system

Abstract

We provide the system. [Solution] The user terminal has means for receiving a keyword entered by the user and generating a request containing the keyword, The server has means for receiving requests sent from user terminals and searching internal databases and internet information sources based on those requests. The server analyzes the acquired information and extracts and formats information that is highly relevant to the user. The server provides a means for transmitting the analyzed information to the user terminal, A means by which the user terminal displays information received from the server, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In order to quickly and efficiently investigate the service contents of other companies on-site, there are problems that a lot of manual operations and information collection are required, which takes time and effort. In addition, the obtained information may be too much and may include a lot of information with low relevance. Therefore, there is a possibility that decision-making on-site may be delayed.

Means for Solving the Problems

[0005] The present invention solves the above problems with a system that includes means for a user terminal to receive keywords entered by the user and generate a request containing the keywords, means for a server to receive a request sent from the user terminal and search an internal database and information sources on the internet based on the request, means for analyzing the information obtained by the server and extracting and formatting information highly relevant to the user, means for the server to send the analyzed information to the user terminal, and means for the user terminal to display the information received from the server. Furthermore, by combining means for evaluating the relevance and importance of information using natural language processing technology and means for obtaining information using an internal database and a search engine API, it is possible to provide information more efficiently and accurately. As a result, on-site decision-making can be made more quickly.

[0006] A "user terminal" refers to a device used by a user to operate the system, and includes personal computers, smartphones, tablets, and the like.

[0007] A "keyword" is a string of characters that a user enters to identify the service or information they are researching.

[0008] A "request" is a request sent from a user's terminal to a server, containing keywords and necessary authentication information.

[0009] A "server" is a computer system that receives requests sent from user terminals and performs data retrieval, analysis, and transmission.

[0010] An "internal database" is a storage location owned by the system, containing data acquired in the past and information that has been registered in advance.

[0011] "Internet information sources" refer to sources of information accessible on the internet, such as websites, APIs, and public databases.

[0012] "Information acquisition" refers to the process by which a server collects data based on a request using methods such as search engine APIs or web scraping.

[0013] "Analysis" refers to the process of processing information acquired by a server, extracting and organizing data that is useful to the user.

[0014] "Natural language processing technology" is a general term for algorithms and techniques used to analyze text data and evaluate its meaning and relationships.

[0015] "Information relevance" is an indicator that shows how well the acquired data matches the keywords entered by the user.

[0016] "Information importance" is an indicator that shows how valuable the acquired data is to the user.

[0017] A "search engine API" is an interface provided by search engines such as Google (registered trademark) and Bing that allows programs to directly submit search queries and retrieve results.

[0018] "Display" refers to the process where a user's terminal outputs data received from a server onto the screen so that the user can view it. [Brief explanation of the drawing]

[0019] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5]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 apparatus 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 apparatus 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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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).

[0026] 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."

[0027] [First Embodiment]

[0028] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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".

[0040] This invention will now describe embodiments for carrying it out. This system consists of a user terminal, a server, an internal database, and an internet-based information source, enabling users to efficiently research the services offered by other companies. The specific processing is carried out as follows.

[0041] System Overview

[0042] The user enters keywords related to the services of another company they want to research from their own device (such as a PC or smartphone). Next, the device generates a request containing these keywords and sends it to the server. The server receives the request and searches its internal database and internet information sources. The retrieved information is analyzed by the server, and information highly relevant to the user is extracted and formatted. Finally, the analyzed information is sent from the server to the user's device, and the device displays that information to the user.

[0043] Program processing

[0044] 1. User input

[0045] The user uses their own terminal to enter keywords related to the services of other companies. For example, they might enter "other company's logistics management system."

[0046] 2. Terminal request generation

[0047] The terminal generates an HTTP request based on the keywords entered by the user. This request includes the following information:

[0048] Keywords entered by the user

[0049] Authentication information (API key, etc.)

[0050] Desired data format (e.g., JSON)

[0051] 3. Sending requests from the terminal to the server

[0052] The device sends the generated request to the server. Requests are often sent via a RESTful API and are transmitted securely using the HTTPS protocol.

[0053] 4. Server receives request

[0054] The server receives the request sent from the terminal. After receiving it, it performs the following actions:

[0055] Check the authentication credentials of the request to verify that it is not unauthorized access.

[0056] Extract the keywords entered by the user.

[0057] 5. Searching for data on the server

[0058] The server searches its internal database and trusted sources on the internet. The specific processing steps are as follows:

[0059] Using search engine APIs: The server uses search engine APIs to retrieve web pages related to keywords.

[0060] Executing queries on internal databases: The server also executes queries on its own internal databases to retrieve relevant information.

[0061] Web scraping: Obtaining information from websites by scraping it as needed.

[0062] 6. Data Analysis

[0063] The server analyzes the acquired data. The specific steps of the analysis are as follows:

[0064] Application of NLP (Natural Language Processing) technology: Use a text analysis engine to evaluate the relevance and importance of information. For example, utilize technologies such as TF-IDF and Word2Vec.

[0065] Data filtering: Remove noise and eliminate information that the user is not looking for.

[0066] Information Classification: Organize information by category, such as service features, pricing plans, and customer reviews.

[0067] 7. Formatting and transmitting information

[0068] The server formats the analysis results into a user-friendly format (e.g., JSON or HTML) and returns them to the terminal.

[0069] 8. Information reception and display by the user terminal

[0070] The terminal receives information sent from the server and displays it to the user. In doing so, it converts the information into a data structure suitable for display and outputs the organized information on the user interface.

[0071] Specific example

[0072] 1. User input

[0073] The user enters "data analysis tools from other companies."

[0074] 2. Terminal request generation and transmission

[0075] The terminal generates a request like this:

[0076] json

[0077] {

[0078] "keyword": "Data analysis tools from other companies",

[0079] "apiKey": "USER_API_KEY",

[0080] "format": "json"

[0081] }

[0082] Send this request to the server.

[0083] 3. Server Request Reception and Search

[0084] The server receives the request, authenticates it, extracts keywords, and uses a search engine API to retrieve information related to "other companies' data analysis tools." It also queries its internal database.

[0085] 4. Data Analysis

[0086] The server uses NLP techniques to analyze the results and classify service features, pricing plans, and customer reviews. For example, it uses TF-IDF to evaluate keyword importance and extract highly relevant information.

[0087] 5. Formatting and transmitting information

[0088] The server generates the following JSON and sends it to the terminal:

[0089] json

[0090] {

[0091] "serviceFeatures": ["Real-time analytics", "Big data compatible"]

[0092] "pricingPlans": ["Basic Plan: ¥10,000 / month", "Premium Plan: ¥30,000 / month"]

[0093] "customerReviews": ["Easy to use", "Good value for money"]

[0094] }

[0095] 6. Receiving and displaying information on the device

[0096] The terminal parses the received data and displays the above information on the screen.

[0097] This system allows for quick and efficient on-site investigation of competitors' services, supporting operational decision-making. In this way, it significantly improves user operational efficiency by reducing the time and effort required for information gathering and providing accurate information.

[0098] The following describes the processing flow.

[0099] Step 1:

[0100] The user uses their own terminal to enter keywords related to the third-party service they want to research. For example, they might enter "third-party logistics management system".

[0101] Step 2:

[0102] The terminal receives the entered keyword and generates an HTTP request. This request includes the keyword entered by the user, authentication information (such as an API key), and the desired data format (e.g., JSON).

[0103] Step 3:

[0104] The terminal generates a request and sends it to the server using HTTPS. The transmission is done via a RESTful API.

[0105] Step 4:

[0106] The server processes the request received from the terminal. First, it checks the authentication information included in the request to verify that it is not an unauthorized access. Next, it extracts the keywords entered by the user.

[0107] Step 5:

[0108] The server searches for data based on the extracted keywords. Specifically, it retrieves information from internal databases and internet sources. In this process, it uses the following methods:

[0109] The server uses a search engine API (e.g., Google API) to retrieve information related to the keyword.

[0110] The server queries the internal database to retrieve relevant information.

[0111] If necessary, the server will perform web scraping from the specified website.

[0112] Step 6:

[0113] The information acquired by the server is analyzed. Specifically, this includes the following processes:

[0114] We use NLP (Natural Language Processing) techniques to evaluate the relevance and importance of information. For example, we use TF-IDF and Word2Vec.

[0115] Filtering is performed to remove unnecessary information that constitutes noise.

[0116] Organize information such as service features, pricing plans, and customer reviews by category.

[0117] Step 7:

[0118] The server formats the parsed data and sends it to the user's terminal. For example, it generates a JSON format like the following:

[0119] json

[0120] {

[0121] "serviceFeatures": ["Real-time analytics", "Big data compatible"]

[0122] "pricingPlans": ["Basic Plan: ¥10,000 / month", "Premium Plan: ¥30,000 / month"]

[0123] "customerReviews": ["Easy to use", "Good value for money"]

[0124] }

[0125] Step 8:

[0126] The server formats the data and sends it to the user's terminal. The transmission is secure using HTTPS.

[0127] Step 9:

[0128] The terminal receives data sent from the server and parses it. It converts the received JSON data into an appropriate data structure and prepares it for display to the user.

[0129] Step 10:

[0130] The terminal analyzes the data and displays it on the user interface. The user then reviews the displayed information and uses it for specific tasks and decision-making.

[0131] (Example 1)

[0132] 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."

[0133] Traditional information gathering systems had the problem that it was time-consuming and laborious for users to efficiently investigate the services offered by other companies. Furthermore, there was a lack of technology to judge the reliability and relevance of the acquired information, making it difficult for users to quickly obtain useful information. In addition, the analysis and classification of search data were not adequately performed, making it difficult for users to obtain the accurate and well-organized information they needed.

[0134] 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.

[0135] In this invention, the server includes means for receiving requests sent from a user terminal and searching an internal database and information sources on the internet based on the requests; means for analyzing the acquired information and extracting and formatting information highly relevant to the user; and means for transmitting the analyzed information to the user terminal. This enables users to quickly and efficiently investigate the services of other companies and accurately acquire highly relevant information.

[0136] A "user terminal" refers to a computing device used by a user, such as a personal computer or smartphone.

[0137] "Keywords" are words or phrases related to the services of other companies that the user is researching.

[0138] A "request" is an HTTP request sent from a user's terminal to a server, and it includes keywords, authentication information, and the desired data format.

[0139] A "server" is a computing system that receives requests from user terminals, searches internal databases and information sources on the internet, analyzes and formats the retrieved information, and sends it to the user terminal.

[0140] An "internal database" is a data storage system that stores data held by a company.

[0141] "Internet information sources" refer to websites and other online information services.

[0142] A "search engine API" is an application interface that uses a search engine to retrieve web pages and information related to specific keywords.

[0143] "Acquired information" refers to data collected by the server from its internal database and information sources on the internet.

[0144] "Analysis" is the process of using natural language processing techniques to evaluate the relevance and importance of information acquired by a server.

[0145] "Natural language processing technology" refers to technologies that process and analyze input text data to understand and evaluate its content, and includes technologies such as TF-IDF and Word2Vec.

[0146] "Extraction" is the process of selecting information that is highly relevant to the user from the analysis results.

[0147] "Formatting" refers to converting extracted information into a format that is easy for the user to understand.

[0148] This invention will now describe embodiments for carrying it out. This system consists of a user terminal, a server, an internal database, and an internet-based information source, enabling users to efficiently research the services offered by other companies. The specific processing is carried out as follows.

[0149] System Overview

[0150] The user uses their own device (such as a PC or smartphone) to enter keywords related to the services of other companies they want to research. For example, they might enter "logistics management system." Next, the device generates a request containing these keywords and sends it to the server. The server receives the request and searches its internal database and internet information sources. The retrieved information is analyzed by the server, and information highly relevant to the user is extracted and formatted. Finally, the analyzed information is sent from the server to the user's device, and the device displays the information to the user. This system can utilize search engine APIs, natural language processing technology, and web scraping techniques.

[0151] Hardware and software to be used

[0152] User terminal: A computing device that can connect to the internet, such as a personal computer, smartphone, or tablet.

[0153] Server: A server for high-performance data processing and database access. Cloud-based servers (e.g., AWS® or Google Cloud Platform) can also be used.

[0154] Internal database: Database management systems such as MySQL®, PostgreSQL, or Oracle Database.

[0155] Search Engine APIs: APIs such as the Google Custom Search API for retrieving web information based on keywords.

[0156] Natural Language Processing Technology: An engine that analyzes information obtained using libraries and models such as TF-IDF, Word2Vec, and BERT.

[0157] Web scraping: A technique for automatically collecting necessary web information using libraries such as Beautiful Soup and Selenium.

[0158] Specific example

[0159] Here's a concrete example of how the system works when a user wants to research "data analysis tools from other companies." The user types "data analysis tools from other companies" into their terminal. The terminal then generates a request like the following and sends it to the server.

[0160] Example of a prompt:

[0161] Keywords: Data analysis tools from other companies

[0162] API Key: USER_API_KEY

[0163] Data format: JSON

[0164] The server receives this request, verifies the authentication information, and then uses its internal database and search engine API to search for information related to "other companies' data analysis tools." It also uses web scraping techniques to collect information from relevant websites. The collected data is analyzed using natural language processing techniques. For example, TF-IDF is used to extract highly relevant information and classify it into categories such as service features, pricing plans, and customer reviews.

[0165] The analyzed information is formatted by the server and sent to the user terminal in the following format:

[0166] Plastic information

[0167] Service features: Real-time analysis, Big data support

[0168] Pricing plans: Basic plan: ¥10,000 / month, Premium plan: ¥30,000 / month

[0169] Customer reviews: Easy to use, good value for money.

[0170] The user's device receives and displays this information. For example, the information is displayed on the UI of a web page in a format appropriate to each information category. This allows users to efficiently understand the services offered by other companies and use that information to help them make informed decisions.

[0171] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0172] Step 1:

[0173] User keyword input

[0174] Input: Users use their own devices (PCs or smartphones) to enter keywords related to the services of other companies they want to research. "Logistics management system" is one example.

[0175] Operation: The user enters keywords into the search field of their device's browser or application and clicks the search button.

[0176] Output: The entered keyword is processed internally by the terminal and ready to be sent to the next step.

[0177] Step 2:

[0178] Request generation by the terminal

[0179] Input: Keyword entered by the user

[0180] Operation: Based on the entered keyword, the terminal generates an HTTP request containing the following information:

[0181] Keywords entered by the user

[0182] API key for system authentication

[0183] Desired data format (e.g., JSON)

[0184] As an example of a specific request, the following JSON will be generated:

[0185] json

[0186] {

[0187] "keyword": "Logistics management system",

[0188] "apiKey": "USER_API_KEY",

[0189] "format": "json"

[0190] }

[0191] Output: The generated HTTP request is ready to be sent to the server.

[0192] Step 3:

[0193] Sending a request from the terminal to the server

[0194] Input: HTTP request (including keywords, API key, and data format)

[0195] Operation: The device sends the generated HTTP request to the server. The HTTPS protocol is used to securely transmit the data. The device sends the request via a RESTful API.

[0196] Output: The request is received by the server and processing begins in the next step.

[0197] Step 4:

[0198] Server request reception and analysis

[0199] Input: HTTP request sent from the terminal

[0200] Operation: The server parses the received request and performs the following processes:

[0201] Verify the API key and confirm that the request is legitimate.

[0202] Extract keywords entered by the user.

[0203] Output: The validated keywords will be used in the next step.

[0204] Step 5:

[0205] Server Data Retrieval

[0206] Input: Extracted keywords

[0207] Operation: The server searches its internal database and trusted sources on the internet. Specifically, it performs the following steps:

[0208] Using search engine APIs: Use APIs such as the Google Custom Search API to retrieve relevant information from the internet.

[0209] Executing queries on internal databases: Execute queries using keywords against internal databases such as MySQL and PostgreSQL to retrieve relevant information.

[0210] Web scraping: If necessary, use Beautiful Soup or Selenium to scrape the required information from specific websites.

[0211] Output: The acquired information will be used for data analysis in the next step.

[0212] Step 6:

[0213] Data analysis

[0214] Input: Data obtained from internal databases and reliable sources on the internet.

[0215] Operation: The server analyzes the acquired data. The specific analysis steps are as follows:

[0216] Application of natural language processing techniques: We evaluate the relevance and importance of information using natural language processing techniques such as TF-IDF, Word2Vec, and BERT. For example, we analyze the importance of keywords using TF-IDF.

[0217] Data filtering: Removes noise and irrelevant data to extract the information the user is looking for.

[0218] Information Classification: Organize information into categories such as service features, pricing plans, and customer reviews.

[0219] Output: The analysis results will be formatted in the next step.

[0220] Step 7:

[0221] Information formatting

[0222] Input: Analyzed data

[0223] Operation: The server formats the analysis results into a user-friendly format, such as JSON or HTML. A concrete example of a response is the following JSON:

[0224] json

[0225] {

[0226] "serviceFeatures": ["Real-time analytics", "Big data compatible"]

[0227] "pricingPlans": ["Basic Plan: ¥10,000 / month", "Premium Plan: ¥30,000 / month"]

[0228] "customerReviews": ["Easy to use", "Good value for money"]

[0229] }

[0230] Output: The formatted data is sent to the user's terminal.

[0231] Step 8:

[0232] Information reception and display by the user terminal

[0233] Input: Formatted data sent from the server

[0234] Operation: The terminal parses the received data and converts it into a format suitable for display. This is then displayed on the user interface. For example, information displayed in the UI of a web page or application is organized into service features, pricing plans, customer reviews, etc.

[0235] Output: Users can view detailed information about the services of other companies they wish to investigate.

[0236] (Application Example 1)

[0237] 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."

[0238] In today's economic environment, efficiently and quickly obtaining and comparing information on other companies' electronic payment services is crucial. However, manual methods and traditional information gathering techniques have limitations in terms of comprehensiveness and accuracy of necessary information, and are time-consuming and labor-intensive. Furthermore, collecting the latest information is particularly difficult in the electronic payment sector, where market trends are constantly changing. This can make accurate market analysis and maintaining competitiveness difficult.

[0239] 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.

[0240] In this invention, the server includes means for generating a request containing keywords entered by the user, means for searching an internal database and information sources on the internet based on the request sent from the user terminal, means for analyzing the acquired information and extracting and formatting information highly relevant to the user, means for transmitting the analyzed information to the user terminal, and means for the user terminal to display information regarding the features, pricing plans, and user reviews of the electronic payment service. This enables the user to efficiently and quickly obtain detailed information on other companies' electronic payment services and compare them.

[0241] A "user terminal" is a device used by a user to operate, and includes computers such as smartphones and personal computers.

[0242] A "keyword" refers to a specific word or phrase that a user enters to search for information.

[0243] A "request" is a communication message containing request information sent from a user's terminal to a server.

[0244] A "server" refers to a central processing unit that receives requests sent from user terminals and performs data retrieval and analysis based on those requests.

[0245] An "internal database" is a database that stores data owned by a company and is used to search for requested information.

[0246] "Internet information sources" refer to websites and online databases that exist on the internet, and are used to obtain necessary information from them.

[0247] "Analysis" refers to the process of classifying information acquired by the server into categories and evaluating their relationships.

[0248] "Highly relevant information" refers to information that is closely related to the keywords the user is searching for and is also useful.

[0249] "Formatting" refers to the process of converting analyzed information into a format that is easy for users to understand.

[0250] "Features" refer to the specific functions or capabilities that an electronic payment service possesses.

[0251] "Pricing plan" refers to the pricing structure and details of a service.

[0252] "User reviews" refer to evaluations and opinions provided by service users.

[0253] This invention will now describe embodiments for carrying out this invention. This invention relates to a system that allows users to efficiently collect and compare information on electronic payment services of other companies. This system consists of a user terminal, a server, an internal database, and information sources on the internet.

[0254] Hardware and software configuration

[0255] User terminal:

[0256] User terminals include computers such as smartphones and personal computers. These terminals are connected to the internet and have an interface for inputting information.

[0257] server:

[0258] The servers are located on cloud infrastructure. Specifically, cloud services such as AWS EC2 are available.

[0259] For the database, we will use an SQL database such as MySQL.

[0260] software:

[0261] To search for information, we utilize our internal database and search engine APIs such as the Google Custom Search API.

[0262] For natural language processing, we use NLP (Natural Language Processing) engines such as TENSORFLOW® and NLTK.

[0263] System operation

[0264] 1. User input:

[0265] The user launches the smartphone app and enters keywords related to other companies' electronic payment services. For example, they might enter the keyword "other companies' mobile payment services."

[0266] 2. Terminal request generation:

[0267] The user's terminal generates an HTTP request based on the keywords entered by the user. This request includes the keywords, the API key, and the desired data format (e.g., JSON).

[0268] 3. Server request reception and data retrieval:

[0269] The server receives requests sent from user terminals. After receiving a request, it executes queries to search engine APIs and internal databases to collect relevant information. It also performs web scraping as needed.

[0270] 4. Data Analysis:

[0271] The server analyzes the collected data using NLP (Neuro-Linguistic Programming) techniques. Specifically, it uses methods such as TF-IDF and Word2Vec to evaluate the relevance and importance of the information, remove noise, and classify it into categories such as service features, pricing plans, and user reviews.

[0272] 5. Formatting and sending information:

[0273] The server formats the analysis results into a user-friendly format (e.g., JSON) and sends it to the terminal.

[0274] 6. Information reception and display by the user terminal:

[0275] The user terminal receives information sent from the server and displays it in a visually easy-to-understand interface. For example, the output might look like this:

[0276] Features of the service:

[0277] High-speed transactions

[0278] Low fees

[0279] Pricing plans:

[0280] Basic plan: \500 / month

[0281] Premium plan: \2,000 / month [[ID=2,2]]

[0282] User reviews:

[0283] Convenient and easy to use

[0284] Good support<00009,00>

[0285] With this system, users can quickly and efficiently collect and compare detailed information on other companies' electronic payment services.

[0286] Examples of prompt texts

[0287] As a specific example, the following are examples of prompt texts when a user enters the keyword "mobile payment of other companies":

[0288] 「Please find competitive mobile payment services including features, pricing plans, and customer reviews.」

[0289] Based on this prompt text, the system collects the necessary information and executes the process to provide it to the user.

[0290] The flow of a specific process in Application Example 1 will be described using FIG. 12.

[0291] Step 1:

[0292] The user launches a smartphone app and enters keywords related to another company's electronic payment service. For example, they might enter the keyword "other company's mobile payment." The input data here is the keyword, and the output is the data that forms the basis of the HTTP request.

[0293] Step 2:

[0294] The user's terminal generates an HTTP request based on the keywords entered by the user. This request includes the following information:

[0295] Keywords (e.g., "other companies' mobile payment services")

[0296] API key (user authentication information)

[0297] Desired data format (e.g., JSON)

[0298] The input for this step is the keyword entered by the user, and the output is the generated HTTP request.

[0299] Step 3:

[0300] The user terminal generates a request and sends it to the server. The request is sent securely using the HTTPS protocol via a RESTful API. The input to this step is the generated HTTP request, and the output is a notification to the server that the transmission is complete.

[0301] Step 4:

[0302] The server receives a request sent from the user's terminal. The received request is authenticated, and keywords are extracted. The input here is the received HTTP request, and the output is the authenticated keywords.

[0303] Step 5:

[0304] The server searches the internal database and information sources on the Internet based on the authenticated keywords. The server performs the following processes:

[0305] Use the search engine API (Google Custom Search API) to obtain relevant web pages.

[0306] Execute a query on the internal database (e.g., MySQL) to obtain relevant information.

[0307] Perform web scraping as necessary to collect relevant information.

[0308] The input for this step is the authenticated keyword, and the output is the raw data collected.

[0309] Step 6:

[0310] The server analyzes the raw data collected. Specifically, the following processes are performed:

[0311] Use the NLP engine (TensorFlow or NLTK) to analyze the acquired data.

[0312] Use TF-IDF or Word2Vec to evaluate the relevance and importance of information.

[0313] Remove noise data and filter out unnecessary information.

[0314] Classify the information for each category of service features, pricing plans, and user reviews.

[0315] The input for this step is the raw data collected, and the output is the analyzed and formatted data.

[0316] Step 7:

[0317] The server formats the parsed information into a user-friendly format (e.g., JSON) and sends it to the user's terminal. The input for this step is the parsed data, and the output is a response containing the formatted data.

[0318] Step 8:

[0319] The user terminal receives information sent from the server and displays it in a visually easy-to-understand interface. The input here is the response data from the server, and the output is the information displayed on the terminal's user interface. For example, service features, pricing plans, and user reviews are displayed in a visually organized manner.

[0320] This series of steps enables the system described in the claims to allow users to efficiently and quickly collect and compare detailed information on other companies' electronic payment services.

[0321] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0322] This invention relates to a system that further enhances the user experience by combining it with an emotion engine that recognizes user emotions. This invention achieves more personalized information delivery by adjusting information display and search results based on the user's emotions.

[0323] System Overview

[0324] This system consists of user terminals, servers, an internal database, and information sources on the internet. Furthermore, the user terminals are equipped with an emotion engine that recognizes the user's emotions in real time and transmits that data to the server.

[0325] Program processing

[0326] 1. User input

[0327] The user uses their own terminal to enter keywords related to the third-party service they want to research. For example, they might enter "third-party logistics management system."

[0328] 2. User emotion recognition

[0329] An emotion engine installed in the user's device recognizes the user's emotions in real time. Using speech recognition and facial expression recognition, it acquires emotional data such as whether the user is stressed or relaxed.

[0330] 3. Terminal request generation

[0331] The terminal generates an HTTP request containing keywords and sentiment data entered by the user. This request includes the following information:

[0332] Keywords entered by the user

[0333] User sentiment data

[0334] Authentication information (API key, etc.)

[0335] Desired data format (e.g., JSON)

[0336] 4. Sending requests from the terminal to the server

[0337] The device sends the generated request to the server. The transmission is done via a RESTful API and is securely sent using the HTTPS protocol.

[0338] 5. Server receives request

[0339] The server processes requests received from the terminal. First, it checks the authentication information of the request to verify that it is not an unauthorized access. Next, it extracts the keywords and sentiment data entered by the user.

[0340] 6. Searching for data on the server

[0341] The server searches for data based on keywords and sentiment data. Specifically, it retrieves information from internal databases and internet sources. In this process, it utilizes the following methods:

[0342] Using search engine APIs: The server uses search engine APIs (e.g., Google API) to retrieve information related to keywords.

[0343] Executing queries on internal databases: The server executes queries on its own internal databases to retrieve relevant information.

[0344] Web scraping: Obtaining information by scraping it from specific websites as needed.

[0345] 7. Data Analysis

[0346] The server analyzes the acquired data. Specifically, it performs the following processes:

[0347] Application of NLP (Natural Language Processing) technology: Use a text analysis engine to evaluate the relevance and importance of information. For example, TF-IDF or Word2Vec may be used.

[0348] Filtering is performed to remove unnecessary information that constitutes noise.

[0349] Organize information such as service features, pricing plans, and customer reviews by category.

[0350] Based on user sentiment data, specific information is highlighted and its display order is adjusted. For example, if a user is feeling stressed, concise and easy-to-read information is prioritized.

[0351] 8. Formatting and transmitting information

[0352] The server formats the analysis results into a user-friendly format (e.g., JSON or HTML) and returns them to the terminal. At this stage, specific formats and display methods can be adopted based on the user's sentiment data.

[0353] 9. Information reception and display by the user terminal

[0354] The terminal receives information sent from the server and displays it to the user. The received data is converted into an appropriate data structure and displayed in the user interface. The display method is adjusted to suit the user's emotions.

[0355] Specific example

[0356] 1. User input and emotion recognition

[0357] As soon as the user types "data analysis tool from another company," the emotion engine recognizes the user's facial expression and acquires emotional data indicating "stress."

[0358] 2. Terminal request generation and transmission

[0359] The terminal generates a request like this:

[0360] json

[0361] {

[0362] "keyword": "Data analysis tools from other companies",

[0363] "emotion": "stress",

[0364] "apiKey": "USER_API_KEY",

[0365] "format": "json"

[0366] }

[0367] Send this request to the server.

[0368] 3. Server Request Reception and Search

[0369] The server receives the request, extracts keywords and sentiment data after authentication, and uses a search engine API to retrieve information related to "other companies' data analysis tools." It also queries its internal database.

[0370] 4. Data Analysis

[0371] The server uses NLP (Neuro-Linguistic Programming) techniques to analyze the results and categorize service features, pricing plans, and customer reviews. Furthermore, it formats the information to display it concisely based on emotional data, specifically "stress."

[0372] 5. Formatting and transmitting information

[0373] The server generates the following JSON and sends it to the terminal:

[0374] json

[0375] {

[0376] "serviceFeatures": ["Real-time analytics", "Big data compatible"]

[0377] "pricingPlans": ["Basic Plan: ¥10,000 / month", "Premium Plan: ¥30,000 / month"]

[0378] "customerReviews": ["Easy to use", "Good value for money"]

[0379] }

[0380] 6. Receiving and displaying information on the device

[0381] The device analyzes the received data and displays the information in a concise and visually easy-to-understand format, taking into account the user's "stress." For example, it highlights important information and collapses detailed information.

[0382] This system enables rapid and efficient on-site research into competitors' services and provides personalized information tailored to the user's emotional state. This reduces the time and effort required for information gathering and significantly improves user efficiency by providing accurate and relevant information.

[0383] The following describes the processing flow.

[0384] Step 1:

[0385] The user uses their own terminal to enter keywords related to the third-party service they want to research. For example, they might enter "third-party logistics management system."

[0386] Step 2:

[0387] An emotion engine installed in the user's device recognizes the user's emotions in real time. Using speech recognition and facial expression recognition, it acquires emotional data such as whether the user is stressed or relaxed.

[0388] Step 3:

[0389] The terminal generates an HTTP request based on the entered keyword and acquired sentiment data. This request includes the following information:

[0390] Keywords entered by the user

[0391] User sentiment data

[0392] Authentication information (API key, etc.)

[0393] Desired data format (e.g., JSON)

[0394] Step 4:

[0395] The terminal generates a request and sends it to the server using HTTPS. The transmission is done via a RESTful API.

[0396] Step 5:

[0397] The server processes the request received from the terminal. First, it checks the authentication information included in the request to verify that it is not an unauthorized access. Next, it extracts the keywords and sentiment data entered by the user.

[0398] Step 6:

[0399] The server searches for data based on extracted keywords and sentiment data. Specifically, it retrieves information from internal databases and internet sources. In this process, it utilizes the following methods:

[0400] Using search engine APIs: The server uses search engine APIs, such as the Google API, to retrieve information related to the keywords.

[0401] Executing queries on internal databases: The server also executes queries on its own internal databases to retrieve relevant information.

[0402] Web scraping: Obtaining information by scraping it from specific websites as needed.

[0403] Step 7:

[0404] The server analyzes the information it has acquired. Specifically, it performs the following processes:

[0405] We use NLP (Natural Language Processing) techniques to evaluate the relevance and importance of information. For example, we use TF-IDF and Word2Vec.

[0406] Filtering is performed to remove unnecessary information that would otherwise be considered noise.

[0407] Organize information such as service features, pricing plans, and customer reviews by category.

[0408] Based on user sentiment data, specific information is highlighted and its display order is adjusted. For example, if a user is feeling stressed, concise and easy-to-read information is prioritized.

[0409] Step 8:

[0410] The server formats the analysis results into a user-friendly format (e.g., JSON or HTML) and sends them to the terminal. In this process, specific formats and display methods can be adopted based on the user's sentiment data.

[0411] Step 9:

[0412] The terminal receives information sent from the server and parses the data. It converts the received JSON data into an appropriate data structure and prepares it for display to the user.

[0413] Step 10:

[0414] The terminal analyzes the data and displays it on the user interface. Users review the displayed information and use it for specific tasks and decision-making. The display method is adjusted according to the user's emotions. For example, if the emotion engine detects user stress, particularly important information is highlighted, and detailed information is collapsed as needed, taking care to reduce the user's burden.

[0415] In this way, this system, which incorporates an emotion engine, can provide information tailored to the user's emotions and support efficient decision-making.

[0416] (Example 2)

[0417] 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".

[0418] Conventional information retrieval systems perform searches based solely on keywords entered by the user, without considering the user's emotional state. This makes it difficult to provide personalized information tailored to the user's emotional state. In particular, if appropriate information is not presented when the user is stressed or relaxed, the user experience may deteriorate. Therefore, there is a need for a system that considers the user's emotional state and provides information more effectively.

[0419] 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.

[0420] In this invention, the server includes means for the user terminal to receive keywords and sentiment data entered by the user and generate a request including said keywords and sentiment data; means for the server to receive a request sent from the user terminal and search an internal database and information sources on the internet based on said request; means for the server to analyze the acquired information, evaluate the relevance and importance of the information using natural language processing techniques, and adjust the information based on the user's sentiment data; means for the server to format the analyzed information, extract information highly relevant to the user, and transmit it to the user terminal; and means for the user terminal to display the information received from the server and adjust the display method based on the user's sentiment. This makes it possible to provide personalized information according to the user's emotional state.

[0421] A "user terminal" is an electronic device used by the user for operations, and is a device that inputs keywords and acquires sentiment data.

[0422] A "keyword" is a word or phrase that a user enters into their device to search for specific information.

[0423] "Emotional data" refers to information that represents the user's emotional state, and is data acquired through voice recognition and facial expression recognition.

[0424] A "request" is request information sent from a user's terminal to a server, and includes keywords and sentiment data.

[0425] A "server" is a device that performs data retrieval and analysis based on requests received from a user terminal and sends the results to the user terminal.

[0426] An "internal database" is a data storage system where data held by a company is stored, and it is used to search for related information.

[0427] "Internet information sources" refer to information resources accessible via the internet that are used to obtain external information.

[0428] "Natural language processing technology" refers to the technology used by computers to understand, interpret, and generate human language, and is a means of evaluating the relevance and importance of information.

[0429] "Highly relevant information" refers to information that is most relevant to the user, filtered based on the user's keywords and sentiment data.

[0430] "Formatting" is the process of reconstructing acquired information into a format that is easy for users to understand.

[0431] "Information adjustment" is the process of determining the display order and highlighting of information based on user sentiment data.

[0432] The system of the present invention consists of a user terminal, a server, an internal database, and an information source on the Internet. Furthermore, the user terminal is equipped with an emotion engine that recognizes the user's emotions in real time and transmits that data to the server. The following describes in detail how this system is implemented.

[0433] First, the user uses their own terminal to enter keywords related to what they want to research. For example, they might enter "other companies' logistics management systems." Simultaneously, an emotion engine installed in the user's terminal recognizes the user's facial expressions and voice in real time and collects emotion data. The emotion engine uses speech recognition technology and facial expression recognition technology, specifically employing AI models such as OpenFace and DeepFace.

[0434] Next, the user terminal generates a request containing the entered keywords and the retrieved sentiment data. This request is in JSON format, and an internal HTTP client library (e.g., Python's requests library) is used to generate the request. The generated JSON request includes the following information:

[0435] Keywords entered by the user

[0436] User sentiment data

[0437] Authentication information (such as API key)

[0438] Desired data format (e.g., JSON)

[0439] This request is sent to the server via the HTTPS protocol through a RESTful API.

[0440] The server processes requests received from user terminals. First, the server verifies the legitimacy of the request based on authentication information to ensure it is not an unauthorized access attempt. Next, it extracts keywords and sentiment data from the request and begins a data search. The search is performed using the following methods:

[0441] Using search engine APIs: Information is retrieved using external search engine APIs such as the Google API.

[0442] Executing queries on internal databases: Retrieving information by executing queries on databases maintained by the company.

[0443] Web scraping: The process of obtaining information by scraping it from a specific website.

[0444] The acquired information is analyzed on the server. The server uses natural language processing (NLP) techniques to evaluate the relevance and importance of the acquired information. In this process, a text analysis engine (e.g., SpaCy, NLTK) is used, employing techniques such as TF-IDF and Word2Vec. Noise is removed through filtering, and necessary information is organized and classified into categories. Furthermore, information is highlighted and its display order is adjusted based on the user's sentiment data. For example, if the user is feeling stressed, important information is summarized concisely and displayed.

[0445] The server reformats the parsed information into a user-friendly format (e.g., JSON or HTML) and returns it to the terminal. This ensures that the information is presented to the user in an appropriate format. Finally, the user terminal displays the information received from the server and adjusts the display method according to the user's emotional state. For example, important information may be highlighted, while detailed information may be displayed in a collapsed format.

[0446] As a concrete example, consider a scenario where a user enters "data analysis tool from another company," and the emotion engine detects a "stressed" state from the user's facial expression. In this case, the device generates and sends the following request to the server:

[0447] Keywords: Data analysis tools from other companies

[0448] Emotional data: stress

[0449] API Key: USER_API_KEY

[0450] Format: json

[0451] The server receives the request, searches for information based on the keywords and sentiment data, and performs analysis. The analysis results provide a concise summary of service features, pricing plans, customer reviews, etc. This information is then formatted as JSON data and sent to the terminal:

[0452] Service features: [Real-time analysis, Big data compatible]

[0453] Pricing plans: [Basic plan: ¥10,000 / month, Premium plan: ¥30,000 / month]

[0454] Customer reviews: [Easy to use, good value for money]

[0455] The terminal receives the transmitted information and displays it concisely and visually, taking into consideration the user's "stress." This allows users to quickly and efficiently understand the services offered by other companies and improve their work efficiency.

[0456] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0457] Step 1: User input

[0458] The user uses their own terminal to enter keywords related to the third-party service they want to investigate. For example, they might enter "third-party logistics management system." This input triggers the system to start. The entered keywords are stored internally on the terminal. Furthermore, user input is primarily done via keyboard text.

[0459] Input: Keywords entered by the user (e.g., "Logistics management system of another company")

[0460] Output: Retained keywords (e.g., "other company's logistics management system")

[0461] Step 2: Recognizing the user's emotions

[0462] The device's built-in emotion engine works to recognize the user's facial expressions and voice tone in real time and generate emotion data. It uses the camera to capture facial expressions and the microphone to recognize voice. The emotion engine uses AI models (e.g., OpenFace, DeepFace) to determine whether the user is stressed or relaxed.

[0463] Input: User's facial expression data, voice data

[0464] Output: Analyzed emotion data (e.g., "stress")

[0465] Step 3: Generate terminal request

[0466] The terminal generates an HTTP request containing the keywords entered by the user and the retrieved sentiment data. The generated request includes the following information: keywords, sentiment data, authentication information (such as an API key), and the desired data format (e.g., JSON). This request is constructed using the system's internal HTTP client library (e.g., Python's requests library).

[0467] Input: Keyword (e.g., "Other company's logistics management system"), Sentiment data (e.g., "Stress"), Authentication information (e.g., "USER_API_KEY")

[0468] Output: HTTP request (e.g., JSON format request)

[0469] Step 4: Sending a request from the terminal to the server

[0470] The terminal sends the generated request to the server. This transmission is performed using a RESTful API and the HTTPS protocol. The use of the HTTPS protocol ensures the security of the transmitted data.

[0471] Input: Generated HTTP request (e.g., a request in JSON format)

[0472] Output: Request sent to the server

[0473] Step 5: Server receives request

[0474] The server receives requests sent from terminals. First, it verifies the authentication credentials to ensure that the access is not unauthorized. Then, it extracts keywords and sentiment data included in the request.

[0475] Input: HTTP request sent from the terminal

[0476] Output: Extracted keywords (e.g., "other company's logistics management system"), sentiment data (e.g., "stress")

[0477] Step 6: Search server data

[0478] The server searches for information from internal databases and internet sources based on extracted keywords and sentiment data. The following methods are used:

[0479] Using search engine APIs: Use the Google API to retrieve information related to keywords.

[0480] Executing queries on internal databases: Retrieving information by executing queries on databases maintained by the company.

[0481] Web scraping: The process of obtaining information by scraping it from a specific website.

[0482] Input: Extracted keywords (e.g., "other company's logistics management system"), sentiment data (e.g., "stress")

[0483] Output: Acquired information data

[0484] Step 7: Data Analysis

[0485] The server analyzes the acquired information. Using a text analysis engine (e.g., SpaCy, NLTK), it evaluates the relevance and importance of the information using natural language processing techniques. Furthermore, it performs noise reduction, categorization, and adjusts the highlighting and display order of information based on sentiment data.

[0486] Input: Acquired information data, emotional data (e.g., "stress")

[0487] Output: Analyzed and formatted information

[0488] Step 8: Format and send information

[0489] The server formats the analyzed information into a user-friendly format (e.g., JSON, HTML) and sends it to the terminal. It adopts an appropriate format and display method based on sentiment data.

[0490] Input: Analyzed and formatted information

[0491] Output: Formatted information data

[0492] Step 9: Information reception and display by the user terminal

[0493] The terminal converts information received from the server into a data structure and displays it on the user interface. Based on the user's emotions, the information is displayed in a concise and visually easy-to-understand manner. Techniques such as highlighting important information and collapsing detailed information are employed.

[0494] Input: Formatted information data

[0495] Output: Displayed information (on the user interface)

[0496] This allows users to quickly and efficiently understand the services offered by other companies, thereby improving operational efficiency.

[0497] (Application Example 2)

[0498] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0499] Traditional content delivery services often fail to consider the user's emotional state, making it difficult to provide content that matches the user's current mood. As a result, users may experience stress or be unable to relax, leading to decreased service satisfaction. Furthermore, it was difficult to quickly and accurately extract highly relevant information from the vast amount of data available.

[0500] 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.

[0501] In this invention, the server includes means for receiving requests sent from a user terminal and searching an internal database and information sources on the internet based on the requests; means for analyzing the acquired information, shaping the information based on the user's emotional data, and extracting information highly relevant to the user; and means for transmitting the analyzed information to the user terminal. This makes it possible to suggest personalized content according to the user's emotional state.

[0502] A "user terminal" is an electronic device used by users to input information and receive and display it, and it is also equipped with an emotion recognition engine.

[0503] "Keywords" are words or phrases that users enter for the purpose of research or searching.

[0504] "Emotional data" refers to data indicating the user's emotional state, acquired in real time by the emotion recognition engine installed in the user's device.

[0505] A "request" is communication data for an information request that includes keywords and sentiment data entered by the user.

[0506] A "server" is a central processing unit that searches internal databases and internet-based information sources based on requests received from user terminals and returns the results to the user terminals.

[0507] An "internal database" is a collection of data that a server maintains for access and retrieval.

[0508] An "information source on the internet" is an external information repository from which a server obtains information through search engine APIs or web scraping.

[0509] An "emotion recognition engine" is a combination of hardware and software that recognizes a user's emotional state in real time from their facial expressions and voice.

[0510] "Natural language processing technology" refers to computational methods and algorithms for analyzing text information acquired by a server and evaluating its relevance and importance.

[0511] "Filtering" is the process of removing noise and unnecessary data from acquired information and extracting information that is highly relevant to the user.

[0512] "Analysis" is the process by which a server analyzes the information it acquires and formats it into an appropriate format based on user sentiment data.

[0513] A "browsing interface" is a screen and operating system that displays information obtained from a server by the user's terminal and adjusts the display method based on the user's emotional state.

[0514] This invention is a system that personalizes content delivery based on user emotions. Its main components are a user terminal, a server, an internal database, an internet-based information source, and an emotion recognition engine.

[0515] The user terminal is equipped with a camera and microphone, and an emotion recognition engine acquires emotion data in real time from the user's facial expressions and voice. A request containing this acquired emotion data and keywords entered by the user is generated and sent to the server. The HTTPS protocol is used to securely communicate when sending requests.

[0516] After receiving a request, the server first verifies the authentication information. If the request is deemed legitimate, it searches its internal database and internet information sources. Information is collected using search engine APIs (e.g., Google API) and web scraping tools. The acquired data is analyzed using NLP (Natural Language Processing) techniques. Specifically, methods such as TF-IDF and Word2Vec are used to evaluate the relevance and importance of the information, and filtering is performed as needed.

[0517] The analysis results are formatted based on the user's emotional data. When the user is stressed, concise and easy-to-understand information is prioritized; when relaxed, detailed and helpful information is displayed preferentially. Finally, the organized information is converted into an appropriate data format, such as JSON, and sent to the user's device.

[0518] The user terminal analyzes information received from the server and displays it on the user interface. During this process, adjustments are made based on the user's emotional state. For example, a user experiencing stress will see a visually simpler UI, with detailed information displayed in a collapsible format.

[0519] Program operation description:

[0520] Emotion Recognition Engine: This engine uses facial recognition and speech recognition technologies to capture user emotions in real time. Specific software examples include OpenCV and TensorFlow.

[0521] Search engine API: Uses the Google API, etc., to retrieve information from internet sources.

[0522] NLP technology: SpaCy and NLTK are used as natural language processing engines. These are used to perform text analysis and evaluate the relevance and importance of information.

[0523] Filtering: Remove noise from collected data and extract only the information most relevant to the user.

[0524] Formatting of analysis results: Convert to HTML or JSON format and send to the user's terminal.

[0525] Specific example:

[0526] For example, if a user types "romantic movie" into their smartphone and the emotion recognition engine obtains the emotion data "relaxed," the server processes the information in the following steps:

[0527] 1. Use a search engine API to collect information about "romantic movies".

[0528] 2. Analyze the collected information using NLP technology.

[0529] 3. Based on the user's emotional data regarding "relaxation," the list of movies that promote relaxation is prioritized and formatted accordingly.

[0530] 4. Input the following prompt message into the AI ​​model to recommend an appropriate movie:

[0531] For users who are looking to relax, recommend relaxing romantic movies. For example, movies with touching and heartwarming stories.

[0532] As a result, users can easily find romantic movies that are suitable for a relaxed state.

[0533] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0534] Step 1:

[0535] User input

[0536] The user uses their device to enter keywords related to the content they want to search for. This input data serves as foundational information for finding appropriate content based on the user's current emotions. The device is equipped with an emotion recognition engine that simultaneously acquires emotion data from the user's facial expressions and voice. The input includes keywords (e.g., "romantic movies") and the user's emotion data (e.g., "relaxed").

[0537] Step 2:

[0538] Recognition of emotions

[0539] The device's built-in emotion recognition engine uses the camera and microphone to capture the user's emotions in real time. Using facial expression recognition and voice analysis technologies (e.g., OpenCV, TensorFlow), it generates emotion data such as whether the user is relaxed or stressed. The output is emotion data such as "relaxed."

[0540] Step 3:

[0541] Request generation

[0542] The user terminal integrates the entered keywords with the retrieved sentiment data to generate a request. This request includes keywords (e.g., "romantic movies"), sentiment data (e.g., "relaxed"), and authentication information (e.g., API key). The request is formatted in JSON format and ready to be sent to the server.

[0543] Step 4:

[0544] Sending a request to the server

[0545] The user terminal sends the generated request to the server. The HTTPS protocol is used for transmission, ensuring secure communication. The input is a request JSON, and the output is the request received by the server.

[0546] Step 5:

[0547] Server receives and authenticates requests.

[0548] The server receives requests from user terminals and first verifies authentication information. After confirming that the request is not malicious, it extracts keywords and sentiment data. The input is a request JSON, and the output is authenticated keywords and sentiment data.

[0549] Step 6:

[0550] Searching for information sources

[0551] The server searches its internal database and internet sources based on the received keywords and sentiment data. It uses search engine APIs (e.g., Google API) to retrieve external information. It also queries its own internal database. The input is authenticated keywords, and the output is a list of relevant information.

[0552] Step 7:

[0553] Data analysis

[0554] The server analyzes the information data obtained through searches. It uses NLP techniques (e.g., SpaCy, NLTK) to evaluate the relevance and importance of the information. Based on sentiment data, it filters the information to extract information that matches the user's interests. The input is the acquired information data, and the output is filtered, relevant information.

[0555] Step 8:

[0556] Formatting of analysis results

[0557] The server formats the analyzed information into a user-friendly format (e.g., JSON). During this process, it adjusts how the information is displayed based on the user's emotional data. In particular, it provides simple and easy-to-understand information to users experiencing stress. The input is filtered relevant information, and the output is formatted JSON data.

[0558] Step 9:

[0559] Sending formatted information

[0560] The server sends the formatted information to the user's terminal. This transmission also uses the HTTPS protocol to ensure secure communication. The input is formatted JSON information, and the output is the data that arrives on the user's terminal.

[0561] Step 10:

[0562] Receiving and displaying information

[0563] The user terminal analyzes information received from the server and displays it on the user interface. The display method is adjusted based on the user's emotional state. For example, a relaxed user is provided with detailed and visually rich information, while a stressed user is provided with concise and easy-to-understand information. The input is the transmitted JSON information, and the output is personalized content displayed on the user interface.

[0564] 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.

[0565] 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.

[0566] 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.

[0567] [Second Embodiment]

[0568] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0569] 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.

[0570] 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).

[0571] 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.

[0572] 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.

[0573] 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).

[0574] 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.

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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".

[0580] This invention will now describe embodiments for carrying it out. This system consists of a user terminal, a server, an internal database, and an internet-based information source, enabling users to efficiently research the services offered by other companies. The specific processing is carried out as follows.

[0581] System Overview

[0582] The user enters keywords related to the services of another company they want to research from their own device (such as a PC or smartphone). Next, the device generates a request containing these keywords and sends it to the server. The server receives the request and searches its internal database and internet information sources. The retrieved information is analyzed by the server, and information highly relevant to the user is extracted and formatted. Finally, the analyzed information is sent from the server to the user's device, and the device displays that information to the user.

[0583] Program processing

[0584] 1. User input

[0585] The user uses their own terminal to enter keywords related to the services of other companies. For example, they might enter "other company's logistics management system."

[0586] 2. Terminal request generation

[0587] The terminal generates an HTTP request based on the keywords entered by the user. This request includes the following information:

[0588] Keywords entered by the user

[0589] Authentication information (API key, etc.)

[0590] Desired data format (e.g., JSON)

[0591] 3. Sending requests from the terminal to the server

[0592] The device sends the generated request to the server. Requests are often sent via a RESTful API and are transmitted securely using the HTTPS protocol.

[0593] 4. Server receives request

[0594] The server receives the request sent from the terminal. After receiving it, it performs the following actions:

[0595] Check the authentication credentials of the request to verify that it is not unauthorized access.

[0596] Extract the keywords entered by the user.

[0597] 5. Searching for data on the server

[0598] The server searches its internal database and trusted sources on the internet. The specific processing steps are as follows:

[0599] Using search engine APIs: The server uses search engine APIs to retrieve web pages related to keywords.

[0600] Executing queries on internal databases: The server also executes queries on its own internal databases to retrieve relevant information.

[0601] Web scraping: Obtaining information from websites by scraping it as needed.

[0602] 6. Data Analysis

[0603] The server analyzes the acquired data. The specific steps of the analysis are as follows:

[0604] Application of NLP (Natural Language Processing) technology: Use a text analysis engine to evaluate the relevance and importance of information. For example, utilize technologies such as TF-IDF and Word2Vec.

[0605] Data filtering: Remove noise and eliminate information that the user is not looking for.

[0606] Information Classification: Organize information by category, such as service features, pricing plans, and customer reviews.

[0607] 7. Formatting and transmitting information

[0608] The server formats the analysis results into a user-friendly format (e.g., JSON or HTML) and returns them to the terminal.

[0609] 8. Information reception and display by the user terminal

[0610] The terminal receives information sent from the server and displays it to the user. In doing so, it converts the information into a data structure suitable for display and outputs the organized information on the user interface.

[0611] Specific example

[0612] 1. User input

[0613] The user enters "data analysis tools from other companies."

[0614] 2. Terminal request generation and transmission

[0615] The terminal generates a request like this:

[0616] json

[0617] {

[0618] "keyword": "Data analysis tools from other companies",

[0619] "apiKey": "USER_API_KEY",

[0620] "format": "json"

[0621] }

[0622] Send this request to the server.

[0623] 3. Server Request Reception and Search

[0624] The server receives the request, authenticates it, extracts keywords, and uses a search engine API to retrieve information related to "other companies' data analysis tools." It also queries its internal database.

[0625] 4. Data Analysis

[0626] The server uses NLP techniques to analyze the results and classify service features, pricing plans, and customer reviews. For example, it uses TF-IDF to evaluate keyword importance and extract highly relevant information.

[0627] 5. Formatting and transmitting information

[0628] The server generates the following JSON and sends it to the terminal:

[0629] json

[0630] {

[0631] "serviceFeatures": ["Real-time analytics", "Big data compatible"]

[0632] "pricingPlans": ["Basic Plan: ¥10,000 / month", "Premium Plan: ¥30,000 / month"]

[0633] "customerReviews": ["Easy to use", "Good value for money"]

[0634] }

[0635] 6. Receiving and displaying information on the device

[0636] The terminal parses the received data and displays the above information on the screen.

[0637] This system allows for quick and efficient on-site investigation of competitors' services, supporting operational decision-making. In this way, it significantly improves user operational efficiency by reducing the time and effort required for information gathering and providing accurate information.

[0638] The following describes the processing flow.

[0639] Step 1:

[0640] The user uses their own terminal to enter keywords related to the third-party service they want to research. For example, they might enter "third-party logistics management system".

[0641] Step 2:

[0642] The terminal receives the entered keyword and generates an HTTP request. This request includes the keyword entered by the user, authentication information (such as an API key), and the desired data format (e.g., JSON).

[0643] Step 3:

[0644] The terminal generates a request and sends it to the server using HTTPS. The transmission is done via a RESTful API.

[0645] Step 4:

[0646] The server processes the request received from the terminal. First, it checks the authentication information included in the request to verify that it is not an unauthorized access. Next, it extracts the keywords entered by the user.

[0647] Step 5:

[0648] The server searches for data based on the extracted keywords. Specifically, it retrieves information from internal databases and internet sources. In this process, it uses the following methods:

[0649] The server uses a search engine API (e.g., Google API) to retrieve information related to the keyword.

[0650] The server queries the internal database to retrieve relevant information.

[0651] If necessary, the server will perform web scraping from the specified website.

[0652] Step 6:

[0653] The information acquired by the server is analyzed. Specifically, this includes the following processes:

[0654] We use NLP (Natural Language Processing) techniques to evaluate the relevance and importance of information. For example, we use TF-IDF and Word2Vec.

[0655] Filtering is performed to remove unnecessary information that constitutes noise.

[0656] Organize information such as service features, pricing plans, and customer reviews by category.

[0657] Step 7:

[0658] The server formats the parsed data and sends it to the user's terminal. For example, it generates a JSON format like the following:

[0659] json

[0660] {

[0661] "serviceFeatures": ["Real-time analytics", "Big data compatible"]

[0662] "pricingPlans": ["Basic Plan: ¥10,000 / month", "Premium Plan: ¥30,000 / month"]

[0663] "customerReviews": ["Easy to use", "Good value for money"]

[0664] }

[0665] Step 8:

[0666] The server formats the data and sends it to the user's terminal. The transmission is secure using HTTPS.

[0667] Step 9:

[0668] The terminal receives data sent from the server and parses it. It converts the received JSON data into an appropriate data structure and prepares it for display to the user.

[0669] Step 10:

[0670] The terminal analyzes the data and displays it on the user interface. The user then reviews the displayed information and uses it for specific tasks and decision-making.

[0671] (Example 1)

[0672] 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".

[0673] Traditional information gathering systems had the problem that it was time-consuming and laborious for users to efficiently investigate the services offered by other companies. Furthermore, there was a lack of technology to judge the reliability and relevance of the acquired information, making it difficult for users to quickly obtain useful information. In addition, the analysis and classification of search data were not adequately performed, making it difficult for users to obtain the accurate and well-organized information they needed.

[0674] 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.

[0675] In this invention, the server includes means for receiving requests sent from a user terminal and searching an internal database and information sources on the internet based on the requests; means for analyzing the acquired information and extracting and formatting information highly relevant to the user; and means for transmitting the analyzed information to the user terminal. This enables users to quickly and efficiently investigate the services of other companies and accurately acquire highly relevant information.

[0676] A "user terminal" refers to a computing device used by a user, such as a personal computer or smartphone.

[0677] "Keywords" are words or phrases related to the services of other companies that the user is researching.

[0678] A "request" is an HTTP request sent from a user's terminal to a server, and it includes keywords, authentication information, and the desired data format.

[0679] A "server" is a computing system that receives requests from user terminals, searches internal databases and information sources on the internet, analyzes and formats the retrieved information, and sends it to the user terminal.

[0680] An "internal database" is a data storage system that stores data held by a company.

[0681] "Internet information sources" refer to websites and other online information services.

[0682] A "search engine API" is an application interface that uses a search engine to retrieve web pages and information related to specific keywords.

[0683] "Acquired information" refers to data collected by the server from its internal database and information sources on the internet.

[0684] "Analysis" is the process of using natural language processing techniques to evaluate the relevance and importance of information acquired by a server.

[0685] "Natural language processing technology" refers to technologies that process and analyze input text data to understand and evaluate its content, and includes technologies such as TF-IDF and Word2Vec.

[0686] "Extraction" is the process of selecting information that is highly relevant to the user from the analysis results.

[0687] "Formatting" refers to converting extracted information into a format that is easy for the user to understand.

[0688] This invention will now describe embodiments for carrying it out. This system consists of a user terminal, a server, an internal database, and an internet-based information source, enabling users to efficiently research the services offered by other companies. The specific processing is carried out as follows.

[0689] System Overview

[0690] The user uses their own device (such as a PC or smartphone) to enter keywords related to the services of other companies they want to research. For example, they might enter "logistics management system." Next, the device generates a request containing these keywords and sends it to the server. The server receives the request and searches its internal database and internet information sources. The retrieved information is analyzed by the server, and information highly relevant to the user is extracted and formatted. Finally, the analyzed information is sent from the server to the user's device, and the device displays the information to the user. This system can utilize search engine APIs, natural language processing technology, and web scraping techniques.

[0691] Hardware and software to be used

[0692] User terminal: A computing device that can connect to the internet, such as a personal computer, smartphone, or tablet.

[0693] Server: A server for high-performance data processing and database access. Cloud-based servers (e.g., AWS or Google Cloud Platform) can also be used.

[0694] Internal database: A database management system such as MySQL, PostgreSQL, or Oracle Database.

[0695] Search Engine APIs: APIs such as the Google Custom Search API for retrieving web information based on keywords.

[0696] Natural Language Processing Technology: An engine that analyzes information obtained using libraries and models such as TF-IDF, Word2Vec, and BERT.

[0697] Web scraping: A technique for automatically collecting necessary web information using libraries such as Beautiful Soup and Selenium.

[0698] Specific example

[0699] Here's a concrete example of how the system works when a user wants to research "data analysis tools from other companies." The user types "data analysis tools from other companies" into their terminal. The terminal then generates a request like the following and sends it to the server.

[0700] Example of a prompt:

[0701] Keywords: Data analysis tools from other companies

[0702] API Key: USER_API_KEY

[0703] Data format: JSON

[0704] The server receives this request, verifies the authentication information, and then uses its internal database and search engine API to search for information related to "other companies' data analysis tools." It also uses web scraping techniques to collect information from relevant websites. The collected data is analyzed using natural language processing techniques. For example, TF-IDF is used to extract highly relevant information and classify it into categories such as service features, pricing plans, and customer reviews.

[0705] The analyzed information is formatted by the server and sent to the user terminal in the following format:

[0706] Plastic information

[0707] Service features: Real-time analysis, Big data support

[0708] Pricing plans: Basic plan: ¥10,000 / month, Premium plan: ¥30,000 / month

[0709] Customer reviews: Easy to use, good value for money.

[0710] The user's device receives and displays this information. For example, the information is displayed on the UI of a web page in a format appropriate to each information category. This allows users to efficiently understand the services offered by other companies and use that information to help them make informed decisions.

[0711] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0712] Step 1:

[0713] User keyword input

[0714] Input: Users use their own devices (PCs or smartphones) to enter keywords related to the services of other companies they want to research. "Logistics management system" is one example.

[0715] Operation: The user enters keywords into the search field of their device's browser or application and clicks the search button.

[0716] Output: The entered keyword is processed internally by the terminal and ready to be sent to the next step.

[0717] Step 2:

[0718] Request generation by the terminal

[0719] Input: Keyword entered by the user

[0720] Operation: Based on the entered keyword, the terminal generates an HTTP request containing the following information:

[0721] Keywords entered by the user

[0722] API key for system authentication

[0723] Desired data format (e.g., JSON)

[0724] As an example of a specific request, the following JSON will be generated:

[0725] json

[0726] {

[0727] "keyword": "Logistics management system",

[0728] "apiKey": "USER_API_KEY",

[0729] "format": "json"

[0730] }

[0731] Output: The generated HTTP request is ready to be sent to the server.

[0732] Step 3:

[0733] Sending a request from the terminal to the server

[0734] Input: HTTP request (including keywords, API key, and data format)

[0735] Operation: The device sends the generated HTTP request to the server. The HTTPS protocol is used to securely transmit the data. The device sends the request via a RESTful API.

[0736] Output: The request is received by the server and processing begins in the next step.

[0737] Step 4:

[0738] Server request reception and analysis

[0739] Input: HTTP request sent from the terminal

[0740] Operation: The server parses the received request and performs the following processes:

[0741] Verify the API key and confirm that the request is legitimate.

[0742] Extract keywords entered by the user.

[0743] Output: The validated keywords will be used in the next step.

[0744] Step 5:

[0745] Server Data Retrieval

[0746] Input: Extracted keywords

[0747] Operation: The server searches its internal database and trusted sources on the internet. Specifically, it performs the following steps:

[0748] Using search engine APIs: Use APIs such as the Google Custom Search API to retrieve relevant information from the internet.

[0749] Executing queries on internal databases: Execute queries using keywords against internal databases such as MySQL and PostgreSQL to retrieve relevant information.

[0750] Web scraping: If necessary, use Beautiful Soup or Selenium to scrape the required information from specific websites.

[0751] Output: The acquired information will be used for data analysis in the next step.

[0752] Step 6:

[0753] Data analysis

[0754] Input: Data obtained from internal databases and reliable sources on the internet.

[0755] Operation: The server analyzes the acquired data. The specific analysis steps are as follows:

[0756] Application of natural language processing techniques: We evaluate the relevance and importance of information using natural language processing techniques such as TF-IDF, Word2Vec, and BERT. For example, we analyze the importance of keywords using TF-IDF.

[0757] Data filtering: Removes noise and irrelevant data to extract the information the user is looking for.

[0758] Information Classification: Organize information into categories such as service features, pricing plans, and customer reviews.

[0759] Output: The analysis results will be formatted in the next step.

[0760] Step 7:

[0761] Information formatting

[0762] Input: Analyzed data

[0763] Operation: The server formats the analysis results into a user-friendly format, such as JSON or HTML. A concrete example of a response is the following JSON:

[0764] json

[0765] {

[0766] "serviceFeatures": ["Real-time analytics", "Big data compatible"]

[0767] "pricingPlans": ["Basic Plan: ¥10,000 / month", "Premium Plan: ¥30,000 / month"]

[0768] "customerReviews": ["Easy to use", "Good value for money"]

[0769] }

[0770] Output: The formatted data is sent to the user's terminal.

[0771] Step 8:

[0772] Information reception and display by the user terminal

[0773] Input: Formatted data sent from the server

[0774] Operation: The terminal parses the received data and converts it into a format suitable for display. This is then displayed on the user interface. For example, information displayed in the UI of a web page or application is organized into service features, pricing plans, customer reviews, etc.

[0775] Output: Users can view detailed information about the services of other companies they wish to investigate.

[0776] (Application Example 1)

[0777] 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."

[0778] In today's economic environment, efficiently and quickly obtaining and comparing information on other companies' electronic payment services is crucial. However, manual methods and traditional information gathering techniques have limitations in terms of comprehensiveness and accuracy of necessary information, and are time-consuming and labor-intensive. Furthermore, collecting the latest information is particularly difficult in the electronic payment sector, where market trends are constantly changing. This can make accurate market analysis and maintaining competitiveness difficult.

[0779] 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.

[0780] In this invention, the server includes means for generating a request containing keywords entered by the user, means for searching an internal database and information sources on the internet based on the request sent from the user terminal, means for analyzing the acquired information and extracting and formatting information highly relevant to the user, means for transmitting the analyzed information to the user terminal, and means for the user terminal to display information regarding the features, pricing plans, and user reviews of the electronic payment service. This enables the user to efficiently and quickly obtain detailed information on other companies' electronic payment services and compare them.

[0781] A "user terminal" is a device used by a user to operate, and includes computers such as smartphones and personal computers.

[0782] A "keyword" refers to a specific word or phrase that a user enters to search for information.

[0783] A "request" is a communication message containing request information sent from a user's terminal to a server.

[0784] A "server" refers to a central processing unit that receives requests sent from user terminals and performs data retrieval and analysis based on those requests.

[0785] An "internal database" is a database that stores data owned by a company and is used to search for requested information.

[0786] "Internet information sources" refer to websites and online databases that exist on the internet, and are used to obtain necessary information from them.

[0787] "Analysis" refers to the process of classifying information acquired by the server into categories and evaluating their relationships.

[0788] "Highly relevant information" refers to information that is closely related to the keywords the user is searching for and is also useful.

[0789] "Formatting" refers to the process of converting analyzed information into a format that is easy for users to understand.

[0790] "Features" refer to the specific functions or capabilities that an electronic payment service possesses.

[0791] "Pricing plan" refers to the pricing structure and details of a service.

[0792] "User reviews" refer to evaluations and opinions provided by service users.

[0793] This invention will now describe embodiments for carrying out this invention. This invention relates to a system that allows users to efficiently collect and compare information on electronic payment services of other companies. This system consists of a user terminal, a server, an internal database, and information sources on the internet.

[0794] Hardware and software configuration

[0795] User terminal:

[0796] User terminals include computers such as smartphones and personal computers. These terminals are connected to the internet and have an interface for inputting information.

[0797] server:

[0798] The servers are located on cloud infrastructure. Specifically, cloud services such as AWS EC2 are available.

[0799] For the database, we will use an SQL database such as MySQL.

[0800] software:

[0801] To search for information, we utilize our internal database and search engine APIs such as the Google Custom Search API.

[0802] For natural language processing, we use NLP (Natural Language Processing) engines such as TensorFlow and NLTK.

[0803] System operation

[0804] 1. User input:

[0805] The user launches the smartphone app and enters keywords related to other companies' electronic payment services. For example, they might enter the keyword "other companies' mobile payment services."

[0806] 2. Terminal request generation:

[0807] The user's terminal generates an HTTP request based on the keywords entered by the user. This request includes the keywords, the API key, and the desired data format (e.g., JSON).

[0808] 3. Server request reception and data retrieval:

[0809] The server receives requests sent from user terminals. After receiving a request, it executes queries to search engine APIs and internal databases to collect relevant information. It also performs web scraping as needed.

[0810] 4. Data Analysis:

[0811] The server analyzes the collected data using NLP (Neuro-Linguistic Programming) techniques. Specifically, it uses methods such as TF-IDF and Word2Vec to evaluate the relevance and importance of the information, remove noise, and classify it into categories such as service features, pricing plans, and user reviews.

[0812] 5. Formatting and sending information:

[0813] The server formats the analysis results into a user-friendly format (e.g., JSON) and sends it to the terminal.

[0814] 6. Information reception and display by the user terminal:

[0815] The user terminal receives information sent from the server and displays it in a visually easy-to-understand interface. For example, the output might look like this:

[0816] Service features:

[0817] High-frequency trading

[0818] Low fees

[0819] Pricing plans:

[0820] Basic plan: ¥500 / month

[0821] Premium plan: ¥2,000 / month

[0822] User reviews:

[0823] Convenient and easy to use

[0824] Good support

[0825] This system allows users to quickly and efficiently gather detailed information on other companies' electronic payment services and compare them.

[0826] Example of a prompt

[0827] As a concrete example, here is an example of the prompt text when a user enters the keyword "mobile payment from another company":

[0828] "Please find competitive mobile payment services including features, pricing plans, and customer reviews."

[0829] Based on this prompt, the system collects the necessary information and performs the necessary processing to provide it to the user.

[0830] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0831] Step 1:

[0832] The user launches a smartphone app and enters keywords related to another company's electronic payment service. For example, they might enter the keyword "other company's mobile payment." The input data here is the keyword, and the output is the data that forms the basis of the HTTP request.

[0833] Step 2:

[0834] The user's terminal generates an HTTP request based on the keywords entered by the user. This request includes the following information:

[0835] Keywords (e.g., "other companies' mobile payment services")

[0836] API key (user authentication information)

[0837] Desired data format (e.g., JSON)

[0838] The input for this step is the keyword entered by the user, and the output is the generated HTTP request.

[0839] Step 3:

[0840] The user terminal generates a request and sends it to the server. The request is sent securely using the HTTPS protocol via a RESTful API. The input to this step is the generated HTTP request, and the output is a notification to the server that the transmission is complete.

[0841] Step 4:

[0842] The server receives a request sent from the user's terminal. The received request is authenticated, and keywords are extracted. The input here is the received HTTP request, and the output is the authenticated keywords.

[0843] Step 5:

[0844] The server searches its internal database and internet sources based on authenticated keywords. The server then performs the following actions:

[0845] Use the search engine API (Google Custom Search API) to retrieve relevant web pages.

[0846] Execute queries against the internal database (e.g., MySQL) to retrieve relevant information.

[0847] Web scraping will be performed as needed to collect relevant information.

[0848] The input for this step is a verified keyword, and the output is the collected raw data.

[0849] Step 6:

[0850] The raw data collected by the server is analyzed. Specifically, the following processes are performed:

[0851] The acquired data is analyzed using an NLP engine (such as TensorFlow or NLTK).

[0852] We use TF-IDF and Word2Vec to evaluate the relevance and importance of the information.

[0853] Removal of noisy data, filtering of unnecessary information.

[0854] Information is categorized by service features, pricing plans, and user reviews.

[0855] The input for this step is the collected raw data, and the output is the analyzed and formatted data.

[0856] Step 7:

[0857] The server formats the parsed information into a user-friendly format (e.g., JSON) and sends it to the user's terminal. The input for this step is the parsed data, and the output is a response containing the formatted data.

[0858] Step 8:

[0859] The user terminal receives information sent from the server and displays it in a visually easy-to-understand interface. The input here is the response data from the server, and the output is the information displayed on the terminal's user interface. For example, service features, pricing plans, and user reviews are displayed in a visually organized manner.

[0860] This series of steps enables the system described in the claims to allow users to efficiently and quickly collect and compare detailed information on other companies' electronic payment services.

[0861] 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.

[0862] This invention relates to a system that further enhances the user experience by combining it with an emotion engine that recognizes user emotions. This invention achieves more personalized information delivery by adjusting information display and search results based on the user's emotions.

[0863] System Overview

[0864] This system consists of user terminals, servers, an internal database, and information sources on the internet. Furthermore, the user terminals are equipped with an emotion engine that recognizes the user's emotions in real time and transmits that data to the server.

[0865] Program processing

[0866] 1. User input

[0867] The user uses their own terminal to enter keywords related to the third-party service they want to research. For example, they might enter "third-party logistics management system."

[0868] 2. User emotion recognition

[0869] An emotion engine installed in the user's device recognizes the user's emotions in real time. Using speech recognition and facial expression recognition, it acquires emotional data such as whether the user is stressed or relaxed.

[0870] 3. Terminal request generation

[0871] The terminal generates an HTTP request containing keywords and sentiment data entered by the user. This request includes the following information:

[0872] Keywords entered by the user

[0873] User sentiment data

[0874] Authentication information (API key, etc.)

[0875] Desired data format (e.g., JSON)

[0876] 4. Sending requests from the terminal to the server

[0877] The device sends the generated request to the server. The transmission is done via a RESTful API and is securely sent using the HTTPS protocol.

[0878] 5. Server receives request

[0879] The server processes requests received from the terminal. First, it checks the authentication information of the request to verify that it is not an unauthorized access. Next, it extracts the keywords and sentiment data entered by the user.

[0880] 6. Searching for data on the server

[0881] The server searches for data based on keywords and sentiment data. Specifically, it retrieves information from internal databases and internet sources. In this process, it utilizes the following methods:

[0882] Using search engine APIs: The server uses search engine APIs (e.g., Google API) to retrieve information related to keywords.

[0883] Executing queries on internal databases: The server executes queries on its own internal databases to retrieve relevant information.

[0884] Web scraping: Obtaining information by scraping it from specific websites as needed.

[0885] 7. Data Analysis

[0886] The server analyzes the acquired data. Specifically, it performs the following processes:

[0887] Application of NLP (Natural Language Processing) technology: Use a text analysis engine to evaluate the relevance and importance of information. For example, TF-IDF or Word2Vec may be used.

[0888] Filtering is performed to remove unnecessary information that constitutes noise.

[0889] Organize information such as service features, pricing plans, and customer reviews by category.

[0890] Based on user sentiment data, specific information is highlighted and its display order is adjusted. For example, if a user is feeling stressed, concise and easy-to-read information is prioritized.

[0891] 8. Formatting and transmitting information

[0892] The server formats the analysis results into a user-friendly format (e.g., JSON or HTML) and returns them to the terminal. At this stage, specific formats and display methods can be adopted based on the user's sentiment data.

[0893] 9. Information reception and display by the user terminal

[0894] The terminal receives information sent from the server and displays it to the user. The received data is converted into an appropriate data structure and displayed in the user interface. The display method is adjusted to suit the user's emotions.

[0895] Specific example

[0896] 1. User input and emotion recognition

[0897] As soon as the user types "data analysis tool from another company," the emotion engine recognizes the user's facial expression and acquires emotional data indicating "stress."

[0898] 2. Terminal request generation and transmission

[0899] The terminal generates a request like this:

[0900] json

[0901] {

[0902] "keyword": "Data analysis tools from other companies",

[0903] "emotion": "stress",

[0904] "apiKey": "USER_API_KEY",

[0905] "format": "json"

[0906] }

[0907] Send this request to the server.

[0908] 3. Server Request Reception and Search

[0909] The server receives the request, extracts keywords and sentiment data after authentication, and uses a search engine API to retrieve information related to "other companies' data analysis tools." It also queries its internal database.

[0910] 4. Data Analysis

[0911] The server uses NLP (Neuro-Linguistic Programming) techniques to analyze the results and categorize service features, pricing plans, and customer reviews. Furthermore, it formats the information to display it concisely based on emotional data, specifically "stress."

[0912] 5. Formatting and transmitting information

[0913] The server generates the following JSON and sends it to the terminal:

[0914] json

[0915] {

[0916] "serviceFeatures": ["Real-time analytics", "Big data compatible"]

[0917] "pricingPlans": ["Basic Plan: ¥10,000 / month", "Premium Plan: ¥30,000 / month"]

[0918] "customerReviews": ["Easy to use", "Good value for money"]

[0919] }

[0920] 6. Receiving and displaying information on the device

[0921] The device analyzes the received data and displays the information in a concise and visually easy-to-understand format, taking into account the user's "stress." For example, it highlights important information and collapses detailed information.

[0922] This system enables rapid and efficient on-site research into competitors' services and provides personalized information tailored to the user's emotional state. This reduces the time and effort required for information gathering and significantly improves user efficiency by providing accurate and relevant information.

[0923] The following describes the processing flow.

[0924] Step 1:

[0925] The user uses their own terminal to enter keywords related to the third-party service they want to research. For example, they might enter "third-party logistics management system."

[0926] Step 2:

[0927] An emotion engine installed in the user's device recognizes the user's emotions in real time. Using speech recognition and facial expression recognition, it acquires emotional data such as whether the user is stressed or relaxed.

[0928] Step 3:

[0929] The terminal generates an HTTP request based on the entered keyword and acquired sentiment data. This request includes the following information:

[0930] Keywords entered by the user

[0931] User sentiment data

[0932] Authentication information (API key, etc.)

[0933] Desired data format (e.g., JSON)

[0934] Step 4:

[0935] The terminal generates a request and sends it to the server using HTTPS. The transmission is done via a RESTful API.

[0936] Step 5:

[0937] The server processes the request received from the terminal. First, it checks the authentication information included in the request to verify that it is not an unauthorized access. Next, it extracts the keywords and sentiment data entered by the user.

[0938] Step 6:

[0939] The server searches for data based on extracted keywords and sentiment data. Specifically, it retrieves information from internal databases and internet sources. In this process, it utilizes the following methods:

[0940] Using search engine APIs: The server uses search engine APIs, such as the Google API, to retrieve information related to the keywords.

[0941] Executing queries on internal databases: The server also executes queries on its own internal databases to retrieve relevant information.

[0942] Web scraping: Obtaining information by scraping it from specific websites as needed.

[0943] Step 7:

[0944] The server analyzes the information it has acquired. Specifically, it performs the following processes:

[0945] We use NLP (Natural Language Processing) techniques to evaluate the relevance and importance of information. For example, we use TF-IDF and Word2Vec.

[0946] Filtering is performed to remove unnecessary information that would otherwise be considered noise.

[0947] Organize information such as service features, pricing plans, and customer reviews by category.

[0948] Based on user sentiment data, specific information is highlighted and its display order is adjusted. For example, if a user is feeling stressed, concise and easy-to-read information is prioritized.

[0949] Step 8:

[0950] The server formats the analysis results into a user-friendly format (e.g., JSON or HTML) and sends them to the terminal. In this process, specific formats and display methods can be adopted based on the user's sentiment data.

[0951] Step 9:

[0952] The terminal receives information sent from the server and parses the data. It converts the received JSON data into an appropriate data structure and prepares it for display to the user.

[0953] Step 10:

[0954] The terminal analyzes the data and displays it on the user interface. Users review the displayed information and use it for specific tasks and decision-making. The display method is adjusted according to the user's emotions. For example, if the emotion engine detects user stress, particularly important information is highlighted, and detailed information is collapsed as needed, taking care to reduce the user's burden.

[0955] In this way, this system, which incorporates an emotion engine, can provide information tailored to the user's emotions and support efficient decision-making.

[0956] (Example 2)

[0957] 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".

[0958] Conventional information retrieval systems perform searches based solely on keywords entered by the user, without considering the user's emotional state. This makes it difficult to provide personalized information tailored to the user's emotional state. In particular, if appropriate information is not presented when the user is stressed or relaxed, the user experience may deteriorate. Therefore, there is a need for a system that considers the user's emotional state and provides information more effectively.

[0959] 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.

[0960] In this invention, the server includes means for the user terminal to receive keywords and sentiment data entered by the user and generate a request including said keywords and sentiment data; means for the server to receive a request sent from the user terminal and search an internal database and information sources on the internet based on said request; means for the server to analyze the acquired information, evaluate the relevance and importance of the information using natural language processing techniques, and adjust the information based on the user's sentiment data; means for the server to format the analyzed information, extract information highly relevant to the user, and transmit it to the user terminal; and means for the user terminal to display the information received from the server and adjust the display method based on the user's sentiment. This makes it possible to provide personalized information according to the user's emotional state.

[0961] A "user terminal" is an electronic device used by the user for operations, and is a device that inputs keywords and acquires sentiment data.

[0962] A "keyword" is a word or phrase that a user enters into their device to search for specific information.

[0963] "Emotional data" refers to information that represents the user's emotional state, and is data acquired through voice recognition and facial expression recognition.

[0964] A "request" is request information sent from a user's terminal to a server, and includes keywords and sentiment data.

[0965] A "server" is a device that performs data retrieval and analysis based on requests received from a user terminal and sends the results to the user terminal.

[0966] An "internal database" is a data storage system where data held by a company is stored, and it is used to search for related information.

[0967] "Internet information sources" refer to information resources accessible via the internet that are used to obtain external information.

[0968] "Natural language processing technology" refers to the technology used by computers to understand, interpret, and generate human language, and is a means of evaluating the relevance and importance of information.

[0969] "Highly relevant information" refers to information that is most relevant to the user, filtered based on the user's keywords and sentiment data.

[0970] "Formatting" is the process of reconstructing acquired information into a format that is easy for users to understand.

[0971] "Information adjustment" is the process of determining the display order and highlighting of information based on user sentiment data.

[0972] The system of the present invention consists of a user terminal, a server, an internal database, and an information source on the Internet. Furthermore, the user terminal is equipped with an emotion engine that recognizes the user's emotions in real time and transmits that data to the server. The following describes in detail how this system is implemented.

[0973] First, the user uses their own terminal to enter keywords related to what they want to research. For example, they might enter "other companies' logistics management systems." Simultaneously, an emotion engine installed in the user's terminal recognizes the user's facial expressions and voice in real time and collects emotion data. The emotion engine uses speech recognition technology and facial expression recognition technology, specifically employing AI models such as OpenFace and DeepFace.

[0974] Next, the user terminal generates a request containing the entered keywords and the retrieved sentiment data. This request is in JSON format, and an internal HTTP client library (e.g., Python's requests library) is used to generate the request. The generated JSON request includes the following information:

[0975] Keywords entered by the user

[0976] User sentiment data

[0977] Authentication information (such as API key)

[0978] Desired data format (e.g., JSON)

[0979] This request is sent to the server via the HTTPS protocol through a RESTful API.

[0980] The server processes requests received from user terminals. First, the server verifies the legitimacy of the request based on authentication information to ensure it is not an unauthorized access attempt. Next, it extracts keywords and sentiment data from the request and begins a data search. The search is performed using the following methods:

[0981] Using search engine APIs: Information is retrieved using external search engine APIs such as the Google API.

[0982] Executing queries on internal databases: Retrieving information by executing queries on databases maintained by the company.

[0983] Web scraping: The process of obtaining information by scraping it from a specific website.

[0984] The acquired information is analyzed on the server. The server uses natural language processing (NLP) techniques to evaluate the relevance and importance of the acquired information. In this process, a text analysis engine (e.g., SpaCy, NLTK) is used, employing techniques such as TF-IDF and Word2Vec. Noise is removed through filtering, and necessary information is organized and classified into categories. Furthermore, information is highlighted and its display order is adjusted based on the user's sentiment data. For example, if the user is feeling stressed, important information is summarized concisely and displayed.

[0985] The server reformats the parsed information into a user-friendly format (e.g., JSON or HTML) and returns it to the terminal. This ensures that the information is presented to the user in an appropriate format. Finally, the user terminal displays the information received from the server and adjusts the display method according to the user's emotional state. For example, important information may be highlighted, while detailed information may be displayed in a collapsed format.

[0986] As a concrete example, consider a scenario where a user enters "data analysis tool from another company," and the emotion engine detects a "stressed" state from the user's facial expression. In this case, the device generates and sends the following request to the server:

[0987] Keywords: Data analysis tools from other companies

[0988] Emotional data: stress

[0989] API Key: USER_API_KEY

[0990] Format: json

[0991] The server receives the request, searches for information based on the keywords and sentiment data, and performs analysis. The analysis results provide a concise summary of service features, pricing plans, customer reviews, etc. This information is then formatted as JSON data and sent to the terminal:

[0992] Service features: [Real-time analysis, Big data compatible]

[0993] Pricing plans: [Basic plan: ¥10,000 / month, Premium plan: ¥30,000 / month]

[0994] Customer reviews: [Easy to use, good value for money]

[0995] The terminal receives the transmitted information and displays it concisely and visually, taking into consideration the user's "stress." This allows users to quickly and efficiently understand the services offered by other companies and improve their work efficiency.

[0996] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0997] Step 1: User input

[0998] The user uses their own terminal to enter keywords related to the third-party service they want to investigate. For example, they might enter "third-party logistics management system." This input triggers the system to start. The entered keywords are stored internally on the terminal. Furthermore, user input is primarily done via keyboard text.

[0999] Input: Keywords entered by the user (e.g., "Logistics management system of another company")

[1000] Output: Retained keywords (e.g., "other company's logistics management system")

[1001] Step 2: Recognizing the user's emotions

[1002] The device's built-in emotion engine works to recognize the user's facial expressions and voice tone in real time and generate emotion data. It uses the camera to capture facial expressions and the microphone to recognize voice. The emotion engine uses AI models (e.g., OpenFace, DeepFace) to determine whether the user is stressed or relaxed.

[1003] Input: User's facial expression data, voice data

[1004] Output: Analyzed emotion data (e.g., "stress")

[1005] Step 3: Generate terminal request

[1006] The terminal generates an HTTP request containing the keywords entered by the user and the retrieved sentiment data. The generated request includes the following information: keywords, sentiment data, authentication information (such as an API key), and the desired data format (e.g., JSON). This request is constructed using the system's internal HTTP client library (e.g., Python's requests library).

[1007] Input: Keyword (e.g., "Other company's logistics management system"), Sentiment data (e.g., "Stress"), Authentication information (e.g., "USER_API_KEY")

[1008] Output: HTTP request (e.g., JSON format request)

[1009] Step 4: Sending a request from the terminal to the server

[1010] The terminal sends the generated request to the server. This transmission is performed using a RESTful API and the HTTPS protocol. The use of the HTTPS protocol ensures the security of the transmitted data.

[1011] Input: Generated HTTP request (e.g., a request in JSON format)

[1012] Output: Request sent to the server

[1013] Step 5: Server receives request

[1014] The server receives requests sent from terminals. First, it verifies the authentication credentials to ensure that the access is not unauthorized. Then, it extracts keywords and sentiment data included in the request.

[1015] Input: HTTP request sent from the terminal

[1016] Output: Extracted keywords (e.g., "other company's logistics management system"), sentiment data (e.g., "stress")

[1017] Step 6: Search server data

[1018] The server searches for information from internal databases and internet sources based on extracted keywords and sentiment data. The following methods are used:

[1019] Using search engine APIs: Use the Google API to retrieve information related to keywords.

[1020] Executing queries on internal databases: Retrieving information by executing queries on databases maintained by the company.

[1021] Web scraping: The process of obtaining information by scraping it from a specific website.

[1022] Input: Extracted keywords (e.g., "other company's logistics management system"), sentiment data (e.g., "stress")

[1023] Output: Acquired information data

[1024] Step 7: Data Analysis

[1025] The server analyzes the acquired information. Using a text analysis engine (e.g., SpaCy, NLTK), it evaluates the relevance and importance of the information using natural language processing techniques. Furthermore, it performs noise reduction, categorization, and adjusts the highlighting and display order of information based on sentiment data.

[1026] Input: Acquired information data, emotional data (e.g., "stress")

[1027] Output: Analyzed and formatted information

[1028] Step 8: Format and send information

[1029] The server formats the analyzed information into a user-friendly format (e.g., JSON, HTML) and sends it to the terminal. It adopts an appropriate format and display method based on sentiment data.

[1030] Input: Analyzed and formatted information

[1031] Output: Formatted information data

[1032] Step 9: Information reception and display by the user terminal

[1033] The terminal converts information received from the server into a data structure and displays it on the user interface. Based on the user's emotions, the information is displayed in a concise and visually easy-to-understand manner. Techniques such as highlighting important information and collapsing detailed information are employed.

[1034] Input: Formatted information data

[1035] Output: Displayed information (on the user interface)

[1036] This allows users to quickly and efficiently understand the services offered by other companies, thereby improving operational efficiency.

[1037] (Application Example 2)

[1038] 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."

[1039] Traditional content delivery services often fail to consider the user's emotional state, making it difficult to provide content that matches the user's current mood. As a result, users may experience stress or be unable to relax, leading to decreased service satisfaction. Furthermore, it was difficult to quickly and accurately extract highly relevant information from the vast amount of data available.

[1040] 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.

[1041] In this invention, the server includes means for receiving requests sent from a user terminal and searching an internal database and information sources on the internet based on the requests; means for analyzing the acquired information, shaping the information based on the user's emotional data, and extracting information highly relevant to the user; and means for transmitting the analyzed information to the user terminal. This makes it possible to suggest personalized content according to the user's emotional state.

[1042] A "user terminal" is an electronic device used by users to input information and receive and display it, and it is also equipped with an emotion recognition engine.

[1043] "Keywords" are words or phrases that users enter for the purpose of research or searching.

[1044] "Emotional data" refers to data indicating the user's emotional state, acquired in real time by the emotion recognition engine installed in the user's device.

[1045] A "request" is communication data for an information request that includes keywords and sentiment data entered by the user.

[1046] A "server" is a central processing unit that searches internal databases and internet-based information sources based on requests received from user terminals and returns the results to the user terminals.

[1047] An "internal database" is a collection of data that a server maintains for access and retrieval.

[1048] An "information source on the internet" is an external information repository from which a server obtains information through search engine APIs or web scraping.

[1049] An "emotion recognition engine" is a combination of hardware and software that recognizes a user's emotional state in real time from their facial expressions and voice.

[1050] "Natural language processing technology" refers to computational methods and algorithms for analyzing text information acquired by a server and evaluating its relevance and importance.

[1051] "Filtering" is the process of removing noise and unnecessary data from acquired information and extracting information that is highly relevant to the user.

[1052] "Analysis" is the process by which a server analyzes the information it acquires and formats it into an appropriate format based on user sentiment data.

[1053] A "browsing interface" is a screen and operating system that displays information obtained from a server by the user's terminal and adjusts the display method based on the user's emotional state.

[1054] This invention is a system that personalizes content delivery based on user emotions. Its main components are a user terminal, a server, an internal database, an internet-based information source, and an emotion recognition engine.

[1055] The user terminal is equipped with a camera and microphone, and an emotion recognition engine acquires emotion data in real time from the user's facial expressions and voice. A request containing this acquired emotion data and keywords entered by the user is generated and sent to the server. The HTTPS protocol is used to securely communicate when sending requests.

[1056] After receiving a request, the server first verifies the authentication information. If the request is deemed legitimate, it searches its internal database and internet information sources. Information is collected using search engine APIs (e.g., Google API) and web scraping tools. The acquired data is analyzed using NLP (Natural Language Processing) techniques. Specifically, methods such as TF-IDF and Word2Vec are used to evaluate the relevance and importance of the information, and filtering is performed as needed.

[1057] The analysis results are formatted based on the user's emotional data. When the user is stressed, concise and easy-to-understand information is prioritized; when relaxed, detailed and helpful information is displayed preferentially. Finally, the organized information is converted into an appropriate data format, such as JSON, and sent to the user's device.

[1058] The user terminal analyzes information received from the server and displays it on the user interface. During this process, adjustments are made based on the user's emotional state. For example, a user experiencing stress will see a visually simpler UI, with detailed information displayed in a collapsible format.

[1059] Program operation description:

[1060] Emotion Recognition Engine: This engine uses facial recognition and speech recognition technologies to capture user emotions in real time. Specific software examples include OpenCV and TensorFlow.

[1061] Search engine API: Uses the Google API, etc., to retrieve information from internet sources.

[1062] NLP technology: SpaCy and NLTK are used as natural language processing engines. These are used to perform text analysis and evaluate the relevance and importance of information.

[1063] Filtering: Remove noise from collected data and extract only the information most relevant to the user.

[1064] Formatting of analysis results: Convert to HTML or JSON format and send to the user's terminal.

[1065] Specific example:

[1066] For example, if a user types "romantic movie" into their smartphone and the emotion recognition engine obtains the emotion data "relaxed," the server processes the information in the following steps:

[1067] 1. Use a search engine API to collect information about "romantic movies".

[1068] 2. Analyze the collected information using NLP technology.

[1069] 3. Based on the user's emotional data regarding "relaxation," the list of movies that promote relaxation is prioritized and formatted accordingly.

[1070] 4. Input the following prompt message into the AI ​​model to recommend an appropriate movie:

[1071] For users who are looking to relax, recommend relaxing romantic movies. For example, movies with touching and heartwarming stories.

[1072] As a result, users can easily find romantic movies that are suitable for a relaxed state.

[1073] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1074] Step 1:

[1075] User input

[1076] The user uses their device to enter keywords related to the content they want to search for. This input data serves as foundational information for finding appropriate content based on the user's current emotions. The device is equipped with an emotion recognition engine that simultaneously acquires emotion data from the user's facial expressions and voice. The input includes keywords (e.g., "romantic movies") and the user's emotion data (e.g., "relaxed").

[1077] Step 2:

[1078] Recognition of emotions

[1079] The device's built-in emotion recognition engine uses the camera and microphone to capture the user's emotions in real time. Using facial expression recognition and voice analysis technologies (e.g., OpenCV, TensorFlow), it generates emotion data such as whether the user is relaxed or stressed. The output is emotion data such as "relaxed."

[1080] Step 3:

[1081] Request generation

[1082] The user terminal integrates the entered keywords with the retrieved sentiment data to generate a request. This request includes keywords (e.g., "romantic movies"), sentiment data (e.g., "relaxed"), and authentication information (e.g., API key). The request is formatted in JSON format and ready to be sent to the server.

[1083] Step 4:

[1084] Sending a request to the server

[1085] The user terminal sends the generated request to the server. The HTTPS protocol is used for transmission, ensuring secure communication. The input is a request JSON, and the output is the request received by the server.

[1086] Step 5:

[1087] Server receives and authenticates requests.

[1088] The server receives requests from user terminals and first verifies authentication information. After confirming that the request is not malicious, it extracts keywords and sentiment data. The input is a request JSON, and the output is authenticated keywords and sentiment data.

[1089] Step 6:

[1090] Searching for information sources

[1091] The server searches its internal database and internet sources based on the received keywords and sentiment data. It uses search engine APIs (e.g., Google API) to retrieve external information. It also queries its own internal database. The input is authenticated keywords, and the output is a list of relevant information.

[1092] Step 7:

[1093] Data analysis

[1094] The server analyzes the information data obtained through searches. It uses NLP techniques (e.g., SpaCy, NLTK) to evaluate the relevance and importance of the information. Based on sentiment data, it filters the information to extract information that matches the user's interests. The input is the acquired information data, and the output is filtered, relevant information.

[1095] Step 8:

[1096] Formatting of analysis results

[1097] The server formats the analyzed information into a user-friendly format (e.g., JSON). During this process, it adjusts how the information is displayed based on the user's emotional data. In particular, it provides simple and easy-to-understand information to users experiencing stress. The input is filtered relevant information, and the output is formatted JSON data.

[1098] Step 9:

[1099] Sending formatted information

[1100] The server sends the formatted information to the user's terminal. This transmission also uses the HTTPS protocol to ensure secure communication. The input is formatted JSON information, and the output is the data that arrives on the user's terminal.

[1101] Step 10:

[1102] Receiving and displaying information

[1103] The user terminal analyzes information received from the server and displays it on the user interface. The display method is adjusted based on the user's emotional state. For example, a relaxed user is provided with detailed and visually rich information, while a stressed user is provided with concise and easy-to-understand information. The input is the transmitted JSON information, and the output is personalized content displayed on the user interface.

[1104] 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.

[1105] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1106] 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.

[1107] [Third Embodiment]

[1108] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1109] 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.

[1110] 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).

[1111] 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.

[1112] 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.

[1113] 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).

[1114] 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.

[1115] 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.

[1116] 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.

[1117] 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.

[1118] 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.

[1119] 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".

[1120] This invention will now describe embodiments for carrying it out. This system consists of a user terminal, a server, an internal database, and an internet-based information source, enabling users to efficiently research the services offered by other companies. The specific processing is carried out as follows.

[1121] System Overview

[1122] The user enters keywords related to the services of another company they want to research from their own device (such as a PC or smartphone). Next, the device generates a request containing these keywords and sends it to the server. The server receives the request and searches its internal database and internet information sources. The retrieved information is analyzed by the server, and information highly relevant to the user is extracted and formatted. Finally, the analyzed information is sent from the server to the user's device, and the device displays that information to the user.

[1123] Program processing

[1124] 1. User input

[1125] The user uses their own terminal to enter keywords related to the services of other companies. For example, they might enter "other company's logistics management system."

[1126] 2. Terminal request generation

[1127] The terminal generates an HTTP request based on the keywords entered by the user. This request includes the following information:

[1128] Keywords entered by the user

[1129] Authentication information (API key, etc.)

[1130] Desired data format (e.g., JSON)

[1131] 3. Sending requests from the terminal to the server

[1132] The device sends the generated request to the server. Requests are often sent via a RESTful API and are transmitted securely using the HTTPS protocol.

[1133] 4. Server receives request

[1134] The server receives the request sent from the terminal. After receiving it, it performs the following actions:

[1135] Check the authentication credentials of the request to verify that it is not unauthorized access.

[1136] Extract the keywords entered by the user.

[1137] 5. Searching for data on the server

[1138] The server searches its internal database and trusted sources on the internet. The specific processing steps are as follows:

[1139] Using search engine APIs: The server uses search engine APIs to retrieve web pages related to keywords.

[1140] Executing queries on internal databases: The server also executes queries on its own internal databases to retrieve relevant information.

[1141] Web scraping: Obtaining information from websites by scraping it as needed.

[1142] 6. Data Analysis

[1143] The server analyzes the acquired data. The specific steps of the analysis are as follows:

[1144] Application of NLP (Natural Language Processing) technology: Use a text analysis engine to evaluate the relevance and importance of information. For example, utilize technologies such as TF-IDF and Word2Vec.

[1145] Data filtering: Remove noise and eliminate information that the user is not looking for.

[1146] Information Classification: Organize information by category, such as service features, pricing plans, and customer reviews.

[1147] 7. Formatting and transmitting information

[1148] The server formats the analysis results into a user-friendly format (e.g., JSON or HTML) and returns them to the terminal.

[1149] 8. Information reception and display by the user terminal

[1150] The terminal receives information sent from the server and displays it to the user. In doing so, it converts the information into a data structure suitable for display and outputs the organized information on the user interface.

[1151] Specific example

[1152] 1. User input

[1153] The user enters "data analysis tools from other companies."

[1154] 2. Terminal request generation and transmission

[1155] The terminal generates a request like this:

[1156] json

[1157] {

[1158] "keyword": "Data analysis tools from other companies",

[1159] "apiKey": "USER_API_KEY",

[1160] "format": "json"

[1161] }

[1162] Send this request to the server.

[1163] 3. Server Request Reception and Search

[1164] The server receives the request, authenticates it, extracts keywords, and uses a search engine API to retrieve information related to "other companies' data analysis tools." It also queries its internal database.

[1165] 4. Data Analysis

[1166] The server uses NLP techniques to analyze the results and classify service features, pricing plans, and customer reviews. For example, it uses TF-IDF to evaluate keyword importance and extract highly relevant information.

[1167] 5. Formatting and transmitting information

[1168] The server generates the following JSON and sends it to the terminal:

[1169] json

[1170] {

[1171] "serviceFeatures": ["Real-time analytics", "Big data compatible"]

[1172] "pricingPlans": ["Basic Plan: ¥10,000 / month", "Premium Plan: ¥30,000 / month"]

[1173] "customerReviews": ["Easy to use", "Good value for money"]

[1174] }

[1175] 6. Receiving and displaying information on the device

[1176] The terminal parses the received data and displays the above information on the screen.

[1177] This system allows for quick and efficient on-site investigation of competitors' services, supporting operational decision-making. In this way, it significantly improves user operational efficiency by reducing the time and effort required for information gathering and providing accurate information.

[1178] The following describes the processing flow.

[1179] Step 1:

[1180] The user uses their own terminal to enter keywords related to the third-party service they want to research. For example, they might enter "third-party logistics management system".

[1181] Step 2:

[1182] The terminal receives the entered keyword and generates an HTTP request. This request includes the keyword entered by the user, authentication information (such as an API key), and the desired data format (e.g., JSON).

[1183] Step 3:

[1184] The terminal generates a request and sends it to the server using HTTPS. The transmission is done via a RESTful API.

[1185] Step 4:

[1186] The server processes the request received from the terminal. First, it checks the authentication information included in the request to verify that it is not an unauthorized access. Next, it extracts the keywords entered by the user.

[1187] Step 5:

[1188] The server searches for data based on the extracted keywords. Specifically, it retrieves information from internal databases and internet sources. In this process, it uses the following methods:

[1189] The server uses a search engine API (e.g., Google API) to retrieve information related to the keyword.

[1190] The server queries the internal database to retrieve relevant information.

[1191] If necessary, the server will perform web scraping from the specified website.

[1192] Step 6:

[1193] The information acquired by the server is analyzed. Specifically, this includes the following processes:

[1194] We use NLP (Natural Language Processing) techniques to evaluate the relevance and importance of information. For example, we use TF-IDF and Word2Vec.

[1195] Filtering is performed to remove unnecessary information that constitutes noise.

[1196] Organize information such as service features, pricing plans, and customer reviews by category.

[1197] Step 7:

[1198] The server formats the parsed data and sends it to the user's terminal. For example, it generates a JSON format like the following:

[1199] json

[1200] {

[1201] "serviceFeatures": ["Real-time analytics", "Big data compatible"]

[1202] "pricingPlans": ["Basic Plan: ¥10,000 / month", "Premium Plan: ¥30,000 / month"]

[1203] "customerReviews": ["Easy to use", "Good value for money"]

[1204] }

[1205] Step 8:

[1206] The server formats the data and sends it to the user's terminal. The transmission is secure using HTTPS.

[1207] Step 9:

[1208] The terminal receives data sent from the server and parses it. It converts the received JSON data into an appropriate data structure and prepares it for display to the user.

[1209] Step 10:

[1210] The terminal analyzes the data and displays it on the user interface. The user then reviews the displayed information and uses it for specific tasks and decision-making.

[1211] (Example 1)

[1212] 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."

[1213] Traditional information gathering systems had the problem that it was time-consuming and laborious for users to efficiently investigate the services offered by other companies. Furthermore, there was a lack of technology to judge the reliability and relevance of the acquired information, making it difficult for users to quickly obtain useful information. In addition, the analysis and classification of search data were not adequately performed, making it difficult for users to obtain the accurate and well-organized information they needed.

[1214] 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.

[1215] In this invention, the server includes means for receiving requests sent from a user terminal and searching an internal database and information sources on the internet based on the requests; means for analyzing the acquired information and extracting and formatting information highly relevant to the user; and means for transmitting the analyzed information to the user terminal. This enables users to quickly and efficiently investigate the services of other companies and accurately acquire highly relevant information.

[1216] A "user terminal" refers to a computing device used by a user, such as a personal computer or smartphone.

[1217] "Keywords" are words or phrases related to the services of other companies that the user is researching.

[1218] A "request" is an HTTP request sent from a user's terminal to a server, and it includes keywords, authentication information, and the desired data format.

[1219] A "server" is a computing system that receives requests from user terminals, searches internal databases and information sources on the internet, analyzes and formats the retrieved information, and sends it to the user terminal.

[1220] An "internal database" is a data storage system that stores data held by a company.

[1221] "Internet information sources" refer to websites and other online information services.

[1222] A "search engine API" is an application interface that uses a search engine to retrieve web pages and information related to specific keywords.

[1223] "Acquired information" refers to data collected by the server from its internal database and information sources on the internet.

[1224] "Analysis" is the process of using natural language processing techniques to evaluate the relevance and importance of information acquired by a server.

[1225] "Natural language processing technology" refers to technologies that process and analyze input text data to understand and evaluate its content, and includes technologies such as TF-IDF and Word2Vec.

[1226] "Extraction" is the process of selecting information that is highly relevant to the user from the analysis results.

[1227] "Formatting" refers to converting extracted information into a format that is easy for the user to understand.

[1228] This invention will now describe embodiments for carrying it out. This system consists of a user terminal, a server, an internal database, and an internet-based information source, enabling users to efficiently research the services offered by other companies. The specific processing is carried out as follows.

[1229] System Overview

[1230] The user uses their own device (such as a PC or smartphone) to enter keywords related to the services of other companies they want to research. For example, they might enter "logistics management system." Next, the device generates a request containing these keywords and sends it to the server. The server receives the request and searches its internal database and internet information sources. The retrieved information is analyzed by the server, and information highly relevant to the user is extracted and formatted. Finally, the analyzed information is sent from the server to the user's device, and the device displays the information to the user. This system can utilize search engine APIs, natural language processing technology, and web scraping techniques.

[1231] Hardware and software to be used

[1232] User terminal: A computing device that can connect to the internet, such as a personal computer, smartphone, or tablet.

[1233] Server: A server for high-performance data processing and database access. Cloud-based servers (e.g., AWS or Google Cloud Platform) can also be used.

[1234] Internal database: A database management system such as MySQL, PostgreSQL, or Oracle Database.

[1235] Search Engine APIs: APIs such as the Google Custom Search API for retrieving web information based on keywords.

[1236] Natural Language Processing Technology: An engine that analyzes information obtained using libraries and models such as TF-IDF, Word2Vec, and BERT.

[1237] Web scraping: A technique for automatically collecting necessary web information using libraries such as Beautiful Soup and Selenium.

[1238] Specific example

[1239] Here's a concrete example of how the system works when a user wants to research "data analysis tools from other companies." The user types "data analysis tools from other companies" into their terminal. The terminal then generates a request like the following and sends it to the server.

[1240] Example of a prompt:

[1241] Keywords: Data analysis tools from other companies

[1242] API Key: USER_API_KEY

[1243] Data format: JSON

[1244] The server receives this request, verifies the authentication information, and then uses its internal database and search engine API to search for information related to "other companies' data analysis tools." It also uses web scraping techniques to collect information from relevant websites. The collected data is analyzed using natural language processing techniques. For example, TF-IDF is used to extract highly relevant information and classify it into categories such as service features, pricing plans, and customer reviews.

[1245] The analyzed information is formatted by the server and sent to the user terminal in the following format:

[1246] Plastic information

[1247] Service features: Real-time analysis, Big data support

[1248] Pricing plans: Basic plan: ¥10,000 / month, Premium plan: ¥30,000 / month

[1249] Customer reviews: Easy to use, good value for money.

[1250] The user's device receives and displays this information. For example, the information is displayed on the UI of a web page in a format appropriate to each information category. This allows users to efficiently understand the services offered by other companies and use that information to help them make informed decisions.

[1251] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1252] Step 1:

[1253] User keyword input

[1254] Input: Users use their own devices (PCs or smartphones) to enter keywords related to the services of other companies they want to research. "Logistics management system" is one example.

[1255] Operation: The user enters keywords into the search field of their device's browser or application and clicks the search button.

[1256] Output: The entered keyword is processed internally by the terminal and ready to be sent to the next step.

[1257] Step 2:

[1258] Request generation by the terminal

[1259] Input: Keyword entered by the user

[1260] Operation: Based on the entered keyword, the terminal generates an HTTP request containing the following information:

[1261] Keywords entered by the user

[1262] API key for system authentication

[1263] Desired data format (e.g., JSON)

[1264] As an example of a specific request, the following JSON will be generated:

[1265] json

[1266] {

[1267] "keyword": "Logistics management system",

[1268] "apiKey": "USER_API_KEY",

[1269] "format": "json"

[1270] }

[1271] Output: The generated HTTP request is ready to be sent to the server.

[1272] Step 3:

[1273] Sending a request from the terminal to the server

[1274] Input: HTTP request (including keywords, API key, and data format)

[1275] Operation: The device sends the generated HTTP request to the server. The HTTPS protocol is used to securely transmit the data. The device sends the request via a RESTful API.

[1276] Output: The request is received by the server and processing begins in the next step.

[1277] Step 4:

[1278] Server request reception and analysis

[1279] Input: HTTP request sent from the terminal

[1280] Operation: The server parses the received request and performs the following processes:

[1281] Verify the API key and confirm that the request is legitimate.

[1282] Extract keywords entered by the user.

[1283] Output: The validated keywords will be used in the next step.

[1284] Step 5:

[1285] Server Data Retrieval

[1286] Input: Extracted keywords

[1287] Operation: The server searches its internal database and trusted sources on the internet. Specifically, it performs the following steps:

[1288] Using search engine APIs: Use APIs such as the Google Custom Search API to retrieve relevant information from the internet.

[1289] Executing queries on internal databases: Execute queries using keywords against internal databases such as MySQL and PostgreSQL to retrieve relevant information.

[1290] Web scraping: If necessary, use Beautiful Soup or Selenium to scrape the required information from specific websites.

[1291] Output: The acquired information will be used for data analysis in the next step.

[1292] Step 6:

[1293] Data analysis

[1294] Input: Data obtained from internal databases and reliable sources on the internet.

[1295] Operation: The server analyzes the acquired data. The specific analysis steps are as follows:

[1296] Application of natural language processing techniques: We evaluate the relevance and importance of information using natural language processing techniques such as TF-IDF, Word2Vec, and BERT. For example, we analyze the importance of keywords using TF-IDF.

[1297] Data filtering: Removes noise and irrelevant data to extract the information the user is looking for.

[1298] Information Classification: Organize information into categories such as service features, pricing plans, and customer reviews.

[1299] Output: The analysis results will be formatted in the next step.

[1300] Step 7:

[1301] Information formatting

[1302] Input: Analyzed data

[1303] Operation: The server formats the analysis results into a user-friendly format, such as JSON or HTML. A concrete example of a response is the following JSON:

[1304] json

[1305] {

[1306] "serviceFeatures": ["Real-time analytics", "Big data compatible"]

[1307] "pricingPlans": ["Basic Plan: ¥10,000 / month", "Premium Plan: ¥30,000 / month"]

[1308] "customerReviews": ["Easy to use", "Good value for money"]

[1309] }

[1310] Output: The formatted data is sent to the user's terminal.

[1311] Step 8:

[1312] Information reception and display by the user terminal

[1313] Input: Formatted data sent from the server

[1314] Operation: The terminal parses the received data and converts it into a format suitable for display. This is then displayed on the user interface. For example, information displayed in the UI of a web page or application is organized into service features, pricing plans, customer reviews, etc.

[1315] Output: Users can view detailed information about the services of other companies they wish to investigate.

[1316] (Application Example 1)

[1317] 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."

[1318] In today's economic environment, efficiently and quickly obtaining and comparing information on other companies' electronic payment services is crucial. However, manual methods and traditional information gathering techniques have limitations in terms of comprehensiveness and accuracy of necessary information, and are time-consuming and labor-intensive. Furthermore, collecting the latest information is particularly difficult in the electronic payment sector, where market trends are constantly changing. This can make accurate market analysis and maintaining competitiveness difficult.

[1319] 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.

[1320] In this invention, the server includes means for generating a request containing keywords entered by the user, means for searching an internal database and information sources on the internet based on the request sent from the user terminal, means for analyzing the acquired information and extracting and formatting information highly relevant to the user, means for transmitting the analyzed information to the user terminal, and means for the user terminal to display information regarding the features, pricing plans, and user reviews of the electronic payment service. This enables the user to efficiently and quickly obtain detailed information on other companies' electronic payment services and compare them.

[1321] A "user terminal" is a device used by a user to operate, and includes computers such as smartphones and personal computers.

[1322] A "keyword" refers to a specific word or phrase that a user enters to search for information.

[1323] A "request" is a communication message containing request information sent from a user's terminal to a server.

[1324] A "server" refers to a central processing unit that receives requests sent from user terminals and performs data retrieval and analysis based on those requests.

[1325] An "internal database" is a database that stores data owned by a company and is used to search for requested information.

[1326] "Internet information sources" refer to websites and online databases that exist on the internet, and are used to obtain necessary information from them.

[1327] "Analysis" refers to the process of classifying information acquired by the server into categories and evaluating their relationships.

[1328] "Highly relevant information" refers to information that is closely related to the keywords the user is searching for and is also useful.

[1329] "Formatting" refers to the process of converting analyzed information into a format that is easy for users to understand.

[1330] "Features" refer to the specific functions or capabilities that an electronic payment service possesses.

[1331] "Pricing plan" refers to the pricing structure and details of a service.

[1332] "User reviews" refer to evaluations and opinions provided by service users.

[1333] This invention will now describe embodiments for carrying out this invention. This invention relates to a system that allows users to efficiently collect and compare information on electronic payment services of other companies. This system consists of a user terminal, a server, an internal database, and information sources on the internet.

[1334] Hardware and software configuration

[1335] User terminal:

[1336] User terminals include computers such as smartphones and personal computers. These terminals are connected to the internet and have an interface for inputting information.

[1337] server:

[1338] The servers are located on cloud infrastructure. Specifically, cloud services such as AWS EC2 are available.

[1339] For the database, we will use an SQL database such as MySQL.

[1340] software:

[1341] To search for information, we utilize our internal database and search engine APIs such as the Google Custom Search API.

[1342] For natural language processing, we use NLP (Natural Language Processing) engines such as TensorFlow and NLTK.

[1343] System operation

[1344] 1. User input:

[1345] The user launches the smartphone app and enters keywords related to other companies' electronic payment services. For example, they might enter the keyword "other companies' mobile payment services."

[1346] 2. Terminal request generation:

[1347] The user's terminal generates an HTTP request based on the keywords entered by the user. This request includes the keywords, the API key, and the desired data format (e.g., JSON).

[1348] 3. Server request reception and data retrieval:

[1349] The server receives requests sent from user terminals. After receiving a request, it executes queries to search engine APIs and internal databases to collect relevant information. It also performs web scraping as needed.

[1350] 4. Data Analysis:

[1351] The server analyzes the collected data using NLP (Neuro-Linguistic Programming) techniques. Specifically, it uses methods such as TF-IDF and Word2Vec to evaluate the relevance and importance of the information, remove noise, and classify it into categories such as service features, pricing plans, and user reviews.

[1352] 5. Formatting and sending information:

[1353] The server formats the analysis results into a user-friendly format (e.g., JSON) and sends it to the terminal.

[1354] 6. Information reception and display by the user terminal:

[1355] The user terminal receives information sent from the server and displays it in a visually easy-to-understand interface. For example, the output might look like this:

[1356] Service features:

[1357] High-frequency trading

[1358] Low fees

[1359] Pricing plans:

[1360] Basic plan: ¥500 / month

[1361] Premium plan: ¥2,000 / month

[1362] User reviews:

[1363] Convenient and easy to use

[1364] Good support

[1365] This system allows users to quickly and efficiently gather detailed information on other companies' electronic payment services and compare them.

[1366] Example of a prompt

[1367] As a concrete example, here is an example of the prompt text when a user enters the keyword "mobile payment from another company":

[1368] "Please find competitive mobile payment services including features, pricing plans, and customer reviews."

[1369] Based on this prompt, the system collects the necessary information and performs the necessary processing to provide it to the user.

[1370] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1371] Step 1:

[1372] The user launches a smartphone app and enters keywords related to another company's electronic payment service. For example, they might enter the keyword "other company's mobile payment." The input data here is the keyword, and the output is the data that forms the basis of the HTTP request.

[1373] Step 2:

[1374] The user's terminal generates an HTTP request based on the keywords entered by the user. This request includes the following information:

[1375] Keywords (e.g., "other companies' mobile payment services")

[1376] API key (user authentication information)

[1377] Desired data format (e.g., JSON)

[1378] The input for this step is the keyword entered by the user, and the output is the generated HTTP request.

[1379] Step 3:

[1380] The user terminal generates a request and sends it to the server. The request is sent securely using the HTTPS protocol via a RESTful API. The input to this step is the generated HTTP request, and the output is a notification to the server that the transmission is complete.

[1381] Step 4:

[1382] The server receives a request sent from the user's terminal. The received request is authenticated, and keywords are extracted. The input here is the received HTTP request, and the output is the authenticated keywords.

[1383] Step 5:

[1384] The server searches its internal database and internet sources based on authenticated keywords. The server then performs the following actions:

[1385] Use the search engine API (Google Custom Search API) to retrieve relevant web pages.

[1386] Execute queries against the internal database (e.g., MySQL) to retrieve relevant information.

[1387] Web scraping will be performed as needed to collect relevant information.

[1388] The input for this step is a verified keyword, and the output is the collected raw data.

[1389] Step 6:

[1390] The raw data collected by the server is analyzed. Specifically, the following processes are performed:

[1391] The acquired data is analyzed using an NLP engine (such as TensorFlow or NLTK).

[1392] We use TF-IDF and Word2Vec to evaluate the relevance and importance of the information.

[1393] Removal of noisy data, filtering of unnecessary information.

[1394] Information is categorized by service features, pricing plans, and user reviews.

[1395] The input for this step is the collected raw data, and the output is the analyzed and formatted data.

[1396] Step 7:

[1397] The server formats the parsed information into a user-friendly format (e.g., JSON) and sends it to the user's terminal. The input for this step is the parsed data, and the output is a response containing the formatted data.

[1398] Step 8:

[1399] The user terminal receives information sent from the server and displays it in a visually easy-to-understand interface. The input here is the response data from the server, and the output is the information displayed on the terminal's user interface. For example, service features, pricing plans, and user reviews are displayed in a visually organized manner.

[1400] This series of steps enables the system described in the claims to allow users to efficiently and quickly collect and compare detailed information on other companies' electronic payment services.

[1401] 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.

[1402] This invention relates to a system that further enhances the user experience by combining it with an emotion engine that recognizes user emotions. This invention achieves more personalized information delivery by adjusting information display and search results based on the user's emotions.

[1403] System Overview

[1404] This system consists of user terminals, servers, an internal database, and information sources on the internet. Furthermore, the user terminals are equipped with an emotion engine that recognizes the user's emotions in real time and transmits that data to the server.

[1405] Program processing

[1406] 1. User input

[1407] The user uses their own terminal to enter keywords related to the third-party service they want to research. For example, they might enter "third-party logistics management system."

[1408] 2. User emotion recognition

[1409] An emotion engine installed in the user's device recognizes the user's emotions in real time. Using speech recognition and facial expression recognition, it acquires emotional data such as whether the user is stressed or relaxed.

[1410] 3. Terminal request generation

[1411] The terminal generates an HTTP request containing keywords and sentiment data entered by the user. This request includes the following information:

[1412] Keywords entered by the user

[1413] User sentiment data

[1414] Authentication information (API key, etc.)

[1415] Desired data format (e.g., JSON)

[1416] 4. Sending requests from the terminal to the server

[1417] The device sends the generated request to the server. The transmission is done via a RESTful API and is securely sent using the HTTPS protocol.

[1418] 5. Server receives request

[1419] The server processes requests received from the terminal. First, it checks the authentication information of the request to verify that it is not an unauthorized access. Next, it extracts the keywords and sentiment data entered by the user.

[1420] 6. Searching for data on the server

[1421] The server searches for data based on keywords and sentiment data. Specifically, it retrieves information from internal databases and internet sources. In this process, it utilizes the following methods:

[1422] Using search engine APIs: The server uses search engine APIs (e.g., Google API) to retrieve information related to keywords.

[1423] Executing queries on internal databases: The server executes queries on its own internal databases to retrieve relevant information.

[1424] Web scraping: Obtaining information by scraping it from specific websites as needed.

[1425] 7. Data Analysis

[1426] The server analyzes the acquired data. Specifically, it performs the following processes:

[1427] Application of NLP (Natural Language Processing) technology: Use a text analysis engine to evaluate the relevance and importance of information. For example, TF-IDF or Word2Vec may be used.

[1428] Filtering is performed to remove unnecessary information that constitutes noise.

[1429] Organize information such as service features, pricing plans, and customer reviews by category.

[1430] Based on user sentiment data, specific information is highlighted and its display order is adjusted. For example, if a user is feeling stressed, concise and easy-to-read information is prioritized.

[1431] 8. Formatting and transmitting information

[1432] The server formats the analysis results into a user-friendly format (e.g., JSON or HTML) and returns them to the terminal. At this stage, specific formats and display methods can be adopted based on the user's sentiment data.

[1433] 9. Information reception and display by the user terminal

[1434] The terminal receives information sent from the server and displays it to the user. The received data is converted into an appropriate data structure and displayed in the user interface. The display method is adjusted to suit the user's emotions.

[1435] Specific example

[1436] 1. User input and emotion recognition

[1437] As soon as the user types "data analysis tool from another company," the emotion engine recognizes the user's facial expression and acquires emotional data indicating "stress."

[1438] 2. Terminal request generation and transmission

[1439] The terminal generates a request like this:

[1440] json

[1441] {

[1442] "keyword": "Data analysis tools from other companies",

[1443] "emotion": "stress",

[1444] "apiKey": "USER_API_KEY",

[1445] "format": "json"

[1446] }

[1447] Send this request to the server.

[1448] 3. Server Request Reception and Search

[1449] The server receives the request, extracts keywords and sentiment data after authentication, and uses a search engine API to retrieve information related to "other companies' data analysis tools." It also queries its internal database.

[1450] 4. Data Analysis

[1451] The server uses NLP (Neuro-Linguistic Programming) techniques to analyze the results and categorize service features, pricing plans, and customer reviews. Furthermore, it formats the information to display it concisely based on emotional data, specifically "stress."

[1452] 5. Formatting and transmitting information

[1453] The server generates the following JSON and sends it to the terminal:

[1454] json

[1455] {

[1456] "serviceFeatures": ["Real-time analytics", "Big data compatible"]

[1457] "pricingPlans": ["Basic Plan: ¥10,000 / month", "Premium Plan: ¥30,000 / month"]

[1458] "customerReviews": ["Easy to use", "Good value for money"]

[1459] }

[1460] 6. Receiving and displaying information on the device

[1461] The device analyzes the received data and displays the information in a concise and visually easy-to-understand format, taking into account the user's "stress." For example, it highlights important information and collapses detailed information.

[1462] This system enables rapid and efficient on-site research into competitors' services and provides personalized information tailored to the user's emotional state. This reduces the time and effort required for information gathering and significantly improves user efficiency by providing accurate and relevant information.

[1463] The following describes the processing flow.

[1464] Step 1:

[1465] The user uses their own terminal to enter keywords related to the third-party service they want to research. For example, they might enter "third-party logistics management system."

[1466] Step 2:

[1467] An emotion engine installed in the user's device recognizes the user's emotions in real time. Using speech recognition and facial expression recognition, it acquires emotional data such as whether the user is stressed or relaxed.

[1468] Step 3:

[1469] The terminal generates an HTTP request based on the entered keyword and acquired sentiment data. This request includes the following information:

[1470] Keywords entered by the user

[1471] User sentiment data

[1472] Authentication information (API key, etc.)

[1473] Desired data format (e.g., JSON)

[1474] Step 4:

[1475] The terminal generates a request and sends it to the server using HTTPS. The transmission is done via a RESTful API.

[1476] Step 5:

[1477] The server processes the request received from the terminal. First, it checks the authentication information included in the request to verify that it is not an unauthorized access. Next, it extracts the keywords and sentiment data entered by the user.

[1478] Step 6:

[1479] The server searches for data based on extracted keywords and sentiment data. Specifically, it retrieves information from internal databases and internet sources. In this process, it utilizes the following methods:

[1480] Using search engine APIs: The server uses search engine APIs, such as the Google API, to retrieve information related to the keywords.

[1481] Executing queries on internal databases: The server also executes queries on its own internal databases to retrieve relevant information.

[1482] Web scraping: Obtaining information by scraping it from specific websites as needed.

[1483] Step 7:

[1484] The server analyzes the information it has acquired. Specifically, it performs the following processes:

[1485] We use NLP (Natural Language Processing) techniques to evaluate the relevance and importance of information. For example, we use TF-IDF and Word2Vec.

[1486] Filtering is performed to remove unnecessary information that would otherwise be considered noise.

[1487] Organize information such as service features, pricing plans, and customer reviews by category.

[1488] Based on user sentiment data, specific information is highlighted and its display order is adjusted. For example, if a user is feeling stressed, concise and easy-to-read information is prioritized.

[1489] Step 8:

[1490] The server formats the analysis results into a user-friendly format (e.g., JSON or HTML) and sends them to the terminal. In this process, specific formats and display methods can be adopted based on the user's sentiment data.

[1491] Step 9:

[1492] The terminal receives information sent from the server and parses the data. It converts the received JSON data into an appropriate data structure and prepares it for display to the user.

[1493] Step 10:

[1494] The terminal analyzes the data and displays it on the user interface. Users review the displayed information and use it for specific tasks and decision-making. The display method is adjusted according to the user's emotions. For example, if the emotion engine detects user stress, particularly important information is highlighted, and detailed information is collapsed as needed, taking care to reduce the user's burden.

[1495] In this way, this system, which incorporates an emotion engine, can provide information tailored to the user's emotions and support efficient decision-making.

[1496] (Example 2)

[1497] 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."

[1498] Conventional information retrieval systems perform searches based solely on keywords entered by the user, without considering the user's emotional state. This makes it difficult to provide personalized information tailored to the user's emotional state. In particular, if appropriate information is not presented when the user is stressed or relaxed, the user experience may deteriorate. Therefore, there is a need for a system that considers the user's emotional state and provides information more effectively.

[1499] 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.

[1500] In this invention, the server includes means for the user terminal to receive keywords and sentiment data entered by the user and generate a request including said keywords and sentiment data; means for the server to receive a request sent from the user terminal and search an internal database and information sources on the internet based on said request; means for the server to analyze the acquired information, evaluate the relevance and importance of the information using natural language processing techniques, and adjust the information based on the user's sentiment data; means for the server to format the analyzed information, extract information highly relevant to the user, and transmit it to the user terminal; and means for the user terminal to display the information received from the server and adjust the display method based on the user's sentiment. This makes it possible to provide personalized information according to the user's emotional state.

[1501] A "user terminal" is an electronic device used by the user for operations, and is a device that inputs keywords and acquires sentiment data.

[1502] A "keyword" is a word or phrase that a user enters into their device to search for specific information.

[1503] "Emotional data" refers to information that represents the user's emotional state, and is data acquired through voice recognition and facial expression recognition.

[1504] A "request" is request information sent from a user's terminal to a server, and includes keywords and sentiment data.

[1505] A "server" is a device that performs data retrieval and analysis based on requests received from a user terminal and sends the results to the user terminal.

[1506] An "internal database" is a data storage system where data held by a company is stored, and it is used to search for related information.

[1507] "Internet information sources" refer to information resources accessible via the internet that are used to obtain external information.

[1508] "Natural language processing technology" refers to the technology used by computers to understand, interpret, and generate human language, and is a means of evaluating the relevance and importance of information.

[1509] "Highly relevant information" refers to information that is most relevant to the user, filtered based on the user's keywords and sentiment data.

[1510] "Formatting" is the process of reconstructing acquired information into a format that is easy for users to understand.

[1511] "Information adjustment" is the process of determining the display order and highlighting of information based on user sentiment data.

[1512] The system of the present invention consists of a user terminal, a server, an internal database, and an information source on the Internet. Furthermore, the user terminal is equipped with an emotion engine that recognizes the user's emotions in real time and transmits that data to the server. The following describes in detail how this system is implemented.

[1513] First, the user uses their own terminal to enter keywords related to what they want to research. For example, they might enter "other companies' logistics management systems." Simultaneously, an emotion engine installed in the user's terminal recognizes the user's facial expressions and voice in real time and collects emotion data. The emotion engine uses speech recognition technology and facial expression recognition technology, specifically employing AI models such as OpenFace and DeepFace.

[1514] Next, the user terminal generates a request containing the entered keywords and the retrieved sentiment data. This request is in JSON format, and an internal HTTP client library (e.g., Python's requests library) is used to generate the request. The generated JSON request includes the following information:

[1515] Keywords entered by the user

[1516] User sentiment data

[1517] Authentication information (such as API key)

[1518] Desired data format (e.g., JSON)

[1519] This request is sent to the server via the HTTPS protocol through a RESTful API.

[1520] The server processes requests received from user terminals. First, the server verifies the legitimacy of the request based on authentication information to ensure it is not an unauthorized access attempt. Next, it extracts keywords and sentiment data from the request and begins a data search. The search is performed using the following methods:

[1521] Using search engine APIs: Information is retrieved using external search engine APIs such as the Google API.

[1522] Executing queries on internal databases: Retrieving information by executing queries on databases maintained by the company.

[1523] Web scraping: The process of obtaining information by scraping it from a specific website.

[1524] The acquired information is analyzed on the server. The server uses natural language processing (NLP) techniques to evaluate the relevance and importance of the acquired information. In this process, a text analysis engine (e.g., SpaCy, NLTK) is used, employing techniques such as TF-IDF and Word2Vec. Noise is removed through filtering, and necessary information is organized and classified into categories. Furthermore, information is highlighted and its display order is adjusted based on the user's sentiment data. For example, if the user is feeling stressed, important information is summarized concisely and displayed.

[1525] The server reformats the parsed information into a user-friendly format (e.g., JSON or HTML) and returns it to the terminal. This ensures that the information is presented to the user in an appropriate format. Finally, the user terminal displays the information received from the server and adjusts the display method according to the user's emotional state. For example, important information may be highlighted, while detailed information may be displayed in a collapsed format.

[1526] As a concrete example, consider a scenario where a user enters "data analysis tool from another company," and the emotion engine detects a "stressed" state from the user's facial expression. In this case, the device generates and sends the following request to the server:

[1527] Keywords: Data analysis tools from other companies

[1528] Emotional data: stress

[1529] API Key: USER_API_KEY

[1530] Format: json

[1531] The server receives the request, searches for information based on the keywords and sentiment data, and performs analysis. The analysis results provide a concise summary of service features, pricing plans, customer reviews, etc. This information is then formatted as JSON data and sent to the terminal:

[1532] Service features: [Real-time analysis, Big data compatible]

[1533] Pricing plans: [Basic plan: ¥10,000 / month, Premium plan: ¥30,000 / month]

[1534] Customer reviews: [Easy to use, good value for money]

[1535] The terminal receives the transmitted information and displays it concisely and visually, taking into consideration the user's "stress." This allows users to quickly and efficiently understand the services offered by other companies and improve their work efficiency.

[1536] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1537] Step 1: User input

[1538] The user uses their own terminal to enter keywords related to the third-party service they want to investigate. For example, they might enter "third-party logistics management system." This input triggers the system to start. The entered keywords are stored internally on the terminal. Furthermore, user input is primarily done via keyboard text.

[1539] Input: Keywords entered by the user (e.g., "Logistics management system of another company")

[1540] Output: Retained keywords (e.g., "other company's logistics management system")

[1541] Step 2: Recognizing the user's emotions

[1542] The device's built-in emotion engine works to recognize the user's facial expressions and voice tone in real time and generate emotion data. It uses the camera to capture facial expressions and the microphone to recognize voice. The emotion engine uses AI models (e.g., OpenFace, DeepFace) to determine whether the user is stressed or relaxed.

[1543] Input: User's facial expression data, voice data

[1544] Output: Analyzed emotion data (e.g., "stress")

[1545] Step 3: Generate terminal request

[1546] The terminal generates an HTTP request containing the keywords entered by the user and the retrieved sentiment data. The generated request includes the following information: keywords, sentiment data, authentication information (such as an API key), and the desired data format (e.g., JSON). This request is constructed using the system's internal HTTP client library (e.g., Python's requests library).

[1547] Input: Keyword (e.g., "Other company's logistics management system"), Sentiment data (e.g., "Stress"), Authentication information (e.g., "USER_API_KEY")

[1548] Output: HTTP request (e.g., JSON format request)

[1549] Step 4: Sending a request from the terminal to the server

[1550] The terminal sends the generated request to the server. This transmission is performed using a RESTful API and the HTTPS protocol. The use of the HTTPS protocol ensures the security of the transmitted data.

[1551] Input: Generated HTTP request (e.g., a request in JSON format)

[1552] Output: Request sent to the server

[1553] Step 5: Server receives request

[1554] The server receives requests sent from terminals. First, it verifies the authentication credentials to ensure that the access is not unauthorized. Then, it extracts keywords and sentiment data included in the request.

[1555] Input: HTTP request sent from the terminal

[1556] Output: Extracted keywords (e.g., "other company's logistics management system"), sentiment data (e.g., "stress")

[1557] Step 6: Search server data

[1558] The server searches for information from internal databases and internet sources based on extracted keywords and sentiment data. The following methods are used:

[1559] Using search engine APIs: Use the Google API to retrieve information related to keywords.

[1560] Executing queries on internal databases: Retrieving information by executing queries on databases maintained by the company.

[1561] Web scraping: The process of obtaining information by scraping it from a specific website.

[1562] Input: Extracted keywords (e.g., "other company's logistics management system"), sentiment data (e.g., "stress")

[1563] Output: Acquired information data

[1564] Step 7: Data Analysis

[1565] The server analyzes the acquired information. Using a text analysis engine (e.g., SpaCy, NLTK), it evaluates the relevance and importance of the information using natural language processing techniques. Furthermore, it performs noise reduction, categorization, and adjusts the highlighting and display order of information based on sentiment data.

[1566] Input: Acquired information data, emotional data (e.g., "stress")

[1567] Output: Analyzed and formatted information

[1568] Step 8: Format and send information

[1569] The server formats the analyzed information into a user-friendly format (e.g., JSON, HTML) and sends it to the terminal. It adopts an appropriate format and display method based on sentiment data.

[1570] Input: Analyzed and formatted information

[1571] Output: Formatted information data

[1572] Step 9: Information reception and display by the user terminal

[1573] The terminal converts information received from the server into a data structure and displays it on the user interface. Based on the user's emotions, the information is displayed in a concise and visually easy-to-understand manner. Techniques such as highlighting important information and collapsing detailed information are employed.

[1574] Input: Formatted information data

[1575] Output: Displayed information (on the user interface)

[1576] This allows users to quickly and efficiently understand the services offered by other companies, thereby improving operational efficiency.

[1577] (Application Example 2)

[1578] 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."

[1579] Traditional content delivery services often fail to consider the user's emotional state, making it difficult to provide content that matches the user's current mood. As a result, users may experience stress or be unable to relax, leading to decreased service satisfaction. Furthermore, it was difficult to quickly and accurately extract highly relevant information from the vast amount of data available.

[1580] 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.

[1581] In this invention, the server includes means for receiving requests sent from a user terminal and searching an internal database and information sources on the internet based on the requests; means for analyzing the acquired information, shaping the information based on the user's emotional data, and extracting information highly relevant to the user; and means for transmitting the analyzed information to the user terminal. This makes it possible to suggest personalized content according to the user's emotional state.

[1582] A "user terminal" is an electronic device used by users to input information and receive and display it, and it is also equipped with an emotion recognition engine.

[1583] "Keywords" are words or phrases that users enter for the purpose of research or searching.

[1584] "Emotional data" refers to data indicating the user's emotional state, acquired in real time by the emotion recognition engine installed in the user's device.

[1585] A "request" is communication data for an information request that includes keywords and sentiment data entered by the user.

[1586] A "server" is a central processing unit that searches internal databases and internet-based information sources based on requests received from user terminals and returns the results to the user terminals.

[1587] An "internal database" is a collection of data that a server maintains for access and retrieval.

[1588] An "information source on the internet" is an external information repository from which a server obtains information through search engine APIs or web scraping.

[1589] An "emotion recognition engine" is a combination of hardware and software that recognizes a user's emotional state in real time from their facial expressions and voice.

[1590] "Natural language processing technology" refers to computational methods and algorithms for analyzing text information acquired by a server and evaluating its relevance and importance.

[1591] "Filtering" is the process of removing noise and unnecessary data from acquired information and extracting information that is highly relevant to the user.

[1592] "Analysis" is the process by which a server analyzes the information it acquires and formats it into an appropriate format based on user sentiment data.

[1593] A "browsing interface" is a screen and operating system that displays information obtained from a server by the user's terminal and adjusts the display method based on the user's emotional state.

[1594] This invention is a system that personalizes content delivery based on user emotions. Its main components are a user terminal, a server, an internal database, an internet-based information source, and an emotion recognition engine.

[1595] The user terminal is equipped with a camera and microphone, and an emotion recognition engine acquires emotion data in real time from the user's facial expressions and voice. A request containing this acquired emotion data and keywords entered by the user is generated and sent to the server. The HTTPS protocol is used to securely communicate when sending requests.

[1596] After receiving a request, the server first verifies the authentication information. If the request is deemed legitimate, it searches its internal database and internet information sources. Information is collected using search engine APIs (e.g., Google API) and web scraping tools. The acquired data is analyzed using NLP (Natural Language Processing) techniques. Specifically, methods such as TF-IDF and Word2Vec are used to evaluate the relevance and importance of the information, and filtering is performed as needed.

[1597] The analysis results are formatted based on the user's emotional data. When the user is stressed, concise and easy-to-understand information is prioritized; when relaxed, detailed and helpful information is displayed preferentially. Finally, the organized information is converted into an appropriate data format, such as JSON, and sent to the user's device.

[1598] The user terminal analyzes information received from the server and displays it on the user interface. During this process, adjustments are made based on the user's emotional state. For example, a user experiencing stress will see a visually simpler UI, with detailed information displayed in a collapsible format.

[1599] Program operation description:

[1600] Emotion Recognition Engine: This engine uses facial recognition and speech recognition technologies to capture user emotions in real time. Specific software examples include OpenCV and TensorFlow.

[1601] Search engine API: Uses the Google API, etc., to retrieve information from internet sources.

[1602] NLP technology: SpaCy and NLTK are used as natural language processing engines. These are used to perform text analysis and evaluate the relevance and importance of information.

[1603] Filtering: Remove noise from collected data and extract only the information most relevant to the user.

[1604] Formatting of analysis results: Convert to HTML or JSON format and send to the user's terminal.

[1605] Specific example:

[1606] For example, if a user types "romantic movie" into their smartphone and the emotion recognition engine obtains the emotion data "relaxed," the server processes the information in the following steps:

[1607] 1. Use a search engine API to collect information about "romantic movies".

[1608] 2. Analyze the collected information using NLP technology.

[1609] 3. Based on the user's emotional data regarding "relaxation," the list of movies that promote relaxation is prioritized and formatted accordingly.

[1610] 4. Input the following prompt message into the AI ​​model to recommend an appropriate movie:

[1611] For users who are looking to relax, recommend relaxing romantic movies. For example, movies with touching and heartwarming stories.

[1612] As a result, users can easily find romantic movies that are suitable for a relaxed state.

[1613] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1614] Step 1:

[1615] User input

[1616] The user uses their device to enter keywords related to the content they want to search for. This input data serves as foundational information for finding appropriate content based on the user's current emotions. The device is equipped with an emotion recognition engine that simultaneously acquires emotion data from the user's facial expressions and voice. The input includes keywords (e.g., "romantic movies") and the user's emotion data (e.g., "relaxed").

[1617] Step 2:

[1618] Recognition of emotions

[1619] The device's built-in emotion recognition engine uses the camera and microphone to capture the user's emotions in real time. Using facial expression recognition and voice analysis technologies (e.g., OpenCV, TensorFlow), it generates emotion data such as whether the user is relaxed or stressed. The output is emotion data such as "relaxed."

[1620] Step 3:

[1621] Request generation

[1622] The user terminal integrates the entered keywords with the retrieved sentiment data to generate a request. This request includes keywords (e.g., "romantic movies"), sentiment data (e.g., "relaxed"), and authentication information (e.g., API key). The request is formatted in JSON format and ready to be sent to the server.

[1623] Step 4:

[1624] Sending a request to the server

[1625] The user terminal sends the generated request to the server. The HTTPS protocol is used for transmission, ensuring secure communication. The input is a request JSON, and the output is the request received by the server.

[1626] Step 5:

[1627] Server receives and authenticates requests.

[1628] The server receives requests from user terminals and first verifies authentication information. After confirming that the request is not malicious, it extracts keywords and sentiment data. The input is a request JSON, and the output is authenticated keywords and sentiment data.

[1629] Step 6:

[1630] Searching for information sources

[1631] The server searches its internal database and internet sources based on the received keywords and sentiment data. It uses search engine APIs (e.g., Google API) to retrieve external information. It also queries its own internal database. The input is authenticated keywords, and the output is a list of relevant information.

[1632] Step 7:

[1633] Data analysis

[1634] The server analyzes the information data obtained through searches. It uses NLP techniques (e.g., SpaCy, NLTK) to evaluate the relevance and importance of the information. Based on sentiment data, it filters the information to extract information that matches the user's interests. The input is the acquired information data, and the output is filtered, relevant information.

[1635] Step 8:

[1636] Formatting of analysis results

[1637] The server formats the analyzed information into a user-friendly format (e.g., JSON). During this process, it adjusts how the information is displayed based on the user's emotional data. In particular, it provides simple and easy-to-understand information to users experiencing stress. The input is filtered relevant information, and the output is formatted JSON data.

[1638] Step 9:

[1639] Sending formatted information

[1640] The server sends the formatted information to the user's terminal. This transmission also uses the HTTPS protocol to ensure secure communication. The input is formatted JSON information, and the output is the data that arrives on the user's terminal.

[1641] Step 10:

[1642] Receiving and displaying information

[1643] The user terminal analyzes information received from the server and displays it on the user interface. The display method is adjusted based on the user's emotional state. For example, a relaxed user is provided with detailed and visually rich information, while a stressed user is provided with concise and easy-to-understand information. The input is the transmitted JSON information, and the output is personalized content displayed on the user interface.

[1644] 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.

[1645] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1646] 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.

[1647] [Fourth Embodiment]

[1648] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1649] 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.

[1650] 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).

[1651] 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.

[1652] 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.

[1653] 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).

[1654] 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.

[1655] 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.

[1656] 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.

[1657] 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.

[1658] 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.

[1659] 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.

[1660] 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".

[1661] This invention will now describe embodiments for carrying it out. This system consists of a user terminal, a server, an internal database, and an internet-based information source, enabling users to efficiently research the services offered by other companies. The specific processing is carried out as follows.

[1662] System Overview

[1663] The user enters keywords related to the services of another company they want to research from their own device (such as a PC or smartphone). Next, the device generates a request containing these keywords and sends it to the server. The server receives the request and searches its internal database and internet information sources. The retrieved information is analyzed by the server, and information highly relevant to the user is extracted and formatted. Finally, the analyzed information is sent from the server to the user's device, and the device displays that information to the user.

[1664] Program processing

[1665] 1. User input

[1666] The user uses their own terminal to enter keywords related to the services of other companies. For example, they might enter "other company's logistics management system."

[1667] 2. Terminal request generation

[1668] The terminal generates an HTTP request based on the keywords entered by the user. This request includes the following information:

[1669] Keywords entered by the user

[1670] Authentication information (API key, etc.)

[1671] Desired data format (e.g., JSON)

[1672] 3. Sending requests from the terminal to the server

[1673] The device sends the generated request to the server. Requests are often sent via a RESTful API and are transmitted securely using the HTTPS protocol.

[1674] 4. Server receives request

[1675] The server receives the request sent from the terminal. After receiving it, it performs the following actions:

[1676] Check the authentication credentials of the request to verify that it is not unauthorized access.

[1677] Extract the keywords entered by the user.

[1678] 5. Searching for data on the server

[1679] The server searches its internal database and trusted sources on the internet. The specific processing steps are as follows:

[1680] Using search engine APIs: The server uses search engine APIs to retrieve web pages related to keywords.

[1681] Executing queries on internal databases: The server also executes queries on its own internal databases to retrieve relevant information.

[1682] Web scraping: Obtaining information from websites by scraping it as needed.

[1683] 6. Data Analysis

[1684] The server analyzes the acquired data. The specific steps of the analysis are as follows:

[1685] Application of NLP (Natural Language Processing) technology: Use a text analysis engine to evaluate the relevance and importance of information. For example, utilize technologies such as TF-IDF and Word2Vec.

[1686] Data filtering: Remove noise and eliminate information that the user is not looking for.

[1687] Information Classification: Organize information by category, such as service features, pricing plans, and customer reviews.

[1688] 7. Formatting and transmitting information

[1689] The server formats the analysis results into a user-friendly format (e.g., JSON or HTML) and returns them to the terminal.

[1690] 8. Information reception and display by the user terminal

[1691] The terminal receives information sent from the server and displays it to the user. In doing so, it converts the information into a data structure suitable for display and outputs the organized information on the user interface.

[1692] Specific example

[1693] 1. User input

[1694] The user enters "data analysis tools from other companies."

[1695] 2. Terminal request generation and transmission

[1696] The terminal generates a request like this:

[1697] json

[1698] {

[1699] "keyword": "Data analysis tools from other companies",

[1700] "apiKey": "USER_API_KEY",

[1701] "format": "json"

[1702] }

[1703] Send this request to the server.

[1704] 3. Server Request Reception and Search

[1705] The server receives the request, authenticates it, extracts keywords, and uses a search engine API to retrieve information related to "other companies' data analysis tools." It also queries its internal database.

[1706] 4. Data Analysis

[1707] The server uses NLP techniques to analyze the results and classify service features, pricing plans, and customer reviews. For example, it uses TF-IDF to evaluate keyword importance and extract highly relevant information.

[1708] 5. Formatting and transmitting information

[1709] The server generates the following JSON and sends it to the terminal:

[1710] json

[1711] {

[1712] "serviceFeatures": ["Real-time analytics", "Big data compatible"]

[1713] "pricingPlans": ["Basic Plan: ¥10,000 / month", "Premium Plan: ¥30,000 / month"]

[1714] "customerReviews": ["Easy to use", "Good value for money"]

[1715] }

[1716] 6. Receiving and displaying information on the device

[1717] The terminal parses the received data and displays the above information on the screen.

[1718] This system allows for quick and efficient on-site investigation of competitors' services, supporting operational decision-making. In this way, it significantly improves user operational efficiency by reducing the time and effort required for information gathering and providing accurate information.

[1719] The following describes the processing flow.

[1720] Step 1:

[1721] The user uses their own terminal to enter keywords related to the third-party service they want to research. For example, they might enter "third-party logistics management system".

[1722] Step 2:

[1723] The terminal receives the entered keyword and generates an HTTP request. This request includes the keyword entered by the user, authentication information (such as an API key), and the desired data format (e.g., JSON).

[1724] Step 3:

[1725] The terminal generates a request and sends it to the server using HTTPS. The transmission is done via a RESTful API.

[1726] Step 4:

[1727] The server processes the request received from the terminal. First, it checks the authentication information included in the request to verify that it is not an unauthorized access. Next, it extracts the keywords entered by the user.

[1728] Step 5:

[1729] The server searches for data based on the extracted keywords. Specifically, it retrieves information from internal databases and internet sources. In this process, it uses the following methods:

[1730] The server uses a search engine API (e.g., Google API) to retrieve information related to the keyword.

[1731] The server queries the internal database to retrieve relevant information.

[1732] If necessary, the server will perform web scraping from the specified website.

[1733] Step 6:

[1734] The information acquired by the server is analyzed. Specifically, this includes the following processes:

[1735] We use NLP (Natural Language Processing) techniques to evaluate the relevance and importance of information. For example, we use TF-IDF and Word2Vec.

[1736] Filtering is performed to remove unnecessary information that constitutes noise.

[1737] Organize information such as service features, pricing plans, and customer reviews by category.

[1738] Step 7:

[1739] The server formats the parsed data and sends it to the user's terminal. For example, it generates a JSON format like the following:

[1740] json

[1741] {

[1742] "serviceFeatures": ["Real-time analytics", "Big data compatible"]

[1743] "pricingPlans": ["Basic Plan: ¥10,000 / month", "Premium Plan: ¥30,000 / month"]

[1744] "customerReviews": ["Easy to use", "Good value for money"]

[1745] }

[1746] Step 8:

[1747] The server formats the data and sends it to the user's terminal. The transmission is secure using HTTPS.

[1748] Step 9:

[1749] The terminal receives data sent from the server and parses it. It converts the received JSON data into an appropriate data structure and prepares it for display to the user.

[1750] Step 10:

[1751] The terminal analyzes the data and displays it on the user interface. The user then reviews the displayed information and uses it for specific tasks and decision-making.

[1752] (Example 1)

[1753] 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".

[1754] Traditional information gathering systems had the problem that it was time-consuming and laborious for users to efficiently investigate the services offered by other companies. Furthermore, there was a lack of technology to judge the reliability and relevance of the acquired information, making it difficult for users to quickly obtain useful information. In addition, the analysis and classification of search data were not adequately performed, making it difficult for users to obtain the accurate and well-organized information they needed.

[1755] 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.

[1756] In this invention, the server includes means for receiving requests sent from a user terminal and searching an internal database and information sources on the internet based on the requests; means for analyzing the acquired information and extracting and formatting information highly relevant to the user; and means for transmitting the analyzed information to the user terminal. This enables users to quickly and efficiently investigate the services of other companies and accurately acquire highly relevant information.

[1757] A "user terminal" refers to a computing device used by a user, such as a personal computer or smartphone.

[1758] "Keywords" are words or phrases related to the services of other companies that the user is researching.

[1759] A "request" is an HTTP request sent from a user's terminal to a server, and it includes keywords, authentication information, and the desired data format.

[1760] A "server" is a computing system that receives requests from user terminals, searches internal databases and information sources on the internet, analyzes and formats the retrieved information, and sends it to the user terminal.

[1761] An "internal database" is a data storage system that stores data held by a company.

[1762] "Internet information sources" refer to websites and other online information services.

[1763] A "search engine API" is an application interface that uses a search engine to retrieve web pages and information related to specific keywords.

[1764] "Acquired information" refers to data collected by the server from its internal database and information sources on the internet.

[1765] "Analysis" is the process of using natural language processing techniques to evaluate the relevance and importance of information acquired by a server.

[1766] "Natural language processing technology" refers to technologies that process and analyze input text data to understand and evaluate its content, and includes technologies such as TF-IDF and Word2Vec.

[1767] "Extraction" is the process of selecting information that is highly relevant to the user from the analysis results.

[1768] "Formatting" refers to converting extracted information into a format that is easy for the user to understand.

[1769] This invention will now describe embodiments for carrying it out. This system consists of a user terminal, a server, an internal database, and an internet-based information source, enabling users to efficiently research the services offered by other companies. The specific processing is carried out as follows.

[1770] System Overview

[1771] The user uses their own device (such as a PC or smartphone) to enter keywords related to the services of other companies they want to research. For example, they might enter "logistics management system." Next, the device generates a request containing these keywords and sends it to the server. The server receives the request and searches its internal database and internet information sources. The retrieved information is analyzed by the server, and information highly relevant to the user is extracted and formatted. Finally, the analyzed information is sent from the server to the user's device, and the device displays the information to the user. This system can utilize search engine APIs, natural language processing technology, and web scraping techniques.

[1772] Hardware and software to be used

[1773] User terminal: A computing device that can connect to the internet, such as a personal computer, smartphone, or tablet.

[1774] Server: A server for high-performance data processing and database access. Cloud-based servers (e.g., AWS or Google Cloud Platform) can also be used.

[1775] Internal database: A database management system such as MySQL, PostgreSQL, or Oracle Database.

[1776] Search Engine APIs: APIs such as the Google Custom Search API for retrieving web information based on keywords.

[1777] Natural Language Processing Technology: An engine that analyzes information obtained using libraries and models such as TF-IDF, Word2Vec, and BERT.

[1778] Web scraping: A technique for automatically collecting necessary web information using libraries such as Beautiful Soup and Selenium.

[1779] Specific example

[1780] Here's a concrete example of how the system works when a user wants to research "data analysis tools from other companies." The user types "data analysis tools from other companies" into their terminal. The terminal then generates a request like the following and sends it to the server.

[1781] Example of a prompt:

[1782] Keywords: Data analysis tools from other companies

[1783] API Key: USER_API_KEY

[1784] Data format: JSON

[1785] The server receives this request, verifies the authentication information, and then uses its internal database and search engine API to search for information related to "other companies' data analysis tools." It also uses web scraping techniques to collect information from relevant websites. The collected data is analyzed using natural language processing techniques. For example, TF-IDF is used to extract highly relevant information and classify it into categories such as service features, pricing plans, and customer reviews.

[1786] The analyzed information is formatted by the server and sent to the user terminal in the following format:

[1787] Plastic information

[1788] Service features: Real-time analysis, Big data support

[1789] Pricing plans: Basic plan: ¥10,000 / month, Premium plan: ¥30,000 / month

[1790] Customer reviews: Easy to use, good value for money.

[1791] The user's device receives and displays this information. For example, the information is displayed on the UI of a web page in a format appropriate to each information category. This allows users to efficiently understand the services offered by other companies and use that information to help them make informed decisions.

[1792] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1793] Step 1:

[1794] User keyword input

[1795] Input: Users use their own devices (PCs or smartphones) to enter keywords related to the services of other companies they want to research. "Logistics management system" is one example.

[1796] Operation: The user enters keywords into the search field of their device's browser or application and clicks the search button.

[1797] Output: The entered keyword is processed internally by the terminal and ready to be sent to the next step.

[1798] Step 2:

[1799] Request generation by the terminal

[1800] Input: Keyword entered by the user

[1801] Operation: Based on the entered keyword, the terminal generates an HTTP request containing the following information:

[1802] Keywords entered by the user

[1803] API key for system authentication

[1804] Desired data format (e.g., JSON)

[1805] As an example of a specific request, the following JSON will be generated:

[1806] json

[1807] {

[1808] "keyword": "Logistics management system",

[1809] "apiKey": "USER_API_KEY",

[1810] "format": "json"

[1811] }

[1812] Output: The generated HTTP request is ready to be sent to the server.

[1813] Step 3:

[1814] Sending a request from the terminal to the server

[1815] Input: HTTP request (including keywords, API key, and data format)

[1816] Operation: The device sends the generated HTTP request to the server. The HTTPS protocol is used to securely transmit the data. The device sends the request via a RESTful API.

[1817] Output: The request is received by the server and processing begins in the next step.

[1818] Step 4:

[1819] Server request reception and analysis

[1820] Input: HTTP request sent from the terminal

[1821] Operation: The server parses the received request and performs the following processes:

[1822] Verify the API key and confirm that the request is legitimate.

[1823] Extract keywords entered by the user.

[1824] Output: The validated keywords will be used in the next step.

[1825] Step 5:

[1826] Server Data Retrieval

[1827] Input: Extracted keywords

[1828] Operation: The server searches its internal database and trusted sources on the internet. Specifically, it performs the following steps:

[1829] Using search engine APIs: Use APIs such as the Google Custom Search API to retrieve relevant information from the internet.

[1830] Executing queries on internal databases: Execute queries using keywords against internal databases such as MySQL and PostgreSQL to retrieve relevant information.

[1831] Web scraping: If necessary, use Beautiful Soup or Selenium to scrape the required information from specific websites.

[1832] Output: The acquired information will be used for data analysis in the next step.

[1833] Step 6:

[1834] Data analysis

[1835] Input: Data obtained from internal databases and reliable sources on the internet.

[1836] Operation: The server analyzes the acquired data. The specific analysis steps are as follows:

[1837] Application of natural language processing techniques: We evaluate the relevance and importance of information using natural language processing techniques such as TF-IDF, Word2Vec, and BERT. For example, we analyze the importance of keywords using TF-IDF.

[1838] Data filtering: Removes noise and irrelevant data to extract the information the user is looking for.

[1839] Information Classification: Organize information into categories such as service features, pricing plans, and customer reviews.

[1840] Output: The analysis results will be formatted in the next step.

[1841] Step 7:

[1842] Information formatting

[1843] Input: Analyzed data

[1844] Operation: The server formats the analysis results into a user-friendly format, such as JSON or HTML. A concrete example of a response is the following JSON:

[1845] json

[1846] {

[1847] "serviceFeatures": ["Real-time analytics", "Big data compatible"]

[1848] "pricingPlans": ["Basic Plan: ¥10,000 / month", "Premium Plan: ¥30,000 / month"]

[1849] "customerReviews": ["Easy to use", "Good value for money"]

[1850] }

[1851] Output: The formatted data is sent to the user's terminal.

[1852] Step 8:

[1853] Information reception and display by the user terminal

[1854] Input: Formatted data sent from the server

[1855] Operation: The terminal parses the received data and converts it into a format suitable for display. This is then displayed on the user interface. For example, information displayed in the UI of a web page or application is organized into service features, pricing plans, customer reviews, etc.

[1856] Output: Users can view detailed information about the services of other companies they wish to investigate.

[1857] (Application Example 1)

[1858] 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".

[1859] In today's economic environment, efficiently and quickly obtaining and comparing information on other companies' electronic payment services is crucial. However, manual methods and traditional information gathering techniques have limitations in terms of comprehensiveness and accuracy of necessary information, and are time-consuming and labor-intensive. Furthermore, collecting the latest information is particularly difficult in the electronic payment sector, where market trends are constantly changing. This can make accurate market analysis and maintaining competitiveness difficult.

[1860] 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.

[1861] In this invention, the server includes means for generating a request containing keywords entered by the user, means for searching an internal database and information sources on the internet based on the request sent from the user terminal, means for analyzing the acquired information and extracting and formatting information highly relevant to the user, means for transmitting the analyzed information to the user terminal, and means for the user terminal to display information regarding the features, pricing plans, and user reviews of the electronic payment service. This enables the user to efficiently and quickly obtain detailed information on other companies' electronic payment services and compare them.

[1862] A "user terminal" is a device used by a user to operate, and includes computers such as smartphones and personal computers.

[1863] A "keyword" refers to a specific word or phrase that a user enters to search for information.

[1864] A "request" is a communication message containing request information sent from a user's terminal to a server.

[1865] A "server" refers to a central processing unit that receives requests sent from user terminals and performs data retrieval and analysis based on those requests.

[1866] An "internal database" is a database that stores data owned by a company and is used to search for requested information.

[1867] "Internet information sources" refer to websites and online databases that exist on the internet, and are used to obtain necessary information from them.

[1868] "Analysis" refers to the process of classifying information acquired by the server into categories and evaluating their relationships.

[1869] "Highly relevant information" refers to information that is closely related to the keywords the user is searching for and is also useful.

[1870] "Formatting" refers to the process of converting analyzed information into a format that is easy for users to understand.

[1871] "Features" refer to the specific functions or capabilities that an electronic payment service possesses.

[1872] "Pricing plan" refers to the pricing structure and details of a service.

[1873] "User reviews" refer to evaluations and opinions provided by service users.

[1874] This invention will now describe embodiments for carrying out this invention. This invention relates to a system that allows users to efficiently collect and compare information on electronic payment services of other companies. This system consists of a user terminal, a server, an internal database, and information sources on the internet.

[1875] Hardware and software configuration

[1876] User terminal:

[1877] User terminals include computers such as smartphones and personal computers. These terminals are connected to the internet and have an interface for inputting information.

[1878] server:

[1879] The servers are located on cloud infrastructure. Specifically, cloud services such as AWS EC2 are available.

[1880] For the database, we will use an SQL database such as MySQL.

[1881] software:

[1882] To search for information, we utilize our internal database and search engine APIs such as the Google Custom Search API.

[1883] For natural language processing, we use NLP (Natural Language Processing) engines such as TensorFlow and NLTK.

[1884] System operation

[1885] 1. User input:

[1886] The user launches the smartphone app and enters keywords related to other companies' electronic payment services. For example, they might enter the keyword "other companies' mobile payment services."

[1887] 2. Terminal request generation:

[1888] The user's terminal generates an HTTP request based on the keywords entered by the user. This request includes the keywords, the API key, and the desired data format (e.g., JSON).

[1889] 3. Server request reception and data retrieval:

[1890] The server receives requests sent from user terminals. After receiving a request, it executes queries to search engine APIs and internal databases to collect relevant information. It also performs web scraping as needed.

[1891] 4. Data Analysis:

[1892] The server analyzes the collected data using NLP (Neuro-Linguistic Programming) techniques. Specifically, it uses methods such as TF-IDF and Word2Vec to evaluate the relevance and importance of the information, remove noise, and classify it into categories such as service features, pricing plans, and user reviews.

[1893] 5. Formatting and sending information:

[1894] The server formats the analysis results into a user-friendly format (e.g., JSON) and sends it to the terminal.

[1895] 6. Information reception and display by the user terminal:

[1896] The user terminal receives information sent from the server and displays it in a visually easy-to-understand interface. For example, the output might look like this:

[1897] Service features:

[1898] High-frequency trading

[1899] Low fees

[1900] Pricing plans:

[1901] Basic plan: ¥500 / month

[1902] Premium plan: ¥2,000 / month

[1903] User reviews:

[1904] Convenient and easy to use

[1905] Good support

[1906] This system allows users to quickly and efficiently gather detailed information on other companies' electronic payment services and compare them.

[1907] Example of a prompt

[1908] As a concrete example, here is an example of the prompt text when a user enters the keyword "mobile payment from another company":

[1909] "Please find competitive mobile payment services including features, pricing plans, and customer reviews."

[1910] Based on this prompt, the system collects the necessary information and performs the necessary processing to provide it to the user.

[1911] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1912] Step 1:

[1913] The user launches a smartphone app and enters keywords related to another company's electronic payment service. For example, they might enter the keyword "other company's mobile payment." The input data here is the keyword, and the output is the data that forms the basis of the HTTP request.

[1914] Step 2:

[1915] The user's terminal generates an HTTP request based on the keywords entered by the user. This request includes the following information:

[1916] Keywords (e.g., "other companies' mobile payment services")

[1917] API key (user authentication information)

[1918] Desired data format (e.g., JSON)

[1919] The input for this step is the keyword entered by the user, and the output is the generated HTTP request.

[1920] Step 3:

[1921] The user terminal generates a request and sends it to the server. The request is sent securely using the HTTPS protocol via a RESTful API. The input to this step is the generated HTTP request, and the output is a notification to the server that the transmission is complete.

[1922] Step 4:

[1923] The server receives a request sent from the user's terminal. The received request is authenticated, and keywords are extracted. The input here is the received HTTP request, and the output is the authenticated keywords.

[1924] Step 5:

[1925] The server searches its internal database and internet sources based on authenticated keywords. The server then performs the following actions:

[1926] Use the search engine API (Google Custom Search API) to retrieve relevant web pages.

[1927] Execute queries against the internal database (e.g., MySQL) to retrieve relevant information.

[1928] Web scraping will be performed as needed to collect relevant information.

[1929] The input for this step is a verified keyword, and the output is the collected raw data.

[1930] Step 6:

[1931] The raw data collected by the server is analyzed. Specifically, the following processes are performed:

[1932] The acquired data is analyzed using an NLP engine (such as TensorFlow or NLTK).

[1933] We use TF-IDF and Word2Vec to evaluate the relevance and importance of the information.

[1934] Removal of noisy data, filtering of unnecessary information.

[1935] Information is categorized by service features, pricing plans, and user reviews.

[1936] The input for this step is the collected raw data, and the output is the analyzed and formatted data.

[1937] Step 7:

[1938] The server formats the parsed information into a user-friendly format (e.g., JSON) and sends it to the user's terminal. The input for this step is the parsed data, and the output is a response containing the formatted data.

[1939] Step 8:

[1940] The user terminal receives information sent from the server and displays it in a visually easy-to-understand interface. The input here is the response data from the server, and the output is the information displayed on the terminal's user interface. For example, service features, pricing plans, and user reviews are displayed in a visually organized manner.

[1941] This series of steps enables the system described in the claims to allow users to efficiently and quickly collect and compare detailed information on other companies' electronic payment services.

[1942] 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.

[1943] This invention relates to a system that further enhances the user experience by combining it with an emotion engine that recognizes user emotions. This invention achieves more personalized information delivery by adjusting information display and search results based on the user's emotions.

[1944] System Overview

[1945] This system consists of user terminals, servers, an internal database, and information sources on the internet. Furthermore, the user terminals are equipped with an emotion engine that recognizes the user's emotions in real time and transmits that data to the server.

[1946] Program processing

[1947] 1. User input

[1948] The user uses their own terminal to enter keywords related to the third-party service they want to research. For example, they might enter "third-party logistics management system."

[1949] 2. User emotion recognition

[1950] An emotion engine installed in the user's device recognizes the user's emotions in real time. Using speech recognition and facial expression recognition, it acquires emotional data such as whether the user is stressed or relaxed.

[1951] 3. Terminal request generation

[1952] The terminal generates an HTTP request containing keywords and sentiment data entered by the user. This request includes the following information:

[1953] Keywords entered by the user

[1954] User sentiment data

[1955] Authentication information (API key, etc.)

[1956] Desired data format (e.g., JSON)

[1957] 4. Sending requests from the terminal to the server

[1958] The device sends the generated request to the server. The transmission is done via a RESTful API and is securely sent using the HTTPS protocol.

[1959] 5. Server receives request

[1960] The server processes requests received from the terminal. First, it checks the authentication information of the request to verify that it is not an unauthorized access. Next, it extracts the keywords and sentiment data entered by the user.

[1961] 6. Searching for data on the server

[1962] The server searches for data based on keywords and sentiment data. Specifically, it retrieves information from internal databases and internet sources. In this process, it utilizes the following methods:

[1963] Using search engine APIs: The server uses search engine APIs (e.g., Google API) to retrieve information related to keywords.

[1964] Executing queries on internal databases: The server executes queries on its own internal databases to retrieve relevant information.

[1965] Web scraping: Obtaining information by scraping it from specific websites as needed.

[1966] 7. Data Analysis

[1967] The server analyzes the acquired data. Specifically, it performs the following processes:

[1968] Application of NLP (Natural Language Processing) technology: Use a text analysis engine to evaluate the relevance and importance of information. For example, TF-IDF or Word2Vec may be used.

[1969] Filtering is performed to remove unnecessary information that constitutes noise.

[1970] Organize information such as service features, pricing plans, and customer reviews by category.

[1971] Based on user sentiment data, specific information is highlighted and its display order is adjusted. For example, if a user is feeling stressed, concise and easy-to-read information is prioritized.

[1972] 8. Formatting and transmitting information

[1973] The server formats the analysis results into a user-friendly format (e.g., JSON or HTML) and returns them to the terminal. At this stage, specific formats and display methods can be adopted based on the user's sentiment data.

[1974] 9. Information reception and display by the user terminal

[1975] The terminal receives information sent from the server and displays it to the user. The received data is converted into an appropriate data structure and displayed in the user interface. The display method is adjusted to suit the user's emotions.

[1976] Specific example

[1977] 1. User input and emotion recognition

[1978] As soon as the user types "data analysis tool from another company," the emotion engine recognizes the user's facial expression and acquires emotional data indicating "stress."

[1979] 2. Terminal request generation and transmission

[1980] The terminal generates a request like this:

[1981] json

[1982] {

[1983] "keyword": "Data analysis tools from other companies",

[1984] "emotion": "stress",

[1985] "apiKey": "USER_API_KEY",

[1986] "format": "json"

[1987] }

[1988] Send this request to the server.

[1989] 3. Server Request Reception and Search

[1990] The server receives the request, extracts keywords and sentiment data after authentication, and uses a search engine API to retrieve information related to "other companies' data analysis tools." It also queries its internal database.

[1991] 4. Data Analysis

[1992] The server uses NLP (Neuro-Linguistic Programming) techniques to analyze the results and categorize service features, pricing plans, and customer reviews. Furthermore, it formats the information to display it concisely based on emotional data, specifically "stress."

[1993] 5. Formatting and transmitting information

[1994] The server generates the following JSON and sends it to the terminal:

[1995] json

[1996] {

[1997] "serviceFeatures": ["Real-time analytics", "Big data compatible"]

[1998] "pricingPlans": ["Basic Plan: ¥10,000 / month", "Premium Plan: ¥30,000 / month"]

[1999] "customerReviews": ["Easy to use", "Good value for money"]

[2000] }

[2001] 6. Receiving and displaying information on the device

[2002] The device analyzes the received data and displays the information in a concise and visually easy-to-understand format, taking into account the user's "stress." For example, it highlights important information and collapses detailed information.

[2003] This system enables rapid and efficient on-site research into competitors' services and provides personalized information tailored to the user's emotional state. This reduces the time and effort required for information gathering and significantly improves user efficiency by providing accurate and relevant information.

[2004] The following describes the processing flow.

[2005] Step 1:

[2006] The user uses their own terminal to enter keywords related to the third-party service they want to research. For example, they might enter "third-party logistics management system."

[2007] Step 2:

[2008] An emotion engine installed in the user's device recognizes the user's emotions in real time. Using speech recognition and facial expression recognition, it acquires emotional data such as whether the user is stressed or relaxed.

[2009] Step 3:

[2010] The terminal generates an HTTP request based on the entered keyword and acquired sentiment data. This request includes the following information:

[2011] Keywords entered by the user

[2012] User sentiment data

[2013] Authentication information (API key, etc.)

[2014] Desired data format (e.g., JSON)

[2015] Step 4:

[2016] The terminal generates a request and sends it to the server using HTTPS. The transmission is done via a RESTful API.

[2017] Step 5:

[2018] The server processes the request received from the terminal. First, it checks the authentication information included in the request to verify that it is not an unauthorized access. Next, it extracts the keywords and sentiment data entered by the user.

[2019] Step 6:

[2020] The server searches for data based on extracted keywords and sentiment data. Specifically, it retrieves information from internal databases and internet sources. In this process, it utilizes the following methods:

[2021] Using search engine APIs: The server uses search engine APIs, such as the Google API, to retrieve information related to the keywords.

[2022] Executing queries on internal databases: The server also executes queries on its own internal databases to retrieve relevant information.

[2023] Web scraping: Obtaining information by scraping it from specific websites as needed.

[2024] Step 7:

[2025] The server analyzes the information it has acquired. Specifically, it performs the following processes:

[2026] We use NLP (Natural Language Processing) techniques to evaluate the relevance and importance of information. For example, we use TF-IDF and Word2Vec.

[2027] Filtering is performed to remove unnecessary information that would otherwise be considered noise.

[2028] Organize information such as service features, pricing plans, and customer reviews by category.

[2029] Based on user sentiment data, specific information is highlighted and its display order is adjusted. For example, if a user is feeling stressed, concise and easy-to-read information is prioritized.

[2030] Step 8:

[2031] The server formats the analysis results into a user-friendly format (e.g., JSON or HTML) and sends them to the terminal. In this process, specific formats and display methods can be adopted based on the user's sentiment data.

[2032] Step 9:

[2033] The terminal receives information sent from the server and parses the data. It converts the received JSON data into an appropriate data structure and prepares it for display to the user.

[2034] Step 10:

[2035] The terminal analyzes the data and displays it on the user interface. Users review the displayed information and use it for specific tasks and decision-making. The display method is adjusted according to the user's emotions. For example, if the emotion engine detects user stress, particularly important information is highlighted, and detailed information is collapsed as needed, taking care to reduce the user's burden.

[2036] In this way, this system, which incorporates an emotion engine, can provide information tailored to the user's emotions and support efficient decision-making.

[2037] (Example 2)

[2038] 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".

[2039] Conventional information retrieval systems perform searches based solely on keywords entered by the user, without considering the user's emotional state. This makes it difficult to provide personalized information tailored to the user's emotional state. In particular, if appropriate information is not presented when the user is stressed or relaxed, the user experience may deteriorate. Therefore, there is a need for a system that considers the user's emotional state and provides information more effectively.

[2040] 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.

[2041] In this invention, the server includes means for the user terminal to receive keywords and sentiment data entered by the user and generate a request including said keywords and sentiment data; means for the server to receive a request sent from the user terminal and search an internal database and information sources on the internet based on said request; means for the server to analyze the acquired information, evaluate the relevance and importance of the information using natural language processing techniques, and adjust the information based on the user's sentiment data; means for the server to format the analyzed information, extract information highly relevant to the user, and transmit it to the user terminal; and means for the user terminal to display the information received from the server and adjust the display method based on the user's sentiment. This makes it possible to provide personalized information according to the user's emotional state.

[2042] A "user terminal" is an electronic device used by the user for operations, and is a device that inputs keywords and acquires sentiment data.

[2043] A "keyword" is a word or phrase that a user enters into their device to search for specific information.

[2044] "Emotional data" refers to information that represents the user's emotional state, and is data acquired through voice recognition and facial expression recognition.

[2045] A "request" is request information sent from a user's terminal to a server, and includes keywords and sentiment data.

[2046] A "server" is a device that performs data retrieval and analysis based on requests received from a user terminal and sends the results to the user terminal.

[2047] An "internal database" is a data storage system where data held by a company is stored, and it is used to search for related information.

[2048] "Internet information sources" refer to information resources accessible via the internet that are used to obtain external information.

[2049] "Natural language processing technology" refers to the technology used by computers to understand, interpret, and generate human language, and is a means of evaluating the relevance and importance of information.

[2050] "Highly relevant information" refers to information that is most relevant to the user, filtered based on the user's keywords and sentiment data.

[2051] "Formatting" is the process of reconstructing acquired information into a format that is easy for users to understand.

[2052] "Information adjustment" is the process of determining the display order and highlighting of information based on user sentiment data.

[2053] The system of the present invention consists of a user terminal, a server, an internal database, and an information source on the Internet. Furthermore, the user terminal is equipped with an emotion engine that recognizes the user's emotions in real time and transmits that data to the server. The following describes in detail how this system is implemented.

[2054] First, the user uses their own terminal to enter keywords related to what they want to research. For example, they might enter "other companies' logistics management systems." Simultaneously, an emotion engine installed in the user's terminal recognizes the user's facial expressions and voice in real time and collects emotion data. The emotion engine uses speech recognition technology and facial expression recognition technology, specifically employing AI models such as OpenFace and DeepFace.

[2055] Next, the user terminal generates a request containing the entered keywords and the retrieved sentiment data. This request is in JSON format, and an internal HTTP client library (e.g., Python's requests library) is used to generate the request. The generated JSON request includes the following information:

[2056] Keywords entered by the user

[2057] User sentiment data

[2058] Authentication information (such as API key)

[2059] Desired data format (e.g., JSON)

[2060] This request is sent to the server via the HTTPS protocol through a RESTful API.

[2061] The server processes requests received from user terminals. First, the server verifies the legitimacy of the request based on authentication information to ensure it is not an unauthorized access attempt. Next, it extracts keywords and sentiment data from the request and begins a data search. The search is performed using the following methods:

[2062] Using search engine APIs: Information is retrieved using external search engine APIs such as the Google API.

[2063] Executing queries on internal databases: Retrieving information by executing queries on databases maintained by the company.

[2064] Web scraping: The process of obtaining information by scraping it from a specific website.

[2065] The acquired information is analyzed on the server. The server uses natural language processing (NLP) techniques to evaluate the relevance and importance of the acquired information. In this process, a text analysis engine (e.g., SpaCy, NLTK) is used, employing techniques such as TF-IDF and Word2Vec. Noise is removed through filtering, and necessary information is organized and classified into categories. Furthermore, information is highlighted and its display order is adjusted based on the user's sentiment data. For example, if the user is feeling stressed, important information is summarized concisely and displayed.

[2066] The server reformats the parsed information into a user-friendly format (e.g., JSON or HTML) and returns it to the terminal. This ensures that the information is presented to the user in an appropriate format. Finally, the user terminal displays the information received from the server and adjusts the display method according to the user's emotional state. For example, important information may be highlighted, while detailed information may be displayed in a collapsed format.

[2067] As a concrete example, consider a scenario where a user enters "data analysis tool from another company," and the emotion engine detects a "stressed" state from the user's facial expression. In this case, the device generates and sends the following request to the server:

[2068] Keywords: Data analysis tools from other companies

[2069] Emotional data: stress

[2070] API Key: USER_API_KEY

[2071] Format: json

[2072] The server receives the request, searches for information based on the keywords and sentiment data, and performs analysis. The analysis results provide a concise summary of service features, pricing plans, customer reviews, etc. This information is then formatted as JSON data and sent to the terminal:

[2073] Service features: [Real-time analysis, Big data compatible]

[2074] Pricing plans: [Basic plan: ¥10,000 / month, Premium plan: ¥30,000 / month]

[2075] Customer reviews: [Easy to use, good value for money]

[2076] The terminal receives the transmitted information and displays it concisely and visually, taking into consideration the user's "stress." This allows users to quickly and efficiently understand the services offered by other companies and improve their work efficiency.

[2077] The flow of the specific processing in Example 2 will be explained using Figure 13.

[2078] Step 1: User input

[2079] The user uses their own terminal to enter keywords related to the third-party service they want to investigate. For example, they might enter "third-party logistics management system." This input triggers the system to start. The entered keywords are stored internally on the terminal. Furthermore, user input is primarily done via keyboard text.

[2080] Input: Keywords entered by the user (e.g., "Logistics management system of another company")

[2081] Output: Retained keywords (e.g., "other company's logistics management system")

[2082] Step 2: Recognizing the user's emotions

[2083] The device's built-in emotion engine works to recognize the user's facial expressions and voice tone in real time and generate emotion data. It uses the camera to capture facial expressions and the microphone to recognize voice. The emotion engine uses AI models (e.g., OpenFace, DeepFace) to determine whether the user is stressed or relaxed.

[2084] Input: User's facial expression data, voice data

[2085] Output: Analyzed emotion data (e.g., "stress")

[2086] Step 3: Generate terminal request

[2087] The terminal generates an HTTP request containing the keywords entered by the user and the retrieved sentiment data. The generated request includes the following information: keywords, sentiment data, authentication information (such as an API key), and the desired data format (e.g., JSON). This request is constructed using the system's internal HTTP client library (e.g., Python's requests library).

[2088] Input: Keyword (e.g., "Other company's logistics management system"), Sentiment data (e.g., "Stress"), Authentication information (e.g., "USER_API_KEY")

[2089] Output: HTTP request (e.g., JSON format request)

[2090] Step 4: Sending a request from the terminal to the server

[2091] The terminal sends the generated request to the server. This transmission is performed using a RESTful API and the HTTPS protocol. The use of the HTTPS protocol ensures the security of the transmitted data.

[2092] Input: Generated HTTP request (e.g., a request in JSON format)

[2093] Output: Request sent to the server

[2094] Step 5: Server receives request

[2095] The server receives requests sent from terminals. First, it verifies the authentication credentials to ensure that the access is not unauthorized. Then, it extracts keywords and sentiment data included in the request.

[2096] Input: HTTP request sent from the terminal

[2097] Output: Extracted keywords (e.g., "other company's logistics management system"), sentiment data (e.g., "stress")

[2098] Step 6: Search server data

[2099] The server searches for information from internal databases and internet sources based on extracted keywords and sentiment data. The following methods are used:

[2100] Using search engine APIs: Use the Google API to retrieve information related to keywords.

[2101] Executing queries on internal databases: Retrieving information by executing queries on databases maintained by the company.

[2102] Web scraping: The process of obtaining information by scraping it from a specific website.

[2103] Input: Extracted keywords (e.g., "other company's logistics management system"), sentiment data (e.g., "stress")

[2104] Output: Acquired information data

[2105] Step 7: Data Analysis

[2106] The server analyzes the acquired information. Using a text analysis engine (e.g., SpaCy, NLTK), it evaluates the relevance and importance of the information using natural language processing techniques. Furthermore, it performs noise reduction, categorization, and adjusts the highlighting and display order of information based on sentiment data.

[2107] Input: Acquired information data, emotional data (e.g., "stress")

[2108] Output: Analyzed and formatted information

[2109] Step 8: Format and send information

[2110] The server formats the analyzed information into a user-friendly format (e.g., JSON, HTML) and sends it to the terminal. It adopts an appropriate format and display method based on sentiment data.

[2111] Input: Analyzed and formatted information

[2112] Output: Formatted information data

[2113] Step 9: Information reception and display by the user terminal

[2114] The terminal converts information received from the server into a data structure and displays it on the user interface. Based on the user's emotions, the information is displayed in a concise and visually easy-to-understand manner. Techniques such as highlighting important information and collapsing detailed information are employed.

[2115] Input: Formatted information data

[2116] Output: Displayed information (on the user interface)

[2117] This allows users to quickly and efficiently understand the services offered by other companies, thereby improving operational efficiency.

[2118] (Application Example 2)

[2119] 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".

[2120] Traditional content delivery services often fail to consider the user's emotional state, making it difficult to provide content that matches the user's current mood. As a result, users may experience stress or be unable to relax, leading to decreased service satisfaction. Furthermore, it was difficult to quickly and accurately extract highly relevant information from the vast amount of data available.

[2121] 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.

[2122] In this invention, the server includes means for receiving requests sent from a user terminal and searching an internal database and information sources on the internet based on the requests; means for analyzing the acquired information, shaping the information based on the user's emotional data, and extracting information highly relevant to the user; and means for transmitting the analyzed information to the user terminal. This makes it possible to suggest personalized content according to the user's emotional state.

[2123] A "user terminal" is an electronic device used by users to input information and receive and display it, and it is also equipped with an emotion recognition engine.

[2124] "Keywords" are words or phrases that users enter for the purpose of research or searching.

[2125] "Emotional data" refers to data indicating the user's emotional state, acquired in real time by the emotion recognition engine installed in the user's device.

[2126] A "request" is communication data for an information request that includes keywords and sentiment data entered by the user.

[2127] A "server" is a central processing unit that searches internal databases and internet-based information sources based on requests received from user terminals and returns the results to the user terminals.

[2128] An "internal database" is a collection of data that a server maintains for access and retrieval.

[2129] An "information source on the internet" is an external information repository from which a server obtains information through search engine APIs or web scraping.

[2130] An "emotion recognition engine" is a combination of hardware and software that recognizes a user's emotional state in real time from their facial expressions and voice.

[2131] "Natural language processing technology" refers to computational methods and algorithms for analyzing text information acquired by a server and evaluating its relevance and importance.

[2132] "Filtering" is the process of removing noise and unnecessary data from acquired information and extracting information that is highly relevant to the user.

[2133] "Analysis" is the process by which a server analyzes the information it acquires and formats it into an appropriate format based on user sentiment data.

[2134] A "browsing interface" is a screen and operating system that displays information obtained from a server by the user's terminal and adjusts the display method based on the user's emotional state.

[2135] This invention is a system that personalizes content delivery based on user emotions. Its main components are a user terminal, a server, an internal database, an internet-based information source, and an emotion recognition engine.

[2136] The user terminal is equipped with a camera and microphone, and an emotion recognition engine acquires emotion data in real time from the user's facial expressions and voice. A request containing this acquired emotion data and keywords entered by the user is generated and sent to the server. The HTTPS protocol is used to securely communicate when sending requests.

[2137] After receiving a request, the server first verifies the authentication information. If the request is deemed legitimate, it searches its internal database and internet information sources. Information is collected using search engine APIs (e.g., Google API) and web scraping tools. The acquired data is analyzed using NLP (Natural Language Processing) techniques. Specifically, methods such as TF-IDF and Word2Vec are used to evaluate the relevance and importance of the information, and filtering is performed as needed.

[2138] The analysis results are formatted based on the user's emotional data. When the user is stressed, concise and easy-to-understand information is prioritized; when relaxed, detailed and helpful information is displayed preferentially. Finally, the organized information is converted into an appropriate data format, such as JSON, and sent to the user's device.

[2139] The user terminal analyzes information received from the server and displays it on the user interface. During this process, adjustments are made based on the user's emotional state. For example, a user experiencing stress will see a visually simpler UI, with detailed information displayed in a collapsible format.

[2140] Program operation description:

[2141] Emotion Recognition Engine: This engine uses facial recognition and speech recognition technologies to capture user emotions in real time. Specific software examples include OpenCV and TensorFlow.

[2142] Search engine API: Uses the Google API, etc., to retrieve information from internet sources.

[2143] NLP technology: SpaCy and NLTK are used as natural language processing engines. These are used to perform text analysis and evaluate the relevance and importance of information.

[2144] Filtering: Remove noise from collected data and extract only the information most relevant to the user.

[2145] Formatting of analysis results: Convert to HTML or JSON format and send to the user's terminal.

[2146] Specific example:

[2147] For example, if a user types "romantic movie" into their smartphone and the emotion recognition engine obtains the emotion data "relaxed," the server processes the information in the following steps:

[2148] 1. Use a search engine API to collect information about "romantic movies".

[2149] 2. Analyze the collected information using NLP technology.

[2150] 3. Based on the user's emotional data regarding "relaxation," the list of movies that promote relaxation is prioritized and formatted accordingly.

[2151] 4. Input the following prompt message into the AI ​​model to recommend an appropriate movie:

[2152] For users who are looking to relax, recommend relaxing romantic movies. For example, movies with touching and heartwarming stories.

[2153] As a result, users can easily find romantic movies that are suitable for a relaxed state.

[2154] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2155] Step 1:

[2156] User input

[2157] The user uses their device to enter keywords related to the content they want to search for. This input data serves as foundational information for finding appropriate content based on the user's current emotions. The device is equipped with an emotion recognition engine that simultaneously acquires emotion data from the user's facial expressions and voice. The input includes keywords (e.g., "romantic movies") and the user's emotion data (e.g., "relaxed").

[2158] Step 2:

[2159] Recognition of emotions

[2160] The device's built-in emotion recognition engine uses the camera and microphone to capture the user's emotions in real time. Using facial expression recognition and voice analysis technologies (e.g., OpenCV, TensorFlow), it generates emotion data such as whether the user is relaxed or stressed. The output is emotion data such as "relaxed."

[2161] Step 3:

[2162] Request generation

[2163] The user terminal integrates the entered keywords with the retrieved sentiment data to generate a request. This request includes keywords (e.g., "romantic movies"), sentiment data (e.g., "relaxed"), and authentication information (e.g., API key). The request is formatted in JSON format and ready to be sent to the server.

[2164] Step 4:

[2165] Sending a request to the server

[2166] The user terminal sends the generated request to the server. The HTTPS protocol is used for transmission, ensuring secure communication. The input is a request JSON, and the output is the request received by the server.

[2167] Step 5:

[2168] Server receives and authenticates requests.

[2169] The server receives requests from user terminals and first verifies authentication information. After confirming that the request is not malicious, it extracts keywords and sentiment data. The input is a request JSON, and the output is authenticated keywords and sentiment data.

[2170] Step 6:

[2171] Searching for information sources

[2172] The server searches its internal database and internet sources based on the received keywords and sentiment data. It uses search engine APIs (e.g., Google API) to retrieve external information. It also queries its own internal database. The input is authenticated keywords, and the output is a list of relevant information.

[2173] Step 7:

[2174] Data analysis

[2175] The server analyzes the information data obtained through searches. It uses NLP techniques (e.g., SpaCy, NLTK) to evaluate the relevance and importance of the information. Based on sentiment data, it filters the information to extract information that matches the user's interests. The input is the acquired information data, and the output is filtered, relevant information.

[2176] Step 8:

[2177] Formatting of analysis results

[2178] The server formats the analyzed information into a user-friendly format (e.g., JSON). During this process, it adjusts how the information is displayed based on the user's emotional data. In particular, it provides simple and easy-to-understand information to users experiencing stress. The input is filtered relevant information, and the output is formatted JSON data.

[2179] Step 9:

[2180] Sending formatted information

[2181] The server sends the formatted information to the user's terminal. This transmission also uses the HTTPS protocol to ensure secure communication. The input is formatted JSON information, and the output is the data that arrives on the user's terminal.

[2182] Step 10:

[2183] Receiving and displaying information

[2184] The user terminal analyzes information received from the server and displays it on the user interface. The display method is adjusted based on the user's emotional state. For example, a relaxed user is provided with detailed and visually rich information, while a stressed user is provided with concise and easy-to-understand information. The input is the transmitted JSON information, and the output is personalized content displayed on the user interface.

[2185] 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.

[2186] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inf...

Claims

1. A user terminal has means for receiving a keyword entered by the user and generating a request containing that keyword, The server has means for receiving requests sent from user terminals and searching internal databases and internet information sources based on those requests. The server analyzes the acquired information and extracts and formats information that is highly relevant to the user. The server provides a means for transmitting the analyzed information to the user terminal, A means by which the user terminal displays information received from the server, A system that includes this.

2. The system according to claim 1, comprising means for evaluating the relevance and importance of information using natural language processing technology.

3. The system according to claim 1, comprising means for obtaining information using an internal database and a search engine API.

Images