system

Abstract

We provide the system. [Solution] A means of automatically collecting information from various sources, A means of analyzing collected information and classifying emotions, A means of summarizing the analysis results and integrating redundant information, Means of providing summarized data to users, A system that includes this.

Description

Technical Field

[0004] ,

[0006]

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

[0007] "Automated data collection" is the process by which machines or programs acquire data from designated sources without human intervention.

[0008] "Analytical techniques" refer to algorithms and statistical methods used to process collected data and extract specific patterns or insights.

[0009] "Emotional classification" is the process of classifying the emotional attributes contained in text data into categories such as positive, negative, and neutral.

[0010] "Summarization" refers to the process of extracting important points from a large amount of information and arranging them in a concise and easy-to-understand format.

[0011] "Information integration" is the process of combining similar data obtained from multiple sources, eliminating redundancy, and presenting it in a consistent format.

[0012] "Reliability" is a measure used to evaluate whether information is accurate and trustworthy.

[0013] A "user interface" is an environment that includes the operating screen and input methods that allow a user to interact effectively with a computer system. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

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

[0018] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] The system of the present invention automatically collects user reviews and word-of-mouth information from various information sources on the internet and provides it to users in a summarized form. This system consists of three main components: a server, a terminal, and the user.

[0036] server

[0037] The server has core functionality for automatically collecting data from various online platforms. It periodically collects new review information by calling specific APIs or utilizing web scraping. The server converts this data into a more manageable format and stores it in a database. It also uses natural language processing techniques to analyze the collected data and perform sentiment analysis. This allows it to evaluate the sentiment of each review and tag them as positive, negative, or neutral.

[0038] Furthermore, the server applies a summarization algorithm to extract key points and summarize the information. This summarization process organizes redundant information and integrates it, preparing it to be presented in an easily understandable format for the user.

[0039] terminal

[0040] The terminal serves to present users with summarized information provided by the server. The user interface, particularly as a mobile or web application, allows users to easily access information about products and services of interest. The display on the terminal is visually intuitive and designed to enhance the user experience.

[0041] User

[0042] This system allows users to obtain information about selected products and services more quickly. If a user wants to research a specific product, they can easily retrieve relevant summary information through their device. This information includes ratings and sentiment analysis results, helping users make informed decisions.

[0043] For example, if a user is considering purchasing a new electronic product, they can simply open a smartphone app and enter the product name to see review summaries collected from various e-commerce sites and social media. This summary includes a statistical overview of the product's strengths and weaknesses, as well as user satisfaction, allowing the user to quickly grasp the necessary information.

[0044] Thus, the present invention strongly supports users' purchasing decisions through efficient information gathering and the provision of a user-friendly interface.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The server automatically collects customer review data about a specified product or service by calling an API on an online platform or by using web scraping techniques.

[0048] Step 2:

[0049] The server cleanses the collected data, removing unnecessary information and noise, and converting it into a format that is easy to analyze. For example, it cleans up HTML tags and special characters.

[0050] Step 3:

[0051] The server uses natural language processing technology to analyze the text data of each review. During this process, sentiment analysis is performed, classifying the reviews as positive, negative, or neutral.

[0052] Step 4:

[0053] The server uses a summarization algorithm to extract important data and frequently occurring themes, and then concisely summarizes the overall content.

[0054] Step 5:

[0055] The server integrates similar information from different sources, groups duplicate content, and stores it in a unified database.

[0056] Step 6:

[0057] The server formats the processing results and converts the data into a format that can be displayed through the user interface.

[0058] Step 7:

[0059] The terminal retrieves summary information from the server and displays it clearly on the user interface. Users can access and obtain the necessary information instantly.

[0060] Step 8:

[0061] Users review the displayed summary information and make decisions based on that information. This allows users to obtain information that helps them avoid regretting their purchase later.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] In today's information society, a vast amount of reviews and word-of-mouth information exists on the internet. However, this information is diverse and its reliability varies, making it difficult for users to quickly retrieve and accurately understand the information they need. In particular, users tend to become confused because the information is often redundant or contradictory. In this situation, there is a need for a system that allows users to acquire information efficiently and concisely and make decisions based on that information.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes means for periodically acquiring information from an information medium, means for converting the acquired information into a data format and storing it in a collection, and means for applying natural language processing to the information and performing sentiment evaluation. This makes it possible to quickly and accurately acquire the information that the user needs and to make appropriate decisions based on the collected information.

[0067] "Information media" refers to online platforms and services used to store and provide information.

[0068] "Acquiring periodically" refers to automatically collecting information at specific time intervals.

[0069] "Converting to a data format" refers to structuring information into a standardized format so that it can be properly stored in a database or similar system.

[0070] "Storing in a set" refers to the process of gathering and saving transformed information in one place.

[0071] "Natural language processing" refers to computer technology used to analyze human language and extract its meaning.

[0072] "Emotional evaluation" refers to a method of identifying and classifying the emotional tendencies inherent in information.

[0073] "Presenting visually" refers to displaying information on a screen in a way that users can easily understand.

[0074] "Reliability" refers to an indicator used to measure the accuracy and credibility of information.

[0075] "Easy information searching and access" refers to functionality that allows users to find and use the information they need without hassle.

[0076] The embodiments for carrying out the present invention are shown below.

[0077] server

[0078] The server plays a central role in collecting data from information sources. Specifically, the server periodically accesses data sources on the internet and retrieves information using APIs or web scraping techniques. For this, software such as Python's BeautifulSoup or Scrapy can be used. The retrieved information is converted into data formats such as JSON or CSV using the Pandas library and stored in a collection. Subsequently, NLTK, TextBlob, or Hugging Face's Transformers can be used for natural language processing and sentiment evaluation, classifying the emotional tendencies of the information. The information processed in this way is then stored in a database.

[0079] terminal

[0080] The terminal is a device for visually presenting data sent from the server to the user. For this purpose, the terminal is equipped with a user interface using React or Flutter®. This interface allows users to easily retrieve information and view details. The visual display is presented in card or list format, designed for intuitive user interaction.

[0081] User

[0082] Users explore information through their devices and make decisions based on collected reviews and word-of-mouth information. For example, if a user is considering purchasing a new electronic product, they can simply type the product name into an app on their device, and relevant information will be summarized and displayed. An example of a prompt that can be used in this case is: "I'm thinking of buying a new electronic product. The product name is XX. Please summarize the reviews from e-commerce sites and social media, showing the main pros and cons and user satisfaction."

[0083] Through the above process, the system of the present invention can provide users with the information they need quickly and accurately, and support them in making decisions based on reliable data.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The server periodically collects data by accessing information sources. It receives API endpoints or web page URLs for each data source as input. Based on this information, the server sends API requests or parses HTML data using web scraping techniques. As output, it obtains raw reviews and user feedback, which is then passed on to the next data transformation step.

[0087] Step 2:

[0088] The server converts the collected raw data into a standardized data format. It uses the raw review and word-of-mouth data obtained in Step 1 as input. The server leverages the Python Pandas library to convert the data into JSON or CSV format, outputting it as easily processable structured data. This data is then ready for storage in the database.

[0089] Step 3:

[0090] The server performs natural language processing on the structured data and conducts sentiment evaluation. The input is the structured data obtained in step 2. The server uses natural language processing libraries such as NLTK, TextBlob, and Hugging Face's Transformers to classify the sentiment of each review as positive, negative, or neutral. This outputs the data with sentiment evaluations and sends it to the next summarization step.

[0091] Step 4:

[0092] The server summarizes the sentiment-rated data. It receives the sentiment-rated data from step 3 as input. Through a summarization algorithm, it extracts key points while streamlining redundant information, generating a user-friendly summary. This summary is then ready to be sent to the terminal.

[0093] Step 5:

[0094] The terminal visually presents the user with summarized information received from the server. The input includes summarized data from the server. The terminal displays the information in card or list format through a user interface using React or Flutter. The output provides the user with easily understandable information.

[0095] Step 6:

[0096] Users explore information and make decisions through an interface on their device. They input product names and service keywords as text. By using prompts to communicate information retrieval requests to the device, users receive optimized information generated by a generative AI model. The output, displayed in a user-friendly format, allows users to easily make decisions such as purchases.

[0097] (Application Example 1)

[0098] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] Consumers face the challenge of having to check numerous reviews before purchasing a product, a process that is time-consuming. Furthermore, it's difficult to interpret individual reviews, making it hard to grasp the overall reputation. Additionally, consumers often lack the time to quickly obtain information at the point of purchase, leading to delays in decision-making. Therefore, there is a need for a system that allows consumers to quickly and efficiently obtain useful product-related information.

[0100] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0101] In this invention, the server includes means for automatically collecting information from various information sources, means for analyzing the collected information and classifying emotions, means for summarizing the analysis results and integrating redundant information, means for identifying objects using an optical device and obtaining relevant summary information, and means for visually displaying the matched information on the user's device. This enables consumers to quickly obtain review information at the point of purchase and make quick purchase decisions based on aggregated evaluations.

[0102] "Various information sources" refers to a variety of platforms and databases on the internet from which information is collected.

[0103] "Methods for automatically collecting information" refers to technologies that collect online information without human intervention, such as by calling specific APIs or using web scraping techniques.

[0104] "Sentiment classification" is the process of analyzing the text of collected information and evaluating whether its content falls into the categories of positive, negative, or neutral.

[0105] "Summarization techniques" are technologies used to analyze information, extract key points, organize redundant data, and present it in a format that is easy for users to understand.

[0106] "Optical devices" refer to visual sensors such as cameras, which are hardware used to identify objects.

[0107] "Matched information" refers to data related to the identified object, and this is used to display context-appropriate information to the user.

[0108] "User's device" refers to devices that users use to receive information, such as smartphones and tablet devices.

[0109] The system implementing this invention mainly consists of three components: a server, a terminal, and a user.

[0110] The server has the capability to automatically collect information, regularly gathering data from various sources using specific APIs and web scraping techniques. The collected data is stored in a database, and sentiment classification is performed using natural language processing. This tags each review as either positive, negative, or neutral. Subsequently, a summarization algorithm extracts and integrates key information, preparing it in a concise but clear format.

[0111] The terminal plays the role of visually displaying summarized information from the server in response to user input. On devices such as smartphones and tablets, it provides immediate access to relevant information by using the terminal's camera to identify objects (e.g., product barcodes). By providing a visually intuitive interface that enhances the user experience, users can easily check review information.

[0112] Users utilize this system to browse and rate products they intend to purchase. For example, a user considering a new gadget can simply scan the product's barcode with their smartphone camera in a store and quickly see a summary of reviews collected online. This is extremely helpful in understanding the product's strengths and weaknesses, allowing for a smoother purchasing decision.

[0113] For example, a user looking for a new smartphone case scans the barcode of a case they're interested in at a store. A summary of reviews regarding the product's texture and durability is then displayed, instantly providing crucial information for their selection. An example of a prompt might be, "How can I generate a review summary and visually present the results based on sentiment analysis?"

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The server collects review information from online platforms by calling an API. The input consists of the URL and API key of the information source, and the server uses these to send a data request to retrieve the information. The output is the raw data of the retrieved reviews.

[0117] Step 2:

[0118] The server stores the collected review information in a database. At this stage, the input is raw data, and the output is structured data stored in the database. Data processing involves converting data in JSON or XML format into a standard database format.

[0119] Step 3:

[0120] The server analyzes the sentiment of reviews using natural language processing. The input is text data of reviews stored in a database, and the output is a sentiment rating (positive, negative, or neutral) for each review. In detail, it uses NLTK and the spacy library to extract sentiment-related keywords and phrases from the text and classifies them using a statistical model.

[0121] Step 4:

[0122] The server summarizes review information based on the results of sentiment analysis. The input is analyzed sentiment data and text data, and the output is a summarized text. This process includes organizing redundant information and extracting and summarizing key points. The summarization algorithm prioritizes important phrases, integrates them, and presents them concisely.

[0123] Step 5:

[0124] The terminal scans the barcode of the target product using its camera, based on user input. This input is barcode information obtained from an optical device. Based on this, the terminal queries the server for information. The specific action taken by the user is to launch the camera app and focus on the product's barcode.

[0125] Step 6:

[0126] The server matches the barcode information with summary reviews and sends them to the terminal. The input is barcode information, and the output is the corresponding product review summary data. In this step, a database search and matching are performed, and the relevant information is sent together.

[0127] Step 7:

[0128] The device visually displays the received summary information to the user. The input is a summary review sent from the server, and the output is the information displayed on the screen to the user. Specifically, the app lays out the summary information and displays it in a way that is easy for the user to understand intuitively.

[0129] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0130] This invention is a system that automatically aggregates word-of-mouth information from multiple sources on the internet and provides information while taking user emotions into consideration. This enables the provision of customized information that meets the individual needs and emotions of each user. The system is mainly composed of three main components: a server, a terminal, and a user, each incorporating an emotion engine.

[0131] server

[0132] The server continuously collects customer reviews about specified products and services from various sources. This often involves API calls and web scraping techniques. The collected data is first cleansed, and then sentiment analysis is performed using natural language processing techniques. Based on this analysis, the reviews are classified as positive, negative, or neutral. The server further summarizes the collected data, integrates duplicate information, and stores it in a database in a more refined form.

[0133] In addition to this process, the server uses an emotion engine to recognize the user's emotional state in real time and learns from the user's past data to improve the quality of the information it provides. This makes it possible to select and provide the most appropriate information based on the user's emotions.

[0134] terminal

[0135] The device retrieves information formatted by the server and displays it in a user-friendly format. The user interface is designed specifically for mobile devices, allowing for intuitive operation. Based on feedback from the emotion engine, the information displayed and how it is displayed are dynamically adjusted to provide the user with the best possible experience.

[0136] User

[0137] Users access this system through their devices to obtain information about products and services. When a user searches for a specific product, a summary of relevant reviews is displayed, and they can also receive personalized feedback from the sentiment engine. For example, if a user is under stress, the system will prioritize displaying calming, positive reviews to support their purchasing decision.

[0138] For example, when a user considering purchasing a new home appliance uses the system, they can open the app and perform a search to obtain a summary that integrates reviews from multiple e-commerce sites and social media. Personalized information generated by an emotion engine is then added, featuring reviews that match the user's preferences.

[0139] In this way, the present invention responds precisely to the user's emotions and needs, and helps to improve their purchasing decisions.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] The server automatically collects data from multiple online sources. It periodically retrieves the latest customer reviews about specified products and services using API calls and web scraping.

[0143] Step 2:

[0144] The server cleanses the collected data and converts it into a structured format. Specifically, it removes unnecessary HTML tags and special characters to prepare the data for application of natural language processing techniques.

[0145] Step 3:

[0146] The server uses sentiment analysis techniques to analyze the review data and classify each review as positive, negative, or neutral. This process identifies the emotional tone of each review.

[0147] Step 4:

[0148] The server summarizes the data and integrates duplicate information. Statistical algorithms are used to extract important keywords and phrases and generate a comprehensive summary.

[0149] Step 5:

[0150] The server uses an emotion engine to assess the user's current emotional state in real time. This allows it to learn from the user's past emotional data and prepare to provide personalized information.

[0151] Step 6:

[0152] The device retrieves summary information provided by the server and displays it in the user interface. Based on feedback from the emotion engine, the information display is adjusted according to the user's emotional needs.

[0153] Step 7:

[0154] Users review the information displayed on their devices and make a purchase decision. Because the information provided is optimized for the user's emotional state, a smoother decision-making process is possible.

[0155] (Example 2)

[0156] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0157] In recent years, a vast amount of information exists on the internet, but it is not easy to select useful and reliable information from this enormous volume. Furthermore, there is a lack of personalized services that cater to individual needs and provide information that matches the user's emotional state. Therefore, there is a need for a system that collects appropriate and reliable information in real time and provides customized information that responds to the user's emotions.

[0158] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0159] In this invention, the server includes means for automatically collecting text data from multiple sources, means for classifying the data into emotional categories through sentiment analysis using natural language processing technology, and means for summarizing the data, integrating related information, and storing it in a database. This enables the rapid collection of reliable information and the provision of personalized information based on the user's emotional state.

[0160] An "information source" is a medium that provides text data obtained from multiple locations on the internet.

[0161] "Text data" refers to data recorded as written information, such as reviews and testimonials.

[0162] "Natural language processing technology" is a technology that enables computers to understand and process the language that people use in everyday life.

[0163] "Sentiment analysis" is the process of extracting opinions and emotions from text data and identifying the type of emotion (positive, negative, neutral, etc.).

[0164] An "emotion category" is a classification that represents the type of emotion identified through emotion analysis.

[0165] A "summary" is data that has been compiled by selecting and prioritizing multiple pieces of information, and summarizing only the main points in a short format.

[0166] "Integration" is the process of combining multiple duplicate or related data into a single entity.

[0167] A "database" is a structured information management system that efficiently manages large amounts of information and allows for searching and retrieval as needed.

[0168] A "server" is a computer system that functions to process data and provide various types of information.

[0169] "Real-time" refers to a process designed to be processed almost simultaneously with the current time.

[0170] "User emotional state" refers to the emotional state a user experiences while using the system.

[0171] Personalization refers to optimizing information and services according to the individual user's needs and preferences.

[0172] The system for implementing this invention mainly consists of three components: a server, a terminal, and a user. The system operates with each component working in coordination to provide the user with optimized information.

[0173] The server automatically collects text data from multiple sources on the internet. This collection utilizes API calls and web scraping techniques. The collected data undergoes sentiment analysis using natural language processing techniques. Generative AI models are used to classify the emotions within the text data as positive, negative, or neutral. The classification results are stored in a database, and further summarization processes extract the key content. By integrating redundant information, only data useful to the user is retained. The server leverages an emotion engine to analyze the user's real-time emotional state and adaptively optimizes information delivery based on historical data.

[0174] The device receives organized information from the server and displays it in a format that is easy for the user to operate intuitively. It provides a user interface specifically designed for mobile devices, enabling real-time information display that reflects the user's emotional state.

[0175] Users can access product reviews and ratings through their devices. This information is personalized using an emotion engine, prioritizing information that matches the user's emotional state. For example, if a user wants to relax, positive reviews will be displayed, prioritizing information that promotes relaxation when selecting a product.

[0176] For example, a user considering purchasing a new electronic device can search for the product name on their device to view reviews aggregated from multiple online stores and social media. This information is filtered by an emotion engine, displaying recommendations tailored to the user's preferences. An example of a prompt to input into the generative AI model is, "Please collect positive reviews related to this product." This prompt efficiently provides information aligned with the specific emotion the user is seeking.

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] The server automatically collects text data from multiple sources on the internet. Specific inputs include API endpoints and website URLs, while the output is the collected raw review and comment text. The server continuously retrieves this information and performs collection processing on a regular schedule to maintain up-to-date data.

[0180] Step 2:

[0181] The server performs a cleansing process on the collected text data. The input is raw text data, and the output is clean text data with noise removed and no duplicates. This cleansing process standardizes the format and removes redundant data.

[0182] Step 3:

[0183] The server performs sentiment analysis using natural language processing techniques based on clean text data. The input is cleansed text data, and the output is sentiment data where the text is classified as positive, negative, or neutral. A generative AI model is used to accurately classify the sentiment within each comment. This analysis reveals the emotional tone of individual reviews, leading to the next steps.

[0184] Step 4:

[0185] The server summarizes data and integrates information based on sentiment analysis results. The input is text data with sentiment data attached, and the output is summarized key review information. The server uses the summarization function of a generative AI model to consolidate redundant information and extract only the information that is important to the user.

[0186] Step 5:

[0187] The server uses an emotion engine to analyze the user's emotional state and optimize the information provided. Inputs include the user's past emotional data and real-time emotional data, while output is customized information tailored to the user's emotional state. Based on this information, the server selects and ultimately delivers the most relevant information to the user.

[0188] Step 6:

[0189] The terminal receives formatted information provided by the server and displays it to the user. The input is summarized information from the server, and the output is information on a user-friendly interface. The terminal is intuitive to operate, and the user interface is updated in real time.

[0190] Step 7:

[0191] Users view information provided through their devices and check reviews and ratings of specific products. The input is the information displayed on the device, and the output is the detailed information and opinions about the product. Users make purchasing decisions based on the presented information. For example, they are more likely to purchase a product with many positive reviews.

[0192] (Application Example 2)

[0193] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0194] While countless reviews and opinions exist online, they are not always presented in a way that is beneficial to users. In particular, the lack of optimization of information based on users' emotional states means that meaningful purchasing support is not being provided. Furthermore, if the user interface is not intuitive, it becomes difficult for users to efficiently acquire information. A system is needed to solve these problems and improve the user experience.

[0195] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0196] In this invention, the server includes means for automatically collecting information from various information sources, means for analyzing the collected information and classifying emotions, and means for summarizing the analysis results and integrating redundant information. This makes it possible to detect the user's emotional state in real time and provide information optimized according to that state.

[0197] "Various information sources" refers to the diverse platforms and databases that exist on the internet, and these are the sources from which word-of-mouth information is collected.

[0198] "Methods for automatically collecting information" refers to methods of efficiently obtaining information from the internet without human intervention by utilizing API calls and scraping techniques.

[0199] "Methods for classifying emotions" refer to natural language processing techniques that analyze collected text data and classify its content as positive, negative, or neutral.

[0200] "Methods for summarizing information and integrating overlapping information" refers to the process of extracting key points from acquired word-of-mouth information and aggregating identical or similar information.

[0201] "Means of providing information to users" refers to interfaces and technologies that display formatted data in a format that is easiest for users to understand.

[0202] "A means of detecting a user's emotional state in real time and optimizing information based on that state" refers to a method that includes algorithms and functions that determine the user's current emotions and adjust the displayed information accordingly.

[0203] A "means of providing purchasing support" refers to a system that provides relevant information and assistance in making decisions so that users can make the best choices when selecting products or services.

[0204] This system consists of three main elements: a server, terminals, and users. The server is responsible for automatically collecting information from various sources on the internet. Information collection utilizes API calls and scraping techniques. The collected data is cleansed using Python, and then sentiment analysis is performed using natural language processing technologies such as Google Cloud Natural Language API. The analysis results are categorized into positive, negative, and neutral, and summaries and duplicate information are consolidated.

[0205] Furthermore, the server also includes processing to recognize the user's emotional state in real time. Based on data provided by the user using smartphone sensors and other means, the emotion engine analyzes the user's current emotions and optimizes the information provided. For example, emotions can be determined using facial recognition technology from the smartphone's camera.

[0206] The terminal has a user interface designed to display formatted data retrieved from the server in a format easily understood by the user. Developed using Flutter and React Native, this interface allows for intuitive operation, reducing user stress while providing optimal information.

[0207] Users can use this system to obtain information related to products and services they are interested in, and it supports their purchasing decisions. Because the system personalizes information based on the user's emotional state, it displays appropriate reviews even to users considering purchasing new home appliances.

[0208] For example, if a user wants to find the "latest rice cooker," the system integrates reviews from e-commerce sites and social media. If it detects that the user is stressed about preparing dinner, it prioritizes featuring positive reviews of easy-to-use rice cookers. In this way, the system can dynamically adjust how information is selected and presented.

[0209] By using a generative AI model, the system can enhance its ability to perform more detailed analysis and provide information based on prompts such as: "If the user is currently experiencing stress, how can we prioritize displaying positive reviews about the latest rice cookers?"

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] The server automatically collects information from various sources. It uses API calls and web scraping to obtain user review data from social media and e-commerce sites. The input is the information source, and the output is the collected raw data.

[0213] Step 2:

[0214] The server performs a cleansing process on the collected raw data. It uses Python libraries to remove unnecessary parts of the data and format it. The input is raw data, and the output is formatted data.

[0215] Step 3:

[0216] The server performs sentiment analysis on the formatted data using natural language processing techniques. It uses the Google Cloud Natural Language API to determine whether each review's text is positive, negative, or neutral. The input is formatted data, and the output is sentiment-labeled data.

[0217] Step 4:

[0218] The server summarizes sentiment-labeled data and merges duplicate information. Using algorithms, it extracts important reviews and groups similar information together. The input is sentiment-labeled data, and the output is summarized data.

[0219] Step 5:

[0220] The server detects the user's emotional state in real time. It uses data from the smartphone's camera and sensors to analyze the user's current psychological state. This information is processed by an emotion engine. The input is the user's emotional state data, and the output is the detected emotion label.

[0221] Step 6:

[0222] The device retrieves summarized data provided by the server and displays information optimized based on the user's emotional state. An interface designed with Flutter and React Native clearly presents the most relevant information to the user. Input is summarized data and emotional labels, while output is the displayed information.

[0223] Step 7:

[0224] Users make purchasing decisions based on this information. The system provides personalized reviews and information to support appropriate choices. The input is the displayed information and the user's selection, and the output is the final purchasing decision.

[0225] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0226] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0227] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0231] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0232] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0233] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0234] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0235] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0236] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0237] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0238] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0239] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0240] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0241] The system of the present invention automatically collects user reviews and word-of-mouth information from various information sources on the internet and provides it to users in a summarized form. This system consists of three main components: a server, a terminal, and the user.

[0242] server

[0243] The server has core functionality for automatically collecting data from various online platforms. It periodically collects new review information by calling specific APIs or utilizing web scraping. The server converts this data into a more manageable format and stores it in a database. It also uses natural language processing techniques to analyze the collected data and perform sentiment analysis. This allows it to evaluate the sentiment of each review and tag them as positive, negative, or neutral.

[0244] Furthermore, the server applies a summarization algorithm to extract key points and summarize the information. This summarization process organizes redundant information and integrates it, preparing it to be presented in an easily understandable format for the user.

[0245] terminal

[0246] The terminal serves to present users with summarized information provided by the server. The user interface, particularly as a mobile or web application, allows users to easily access information about products and services of interest. The display on the terminal is visually intuitive and designed to enhance the user experience.

[0247] User

[0248] This system allows users to obtain information about selected products and services more quickly. If a user wants to research a specific product, they can easily retrieve relevant summary information through their device. This information includes ratings and sentiment analysis results, helping users make informed decisions.

[0249] For example, if a user is considering purchasing a new electronic product, they can simply open a smartphone app and enter the product name to see review summaries collected from various e-commerce sites and social media. This summary includes a statistical overview of the product's strengths and weaknesses, as well as user satisfaction, allowing the user to quickly grasp the necessary information.

[0250] Thus, the present invention strongly supports users' purchasing decisions through efficient information gathering and the provision of a user-friendly interface.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] The server automatically collects customer review data about a specified product or service by calling an API on an online platform or by using web scraping techniques.

[0254] Step 2:

[0255] The server cleanses the collected data, removing unnecessary information and noise, and converting it into a format that is easy to analyze. For example, it cleans up HTML tags and special characters.

[0256] Step 3:

[0257] The server uses natural language processing technology to analyze the text data of each review. During this process, sentiment analysis is performed, classifying the reviews as positive, negative, or neutral.

[0258] Step 4:

[0259] The server uses a summarization algorithm to extract important data and frequently occurring themes, and then concisely summarizes the overall content.

[0260] Step 5:

[0261] The server integrates similar information from different sources, groups duplicate content, and stores it in a unified database.

[0262] Step 6:

[0263] The server formats the processing results and converts the data into a format that can be displayed through the user interface.

[0264] Step 7:

[0265] The terminal retrieves summary information from the server and displays it clearly on the user interface. Users can access and obtain the necessary information instantly.

[0266] Step 8:

[0267] Users review the displayed summary information and make decisions based on that information. This allows users to obtain information that helps them avoid regretting their purchase later.

[0268] (Example 1)

[0269] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0270] In today's information society, a vast amount of reviews and word-of-mouth information exists on the internet. However, this information is diverse and its reliability varies, making it difficult for users to quickly retrieve and accurately understand the information they need. In particular, users tend to become confused because the information is often redundant or contradictory. In this situation, there is a need for a system that allows users to acquire information efficiently and concisely and make decisions based on that information.

[0271] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0272] In this invention, the server includes means for periodically acquiring information from an information medium, means for converting the acquired information into a data format and storing it in a collection, and means for applying natural language processing to the information and performing sentiment evaluation. This makes it possible to quickly and accurately acquire the information that the user needs and to make appropriate decisions based on the collected information.

[0273] "Information media" refers to online platforms and services used to store and provide information.

[0274] "Acquiring periodically" refers to automatically collecting information at specific time intervals.

[0275] "Converting to a data format" refers to structuring information into a standardized format so that it can be properly stored in a database or similar system.

[0276] "Storing in a set" refers to the process of gathering and saving transformed information in one place.

[0277] "Natural language processing" refers to computer technology used to analyze human language and extract its meaning.

[0278] "Emotional evaluation" refers to a method of identifying and classifying the emotional tendencies inherent in information.

[0279] "Presenting visually" refers to displaying information on a screen in a way that users can easily understand.

[0280] "Reliability" refers to an indicator used to measure the accuracy and credibility of information.

[0281] "Easy information searching and access" refers to functionality that allows users to find and use the information they need without hassle.

[0282] The embodiments for carrying out the present invention are shown below.

[0283] Server

[0284] The server plays a central role in collecting data from information media. Specifically, the server periodically accesses data sources on the Internet and obtains information using API utilization or web scraping techniques. In contrast, it is possible to use software such as Python's BeautifulSoup or Scrapy. The acquired information is converted into data formats such as JSON or CSV using the Pandas library and stored in a collection. Then, in order to perform natural language processing and sentiment evaluation, NLTK, TextBlob, or Hugging Face's Transformers can be utilized, and the sentiment tendency of the information is classified. The information processed in this way is stored in a database.

[0285] Terminal

[0286] The terminal is a device for visually presenting the data sent from the server to the user. For this purpose, the terminal has a user interface using React or Flutter. Through this interface, the user can easily obtain information and check the details. The visual display is performed in the form of cards or lists and is designed to be intuitively operable by the user.

[0287] User

[0288] The user explores information via the terminal and makes decisions based on the collected review and word-of-mouth information. As a specific example, when a user is considering purchasing a new electronic product, by simply entering the product name in text in the app on the terminal, the relevant information is summarized and displayed. An example of a prompt sentence that can be used at this time is, "I am considering purchasing a new electronic product. The product name is 〇〇.Please summarize the reviews on e-commerce sites and social media and show the main advantages and disadvantages and the user satisfaction."

[0289] Through the above process, the system of the present invention can provide users with the information they need quickly and accurately, and support them in making decisions based on reliable data.

[0290] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0291] Step 1:

[0292] The server periodically collects data by accessing information sources. It receives API endpoints or web page URLs for each data source as input. Based on this information, the server sends API requests or parses HTML data using web scraping techniques. As output, it obtains raw reviews and user feedback, which is then passed on to the next data transformation step.

[0293] Step 2:

[0294] The server converts the collected raw data into a standardized data format. It uses the raw review and word-of-mouth data obtained in Step 1 as input. The server leverages the Python Pandas library to convert the data into JSON or CSV format, outputting it as easily processable structured data. This data is then ready for storage in the database.

[0295] Step 3:

[0296] The server performs natural language processing on the structured data and conducts sentiment evaluation. The input is the structured data obtained in step 2. The server uses natural language processing libraries such as NLTK, TextBlob, and Hugging Face's Transformers to classify the sentiment of each review as positive, negative, or neutral. This outputs the data with sentiment evaluations and sends it to the next summarization step.

[0297] Step 4:

[0298] The server summarizes the sentiment-rated data. It receives the sentiment-rated data from step 3 as input. Through a summarization algorithm, it extracts key points while streamlining redundant information, generating a user-friendly summary. This summary is then ready to be sent to the terminal.

[0299] Step 5:

[0300] The terminal visually presents the user with summarized information received from the server. The input includes summarized data from the server. The terminal displays the information in card or list format through a user interface using React or Flutter. The output provides the user with easily understandable information.

[0301] Step 6:

[0302] Users explore information and make decisions through an interface on their device. They input product names and service keywords as text. By using prompts to communicate information retrieval requests to the device, users receive optimized information generated by a generative AI model. The output, displayed in a user-friendly format, allows users to easily make decisions such as purchases.

[0303] (Application Example 1)

[0304] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0305] Consumers face the challenge of having to check numerous reviews before purchasing a product, a process that is time-consuming. Furthermore, it's difficult to interpret individual reviews, making it hard to grasp the overall reputation. Additionally, consumers often lack the time to quickly obtain information at the point of purchase, leading to delays in decision-making. Therefore, there is a need for a system that allows consumers to quickly and efficiently obtain useful product-related information.

[0306] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means respectively.

[0307] In this invention, the server includes means for automatically collecting information from various information sources, means for analyzing the collected information and performing sentiment classification, means for summarizing the analysis results and integrating duplicate information, means for identifying an object by an optical device and obtaining related summary information, and means for visually displaying the collated information on the user's device. Thereby, consumers can quickly obtain review information at the point of purchase and make a quick purchase decision based on the aggregated evaluations.

[0308] "Various information sources" refers to various platforms and databases on the Internet, from which information is collected.

[0309] "Means for automatically collecting information" refers to a technology for collecting online information without manual intervention by using the call of a specific API or web scraping technology.

[0310] "Sentiment classification" is a process of analyzing the text of the collected information and evaluating whether its content corresponds to positive, negative, or neutral sentiment.

[0311] "Summarization means" is a technology for analyzing information, extracting important points, organizing redundant data, and providing it in a form that is easy for users to understand.

[0312] "Optical device" refers to a visual sensor such as a camera, which is hardware used to identify an object.

[0313] "Collated information" is data related to the identified object, and based on this, information corresponding to the context is displayed to the user.

[0314] "User's device" refers to devices that users use to receive information, such as smartphones and tablet devices.

[0315] The system implementing this invention mainly consists of three components: a server, a terminal, and a user.

[0316] The server has the capability to automatically collect information, regularly gathering data from various sources using specific APIs and web scraping techniques. The collected data is stored in a database, and sentiment classification is performed using natural language processing. This tags each review as either positive, negative, or neutral. Subsequently, a summarization algorithm extracts and integrates key information, preparing it in a concise but clear format.

[0317] The terminal plays the role of visually displaying summarized information from the server in response to user input. On devices such as smartphones and tablets, it provides immediate access to relevant information by using the terminal's camera to identify objects (e.g., product barcodes). By providing a visually intuitive interface that enhances the user experience, users can easily check review information.

[0318] Users utilize this system to browse and rate products they intend to purchase. For example, a user considering a new gadget can simply scan the product's barcode with their smartphone camera in a store and quickly see a summary of reviews collected online. This is extremely helpful in understanding the product's strengths and weaknesses, allowing for a smoother purchasing decision.

[0319] For example, a user looking for a new smartphone case scans the barcode of a case they're interested in at a store. A summary of reviews regarding the product's texture and durability is then displayed, instantly providing crucial information for their selection. An example of a prompt might be, "How can I generate a review summary and visually present the results based on sentiment analysis?"

[0320] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0321] Step 1:

[0322] The server collects review information from online platforms by calling an API. The input consists of the URL and API key of the information source, and the server uses these to send a data request to retrieve the information. The output is the raw data of the retrieved reviews.

[0323] Step 2:

[0324] The server stores the collected review information in a database. At this stage, the input is raw data, and the output is structured data stored in the database. Data processing involves converting data in JSON or XML format into a standard database format.

[0325] Step 3:

[0326] The server analyzes the sentiment of reviews using natural language processing. The input is text data of reviews stored in a database, and the output is a sentiment rating (positive, negative, or neutral) for each review. In detail, it uses NLTK and the spacy library to extract sentiment-related keywords and phrases from the text and classifies them using a statistical model.

[0327] Step 4:

[0328] The server summarizes review information based on the results of sentiment analysis. The input is analyzed sentiment data and text data, and the output is a summarized text. This process includes organizing redundant information and extracting and summarizing key points. The summarization algorithm prioritizes important phrases, integrates them, and presents them concisely.

[0329] Step 5:

[0330] The terminal scans the barcode of the target product using its camera, based on user input. This input is barcode information obtained from an optical device. Based on this, the terminal queries the server for information. The specific action taken by the user is to launch the camera app and focus on the product's barcode.

[0331] Step 6:

[0332] The server matches the barcode information with summary reviews and sends them to the terminal. The input is barcode information, and the output is the corresponding product review summary data. In this step, a database search and matching are performed, and the relevant information is sent together.

[0333] Step 7:

[0334] The device visually displays the received summary information to the user. The input is a summary review sent from the server, and the output is the information displayed on the screen to the user. Specifically, the app lays out the summary information and displays it in a way that is easy for the user to understand intuitively.

[0335] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0336] This invention is a system that automatically aggregates word-of-mouth information from multiple sources on the internet and provides information while taking user emotions into consideration. This enables the provision of customized information that meets the individual needs and emotions of each user. The system is mainly composed of three main components: a server, a terminal, and a user, each incorporating an emotion engine.

[0337] server

[0338] The server continuously collects customer reviews about specified products and services from various sources. This often involves API calls and web scraping techniques. The collected data is first cleansed, and then sentiment analysis is performed using natural language processing techniques. Based on this analysis, the reviews are classified as positive, negative, or neutral. The server further summarizes the collected data, integrates duplicate information, and stores it in a database in a more refined form.

[0339] In addition to this process, the server uses an emotion engine to recognize the user's emotional state in real time and learns from the user's past data to improve the quality of the information it provides. This makes it possible to select and provide the most appropriate information based on the user's emotions.

[0340] terminal

[0341] The device retrieves information formatted by the server and displays it in a user-friendly format. The user interface is designed specifically for mobile devices, allowing for intuitive operation. Based on feedback from the emotion engine, the information displayed and how it is displayed are dynamically adjusted to provide the user with the best possible experience.

[0342] User

[0343] Users access this system through their devices to obtain information about products and services. When a user searches for a specific product, a summary of relevant reviews is displayed, and they can also receive personalized feedback from the sentiment engine. For example, if a user is under stress, the system will prioritize displaying calming, positive reviews to support their purchasing decision.

[0344] For example, when a user considering purchasing a new home appliance uses the system, they can open the app and perform a search to obtain a summary that integrates reviews from multiple e-commerce sites and social media. Personalized information generated by an emotion engine is then added, featuring reviews that match the user's preferences.

[0345] In this way, the present invention responds precisely to the user's emotions and needs, and helps to improve their purchasing decisions.

[0346] The following describes the processing flow.

[0347] Step 1:

[0348] The server automatically collects data from multiple online sources. It periodically retrieves the latest customer reviews about specified products and services using API calls and web scraping.

[0349] Step 2:

[0350] The server cleanses the collected data and converts it into a structured format. Specifically, it removes unnecessary HTML tags and special characters to prepare the data for application of natural language processing techniques.

[0351] Step 3:

[0352] The server uses sentiment analysis techniques to analyze the review data and classify each review as positive, negative, or neutral. This process identifies the emotional tone of each review.

[0353] Step 4:

[0354] The server summarizes the data and integrates duplicate information. Statistical algorithms are used to extract important keywords and phrases and generate a comprehensive summary.

[0355] Step 5:

[0356] The server uses an emotion engine to assess the user's current emotional state in real time. This allows it to learn from the user's past emotional data and prepare to provide personalized information.

[0357] Step 6:

[0358] The device retrieves summary information provided by the server and displays it in the user interface. Based on feedback from the emotion engine, the information display is adjusted according to the user's emotional needs.

[0359] Step 7:

[0360] Users review the information displayed on their devices and make a purchase decision. Because the information provided is optimized for the user's emotional state, a smoother decision-making process is possible.

[0361] (Example 2)

[0362] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0363] In recent years, a vast amount of information exists on the internet, but it is not easy to select useful and reliable information from this enormous volume. Furthermore, there is a lack of personalized services that cater to individual needs and provide information that matches the user's emotional state. Therefore, there is a need for a system that collects appropriate and reliable information in real time and provides customized information that responds to the user's emotions.

[0364] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0365] In this invention, the server includes means for automatically collecting text data from multiple sources, means for classifying the data into emotional categories through sentiment analysis using natural language processing technology, and means for summarizing the data, integrating related information, and storing it in a database. This enables the rapid collection of reliable information and the provision of personalized information based on the user's emotional state.

[0366] An "information source" is a medium that provides text data obtained from multiple locations on the internet.

[0367] "Text data" refers to data recorded as written information, such as reviews and testimonials.

[0368] "Natural language processing technology" is a technology that enables computers to understand and process the language that people use in everyday life.

[0369] "Sentiment analysis" is the process of extracting opinions and emotions from text data and identifying the type of emotion (positive, negative, neutral, etc.).

[0370] An "emotion category" is a classification that represents the type of emotion identified through emotion analysis.

[0371] A "summary" is data that has been compiled by selecting and prioritizing multiple pieces of information, and summarizing only the main points in a short format.

[0372] "Integration" is the process of combining multiple duplicate or related data into a single entity.

[0373] A "database" is a structured information management system that efficiently manages large amounts of information and allows for searching and retrieval as needed.

[0374] A "server" is a computer system that functions to process data and provide various types of information.

[0375] "Real-time" refers to a process designed to be processed almost simultaneously with the current time.

[0376] "User emotional state" refers to the emotional state a user experiences while using the system.

[0377] Personalization refers to optimizing information and services according to the individual user's needs and preferences.

[0378] The system for implementing this invention mainly consists of three components: a server, a terminal, and a user. The system operates with each component working in coordination to provide the user with optimized information.

[0379] The server automatically collects text data from multiple sources on the internet. This collection utilizes API calls and web scraping techniques. The collected data undergoes sentiment analysis using natural language processing techniques. Generative AI models are used to classify the emotions within the text data as positive, negative, or neutral. The classification results are stored in a database, and further summarization processes extract the key content. By integrating redundant information, only data useful to the user is retained. The server leverages an emotion engine to analyze the user's real-time emotional state and adaptively optimizes information delivery based on historical data.

[0380] The device receives organized information from the server and displays it in a format that is easy for the user to operate intuitively. It provides a user interface specifically designed for mobile devices, enabling real-time information display that reflects the user's emotional state.

[0381] Users can access product reviews and ratings through their devices. This information is personalized using an emotion engine, prioritizing information that matches the user's emotional state. For example, if a user wants to relax, positive reviews will be displayed, prioritizing information that promotes relaxation when selecting a product.

[0382] For example, a user considering purchasing a new electronic device can search for the product name on their device to view reviews aggregated from multiple online stores and social media. This information is filtered by an emotion engine, displaying recommendations tailored to the user's preferences. An example of a prompt to input into the generative AI model is, "Please collect positive reviews related to this product." This prompt efficiently provides information aligned with the specific emotion the user is seeking.

[0383] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0384] Step 1:

[0385] The server automatically collects text data from multiple sources on the internet. Specific inputs include API endpoints and website URLs, while the output is the collected raw review and comment text. The server continuously retrieves this information and performs collection processing on a regular schedule to maintain up-to-date data.

[0386] Step 2:

[0387] The server performs a cleansing process on the collected text data. The input is raw text data, and the output is clean text data with noise removed and no duplicates. This cleansing process standardizes the format and removes redundant data.

[0388] Step 3:

[0389] The server performs sentiment analysis using natural language processing techniques based on clean text data. The input is cleansed text data, and the output is sentiment data where the text is classified as positive, negative, or neutral. A generative AI model is used to accurately classify the sentiment within each comment. This analysis reveals the emotional tone of individual reviews, leading to the next steps.

[0390] Step 4:

[0391] The server summarizes data and integrates information based on sentiment analysis results. The input is text data with sentiment data attached, and the output is summarized key review information. The server uses the summarization function of a generative AI model to consolidate redundant information and extract only the information that is important to the user.

[0392] Step 5:

[0393] The server uses an emotion engine to analyze the user's emotional state and optimize the information provided. Inputs include the user's past emotional data and real-time emotional data, while output is customized information tailored to the user's emotional state. Based on this information, the server selects and ultimately delivers the most relevant information to the user.

[0394] Step 6:

[0395] The terminal receives formatted information provided by the server and displays it to the user. The input is summarized information from the server, and the output is information on a user-friendly interface. The terminal is intuitive to operate, and the user interface is updated in real time.

[0396] Step 7:

[0397] Users view information provided through their devices and check reviews and ratings of specific products. The input is the information displayed on the device, and the output is the detailed information and opinions about the product. Users make purchasing decisions based on the presented information. For example, they are more likely to purchase a product with many positive reviews.

[0398] (Application Example 2)

[0399] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0400] While countless reviews and opinions exist online, they are not always presented in a way that is beneficial to users. In particular, the lack of optimization of information based on users' emotional states means that meaningful purchasing support is not being provided. Furthermore, if the user interface is not intuitive, it becomes difficult for users to efficiently acquire information. A system is needed to solve these problems and improve the user experience.

[0401] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0402] In this invention, the server includes means for automatically collecting information from various information sources, means for analyzing the collected information and classifying emotions, and means for summarizing the analysis results and integrating redundant information. This makes it possible to detect the user's emotional state in real time and provide information optimized according to that state.

[0403] "Various information sources" refers to the diverse platforms and databases that exist on the internet, and these are the sources from which word-of-mouth information is collected.

[0404] "Methods for automatically collecting information" refers to methods of efficiently obtaining information from the internet without human intervention by utilizing API calls and scraping techniques.

[0405] "Methods for classifying emotions" refer to natural language processing techniques that analyze collected text data and classify its content as positive, negative, or neutral.

[0406] "Methods for summarizing information and integrating overlapping information" refers to the process of extracting key points from acquired word-of-mouth information and aggregating identical or similar information.

[0407] "Means of providing information to users" refers to interfaces and technologies that display formatted data in a format that is easiest for users to understand.

[0408] "A means of detecting a user's emotional state in real time and optimizing information based on that state" refers to a method that includes algorithms and functions that determine the user's current emotions and adjust the displayed information accordingly.

[0409] A "means of providing purchasing support" refers to a system that provides relevant information and assistance in making decisions so that users can make the best choices when selecting products or services.

[0410] This system consists of three main elements: a server, terminals, and users. The server is responsible for automatically collecting information from various sources on the internet. Information collection utilizes API calls and scraping techniques. The collected data is cleansed using Python, and then sentiment analysis is performed using natural language processing technologies such as the Google Cloud Natural Language API. The analysis results are categorized into positive, negative, and neutral, and summaries and duplicate information are consolidated.

[0411] Furthermore, the server also includes processing to recognize the user's emotional state in real time. Based on data provided by the user using smartphone sensors and other means, the emotion engine analyzes the user's current emotions and optimizes the information provided. For example, emotions can be determined using facial recognition technology from the smartphone's camera.

[0412] The terminal has a user interface designed to display formatted data retrieved from the server in a format easily understood by the user. Developed using Flutter and React Native, this interface allows for intuitive operation, reducing user stress while providing optimal information.

[0413] Users can use this system to obtain information related to products and services they are interested in, and it supports their purchasing decisions. Because the system personalizes information based on the user's emotional state, it displays appropriate reviews even to users considering purchasing new home appliances.

[0414] For example, if a user wants to find the "latest rice cooker," the system integrates reviews from e-commerce sites and social media. If it detects that the user is stressed about preparing dinner, it prioritizes featuring positive reviews of easy-to-use rice cookers. In this way, the system can dynamically adjust how information is selected and presented.

[0415] By using a generative AI model, the system can enhance its ability to perform more detailed analysis and provide information based on prompts such as: "If the user is currently experiencing stress, how can we prioritize displaying positive reviews about the latest rice cookers?"

[0416] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0417] Step 1:

[0418] The server automatically collects information from various sources. It uses API calls and web scraping to obtain user review data from social media and e-commerce sites. The input is the information source, and the output is the collected raw data.

[0419] Step 2:

[0420] The server performs a cleansing process on the collected raw data. It uses Python libraries to remove unnecessary parts of the data and format it. The input is raw data, and the output is formatted data.

[0421] Step 3:

[0422] The server performs sentiment analysis on the formatted data using natural language processing techniques. It uses the Google Cloud Natural Language API to determine whether each review's text is positive, negative, or neutral. The input is formatted data, and the output is sentiment-labeled data.

[0423] Step 4:

[0424] The server summarizes sentiment-labeled data and merges duplicate information. Using algorithms, it extracts important reviews and groups similar information together. The input is sentiment-labeled data, and the output is summarized data.

[0425] Step 5:

[0426] The server detects the user's emotional state in real time. It uses data from the smartphone's camera and sensors to analyze the user's current psychological state. This information is processed by an emotion engine. The input is the user's emotional state data, and the output is the detected emotion label.

[0427] Step 6:

[0428] The device retrieves summarized data provided by the server and displays information optimized based on the user's emotional state. An interface designed with Flutter and React Native clearly presents the most relevant information to the user. Input is summarized data and emotional labels, while output is the displayed information.

[0429] Step 7:

[0430] Users make purchasing decisions based on this information. The system provides personalized reviews and information to support appropriate choices. The input is the displayed information and the user's selection, and the output is the final purchasing decision.

[0431] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0432] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0433] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0434] [Third Embodiment]

[0435] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0436] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0437] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0438] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0439] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0440] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0441] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0442] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0443] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0444] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0445] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0446] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0447] The system of the present invention automatically collects user reviews and word-of-mouth information from various information sources on the internet and provides it to users in a summarized form. This system consists of three main components: a server, a terminal, and the user.

[0448] server

[0449] The server has core functionality for automatically collecting data from various online platforms. It periodically collects new review information by calling specific APIs or utilizing web scraping. The server converts this data into a more manageable format and stores it in a database. It also uses natural language processing techniques to analyze the collected data and perform sentiment analysis. This allows it to evaluate the sentiment of each review and tag them as positive, negative, or neutral.

[0450] Furthermore, the server applies a summarization algorithm to extract key points and summarize the information. This summarization process organizes redundant information and integrates it, preparing it to be presented in an easily understandable format for the user.

[0451] terminal

[0452] The terminal serves to present users with summarized information provided by the server. The user interface, particularly as a mobile or web application, allows users to easily access information about products and services of interest. The display on the terminal is visually intuitive and designed to enhance the user experience.

[0453] User

[0454] This system allows users to obtain information about selected products and services more quickly. If a user wants to research a specific product, they can easily retrieve relevant summary information through their device. This information includes ratings and sentiment analysis results, helping users make informed decisions.

[0455] For example, if a user is considering purchasing a new electronic product, they can simply open a smartphone app and enter the product name to see review summaries collected from various e-commerce sites and social media. This summary includes a statistical overview of the product's strengths and weaknesses, as well as user satisfaction, allowing the user to quickly grasp the necessary information.

[0456] Thus, the present invention strongly supports users' purchasing decisions through efficient information gathering and the provision of a user-friendly interface.

[0457] The following describes the processing flow.

[0458] Step 1:

[0459] The server automatically collects customer review data about a specified product or service by calling an API on an online platform or by using web scraping techniques.

[0460] Step 2:

[0461] The server cleanses the collected data, removing unnecessary information and noise, and converting it into a format that is easy to analyze. For example, it cleans up HTML tags and special characters.

[0462] Step 3:

[0463] The server uses natural language processing technology to analyze the text data of each review. During this process, sentiment analysis is performed, classifying the reviews as positive, negative, or neutral.

[0464] Step 4:

[0465] The server uses a summarization algorithm to extract important data and frequently occurring themes, and then concisely summarizes the overall content.

[0466] Step 5:

[0467] The server integrates similar information from different sources, groups duplicate content, and stores it in a unified database.

[0468] Step 6:

[0469] The server formats the processing results and converts the data into a format that can be displayed through the user interface.

[0470] Step 7:

[0471] The terminal retrieves summary information from the server and displays it clearly on the user interface. Users can access and obtain the necessary information instantly.

[0472] Step 8:

[0473] Users review the displayed summary information and make decisions based on that information. This allows users to obtain information that helps them avoid regretting their purchase later.

[0474] (Example 1)

[0475] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0476] In today's information society, a vast amount of reviews and word-of-mouth information exists on the internet. However, this information is diverse and its reliability varies, making it difficult for users to quickly retrieve and accurately understand the information they need. In particular, users tend to become confused because the information is often redundant or contradictory. In this situation, there is a need for a system that allows users to acquire information efficiently and concisely and make decisions based on that information.

[0477] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0478] In this invention, the server includes means for periodically acquiring information from an information medium, means for converting the acquired information into a data format and storing it in a collection, and means for applying natural language processing to the information and performing sentiment evaluation. This makes it possible to quickly and accurately acquire the information that the user needs and to make appropriate decisions based on the collected information.

[0479] "Information media" refers to online platforms and services used to store and provide information.

[0480] "Acquiring periodically" refers to automatically collecting information at specific time intervals.

[0481] "Converting to a data format" refers to structuring information into a standardized format so that it can be properly stored in a database or similar system.

[0482] "Storing in a set" refers to the process of gathering and saving transformed information in one place.

[0483] "Natural language processing" refers to computer technology used to analyze human language and extract its meaning.

[0484] "Emotional evaluation" refers to a method of identifying and classifying the emotional tendencies inherent in information.

[0485] "Presenting visually" refers to displaying information on a screen in a way that users can easily understand.

[0486] "Reliability" refers to an indicator used to measure the accuracy and credibility of information.

[0487] "Easy information searching and access" refers to functionality that allows users to find and use the information they need without hassle.

[0488] The embodiments for carrying out the present invention are shown below.

[0489] server

[0490] The server plays a central role in collecting data from information sources. Specifically, the server periodically accesses data sources on the internet and retrieves information using APIs or web scraping techniques. For this, software such as Python's BeautifulSoup or Scrapy can be used. The retrieved information is converted into data formats such as JSON or CSV using the Pandas library and stored in a collection. Subsequently, NLTK, TextBlob, or Hugging Face's Transformers can be used for natural language processing and sentiment evaluation, classifying the emotional tendencies of the information. The information processed in this way is then stored in a database.

[0491] terminal

[0492] A terminal is a device that visually presents data sent from a server to the user. For this purpose, the terminal is equipped with a user interface using React or Flutter. This interface allows the user to easily retrieve information and view details. The visual display is in card or list format and is designed to be intuitive for the user to operate.

[0493] User

[0494] Users explore information through their devices and make decisions based on collected reviews and word-of-mouth information. For example, if a user is considering purchasing a new electronic product, they can simply type the product name into an app on their device, and relevant information will be summarized and displayed. An example of a prompt that can be used in this case is: "I'm thinking of buying a new electronic product. The product name is XX. Please summarize the reviews from e-commerce sites and social media, showing the main pros and cons and user satisfaction."

[0495] Through the above process, the system of the present invention can provide users with the information they need quickly and accurately, and support them in making decisions based on reliable data.

[0496] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0497] Step 1:

[0498] The server periodically collects data by accessing information sources. It receives API endpoints or web page URLs for each data source as input. Based on this information, the server sends API requests or parses HTML data using web scraping techniques. As output, it obtains raw reviews and user feedback, which is then passed on to the next data transformation step.

[0499] Step 2:

[0500] The server converts the collected raw data into a standardized data format. It uses the raw review and word-of-mouth data obtained in Step 1 as input. The server leverages the Python Pandas library to convert the data into JSON or CSV format, outputting it as easily processable structured data. This data is then ready for storage in the database.

[0501] Step 3:

[0502] The server performs natural language processing on the structured data and conducts sentiment evaluation. The input is the structured data obtained in step 2. The server uses natural language processing libraries such as NLTK, TextBlob, and Hugging Face's Transformers to classify the sentiment of each review as positive, negative, or neutral. This outputs the data with sentiment evaluations and sends it to the next summarization step.

[0503] Step 4:

[0504] The server summarizes the sentiment-rated data. It receives the sentiment-rated data from step 3 as input. Through a summarization algorithm, it extracts key points while streamlining redundant information, generating a user-friendly summary. This summary is then ready to be sent to the terminal.

[0505] Step 5:

[0506] The terminal visually presents the user with summarized information received from the server. The input includes summarized data from the server. The terminal displays the information in card or list format through a user interface using React or Flutter. The output provides the user with easily understandable information.

[0507] Step 6:

[0508] Users explore information and make decisions through an interface on their device. They input product names and service keywords as text. By using prompts to communicate information retrieval requests to the device, users receive optimized information generated by a generative AI model. The output, displayed in a user-friendly format, allows users to easily make decisions such as purchases.

[0509] (Application Example 1)

[0510] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0511] Consumers face the challenge of having to check numerous reviews before purchasing a product, a process that is time-consuming. Furthermore, it's difficult to interpret individual reviews, making it hard to grasp the overall reputation. Additionally, consumers often lack the time to quickly obtain information at the point of purchase, leading to delays in decision-making. Therefore, there is a need for a system that allows consumers to quickly and efficiently obtain useful product-related information.

[0512] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0513] In this invention, the server includes means for automatically collecting information from various information sources, means for analyzing the collected information and classifying emotions, means for summarizing the analysis results and integrating redundant information, means for identifying objects using an optical device and obtaining relevant summary information, and means for visually displaying the matched information on the user's device. This enables consumers to quickly obtain review information at the point of purchase and make quick purchase decisions based on aggregated evaluations.

[0514] "Various information sources" refers to a variety of platforms and databases on the internet from which information is collected.

[0515] "Methods for automatically collecting information" refers to technologies that collect online information without human intervention, such as by calling specific APIs or using web scraping techniques.

[0516] "Sentiment classification" is the process of analyzing the text of collected information and evaluating whether its content falls into the categories of positive, negative, or neutral.

[0517] "Summarization techniques" are technologies used to analyze information, extract key points, organize redundant data, and present it in a format that is easy for users to understand.

[0518] "Optical devices" refer to visual sensors such as cameras, which are hardware used to identify objects.

[0519] "Matched information" refers to data related to the identified object, and this is used to display context-appropriate information to the user.

[0520] "User's device" refers to devices that users use to receive information, such as smartphones and tablet devices.

[0521] The system implementing this invention mainly consists of three components: a server, a terminal, and a user.

[0522] The server has the capability to automatically collect information, regularly gathering data from various sources using specific APIs and web scraping techniques. The collected data is stored in a database, and sentiment classification is performed using natural language processing. This tags each review as either positive, negative, or neutral. Subsequently, a summarization algorithm extracts and integrates key information, preparing it in a concise but clear format.

[0523] The terminal plays the role of visually displaying summarized information from the server in response to user input. On devices such as smartphones and tablets, it provides immediate access to relevant information by using the terminal's camera to identify objects (e.g., product barcodes). By providing a visually intuitive interface that enhances the user experience, users can easily check review information.

[0524] Users utilize this system to browse and rate products they intend to purchase. For example, a user considering a new gadget can simply scan the product's barcode with their smartphone camera in a store and quickly see a summary of reviews collected online. This is extremely helpful in understanding the product's strengths and weaknesses, allowing for a smoother purchasing decision.

[0525] For example, a user looking for a new smartphone case scans the barcode of a case they're interested in at a store. A summary of reviews regarding the product's texture and durability is then displayed, instantly providing crucial information for their selection. An example of a prompt might be, "How can I generate a review summary and visually present the results based on sentiment analysis?"

[0526] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0527] Step 1:

[0528] The server collects review information from online platforms by calling an API. The input consists of the URL and API key of the information source, and the server uses these to send a data request to retrieve the information. The output is the raw data of the retrieved reviews.

[0529] Step 2:

[0530] The server stores the collected review information in a database. At this stage, the input is raw data, and the output is structured data stored in the database. Data processing involves converting data in JSON or XML format into a standard database format.

[0531] Step 3:

[0532] The server analyzes the sentiment of reviews using natural language processing. The input is text data of reviews stored in a database, and the output is a sentiment rating (positive, negative, or neutral) for each review. In detail, it uses NLTK and the spacy library to extract sentiment-related keywords and phrases from the text and classifies them using a statistical model.

[0533] Step 4:

[0534] The server summarizes review information based on the results of sentiment analysis. The input is analyzed sentiment data and text data, and the output is a summarized text. This process includes organizing redundant information and extracting and summarizing key points. The summarization algorithm prioritizes important phrases, integrates them, and presents them concisely.

[0535] Step 5:

[0536] The terminal scans the barcode of the target product using its camera, based on user input. This input is barcode information obtained from an optical device. Based on this, the terminal queries the server for information. The specific action taken by the user is to launch the camera app and focus on the product's barcode.

[0537] Step 6:

[0538] The server matches the barcode information with summary reviews and sends them to the terminal. The input is barcode information, and the output is the corresponding product review summary data. In this step, a database search and matching are performed, and the relevant information is sent together.

[0539] Step 7:

[0540] The device visually displays the received summary information to the user. The input is a summary review sent from the server, and the output is the information displayed on the screen to the user. Specifically, the app lays out the summary information and displays it in a way that is easy for the user to understand intuitively.

[0541] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0542] This invention is a system that automatically aggregates word-of-mouth information from multiple sources on the internet and provides information while taking user emotions into consideration. This enables the provision of customized information that meets the individual needs and emotions of each user. The system is mainly composed of three main components: a server, a terminal, and a user, each incorporating an emotion engine.

[0543] server

[0544] The server continuously collects customer reviews about specified products and services from various sources. This often involves API calls and web scraping techniques. The collected data is first cleansed, and then sentiment analysis is performed using natural language processing techniques. Based on this analysis, the reviews are classified as positive, negative, or neutral. The server further summarizes the collected data, integrates duplicate information, and stores it in a database in a more refined form.

[0545] In addition to this process, the server uses an emotion engine to recognize the user's emotional state in real time and learns from the user's past data to improve the quality of the information it provides. This makes it possible to select and provide the most appropriate information based on the user's emotions.

[0546] terminal

[0547] The device retrieves information formatted by the server and displays it in a user-friendly format. The user interface is designed specifically for mobile devices, allowing for intuitive operation. Based on feedback from the emotion engine, the information displayed and how it is displayed are dynamically adjusted to provide the user with the best possible experience.

[0548] User

[0549] Users access this system through their devices to obtain information about products and services. When a user searches for a specific product, a summary of relevant reviews is displayed, and they can also receive personalized feedback from the sentiment engine. For example, if a user is under stress, the system will prioritize displaying calming, positive reviews to support their purchasing decision.

[0550] For example, when a user considering purchasing a new home appliance uses the system, they can open the app and perform a search to obtain a summary that integrates reviews from multiple e-commerce sites and social media. Personalized information generated by an emotion engine is then added, featuring reviews that match the user's preferences.

[0551] In this way, the present invention responds precisely to the user's emotions and needs, and helps to improve their purchasing decisions.

[0552] The following describes the processing flow.

[0553] Step 1:

[0554] The server automatically collects data from multiple online sources. It periodically retrieves the latest customer reviews about specified products and services using API calls and web scraping.

[0555] Step 2:

[0556] The server cleanses the collected data and converts it into a structured format. Specifically, it removes unnecessary HTML tags and special characters to prepare the data for application of natural language processing techniques.

[0557] Step 3:

[0558] The server uses sentiment analysis techniques to analyze the review data and classify each review as positive, negative, or neutral. This process identifies the emotional tone of each review.

[0559] Step 4:

[0560] The server summarizes the data and integrates duplicate information. Statistical algorithms are used to extract important keywords and phrases and generate a comprehensive summary.

[0561] Step 5:

[0562] The server uses an emotion engine to assess the user's current emotional state in real time. This allows it to learn from the user's past emotional data and prepare to provide personalized information.

[0563] Step 6:

[0564] The device retrieves summary information provided by the server and displays it in the user interface. Based on feedback from the emotion engine, the information display is adjusted according to the user's emotional needs.

[0565] Step 7:

[0566] Users review the information displayed on their devices and make a purchase decision. Because the information provided is optimized for the user's emotional state, a smoother decision-making process is possible.

[0567] (Example 2)

[0568] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0569] In recent years, a vast amount of information exists on the internet, but it is not easy to select useful and reliable information from this enormous volume. Furthermore, there is a lack of personalized services that cater to individual needs and provide information that matches the user's emotional state. Therefore, there is a need for a system that collects appropriate and reliable information in real time and provides customized information that responds to the user's emotions.

[0570] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0571] In this invention, the server includes means for automatically collecting text data from multiple sources, means for classifying the data into emotional categories through sentiment analysis using natural language processing technology, and means for summarizing the data, integrating related information, and storing it in a database. This enables the rapid collection of reliable information and the provision of personalized information based on the user's emotional state.

[0572] An "information source" is a medium that provides text data obtained from multiple locations on the internet.

[0573] "Text data" refers to data recorded as written information, such as reviews and testimonials.

[0574] "Natural language processing technology" is a technology that enables computers to understand and process the language that people use in everyday life.

[0575] "Sentiment analysis" is the process of extracting opinions and emotions from text data and identifying the type of emotion (positive, negative, neutral, etc.).

[0576] An "emotion category" is a classification that represents the type of emotion identified through emotion analysis.

[0577] A "summary" is data that has been compiled by selecting and prioritizing multiple pieces of information, and summarizing only the main points in a short format.

[0578] "Integration" is the process of combining multiple duplicate or related data into a single entity.

[0579] A "database" is a structured information management system that efficiently manages large amounts of information and allows for searching and retrieval as needed.

[0580] A "server" is a computer system that functions to process data and provide various types of information.

[0581] "Real-time" refers to a process designed to be processed almost simultaneously with the current time.

[0582] "User emotional state" refers to the emotional state a user experiences while using the system.

[0583] Personalization refers to optimizing information and services according to the individual user's needs and preferences.

[0584] The system for implementing this invention mainly consists of three components: a server, a terminal, and a user. The system operates with each component working in coordination to provide the user with optimized information.

[0585] The server automatically collects text data from multiple sources on the internet. This collection utilizes API calls and web scraping techniques. The collected data undergoes sentiment analysis using natural language processing techniques. Generative AI models are used to classify the emotions within the text data as positive, negative, or neutral. The classification results are stored in a database, and further summarization processes extract the key content. By integrating redundant information, only data useful to the user is retained. The server leverages an emotion engine to analyze the user's real-time emotional state and adaptively optimizes information delivery based on historical data.

[0586] The device receives organized information from the server and displays it in a format that is easy for the user to operate intuitively. It provides a user interface specifically designed for mobile devices, enabling real-time information display that reflects the user's emotional state.

[0587] Users can access product reviews and ratings through their devices. This information is personalized using an emotion engine, prioritizing information that matches the user's emotional state. For example, if a user wants to relax, positive reviews will be displayed, prioritizing information that promotes relaxation when selecting a product.

[0588] For example, a user considering purchasing a new electronic device can search for the product name on their device to view reviews aggregated from multiple online stores and social media. This information is filtered by an emotion engine, displaying recommendations tailored to the user's preferences. An example of a prompt to input into the generative AI model is, "Please collect positive reviews related to this product." This prompt efficiently provides information aligned with the specific emotion the user is seeking.

[0589] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0590] Step 1:

[0591] The server automatically collects text data from multiple sources on the internet. Specific inputs include API endpoints and website URLs, while the output is the collected raw review and comment text. The server continuously retrieves this information and performs collection processing on a regular schedule to maintain up-to-date data.

[0592] Step 2:

[0593] The server performs a cleansing process on the collected text data. The input is raw text data, and the output is clean text data with noise removed and no duplicates. This cleansing process standardizes the format and removes redundant data.

[0594] Step 3:

[0595] The server performs sentiment analysis using natural language processing techniques based on clean text data. The input is cleansed text data, and the output is sentiment data where the text is classified as positive, negative, or neutral. A generative AI model is used to accurately classify the sentiment within each comment. This analysis reveals the emotional tone of individual reviews, leading to the next steps.

[0596] Step 4:

[0597] The server summarizes data and integrates information based on sentiment analysis results. The input is text data with sentiment data attached, and the output is summarized key review information. The server uses the summarization function of a generative AI model to consolidate redundant information and extract only the information that is important to the user.

[0598] Step 5:

[0599] The server uses an emotion engine to analyze the user's emotional state and optimize the information provided. Inputs include the user's past emotional data and real-time emotional data, while output is customized information tailored to the user's emotional state. Based on this information, the server selects and ultimately delivers the most relevant information to the user.

[0600] Step 6:

[0601] The terminal receives formatted information provided by the server and displays it to the user. The input is summarized information from the server, and the output is information on a user-friendly interface. The terminal is intuitive to operate, and the user interface is updated in real time.

[0602] Step 7:

[0603] Users view information provided through their devices and check reviews and ratings of specific products. The input is the information displayed on the device, and the output is the detailed information and opinions about the product. Users make purchasing decisions based on the presented information. For example, they are more likely to purchase a product with many positive reviews.

[0604] (Application Example 2)

[0605] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0606] While countless reviews and opinions exist online, they are not always presented in a way that is beneficial to users. In particular, the lack of optimization of information based on users' emotional states means that meaningful purchasing support is not being provided. Furthermore, if the user interface is not intuitive, it becomes difficult for users to efficiently acquire information. A system is needed to solve these problems and improve the user experience.

[0607] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0608] In this invention, the server includes means for automatically collecting information from various information sources, means for analyzing the collected information and classifying emotions, and means for summarizing the analysis results and integrating redundant information. This makes it possible to detect the user's emotional state in real time and provide information optimized according to that state.

[0609] "Various information sources" refers to the diverse platforms and databases that exist on the internet, and these are the sources from which word-of-mouth information is collected.

[0610] "Methods for automatically collecting information" refers to methods of efficiently obtaining information from the internet without human intervention by utilizing API calls and scraping techniques.

[0611] "Methods for classifying emotions" refer to natural language processing techniques that analyze collected text data and classify its content as positive, negative, or neutral.

[0612] "Methods for summarizing information and integrating overlapping information" refers to the process of extracting key points from acquired word-of-mouth information and aggregating identical or similar information.

[0613] "Means of providing information to users" refers to interfaces and technologies that display formatted data in a format that is easiest for users to understand.

[0614] "A means of detecting a user's emotional state in real time and optimizing information based on that state" refers to a method that includes algorithms and functions that determine the user's current emotions and adjust the displayed information accordingly.

[0615] A "means of providing purchasing support" refers to a system that provides relevant information and assistance in making decisions so that users can make the best choices when selecting products or services.

[0616] This system consists of three main elements: a server, terminals, and users. The server is responsible for automatically collecting information from various sources on the internet. Information collection utilizes API calls and scraping techniques. The collected data is cleansed using Python, and then sentiment analysis is performed using natural language processing technologies such as the Google Cloud Natural Language API. The analysis results are categorized into positive, negative, and neutral, and summaries and duplicate information are consolidated.

[0617] Furthermore, the server also includes processing to recognize the user's emotional state in real time. Based on data provided by the user using smartphone sensors and other means, the emotion engine analyzes the user's current emotions and optimizes the information provided. For example, emotions can be determined using facial recognition technology from the smartphone's camera.

[0618] The terminal has a user interface designed to display formatted data retrieved from the server in a format easily understood by the user. Developed using Flutter and React Native, this interface allows for intuitive operation, reducing user stress while providing optimal information.

[0619] Users can use this system to obtain information related to products and services they are interested in, and it supports their purchasing decisions. Because the system personalizes information based on the user's emotional state, it displays appropriate reviews even to users considering purchasing new home appliances.

[0620] For example, if a user wants to find the "latest rice cooker," the system integrates reviews from e-commerce sites and social media. If it detects that the user is stressed about preparing dinner, it prioritizes featuring positive reviews of easy-to-use rice cookers. In this way, the system can dynamically adjust how information is selected and presented.

[0621] By using a generative AI model, the system can enhance its ability to perform more detailed analysis and provide information based on prompts such as: "If the user is currently experiencing stress, how can we prioritize displaying positive reviews about the latest rice cookers?"

[0622] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0623] Step 1:

[0624] The server automatically collects information from various sources. It uses API calls and web scraping to obtain user review data from social media and e-commerce sites. The input is the information source, and the output is the collected raw data.

[0625] Step 2:

[0626] The server performs a cleansing process on the collected raw data. It uses Python libraries to remove unnecessary parts of the data and format it. The input is raw data, and the output is formatted data.

[0627] Step 3:

[0628] The server performs sentiment analysis on the formatted data using natural language processing techniques. It uses the Google Cloud Natural Language API to determine whether each review's text is positive, negative, or neutral. The input is formatted data, and the output is sentiment-labeled data.

[0629] Step 4:

[0630] The server summarizes sentiment-labeled data and merges duplicate information. Using algorithms, it extracts important reviews and groups similar information together. The input is sentiment-labeled data, and the output is summarized data.

[0631] Step 5:

[0632] The server detects the user's emotional state in real time. It uses data from the smartphone's camera and sensors to analyze the user's current psychological state. This information is processed by an emotion engine. The input is the user's emotional state data, and the output is the detected emotion label.

[0633] Step 6:

[0634] The device retrieves summarized data provided by the server and displays information optimized based on the user's emotional state. An interface designed with Flutter and React Native clearly presents the most relevant information to the user. Input is summarized data and emotional labels, while output is the displayed information.

[0635] Step 7:

[0636] Users make purchasing decisions based on this information. The system provides personalized reviews and information to support appropriate choices. The input is the displayed information and the user's selection, and the output is the final purchasing decision.

[0637] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0638] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0639] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0640] [Fourth Embodiment]

[0641] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0642] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0643] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0644] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0645] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0646] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0647] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0648] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0649] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0650] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0651] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0652] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0653] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0654] The system of the present invention automatically collects user reviews and word-of-mouth information from various information sources on the internet and provides it to users in a summarized form. This system consists of three main components: a server, a terminal, and the user.

[0655] server

[0656] The server has core functionality for automatically collecting data from various online platforms. It periodically collects new review information by calling specific APIs or utilizing web scraping. The server converts this data into a more manageable format and stores it in a database. It also uses natural language processing techniques to analyze the collected data and perform sentiment analysis. This allows it to evaluate the sentiment of each review and tag them as positive, negative, or neutral.

[0657] Furthermore, the server applies a summarization algorithm to extract key points and summarize the information. This summarization process organizes redundant information and integrates it, preparing it to be presented in an easily understandable format for the user.

[0658] terminal

[0659] The terminal serves to present users with summarized information provided by the server. The user interface, particularly as a mobile or web application, allows users to easily access information about products and services of interest. The display on the terminal is visually intuitive and designed to enhance the user experience.

[0660] User

[0661] This system allows users to obtain information about selected products and services more quickly. If a user wants to research a specific product, they can easily retrieve relevant summary information through their device. This information includes ratings and sentiment analysis results, helping users make informed decisions.

[0662] For example, if a user is considering purchasing a new electronic product, they can simply open a smartphone app and enter the product name to see review summaries collected from various e-commerce sites and social media. This summary includes a statistical overview of the product's strengths and weaknesses, as well as user satisfaction, allowing the user to quickly grasp the necessary information.

[0663] Thus, the present invention strongly supports users' purchasing decisions through efficient information gathering and the provision of a user-friendly interface.

[0664] The following describes the processing flow.

[0665] Step 1:

[0666] The server automatically collects customer review data about a specified product or service by calling an API on an online platform or by using web scraping techniques.

[0667] Step 2:

[0668] The server cleanses the collected data, removing unnecessary information and noise, and converting it into a format that is easy to analyze. For example, it cleans up HTML tags and special characters.

[0669] Step 3:

[0670] The server uses natural language processing technology to analyze the text data of each review. During this process, sentiment analysis is performed, classifying the reviews as positive, negative, or neutral.

[0671] Step 4:

[0672] The server uses a summarization algorithm to extract important data and frequently occurring themes, and then concisely summarizes the overall content.

[0673] Step 5:

[0674] The server integrates similar information from different sources, groups duplicate content, and stores it in a unified database.

[0675] Step 6:

[0676] The server formats the processing results and converts the data into a format that can be displayed through the user interface.

[0677] Step 7:

[0678] The terminal retrieves summary information from the server and displays it clearly on the user interface. Users can access and obtain the necessary information instantly.

[0679] Step 8:

[0680] Users review the displayed summary information and make decisions based on that information. This allows users to obtain information that helps them avoid regretting their purchase later.

[0681] (Example 1)

[0682] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0683] In today's information society, a vast amount of reviews and word-of-mouth information exists on the internet. However, this information is diverse and its reliability varies, making it difficult for users to quickly retrieve and accurately understand the information they need. In particular, users tend to become confused because the information is often redundant or contradictory. In this situation, there is a need for a system that allows users to acquire information efficiently and concisely and make decisions based on that information.

[0684] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0685] In this invention, the server includes means for periodically acquiring information from an information medium, means for converting the acquired information into a data format and storing it in a collection, and means for applying natural language processing to the information and performing sentiment evaluation. This makes it possible to quickly and accurately acquire the information that the user needs and to make appropriate decisions based on the collected information.

[0686] "Information media" refers to online platforms and services used to store and provide information.

[0687] "Acquiring periodically" refers to automatically collecting information at specific time intervals.

[0688] "Converting to a data format" refers to structuring information into a standardized format so that it can be properly stored in a database or similar system.

[0689] "Storing in a set" refers to the process of gathering and saving transformed information in one place.

[0690] "Natural language processing" refers to computer technology used to analyze human language and extract its meaning.

[0691] "Emotional evaluation" refers to a method of identifying and classifying the emotional tendencies inherent in information.

[0692] "Presenting visually" refers to displaying information on a screen in a way that users can easily understand.

[0693] "Reliability" refers to an indicator used to measure the accuracy and credibility of information.

[0694] "Easy information searching and access" refers to functionality that allows users to find and use the information they need without hassle.

[0695] The embodiments for carrying out the present invention are shown below.

[0696] server

[0697] The server plays a central role in collecting data from information sources. Specifically, the server periodically accesses data sources on the internet and retrieves information using APIs or web scraping techniques. For this, software such as Python's BeautifulSoup or Scrapy can be used. The retrieved information is converted into data formats such as JSON or CSV using the Pandas library and stored in a collection. Subsequently, NLTK, TextBlob, or Hugging Face's Transformers can be used for natural language processing and sentiment evaluation, classifying the emotional tendencies of the information. The information processed in this way is then stored in a database.

[0698] terminal

[0699] A terminal is a device that visually presents data sent from a server to the user. For this purpose, the terminal is equipped with a user interface using React or Flutter. This interface allows the user to easily retrieve information and view details. The visual display is in card or list format and is designed to be intuitive for the user to operate.

[0700] User

[0701] Users explore information through their devices and make decisions based on collected reviews and word-of-mouth information. For example, if a user is considering purchasing a new electronic product, they can simply type the product name into an app on their device, and relevant information will be summarized and displayed. An example of a prompt that can be used in this case is: "I'm thinking of buying a new electronic product. The product name is XX. Please summarize the reviews from e-commerce sites and social media, showing the main pros and cons and user satisfaction."

[0702] Through the above process, the system of the present invention can provide users with the information they need quickly and accurately, and support them in making decisions based on reliable data.

[0703] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0704] Step 1:

[0705] The server periodically collects data by accessing information sources. It receives API endpoints or web page URLs for each data source as input. Based on this information, the server sends API requests or parses HTML data using web scraping techniques. As output, it obtains raw reviews and user feedback, which is then passed on to the next data transformation step.

[0706] Step 2:

[0707] The server converts the collected raw data into a standardized data format. It uses the raw review and word-of-mouth data obtained in Step 1 as input. The server leverages the Python Pandas library to convert the data into JSON or CSV format, outputting it as easily processable structured data. This data is then ready for storage in the database.

[0708] Step 3:

[0709] The server performs natural language processing on the structured data and conducts sentiment evaluation. The input is the structured data obtained in step 2. The server uses natural language processing libraries such as NLTK, TextBlob, and Hugging Face's Transformers to classify the sentiment of each review as positive, negative, or neutral. This outputs the data with sentiment evaluations and sends it to the next summarization step.

[0710] Step 4:

[0711] The server summarizes the sentiment-rated data. It receives the sentiment-rated data from step 3 as input. Through a summarization algorithm, it extracts key points while streamlining redundant information, generating a user-friendly summary. This summary is then ready to be sent to the terminal.

[0712] Step 5:

[0713] The terminal visually presents the user with summarized information received from the server. The input includes summarized data from the server. The terminal displays the information in card or list format through a user interface using React or Flutter. The output provides the user with easily understandable information.

[0714] Step 6:

[0715] Users explore information and make decisions through an interface on their device. They input product names and service keywords as text. By using prompts to communicate information retrieval requests to the device, users receive optimized information generated by a generative AI model. The output, displayed in a user-friendly format, allows users to easily make decisions such as purchases.

[0716] (Application Example 1)

[0717] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0718] Consumers face the challenge of having to check numerous reviews before purchasing a product, a process that is time-consuming. Furthermore, it's difficult to interpret individual reviews, making it hard to grasp the overall reputation. Additionally, consumers often lack the time to quickly obtain information at the point of purchase, leading to delays in decision-making. Therefore, there is a need for a system that allows consumers to quickly and efficiently obtain useful product-related information.

[0719] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0720] In this invention, the server includes means for automatically collecting information from various information sources, means for analyzing the collected information and classifying emotions, means for summarizing the analysis results and integrating redundant information, means for identifying objects using an optical device and obtaining relevant summary information, and means for visually displaying the matched information on the user's device. This enables consumers to quickly obtain review information at the point of purchase and make quick purchase decisions based on aggregated evaluations.

[0721] "Various information sources" refers to a variety of platforms and databases on the internet from which information is collected.

[0722] "Methods for automatically collecting information" refers to technologies that collect online information without human intervention, such as by calling specific APIs or using web scraping techniques.

[0723] "Sentiment classification" is the process of analyzing the text of collected information and evaluating whether its content falls into the categories of positive, negative, or neutral.

[0724] "Summarization techniques" are technologies used to analyze information, extract key points, organize redundant data, and present it in a format that is easy for users to understand.

[0725] "Optical devices" refer to visual sensors such as cameras, which are hardware used to identify objects.

[0726] "Matched information" refers to data related to the identified object, and this is used to display context-appropriate information to the user.

[0727] "User's device" refers to devices that users use to receive information, such as smartphones and tablet devices.

[0728] The system implementing this invention mainly consists of three components: a server, a terminal, and a user.

[0729] The server has the capability to automatically collect information, regularly gathering data from various sources using specific APIs and web scraping techniques. The collected data is stored in a database, and sentiment classification is performed using natural language processing. This tags each review as either positive, negative, or neutral. Subsequently, a summarization algorithm extracts and integrates key information, preparing it in a concise but clear format.

[0730] The terminal plays the role of visually displaying summarized information from the server in response to user input. On devices such as smartphones and tablets, it provides immediate access to relevant information by using the terminal's camera to identify objects (e.g., product barcodes). By providing a visually intuitive interface that enhances the user experience, users can easily check review information.

[0731] Users utilize this system to browse and rate products they intend to purchase. For example, a user considering a new gadget can simply scan the product's barcode with their smartphone camera in a store and quickly see a summary of reviews collected online. This is extremely helpful in understanding the product's strengths and weaknesses, allowing for a smoother purchasing decision.

[0732] For example, a user looking for a new smartphone case scans the barcode of a case they're interested in at a store. A summary of reviews regarding the product's texture and durability is then displayed, instantly providing crucial information for their selection. An example of a prompt might be, "How can I generate a review summary and visually present the results based on sentiment analysis?"

[0733] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0734] Step 1:

[0735] The server collects review information from online platforms by calling an API. The input consists of the URL and API key of the information source, and the server uses these to send a data request to retrieve the information. The output is the raw data of the retrieved reviews.

[0736] Step 2:

[0737] The server stores the collected review information in a database. At this stage, the input is raw data, and the output is structured data stored in the database. Data processing involves converting data in JSON or XML format to a standard database format.

[0738] Step 3:

[0739] The server analyzes the sentiment of reviews using natural language processing. The input is text data of reviews stored in a database, and the output is a sentiment rating (positive, negative, or neutral) for each review. In detail, it uses NLTK and the spacy library to extract sentiment-related keywords and phrases from the text and classifies them using a statistical model.

[0740] Step 4:

[0741] The server summarizes review information based on the results of sentiment analysis. The input is analyzed sentiment data and text data, and the output is a summarized text. This process includes organizing redundant information and extracting and summarizing key points. The summarization algorithm prioritizes important phrases, integrates them, and presents them concisely.

[0742] Step 5:

[0743] The terminal scans the barcode of the target product using its camera, based on user input. This input is barcode information obtained from an optical device. Based on this, the terminal queries the server for information. The specific action taken by the user is to launch the camera app and focus on the product's barcode.

[0744] Step 6:

[0745] The server matches the barcode information with summary reviews and sends them to the terminal. The input is barcode information, and the output is the corresponding product review summary data. In this step, a database search and matching are performed, and the relevant information is sent together.

[0746] Step 7:

[0747] The device visually displays the received summary information to the user. The input is a summary review sent from the server, and the output is the information displayed on the screen to the user. Specifically, the application lays out the summary information and displays it in a way that is easy for the user to understand intuitively.

[0748] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0749] This invention is a system that automatically aggregates word-of-mouth information from multiple sources on the internet and provides information while taking user emotions into consideration. This enables the provision of customized information that meets the individual needs and emotions of each user. The system is mainly composed of three main components: a server, a terminal, and a user, each incorporating an emotion engine.

[0750] server

[0751] The server continuously collects customer reviews about specified products and services from various sources. This often involves API calls and web scraping techniques. The collected data is first cleansed, and then sentiment analysis is performed using natural language processing techniques. Based on this analysis, the reviews are classified as positive, negative, or neutral. The server further summarizes the collected data, integrates duplicate information, and stores it in a database in a more refined form.

[0752] In addition to this process, the server uses an emotion engine to recognize the user's emotional state in real time and learns from the user's past data to improve the quality of the information it provides. This makes it possible to select and provide the most appropriate information based on the user's emotions.

[0753] terminal

[0754] The device retrieves information formatted by the server and displays it in a user-friendly format. The user interface is designed specifically for mobile devices, allowing for intuitive operation. Based on feedback from the emotion engine, the information displayed and how it is displayed are dynamically adjusted to provide the user with the best possible experience.

[0755] User

[0756] Users access this system through their devices to obtain information about products and services. When a user searches for a specific product, a summary of relevant reviews is displayed, and they can also receive personalized feedback from the sentiment engine. For example, if a user is under stress, the system will prioritize displaying calming, positive reviews to support their purchasing decision.

[0757] For example, when a user considering purchasing a new home appliance uses the system, they can open the app and perform a search to obtain a summary that integrates reviews from multiple e-commerce sites and social media. Personalized information generated by an emotion engine is then added, featuring reviews that match the user's preferences.

[0758] In this way, the present invention responds precisely to the user's emotions and needs, and helps to improve their purchasing decisions.

[0759] The following describes the processing flow.

[0760] Step 1:

[0761] The server automatically collects data from multiple online sources. It periodically retrieves the latest customer reviews about specified products and services using API calls and web scraping.

[0762] Step 2:

[0763] The server cleanses the collected data and converts it into a structured format. Specifically, it removes unnecessary HTML tags and special characters to prepare the data for application of natural language processing techniques.

[0764] Step 3:

[0765] The server uses sentiment analysis techniques to analyze the review data and classify each review as positive, negative, or neutral. This process identifies the emotional tone of each review.

[0766] Step 4:

[0767] The server summarizes the data and integrates duplicate information. Statistical algorithms are used to extract important keywords and phrases and generate a comprehensive summary.

[0768] Step 5:

[0769] The server uses an emotion engine to assess the user's current emotional state in real time. This allows it to learn from the user's past emotional data and prepare to provide personalized information.

[0770] Step 6:

[0771] The device retrieves summary information provided by the server and displays it in the user interface. Based on feedback from the emotion engine, the information display is adjusted according to the user's emotional needs.

[0772] Step 7:

[0773] Users review the information displayed on their devices and make a purchase decision. Because the information provided is optimized for the user's emotional state, a smoother decision-making process is possible.

[0774] (Example 2)

[0775] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0776] In recent years, a vast amount of information exists on the internet, but it is not easy to select useful and reliable information from this enormous volume. Furthermore, there is a lack of personalized services that cater to individual needs and provide information that matches the user's emotional state. Therefore, there is a need for a system that collects appropriate and reliable information in real time and provides customized information that responds to the user's emotions.

[0777] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0778] In this invention, the server includes means for automatically collecting text data from multiple sources, means for classifying the data into emotional categories through sentiment analysis using natural language processing technology, and means for summarizing the data, integrating related information, and storing it in a database. This enables the rapid collection of reliable information and the provision of personalized information based on the user's emotional state.

[0779] An "information source" is a medium that provides text data obtained from multiple locations on the internet.

[0780] "Text data" refers to data recorded as written information, such as reviews and testimonials.

[0781] "Natural language processing technology" is a technology that enables computers to understand and process the language that people use in everyday life.

[0782] "Sentiment analysis" is the process of extracting opinions and emotions from text data and identifying the type of emotion (positive, negative, neutral, etc.).

[0783] An "emotion category" is a classification that represents the type of emotion identified through emotion analysis.

[0784] A "summary" is data that has been compiled by selecting and prioritizing multiple pieces of information, and summarizing only the main points in a short format.

[0785] "Integration" is the process of combining multiple duplicate or related data into a single entity.

[0786] A "database" is a structured information management system that efficiently manages large amounts of information and allows for searching and retrieval as needed.

[0787] A "server" is a computer system that functions to process data and provide various types of information.

[0788] "Real-time" refers to a process designed to be processed almost simultaneously with the current time.

[0789] "User emotional state" refers to the emotional state a user experiences while using the system.

[0790] Personalization refers to optimizing information and services according to the individual user's needs and preferences.

[0791] The system for implementing this invention mainly consists of three components: a server, a terminal, and a user. The system operates with each component working in coordination to provide the user with optimized information.

[0792] The server automatically collects text data from multiple sources on the internet. This collection utilizes API calls and web scraping techniques. The collected data undergoes sentiment analysis using natural language processing techniques. Generative AI models are used to classify the emotions within the text data as positive, negative, or neutral. The classification results are stored in a database, and further summarization processes extract the key content. By integrating redundant information, only data useful to the user is retained. The server leverages an emotion engine to analyze the user's real-time emotional state and adaptively optimizes information delivery based on historical data.

[0793] The device receives organized information from the server and displays it in a format that is easy for the user to operate intuitively. It provides a user interface specifically designed for mobile devices, enabling real-time information display that reflects the user's emotional state.

[0794] Users can access product reviews and ratings through their devices. This information is personalized using an emotion engine, prioritizing information that matches the user's emotional state. For example, if a user wants to relax, positive reviews will be displayed, prioritizing information that promotes relaxation when selecting a product.

[0795] For example, a user considering purchasing a new electronic device can search for the product name on their device to view reviews aggregated from multiple online stores and social media. This information is filtered by an emotion engine, displaying recommendations tailored to the user's preferences. An example of a prompt to input into the generative AI model is, "Please collect positive reviews related to this product." This prompt efficiently provides information aligned with the specific emotion the user is seeking.

[0796] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0797] Step 1:

[0798] The server automatically collects text data from multiple sources on the internet. Specific inputs include API endpoints and website URLs, while the output is the collected raw review and comment text. The server continuously retrieves this information and performs collection processing on a regular schedule to maintain up-to-date data.

[0799] Step 2:

[0800] The server performs a cleansing process on the collected text data. The input is raw text data, and the output is clean text data with noise removed and no duplicates. This cleansing process standardizes the format and removes redundant data.

[0801] Step 3:

[0802] The server performs sentiment analysis using natural language processing techniques based on clean text data. The input is cleansed text data, and the output is sentiment data where the text is classified as positive, negative, or neutral. A generative AI model is used to accurately classify the sentiment within each comment. This analysis reveals the emotional tone of individual reviews, leading to the next steps.

[0803] Step 4:

[0804] The server summarizes data and integrates information based on sentiment analysis results. The input is text data with sentiment data attached, and the output is summarized key review information. The server uses the summarization function of a generative AI model to consolidate redundant information and extract only the information that is important to the user.

[0805] Step 5:

[0806] The server uses an emotion engine to analyze the user's emotional state and optimize the information provided. Inputs include the user's past emotional data and real-time emotional data, while output is customized information tailored to the user's emotional state. Based on this information, the server selects and ultimately delivers the most relevant information to the user.

[0807] Step 6:

[0808] The terminal receives formatted information provided by the server and displays it to the user. The input is summarized information from the server, and the output is information on a user-friendly interface. The terminal is intuitive to operate, and the user interface is updated in real time.

[0809] Step 7:

[0810] Users view information provided through their devices and check reviews and ratings of specific products. The input is the information displayed on the device, and the output is the detailed information and opinions about the product. Users make purchasing decisions based on the presented information. For example, they are more likely to purchase a product with many positive reviews.

[0811] (Application Example 2)

[0812] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0813] While countless reviews and opinions exist online, they are not always presented in a way that is beneficial to users. In particular, the lack of optimization of information based on users' emotional states means that meaningful purchasing support is not being provided. Furthermore, if the user interface is not intuitive, it becomes difficult for users to efficiently acquire information. A system is needed to solve these problems and improve the user experience.

[0814] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0815] In this invention, the server includes means for automatically collecting information from various information sources, means for analyzing the collected information and classifying emotions, and means for summarizing the analysis results and integrating redundant information. This makes it possible to detect the user's emotional state in real time and provide information optimized according to that state.

[0816] "Various information sources" refers to the diverse platforms and databases that exist on the internet, and these are the sources from which word-of-mouth information is collected.

[0817] "Methods for automatically collecting information" refers to methods of efficiently obtaining information from the internet without human intervention by utilizing API calls and scraping techniques.

[0818] "Methods for classifying emotions" refer to natural language processing techniques that analyze collected text data and classify its content as positive, negative, or neutral.

[0819] "Methods for summarizing information and integrating overlapping information" refers to the process of extracting key points from acquired word-of-mouth information and aggregating identical or similar information.

[0820] "Means of providing information to users" refers to interfaces and technologies that display formatted data in a format that is easiest for users to understand.

[0821] "A means of detecting a user's emotional state in real time and optimizing information based on that state" refers to a method that includes algorithms and functions that determine the user's current emotions and adjust the displayed information accordingly.

[0822] A "means of providing purchasing support" refers to a system that provides relevant information and assistance in making decisions so that users can make the best choices when selecting products or services.

[0823] This system consists of three main elements: a server, terminals, and users. The server is responsible for automatically collecting information from various sources on the internet. Information collection utilizes API calls and scraping techniques. The collected data is cleansed using Python, and then sentiment analysis is performed using natural language processing technologies such as the Google Cloud Natural Language API. The analysis results are categorized into positive, negative, and neutral, and summaries and duplicate information are consolidated.

[0824] Furthermore, the server also includes processing to recognize the user's emotional state in real time. Based on data provided by the user using smartphone sensors and other means, the emotion engine analyzes the user's current emotions and optimizes the information provided. For example, emotions can be determined using facial recognition technology from the smartphone's camera.

[0825] The terminal has a user interface designed to display formatted data retrieved from the server in a format easily understood by the user. Developed using Flutter and React Native, this interface allows for intuitive operation, reducing user stress while providing optimal information.

[0826] Users can use this system to obtain information related to products and services they are interested in, and it supports their purchasing decisions. Because the system personalizes information based on the user's emotional state, it displays appropriate reviews even to users considering purchasing new home appliances.

[0827] For example, if a user wants to find the "latest rice cooker," the system integrates reviews from e-commerce sites and social media. If it detects that the user is stressed about preparing dinner, it prioritizes featuring positive reviews of easy-to-use rice cookers. In this way, the system can dynamically adjust how information is selected and presented.

[0828] By using a generative AI model, the system can enhance its ability to perform more detailed analysis and provide information based on prompts such as: "If the user is currently experiencing stress, how can we prioritize displaying positive reviews about the latest rice cookers?"

[0829] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0830] Step 1:

[0831] The server automatically collects information from various sources. It uses API calls and web scraping to obtain user review data from social media and e-commerce sites. The input is the information source, and the output is the collected raw data.

[0832] Step 2:

[0833] The server performs a cleansing process on the collected raw data. It uses Python libraries to remove unnecessary parts of the data and format it. The input is raw data, and the output is formatted data.

[0834] Step 3:

[0835] The server performs sentiment analysis on the formatted data using natural language processing techniques. It uses the Google Cloud Natural Language API to determine whether each review's text is positive, negative, or neutral. The input is formatted data, and the output is sentiment-labeled data.

[0836] Step 4:

[0837] The server summarizes sentiment-labeled data and merges duplicate information. Using algorithms, it extracts important reviews and groups similar information together. The input is sentiment-labeled data, and the output is summarized data.

[0838] Step 5:

[0839] The server detects the user's emotional state in real time. It uses data from the smartphone's camera and sensors to analyze the user's current psychological state. This information is processed by an emotion engine. The input is the user's emotional state data, and the output is the detected emotion label.

[0840] Step 6:

[0841] The device retrieves summarized data provided by the server and displays information optimized based on the user's emotional state. An interface designed with Flutter and React Native clearly presents the most relevant information to the user. Input is summarized data and emotional labels, while output is the displayed information.

[0842] Step 7:

[0843] Users make purchasing decisions based on this information. The system provides personalized reviews and information to support appropriate choices. The input is the displayed information and the user's selection, and the output is the final purchasing decision.

[0844] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0845] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0846] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0847] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0848] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0849] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0850] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0851] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0852] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0853] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0854] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0855] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0856] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0857] 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.

[0858] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0859] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0860] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0861] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0862] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0863] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0864] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0865] The following is further disclosed regarding the embodiments described above.

[0866] (Claim 1)

[0867] A means of automatically collecting information from various sources,

[0868] A means of analyzing collected information and classifying emotions,

[0869] A means of summarizing the analysis results and integrating redundant information,

[0870] Means of providing summarized data to users,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, which evaluates the reliability of analysis results and prioritizes providing highly reliable information.

[0874] (Claim 3)

[0875] The system according to claim 1, which is configured to allow users to easily view information through a user interface.

[0876] "Example 1"

[0877] (Claim 1)

[0878] Means of periodically acquiring information from information media,

[0879] A means for converting acquired information into a data format and storing it in a set,

[0880] A method for performing sentiment evaluation by applying natural language processing to information,

[0881] A means of selecting and integrating the information after evaluation into an easy-to-understand format using a summarization algorithm,

[0882] A means of visually presenting the processed data to the user's terminal,

[0883] An information processing system that includes this.

[0884] (Claim 2)

[0885] The information processing system according to claim 1, which analyzes summarized and proposed information from a reliability standpoint and prioritizes the presentation of highly reliable information.

[0886] (Claim 3)

[0887] The information processing system according to claim 1, comprising a device that enables users to easily search for and access information through visual display.

[0888] "Application Example 1"

[0889] (Claim 1)

[0890] A means of automatically collecting information from various sources,

[0891] A means of analyzing collected information and classifying emotions,

[0892] A means of summarizing the analysis results and integrating redundant information,

[0893] Means of providing summarized data to users,

[0894] A means for identifying an object using an optical device and obtaining related summary information,

[0895] A means of visually displaying the verified information on the user's device,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, which evaluates the reliability of analysis results and prioritizes providing highly reliable information.

[0899] (Claim 3)

[0900] The system according to claim 1, which is configured to allow users to easily view information through a user interface.

[0901] "Example 2 of combining an emotion engine"

[0902] (Claim 1)

[0903] A means of automatically collecting text data from multiple sources,

[0904] A method for processing collected text data, performing sentiment analysis using natural language processing techniques, and classifying it into sentiment categories,

[0905] A means of summarizing data based on sentiment analysis, integrating relevant information, and storing it in a database,

[0906] A means of adaptively optimizing information provision by using an emotion engine to detect the user's emotional state in real time and learning from past emotional data,

[0907] A means for dynamically displaying formatted information through a user interface on a user device,

[0908] A system that includes this.

[0909] (Claim 2)

[0910] The system according to claim 1, which performs reliability evaluation and selectively provides information based on highly reliable sentiment analysis results.

[0911] (Claim 3)

[0912] The system according to claim 1, which dynamically adjusts the displayed information and its layout based on the user's emotional state, and provides an interface that enables intuitive operation.

[0913] "Application example 2 when combining with an emotional engine"

[0914] (Claim 1)

[0915] A means of automatically collecting information from various sources,

[0916] A means of analyzing collected information and classifying emotions,

[0917] A means of summarizing the analysis results and integrating redundant information,

[0918] Means of providing summarized data to users,

[0919] A means for detecting the user's emotional state in real time and optimizing information based on that state,

[0920] A means of providing purchasing support while considering the user's current emotional state,

[0921] A system that includes this.

[0922] (Claim 2)

[0923] The system according to claim 1, which evaluates the reliability of analysis results and prioritizes providing highly reliable information.

[0924] (Claim 3)

[0925] The system according to claim 1, which is configured to allow users to easily view information through a user interface. [Explanation of Symbols]

[0926] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of automatically collecting information from various sources, A means of analyzing collected information and classifying emotions, A means of summarizing the analysis results and integrating redundant information, Means of providing summarized data to users, A system that includes this.

2. The system according to claim 1, which evaluates the reliability of analysis results and prioritizes providing highly reliable information.

3. The system according to claim 1, which is configured to allow users to easily view information through a user interface.

Images