Specific behavior:

[1221] A user schedules a meeting to launch a "new AI project."

[1222] (Application example 2)

[1223] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1224] Conventional systems for grasping industry trends do not suggest specific actions that take into account the user's emotional state. As a result, even if users can obtain materials to understand industry trends, it is difficult for them to take appropriate actions based on their individual emotional state. Furthermore, when dealing with customers in physical stores, it is difficult to provide appropriate customer service that matches the customer's emotional state. A system that solves these problems and suggests specific actions based on the user's emotional state is needed.

[1225] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for automatically collecting data from specified information sources on the Internet, means for preprocessing the collected data and analyzing it using natural language processing technology, means for detecting industry trends based on the analysis results and generating a report, means for displaying the generated report on a user interface and presenting specific action ideas to the user, and means for recognizing the user's emotional state and suggesting appropriate actions. This makes it possible not only to grasp industry trends but also to suggest optimal actions based on the user's emotional state. This enables optimal customer service tailored to the emotional state of customers, especially in physical stores.

[1226] "Designated internet sources" refers to specific data collection targets, such as pre-defined websites or social networking services.

[1227] "Means for automatically collecting data" refers to the function of launching crawlers or other machines according to a specified schedule to collect text data from sources on the Internet.

[1228] "Preprocessing" refers to the process of removing unnecessary information from collected data and preparing it in a format suitable for analysis, such as removing HTML tags, deleting duplicate data, and normalizing text.

[1229] "Natural language processing technology" refers to a set of techniques for understanding and analyzing text data, including means for extracting key keywords and topics.

[1230] "Industry trends" refers to fluctuations and trends in the frequency of occurrence of key keywords and topics within a particular industry.

[1231] "Report" refers to a written report generated based on collected and analyzed data, which includes industry trends and action ideas.

[1232] "User interface" refers to the screens and applications that users can interact with directly and check and manipulate information.

[1233] "User's emotional state" refers to the psychological tendency, such as positive, negative, or neutral, displayed by the user.

[1234] "Means to suggest appropriate actions" refers to a function that suggests specific actions based on the user's emotional state and industry trends.

[1235] The present invention is embodied in a specific form as a system that automatically collects and analyzes data from designated internet sources to understand industry trends, and further recognizes the user's emotional state and suggests actions.

[1236] System Program Overview

[1237] The server first launches a crawler based on a specified schedule to collect text data from relevant websites and social networking services. The collected data is preprocessed to remove HTML tags, delete duplicate data, and normalize the text. Natural language processing techniques (e.g., spaCy and TextBlob) are then used to extract key keywords and topics, analyze their frequency over time, and graph industry trends.

[1238] The server then analyzes the collected text data and user feedback using an emotion engine, which identifies emotional trends such as positive, negative, and neutral, and estimates the user's emotional state. Based on this information, the content of reports and action ideas is optimized.

[1239] The generated report is saved in PDF or HTML format and sent to the device, where it is displayed on the user interface, allowing users to easily check industry trends and emotional tendencies. The device also suggests specific action ideas to users based on the information obtained from the emotion engine.

[1240] Hardware and Software Examples

[1241] Hardware: Server, smart glasses

[1242] Software: Python, spaCy, TextBlob, Requests library

[1243] Specific examples

[1244] For example, to gather the latest trends in the IT industry, the server collects data from tech news sites and related social media feeds at 8:00 every morning. NLP technology is used to extract key technology keywords and analyze trends. Using the analysis results and an emotion engine, the system takes into account the user's emotional tendencies and suggests new projects or breaks to refresh them.

[1245] Store staff wearing smart glasses are dynamically suggested appropriate ways to serve customers based on the emotional state of the customer. For example, if a customer says, "I was really looking forward to visiting this store today!", the smart glasses' display will suggest, "Proactively introduce new products."

[1246] Explicit prompt example

[1247] "Analyze your customers' emotional state in real time and suggest the best way to serve them based on the latest trend information. Latest customer feedback: {Customer feedback}"

[1248] This will enable brick-and-mortar stores to provide optimal responses according to the emotional state of customers. Furthermore, understanding industry trends and proposing specific actions based on those trends will support user decision-making.

[1249] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1250] Step 1:

[1251] The server launches a crawler according to a specified schedule. The crawler collects text data from specified Internet sources (such as news sites and social network feeds). The inputs to this step are the specified URLs and schedule information, and the output is the collected text data.

[1252] Step 2:

[1253] The server preprocesses the collected text data. Preprocessing includes removing HTML tags, deleting duplicate data, and normalizing the text (converting to lowercase and removing stop words). The input of this step is the collected text data, and the output is the preprocessed, clean text data.

[1254] Step 3:

[1255] The server applies natural language processing (NLP) technology to the preprocessed data. Specifically, it uses tools such as spaCy and TextBlob to extract key keywords and topics from the text data. It also analyzes fluctuations in their frequency of appearance over time. The input for this step is the preprocessed text data, and the output is key keywords and topics and information on fluctuations in their frequency of appearance.

[1256] Step 4:

[1257] The server analyzes the collected text data and user feedback using an emotion engine, which identifies the emotional tendencies of the text and classifies them into categories such as positive, negative, and neutral. The inputs of this step are the preprocessed text data and user feedback, and the output is the sentiment analysis result of the text.

[1258] Step 5:

[1259] The server detects industry trends based on the analysis results and generates a report that includes analysis results of key keywords and topics, sentiment analysis results, and insights into industry trends. The inputs to this step are the sentiment analysis results and NLP analysis results, and the output is an industry trend report.

[1260] Step 6:

[1261] The server periodically saves the generated reports in PDF or HTML format and sends them to the terminal. The input of this step is the generated report, and the output is the saved and sent report.

[1262] Step 7:

[1263] The terminal displays the received report on the user interface, allowing the user to check industry trends and sentiment. The input of this step is the report sent from the server, and the output is the report displayed on the user interface.

[1264] Step 8:

[1265] The terminal proposes specific action ideas to the user based on the information obtained from the emotion engine. The input of this step is the emotion analysis result and the generated report, and the output is the action ideas presented to the user.

[1266] Step 9:

[1267] The user considers and takes specific actions based on the report and proposals displayed on the device. If necessary, the user may launch a new project or revise their market strategy. The input for this step is the report and proposed actions displayed on the device, and the output is the specific action taken by the user.

[1268] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1269] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1270] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1271] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1272] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1273] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1274] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1275] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1276] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1277] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1278] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1279] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1280] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1281] 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.

[1282] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1283] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1284] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1285] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1286] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1287] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1288] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1289] The following is further disclosed regarding the above embodiment.

[1290] (Claim 1)

[1291] means of automatically collecting data from designated internet sources;

[1292] A means for preprocessing the collected data and analyzing it using natural language processing techniques;

[1293] A means of detecting industry trends and generating reports based on the analysis results;

[1294] a means for displaying the generated report on a user interface and presenting specific action ideas to the user;

[1295] A system including:

[1296] (Claim 2)

[1297] 2. The system according to claim 1, wherein the means for collecting data includes crawler means for launching a crawler according to a specified schedule and collecting text data from specified websites and social network services.

[1298] (Claim 3)

[1299] 2. The system according to claim 1, wherein the natural language processing technology includes an analysis means for extracting main keywords and topics from text data and analyzing fluctuations in frequency of appearance.

[1300] "Example 1"

[1301] (Claim 1)

[1302] means of automatically collecting data from designated internet sources;

[1303] A means for preprocessing the collected data and analyzing it using natural language processing techniques;

[1304] A means of detecting industry trends and generating reports based on the analysis results;

[1305] a means for displaying the generated report on a user interface and presenting specific action ideas to the user;

[1306] A crawler or a means of retrieving data according to a specified schedule for data collection;

[1307] Preprocessing involves removing HTML tags, eliminating duplicate data, and normalizing text.

[1308] In the analysis, a method for extracting and graphing important keywords using TF-IDF and Word2Vec is used.

[1309] A means to save the generated report in PDF or HTML format,

[1310] A system including:

[1311] (Claim 2)

[1312] 2. The system according to claim 1, wherein the means for collecting data includes crawler means for launching a crawler according to a specified schedule and collecting text data from specified websites and social network services.

[1313] (Claim 3)

[1314] 2. The system according to claim 1, wherein the natural language processing technology includes an analysis means for extracting main keywords and topics from text data and analyzing fluctuations in frequency of appearance.

[1315] "Application Example 1"

[1316] (Claim 1)

[1317] means of automatically collecting data from designated internet sources;

[1318] A means for preprocessing the collected data and analyzing it using natural language processing techniques;

[1319] A means of detecting industry trends and generating reports based on the analysis results;

[1320] a means for displaying the generated report on a user interface and presenting specific action ideas to the user;

[1321] The generated reports will be displayed on smart glasses worn by factory workers, allowing them to grasp industry trends in real time and propose specific actions for manufacturing processes and the introduction of new technologies.

[1322] A system including:

[1323] (Claim 2)

[1324] 2. The system according to claim 1, wherein the means for collecting data includes crawler means for launching a crawler according to a specified schedule and collecting text data from specified websites and social network services.

[1325] (Claim 3)

[1326] 2. The system according to claim 1, wherein the natural language processing technology includes an analysis means for extracting main keywords and topics from text data and analyzing fluctuations in frequency of appearance.

[1327] "Example 2: Combining Emotion Engines"

[1328] (Claim 1)

[1329] means of automatically collecting data from designated internet sources;

[1330] A means for preprocessing the collected data and analyzing it using natural language processing techniques;

[1331] A means of detecting industry trends and generating reports based on the analysis results;

[1332] a means for displaying the generated report on a user interface and presenting specific action ideas to the user;

[1333] means for analyzing the collected text data and user feedback to identify an emotional state;

[1334] A means to optimize the content of reports and suggest action ideas taking into account emotional states;

[1335] A system including:

[1336] (Claim 2)

[1337] 2. The system according to claim 1, further comprising crawler means for launching a crawler according to a specified schedule and collecting text data from specified websites and social network services.

[1338] (Claim 3)

[1339] 2. The system according to claim 1, further comprising an analysis means for extracting main keywords and topics from the text data and analyzing fluctuations in frequency of appearance.

[1340] "Application example 2 when combining emotion engines"

[1341] (Claim 1)

[1342] means of automatically collecting data from designated internet sources;

[1343] A means for preprocessing the collected data and analyzing it using natural language processing techniques;

[1344] A means of detecting industry trends and generating reports based on the analysis results;

[1345] a means for displaying the generated report on a user interface and presenting specific action ideas to the user;

[1346] A means of recognizing the user's emotional state and suggesting appropriate actions;

[1347] A system including:

[1348] (Claim 2)

[1349] 2. The system according to claim 1, further comprising crawler means for launching a crawler according to a specified schedule and collecting text data from specified websites and social network services.

[1350] (Claim 3)

[1351] 2. The system according to claim 1, further comprising an analysis means for extracting main keywords and topics from the text data and analyzing fluctuations in frequency of appearance. [Explanation of symbols]

[1352] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means of automatically collecting data from designated internet sources; A means for preprocessing the collected data and analyzing it using natural language processing techniques; A means of detecting industry trends and generating reports based on the analysis results; a means for displaying the generated report on a user interface and presenting specific action ideas to the user; A system including:

2. The system according to claim 1 , wherein the means for collecting data includes crawler means for launching a crawler according to a specified schedule and collecting text data from specified websites and social network services.

3. The system according to claim 1 , wherein the natural language processing technology includes an analysis means for extracting main keywords and topics from text data and analyzing fluctuations in frequency of appearance.

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