System

Abstract

A system is provided.SOLUTION: A system comprising: means for providing a user interface for entering a search query; means for receiving the search query and sending a request to a web search engine to obtain search results; means for analyzing the search results and extracting metrics such as topic, relevance, and credibility for each page; means for evaluating the goodness and badness of each search result based on the extracted metrics; and means for generating and displaying a report in the user interface based on the evaluation results.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's information society, Internet-based search engines are an important means of gathering information. However, the wide variety of search results makes it difficult to accurately and quickly find useful information. In particular, search results may contain commercial advertisements or unreliable information, making it difficult for users to access the high-quality information they need. To solve this problem, an effective system is needed to analyze search results and provide both good and bad points. [Means for solving the problem]

[0005] The present invention provides a user interface for inputting a search query and includes means for receiving the query and then sending a request to a web search engine to retrieve search results. It also includes means for analyzing the search results and extracting indicators such as the topic, relevance, and reliability of each page. Based on this, a system is provided that evaluates the pros and cons of each search result, generates a report based on the evaluation results, and displays it on the user interface, thereby providing a means for users to quickly and accurately obtain useful information. The evaluation of pros is based on the freshness, originality, depth, and accuracy of the information, while the evaluation of cons is based on the presence or absence of commercial advertising, the reliability of the information, and excessive technical content of the article.

[0006] A "search query" is a series of keywords or phrases entered into a web search engine to identify the information a user is seeking.

[0007] A "user interface" is a computer graphic display that includes screens, input fields, buttons, and other elements that allow a user to interact with a system.

[0008] A "web search engine" is a system that indexes information on the Internet and provides relevant information based on a user's search query.

[0009] A "search result" is a list of results that a web search engine returns in response to a user's search query; typically, a set of pages containing links, titles, and snippets.

[0010] "Analysis" is the process of examining the content of the search results in detail to find specific indicators and patterns.

[0011] A "topic" is the subject or theme that a piece of writing or page focuses on.

[0012] "Relevance" is the degree to which the content of a search result matches the search query.

[0013] "Credibility" is a measure of information's accuracy, reliability, and whether it is based on a reliable source.

[0014] "Rating" is the act of judging each search result as good or bad based on specific criteria.

[0015] A "report" is a document summarizing the evaluation results of the good and bad points, and is the form of information provided to the user. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The search result evaluation system of the present invention is for analyzing search queries, evaluating the results, and generating reports, and operates through a terminal, a server, and a user interface.

[0038] User Interface

[0039] A user enters a search query, such as "AI ideas," through a user interface on the device, which includes a search field and a search button for input and submission.

[0040] Sending a search query and getting results

[0041] After entering a search query, the device sends it to a server, which then sends the received search query as a request to a web search engine to retrieve relevant search results, using APIs provided by the search engine or web scraping technology.

[0042] Parsing search results

[0043] The server analyzes the search results and extracts metrics such as topic, relevance, and credibility for each page. The page content is then processed using text analysis algorithms, extracting the required text from the HTML document. Metrics used include keyword frequency, page metadata, and external links.

[0044] Evaluation of the good and bad points

[0045] Based on the analysis, the server evaluates each search result's pros and cons. Pros are assessed based on factors such as the freshness, uniqueness, depth, and accuracy of the information. Cons include the presence or absence of commercial advertising, the reliability of the information, and the excessive technical content of the article.

[0046] Generate and view reports

[0047] The server generates a report summarizing the evaluation results. The report clearly lists the pros and cons of each search result and is presented in a user-friendly format. The report is generated in HTML, PDF, or other formats and sent to the device.

[0048] The user can receive the report on their device and view it through the user interface, allowing the user to quickly obtain useful information and efficiently evaluate the quality of search results.

[0049] Specific examples

[0050] For example, if a user enters the search query "AI ideas," the device sends this query to the server. The server then sends a request to the Google search engine to retrieve relevant search results. The server then analyzes the search results and evaluates each result's topic, relevance, and credibility. The server then lists the positives (e.g., introduction of new ideas, detailed examples, information about the latest technology) and negatives (e.g., outdated information, excessive commercial advertising, overly technical content). Finally, the server generates a report, sends it to the device, and displays it to the user.

[0051] The above is an embodiment of the search result evaluation system, which allows users to efficiently access high-quality information.

[0052] The processing flow will be explained below.

[0053] Step 1:

[0054] A user enters a search query "AI ideas" on a terminal and clicks the search button. Here, the user interface receives the user's input from the search field and monitors the click event of the search button.

[0055] Step 2:

[0056] The terminal sends the search query entered by the user to the server. Specifically, it generates an HTTP request including the search query and sends it to the specified endpoint of the server.

[0057] Step 3:

[0058] The server sends a request to a web search engine based on the received search query, using Google's search engine API and scraping techniques to retrieve results corresponding to the search query.

[0059] Step 4:

[0060] The server receives the response from the search engine and retrieves the search results, which are often returned in the form of links, titles, snippets, etc.

[0061] Step 5:

[0062] The server analyzes the search results and extracts the content of each page by visiting each linked page and parsing the HTML document to extract the body of the text and important metadata.

[0063] Step 6:

[0064] The server calculates metrics such as topic, relevance, and credibility for each page, and assigns a score to each page through keyword frequency analysis, external link evaluation, and metadata checks.

[0065] Step 7:

[0066] The server then uses the analytics data to rate the results on their merits and demerits, with the merits including freshness, uniqueness, depth, and accuracy of the information, and the demerits including the presence or absence of commercial advertising, the reliability of the information, and the excessive technical content of the article.

[0067] Step 8:

[0068] The server lists the pros and cons and embeds these ratings in a report template, which is then generated in a user-friendly format.

[0069] Step 9:

[0070] The server sends the generated report to the terminal by returning an HTTP response containing the report data in HTML or PDF format.

[0071] Step 10:

[0072] The user receives the report on their device and displays it through a user interface, which allows the user to view a visually arranged report that allows them to see at a glance the pros and cons of the search results.

[0073] Example 1

[0074] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0075] Current search systems provide search results as they are, forcing users to review each of the numerous results and evaluate their reliability and relevance. This takes time and effort, making it difficult for users to efficiently obtain high-quality information. Furthermore, the lack of a function for comprehensively evaluating the positive and negative aspects of search results makes it difficult for users to quickly find the information they need.

[0076] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0077] In this invention, the server includes means for receiving a search query and transmitting a request to a data processing device to obtain search results, means for analyzing the search results and extracting indicators such as the content, relevance, and reliability of each result, and means for evaluating the advantages and disadvantages of each search result based on the extracted indicators, thereby enabling users to quickly and efficiently obtain highly reliable and relevant information.

[0078] A "search query" is a keyword or phrase that a user enters to search for information.

[0079] "User interface" refers to the interface through which a user interacts with the system, including the search field and search button.

[0080] A "data processing device" is a device that receives a search query, sends a request to a search engine, and retrieves results.

[0081] A "search result" is a list of information returned by a search engine based on a search query, with each item including the title, URL, snippet, etc. of a web page.

[0082] "Analysis" is the process of examining the search results in detail and extracting indicators such as the content, relevance, and reliability of each result.

[0083] An "indicator" is an element extracted from analyzed data, and serves as a standard for evaluating relevance, reliability, advantages, disadvantages, etc.

[0084] "Good points" are positive elements that are evaluated in search results, including freshness, uniqueness, depth, and accuracy.

[0085] "Bad points" are negative elements that are evaluated in search results, including commercial advertising, unreliable information, and overly technical content.

[0086] "Evaluation" is the process of determining the good and bad points of a search result based on analyzed metrics.

[0087] A "report" is a document summarizing the evaluation results, and provides the user with the evaluation of the search results.

[0088] The search result evaluation system of the present invention analyzes search queries, evaluates the results, and generates reports. This system operates through a terminal, a server, and a user interface. Each component and its specific processing method are described in detail below.

[0089] First, a user enters a search query through the device's user interface. The user interface includes a search field and a search button, allowing for easy input and submission. For example, a user enters the query "AI ideas" and presses the search button.

[0090] The device then takes the entered search query and sends it to the server using an HTTP request, packaging the query data in JSON format.

[0091] The server processes the received search query and sends an API request to the specified web search engine (e.g., Google Search) using an official API such as the Google Search API. As a result of this request, the search engine returns search results in JSON format.

[0092] To analyze the received search results, the server first extracts the necessary text from the HTML document, then uses a natural language processing library (e.g., NLTK or spaCy) to evaluate the content, relevance, and credibility of each search result. This analysis extracts metrics such as each page's topic, keyword frequency, metadata, and external links.

[0093] The server then evaluates each search result based on its analysis, scoring it on its pros and cons: pros include freshness, uniqueness, depth, and accuracy of the information, while cons include the presence or absence of commercial advertising, reliability of the information, and excessive technical content.

[0094] Based on these evaluation results, the server generates a detailed report that clearly lists the pros and cons of each search result and is provided in a user-friendly format (e.g., HTML or PDF).The generated report is then sent back to the device.

[0095] Finally, the user can receive the report on their terminal and view it through a user interface, allowing the user to quickly obtain useful information and efficiently evaluate the quality of search results.

[0096] Specific examples

[0097] For example, if a user enters the search query "AI ideas," the following prompt will be generated:

[0098] "Find information about the following search query 'AI ideas', analyze the results, evaluate their pros and cons, and generate a report."

[0099] Based on this prompt, a series of processes are executed between the terminal and the server, and highly rated information is provided to the user.

[0100] The above is an embodiment of the present invention. This system allows users to quickly and accurately obtain the information they need.

[0101] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0102] Step 1:

[0103] A user inputs a search query through a user interface of a terminal, for example, by inputting "AI ideas" and pressing a search button, and the query is submitted. At this time, the user interface provides a search field and a search button.

[0104] Input: Search query (e.g. "AI ideas")

[0105] Output: The action to which the search query is sent

[0106] Step 2:

[0107] The device receives the search query entered in the user interface and sends it to the server using an HTTP request, packaging the query data in JSON format.

[0108] Input: The search query submitted by the user

[0109] Output: HTTP POST request containing a search query

[0110] Step 3:

[0111] The server processes the received search query and sends an API request to the specified web search engine, for example, sending the search query "AI ideas" as a request to the Google Search API.

[0112] Input: Search query sent from the device

[0113] Output: API request to search engine

[0114] Step 4:

[0115] The server receives search results returned by the search engine. The results are returned in JSON format and include information such as title, URL, and snippet.

[0116] Input: API response from search engine

[0117] Output: Search result data (JSON format)

[0118] Step 5:

[0119] The server analyzes the received search results by first extracting the required text from the HTML document and then analyzing the text using a natural language processing library (NLTK or spaCy).

[0120] Input: Search result data (JSON format)

[0121] Output: Analyzed data (e.g., keyword frequencies, topic modeling results)

[0122] Step 6:

[0123] The server then evaluates each search result based on its analysis, assessing its merits and demerits. For example, new ideas and detailed examples are evaluated as positive, while advertisements and outdated information are evaluated as negative.

[0124] Input: Parsed data

[0125] Output: Evaluation results (list of good and bad points)

[0126] Step 7:

[0127] The server generates a report based on the evaluation results, which is output in HTML or PDF format for easy viewing by the user.

[0128] Input: Evaluation results (list of good and bad points)

[0129] Output: Report (HTML or PDF format)

[0130] Step 8:

[0131] The server transmits the generated report to the terminal.

[0132] Input: Report

[0133] Output: Report sent to terminal

[0134] Step 9:

[0135] The terminal displays the report received from the server through a user interface, allowing the user to check the evaluation of search results and quickly obtain high-quality information.

[0136] Input: Report sent from server

[0137] Output: Report display on the user interface

[0138] (Application example 1)

[0139] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0140] Conventional search result systems make it difficult to evaluate the reliability and relevance of results based on a user's search query. Furthermore, especially on online shopping sites, search results are provided without considering factors such as product freshness or the reliability of reviews, making it difficult for users to select the optimal product. Furthermore, excessive advertising and technical content often impair user convenience.

[0141] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0142] In this invention, the server includes means for providing a user interface for inputting a search query, means for receiving the search query and sending a request to a web search engine to obtain search results, and means for analyzing the search results and extracting indicators such as the topic, relevance, and reliability of each page. This makes it possible to analyze the freshness, rating, price, and reliability of reviews of products based on search results containing the content of the search query, and to suggest optimal options to the user.

[0143] A "user interface" is a mechanism that provides a screen and operating means for users to interact with a system or application.

[0144] A "search query" refers to a keyword or phrase that a user enters into a search engine.

[0145] A "web search engine" is a system that automatically collects and indexes information on the Internet and provides relevant web pages in response to a user's search query.

[0146] "Metrics" refers to the criteria used to evaluate the quality and reliability of search results.

[0147] "Freshness" is an indicator of how up-to-date the information is.

[0148] "Rating" refers to reviews or scores given by users that indicate the satisfaction or quality of a product or service.

[0149] "Price" refers to the amount paid to purchase a product or service.

[0150] A "review" refers to an evaluation or impression written by a user after using a product or service.

[0151] "Report" refers to a document or presentation that provides a summary of the analysis and evaluation of search results.

[0152] "Good points" refer to the parts of the search results that are particularly outstanding or highly rated.

[0153] "Bad points" refer to problematic or poorly rated parts of search results.

[0154] "Product freshness" is an index that evaluates how recent the product information provided is.

[0155] "Credibility" is an indicator of how reliable the information and reviews provided are.

[0156] "Best choice" refers to the search results or products that best meet the user's needs and evaluation criteria.

[0157] This invention is a system in which a user inputs a search query, a server evaluates results based on the search query, and provides optimal search results. The configuration and operation of this system will be described in detail below.

[0158] The server provides a user interface for inputting a search query. The user interface includes a search field and a search button, and is designed to allow a user to easily input and submit a search query. The user inputs a search query such as "smartphone reviews" using a smartphone application.

[0159] When a search query is entered, the device receives it and sends it to a server. The server then sends the received search query as a request to a web search engine to retrieve related search results. This can be done using the API provided by the search engine. An example of a specific API is the Google Custom Search JSON API.

[0160] The server analyzes the search results and extracts metrics such as the topic, relevance, and credibility of each page. This analysis uses HTML parsing libraries such as BeautifulSoup and text analysis algorithms. For example, the analysis is based on information such as each page's metadata, keyword frequency, and external links.

[0161] To rate the quality of search results, the server uses criteria such as freshness, uniqueness, depth, and accuracy, taking into account the recency of the ratings, the quality of the reviews, the price, and the trustworthiness of external links. This rating is typically achieved using text analysis algorithms.

[0162] A report is generated based on the evaluation results and provided to the user through a user interface. This report is often generated in HTML format and clearly shows the good and bad points of the search results in a user-friendly format.

[0163] (Example)

[0164] For example, if a user searches for "new smartphone," the server sends a request to the Google search engine to retrieve relevant search results. The server then analyzes the retrieved search results and evaluates each page's topic, relevance, and credibility. It then generates a list of highly rated products based on factors such as product freshness and review credibility, allowing users to easily select the best product.

[0165] (Example of a prompt)

[0166] The following Python code retrieves data from the API based on a search query, analyzes it, and evaluates it. This code evaluates the freshness, uniqueness, depth, and accuracy of search results, and provides them to the user in the form of a report.

[0167] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0168] Step 1:

[0169] A user inputs and submits a search query through a user interface, for example, using the search query "new smartphone." The user interface includes a search field and a search button.

[0170] Step 2:

[0171] The terminal receives the search query entered by the user and sends it to the server. The input is the search query entered by the user, and the output is the query sent to the server. The data is sent to the server in the form of a query.

[0172] Step 3:

[0173] The server sends the received search query to a web search engine and retrieves related search results. The input is the search query sent to the server, and the output is the search results. Specifically, the data is retrieved using a search engine API such as Google Custom Search JSON API.

[0174] Step 4:

[0175] The server analyzes the search results and extracts metrics such as the topic, relevance, and credibility of each page. The input is the search results, and the output is analytical data for each page. This analysis uses HTML analysis libraries such as BeautifulSoup to process the data based on information such as metadata, keyword frequency, and external links.

[0176] Step 5:

[0177] The server evaluates the pros and cons of each search result based on the extracted metrics. The input is the extracted metrics data, and the output is the evaluation results. Each page is evaluated using criteria such as freshness, uniqueness, depth, and accuracy.

[0178] Step 6:

[0179] The server generates a report based on the evaluation results and displays it in the user interface. The input is the evaluation results and the output is the generated report. The report is generated in HTML format and explicitly shows the pros and cons of each search result.

[0180] Step 7:

[0181] The user views the generated report through the user interface on the terminal. The input is the report data sent from the server, and the output is the report displayed to the user. This allows the user to select the most suitable products and information based on the analyzed search results.

[0182] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0183] The present invention is a system that analyzes search queries, evaluates search results in combination with an emotion engine, and adds recommendations based on the user's emotions to the generated report. The system operates through a terminal, a server, and a user interface, as well as the emotion engine.

[0184] User Interface

[0185] A user inputs a search query such as "AI ideas" through the device's user interface. The user interface includes a search field and a search button, allowing input and submission. The emotion engine also recognizes the user's emotion when inputting and captures that information.

[0186] Sending a search query and getting results

[0187] After entering a search query, the device sends it to the server. The server then sends the received search query as a request to a web search engine to retrieve related search results. The server also receives emotion data from the emotion engine and begins analyzing it together with the search results.

[0188] Parsing search results

[0189] The server analyzes the search results and extracts the content of each page. Specifically, it accesses each linked page, parses the HTML document, and extracts the main text and important metadata. It also evaluates the appropriateness of the search results based on the user's emotional data obtained from the emotion engine.

[0190] Evaluation of the good and bad points

[0191] The server evaluates the positive and negative aspects of the search results based on the analysis data. The positive aspects include the freshness, uniqueness, depth, and accuracy of the information, while the negative aspects include the presence or absence of commercial advertisements, the reliability of the information, and the excessive technical content of the article. The server also takes into account the user's emotional data provided by the emotion engine and evaluates the results according to the user's emotions.

[0192] Generate and view reports

[0193] The server generates a report summarizing the evaluation results. The report clearly lists the pros and cons of each search result and is presented in an easy-to-read format for the user. In addition, additional recommendations based on the user's sentiment are added using data from the sentiment engine. The report is generated in formats such as HTML or PDF and sent to the device.

[0194] Users can receive the report on their device and view it through the user interface, allowing them to quickly obtain useful information and efficiently evaluate the quality of search results. Customized recommendations based on the user's emotional state also promote more effective use of search results.

[0195] Specific examples

[0196] For example, if a user enters the search query "AI ideas," the device sends this query to the server. The emotion engine collects emotion data from the user's facial expressions and tone of voice, and sends it to the server. The server then sends a request to the Google search engine to retrieve relevant search results. The server then analyzes the search results, assessing each result's topic, relevance, and credibility, and providing a more detailed evaluation based on the emotion data from the emotion engine. The server then generates a report listing the positives (e.g., introduction of new ideas, detailed examples, and information on the latest technology) and negatives (e.g., outdated information, excessive commercial advertising, and overly technical content) and adding recommendations based on the user's emotion (e.g., encouraging messages and motivational information for a depressed user). Finally, the server sends the report to the device and displays it to the user.

[0197] The above is an embodiment of a search result evaluation system that combines an emotion engine. This system not only allows users to efficiently access high-quality information, but also provides recommendations that correspond to individual emotions.

[0198] The processing flow will be explained below.

[0199] Step 1:

[0200] A user enters the search query "AI ideas" on a device and clicks the search button. The user interface receives user input from the search field and monitors the click event of the search button. The emotion engine also analyzes the user's facial expressions and tone of voice to collect emotion data in real time.

[0201] Step 2:

[0202] The device sends the search query entered by the user and the emotion data acquired by the emotion engine to the server. Specifically, it generates an HTTP request including the search query and emotion data and sends it to the specified endpoint of the server.

[0203] Step 3:

[0204] The server sends a request to a web search engine based on the received search query, using the Google search engine API to retrieve search results corresponding to the entered search query.

[0205] Step 4:

[0206] The server receives the response from the search engine and retrieves the search results, which include links, titles, snippets, etc.

[0207] Step 5:

[0208] The server visits each page of the retrieved search results and parses the HTML document to extract the body of the text and important metadata, using web scraping techniques to analyze the page content.

[0209] Step 6:

[0210] The server calculates metrics such as topic, relevance, and credibility for each page, based on information such as the page's keyword frequency, link structure, and metadata.

[0211] Step 7:

[0212] The server re-evaluates the relevance of search results based on the emotional data from the emotion engine, adjusting the ranking and filtering of search results according to the user's emotional state.

[0213] Step 8:

[0214] The server rates each search result on its pros and cons: pros include freshness, uniqueness, depth, and accuracy of the information, while cons include the presence or absence of commercial advertising, the reliability of the information, and the excessive technical content of the article.

[0215] Step 9:

[0216] The server uses the emotion data to make recommendations based on the user's feelings, such as encouraging messages for positive points and alternatives for negative points.

[0217] Step 10:

[0218] The server generates a report summarizing the assessment results and recommendations, which can be generated in HTML or PDF format.

[0219] Step 11:

[0220] The server sends the generated report to the terminal. Specifically, the server returns an HTTP response including the generated report to the terminal.

[0221] Step 12:

[0222] Users receive the report on their device and view it through a user interface, which provides a visually organized report that gives users an at-a-glance view of the pros and cons of search results, as well as sentiment-based recommendations.

[0223] Example 2

[0224] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0225] Conventional search systems are unable to fully meet user needs because they are unable to take into account the user's emotional state when evaluating search results. Furthermore, the quality evaluation of search results is uniform, making it impossible to provide recommendations that are adapted to individual users' characteristics and emotions. Therefore, there is a need for a system that can improve the quality of search results and effectively provide customized feedback based on the user's emotions.

[0226] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0227] In this invention, the server includes means for providing a user interface for inputting a search query, means for receiving the search query and sending a request to a web search engine to obtain search results, means for analyzing the search results and extracting indicators such as the topic, relevance, and reliability of each page, means for evaluating the pros and cons of each search result based on the extracted indicators, means for acquiring emotional data from the user at the time of input and including this emotional data in the analysis, and means for generating a report based on the evaluation results and the emotional data and displaying it on the user interface. This allows users to efficiently evaluate the quality of search results and receive recommendations that correspond to their emotions.

[0228] A "search query" refers to text or a phrase that a user types into a search engine to retrieve specific information.

[0229] "User interface" refers to the interface through which a user provides input to a system and receives output from the system, including input fields and search buttons on a computer screen.

[0230] "Server" refers to a computing device over a computer network that receives search queries, transmits requests to web search engines, analyzes search results, and so on.

[0231] A "web search engine" refers to a system for searching for information on the Internet, such as the Google search engine, that provides relevant information based on a search query.

[0232] "Emotional data" refers to data that indicates the user's emotional state at the time of input. It is captured by the emotion engine and analyzed from facial expressions and vocal tone.

[0233] "Analysis" refers to the process of extracting useful information from search results and analyzing that information for topic, relevance, reliability, etc.

[0234] "Metrics" refer to the criteria used to evaluate search results, such as topicality, relevance, and credibility.

[0235] "Evaluation" refers to the process of determining the pros and cons of search results based on extracted metrics and sentiment data.

[0236] "Report" refers to a document containing the evaluation results and sentiment-based recommendations, presented to the user in a user-friendly format.

[0237] MODE FOR CARRYING OUT THE INVENTION

[0238] The present invention is a system that analyzes search queries, evaluates search results in combination with an emotion engine, and adds recommendations based on the user's emotions to the generated report. Specific embodiments of this system are described below.

[0239] Hardware and Software Configuration

[0240] A user enters a search query using a device such as a PC or smartphone. This device has a web browser that provides the user interface. The user interface is composed of HTML, CSS, and JavaScript. It includes a search field and a search button, allowing input and submission. The emotion engine also uses a camera and microphone to analyze the user's facial expressions and tone of voice.

[0241] The device receives a search query entered by the user and sends it to the server, where the emotion engine analyzes the emotion data entered by the user and also sends the emotion data to the server.

[0242] The server requires high computing power, but is often configured as a general-purpose cloud server. The server sends the search query as a request to a web search engine and retrieves relevant search results. The search query is sent using an existing web search engine API, such as the Google Search API.

[0243] After retrieving the search results, the server performs an analysis process. Here, it uses an HTML parsing library such as BeautifulSoup to parse the HTML document of each search result page and extract the body of the text and metadata. Furthermore, it uses the results of this analysis to extract metrics such as the topic, relevance, and credibility of each page.

[0244] Based on the acquired analytical data and emotion data, the server evaluates the positive and negative aspects of each search result. The positive aspects include the freshness, uniqueness, depth, and accuracy of the information, while the negative aspects include the presence or absence of commercial advertisements, the reliability of the information, and the excessive technical content of the article. The server also takes into account the user's emotion data provided by the emotion engine and evaluates the results according to the user's emotions.

[0245] Finally, the server generates a report summarizing the results, clearly listing the pros and cons of each search result, in a user-friendly format, and in formats such as HTML and PDF.

[0246] The terminal receives the generated report and displays it through a user interface, allowing the user to quickly obtain useful information and efficiently evaluate the quality of search results. In addition, customized recommendations based on the user's emotional state promote more effective use of search results.

[0247] Specific examples

[0248] For example, consider a user entering the search query "AI ideas." The device sends this query along with emotional data collected by the emotion engine from facial expressions and voice tone to the server. The server then sends a request to the Google search engine to retrieve relevant search results. The server then uses BeautifulSoup to analyze the search results and evaluate each result's topic, relevance, and trustworthiness. Based on the emotional data, the server generates a report, including optional recommendations, which is finally sent to the device and displayed to the user.

[0249] Example prompts for generative AI models

[0250] "Work with the sentiment engine to evaluate search results for AI ideas and generate reports that provide customized recommendations to users."

[0251] The above is an embodiment of a search result evaluation system that combines an emotion engine. This system allows users to efficiently obtain high-quality information and receive recommendations that correspond to their emotions.

[0252] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0253] Specific explanation of processing steps

[0254] Step 1: Entering a User Query

[0255] A user uses a terminal to enter a search query into an input field in the user interface, for example, "AI ideas," and clicks the search button, which sends the search query to the system.

[0256] Input: A search query such as "AI ideas"

[0257] Output: Sending a search query based on the search button click event

[0258] Step 2: Submitting a query and sentiment data

[0259] The device sends the search query entered by the user to the server. At the same time, the emotion engine also sends the user's emotion data, which is analyzed based on facial expressions, voice tone, etc.

[0260] Input: Search query and emotional data (facial expression, voice tone)

[0261] Output: Search query and sentiment data are sent to the server.

[0262] Step 3: Processing the search request

[0263] The server sends a request to a web search engine (e.g., Google Search API) using the received search query. Once the search results are retrieved, the server stores them.

[0264] Input: search query

[0265] Output: A list of search results

[0266] Step 4: Parse the search results

[0267] The server accesses the URLs of the search results and retrieves the HTML documents. Using an HTML parser library such as BeautifulSoup, the body of each page and important metadata are extracted. This analysis provides metrics such as topic, relevance, and authority.

[0268] Input: Search result URL list

[0269] Output: Extracted data such as the body of each page, metadata, topics, relevance, and credibility

[0270] Step 5: Evaluate the search results

[0271] The server evaluates each search result's pros and cons based on analytical and sentiment data. Pros include freshness, uniqueness, depth, and accuracy of the information, while cons include the presence or absence of commercial advertising, the reliability of the information, and excessive technical content of the article. The server also customizes the evaluation based on the user's sentiment using data from the sentiment engine.

[0272] Input: Analysis data, emotion data

[0273] Output: A list of pros and cons for each search result

[0274] Step 6: Generate reports

[0275] The server compiles the results and generates a user-friendly report, which includes a rating for each search result and recommendations based on user sentiment. The report is generated in HTML and PDF formats.

[0276] Input: Evaluation results, emotion data

[0277] Output: HTML or PDF report

[0278] Step 7: Send and view the report

[0279] The server sends the generated report to the terminal, where the user can view the report through the user interface.

[0280] Input: Report in HTML or PDF format

[0281] Output: Report display in the user interface

[0282] Specific actions

[0283] User types in "AI Ideas" and clicks the search button

[0284] The device sends the search query and emotion data to the server.

[0285] The server executes a search using the Google Search API and retrieves the results.

[0286] The server uses BeautifulSoup to parse the HTML.

[0287] The server evaluates the content based on criteria such as freshness and uniqueness, and also takes into account data from the emotion engine.

[0288] The server generates a report summarizing the evaluation results and sentiment-based recommendations.

[0289] The server sends the generated report to the terminal, where the user can check it.

[0290] The above is the specific process flow that combines the search result evaluation system and emotion engine. This system allows users to easily evaluate the quality of search results and receive recommendations that correspond to their emotions.

[0291] (Application example 2)

[0292] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0293] Conventional food delivery services only provide uniform search results for user queries, and have the problem of being unable to make optimal suggestions based on the user's emotions or mood. In particular, they are unable to suggest dishes that correspond to a user's specific emotional state, such as when the user is stressed or tired, which limits the user experience and makes it difficult to improve satisfaction.

[0294] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for providing a user interface for inputting a search query, means for receiving the search query and sending a request to a network search engine to obtain search results, means for analyzing the search results and extracting indicators such as the topic, relevance, and reliability of each page, an emotion engine for recognizing the user's emotional state and obtaining that data, means for considering the user's emotional data in evaluating the search results, and means for generating a report based on the evaluation results and the emotional data, adding additional recommendations, and displaying the report on the user interface. This makes it possible to suggest optimal dishes based on the user's emotional state, thereby improving the food delivery experience.

[0295] A "search query" is the text or keywords a user enters to search for specific information.

[0296] A "user interface" refers to a screen or device that allows a user to perform operations or input data into a system.

[0297] A "network search engine" is a system that searches for information on the Internet and provides relevant results.

[0298] "Search Results" means information obtained by a network search engine based on a search query.

[0299] An "emotion engine" is a system that analyzes a user's emotions and provides data based on that.

[0300] "Indicators" are information that serves as a basis for evaluating search results.

[0301] A "report" is a document or data generated based on evaluation information and sentiment data of search results.

[0302] "Recommendations" are advice or suggestions provided based on a user's emotional state and evaluation of search results.

[0303] "User emotional state" refers to the current psychological and physiological state of the user performing a search or operation.

[0304] The present invention provides a system for providing search results that take into account a user's emotional state when using a food delivery app. The system includes a user interface, a server, and an emotion engine.

[0305] User Interface

[0306] The user interface is a screen where users can enter search queries through their devices. Users enter keywords such as cuisine or restaurant and press the search button. At this time, emotional data such as the user's facial expression and tone of voice are also acquired.

[0307] Emotion Engine

[0308] The emotion engine is a system for analyzing the user's emotional state. It uses the Google Cloud Vision API and Google Cloud Speech-to-Text API to recognize the user's emotional state from image and audio data. For example, if the user's stress level is determined to be high, that information is fed back to the system.

[0309] Sending a search query and getting results

[0310] When a user enters a search query, the device sends it to the server, which then sends a request to a network search engine to retrieve relevant search results.

[0311] Parsing search results

[0312] The server analyzes the search results and extracts indicators such as the topic, relevance, and reliability of each page. It also combines this with emotional data obtained from the emotion engine to evaluate the search results from multiple angles.

[0313] Evaluation of the good and bad points

[0314] The server evaluates the search results based on the analysis data. The evaluation of positive and negative aspects includes freshness, uniqueness, depth, and accuracy of the information, while the evaluation of negative aspects includes the presence or absence of commercial advertisements, the reliability of the information, and excessive technical content of the article. The server also evaluates the results based on the user's emotional state using emotional data.

[0315] Generate and view reports

[0316] The server generates a report summarizing the evaluation results. The report clearly lists the good and bad points of the search results and is presented to the user in an easy-to-read format. It also adds additional recommendations based on the emotional data, corresponding to the user's emotional state. The report is generated in a format such as HTML or PDF and sent to the device. The user can receive the report on their device and view it through the user interface.

[0317] Specific examples

[0318] For example, if a user enters the search query "relaxing food" and the emotion engine recognizes that the user is feeling stressed based on their facial expression and voice, the server will prioritize suggesting dishes with a relaxing effect (e.g., dishes using herbs and lemon).If the user is tired, the server will recommend dishes that will replenish energy (e.g., high-protein dishes).

[0319] Prompt Sentence Examples

[0320] "Show relaxing dishes. If a user is feeling stressed, prioritize dishes that will help relieve stress and provide recipes."

[0321] This makes it possible to provide optimal information based on the user's emotions, improving the food delivery experience.

[0322] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0323] Step 1:

[0324] A user enters a search query through the user interface of a food delivery app. The search query is a keyword such as "relaxing food." Emotional data, such as the user's facial expressions and voice tone, is also captured. The input consists of a text query and emotional data in the form of images and audio. The emotional data is sent in preparation for sending the image and audio input to the emotion engine.

[0325] Step 2:

[0326] The device sends a search query and emotion data to the server. The input is a text search query and emotion data in the form of images or audio. The output is the search query and emotion data sent to the server. The device performs this process asynchronously and notifies the user of the response.

[0327] Step 3:

[0328] The server receives a search query, sends a request to a network search engine, and retrieves relevant search results. The server stores the sentiment data separately. The input is a text search query, and the output is a list of search results in JSON format.

[0329] Step 4:

[0330] The server analyzes the image and audio data sent to the emotion engine and recognizes the user's emotional state. The input is emotional data in the form of images and audio. The output is emotional information returned by the emotion engine (e.g., stress level, high).

[0331] Step 5:

[0332] The server analyzes the search results and extracts metrics such as the topic, relevance, and credibility of each page. The input is a list of search results in JSON format, and the output is data containing evaluation metrics for each search result. This analysis includes text mining of the page content using natural language processing techniques.

[0333] Step 6:

[0334] The server evaluates the pros and cons of each search result based on the evaluation index and emotion data. The input is data including the evaluation index and emotion information from the emotion engine. The output is report data evaluating the pros and cons of each search result. The evaluation criteria used include the freshness of the information, uniqueness, depth, accuracy, the presence or absence of commercial advertisements, the reliability of the information, and whether the article contains excessive technical content.

[0335] Step 7:

[0336] The server generates a report based on the evaluation results and emotion data, and adds additional recommendations. The input is the evaluation data and emotion data. The output is a report file in HTML or PDF format. Specifically, if the user is feeling stressed, the report will include recipes that will help relieve stress.

[0337] Step 8:

[0338] The server sends the generated report to the terminal, which displays it on the user interface. The input is a report file in HTML or PDF format, and the output is a report that is displayed to the user. The user receives the report on the terminal and can choose the best dish based on the displayed information.

[0339] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0340] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0341] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0342] [Second embodiment]

[0343] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0344] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0345] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0346] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0347] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0348] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0349] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0350] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0351] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0352] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0353] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0354] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0355] The search result evaluation system of the present invention is for analyzing search queries, evaluating the results, and generating reports, and operates through a terminal, a server, and a user interface.

[0356] User Interface

[0357] A user enters a search query, such as "AI ideas," through a user interface on the device, which includes a search field and a search button for input and submission.

[0358] Sending a search query and getting results

[0359] After entering a search query, the device sends it to a server, which then sends the received search query as a request to a web search engine to retrieve relevant search results, using APIs provided by the search engine or web scraping technology.

[0360] Parsing search results

[0361] The server analyzes the search results and extracts metrics such as topic, relevance, and credibility for each page. The page content is then processed using text analysis algorithms, extracting the required text from the HTML document. Metrics used include keyword frequency, page metadata, and external links.

[0362] Evaluation of the good and bad points

[0363] Based on the analysis, the server evaluates each search result's pros and cons. Pros are assessed based on factors such as the freshness, uniqueness, depth, and accuracy of the information. Cons include the presence or absence of commercial advertising, the reliability of the information, and the excessive technical content of the article.

[0364] Generate and view reports

[0365] The server generates a report summarizing the evaluation results. The report clearly lists the pros and cons of each search result and is presented in a user-friendly format. The report is generated in HTML, PDF, or other formats and sent to the device.

[0366] The user can receive the report on their device and view it through the user interface, allowing the user to quickly obtain useful information and efficiently evaluate the quality of search results.

[0367] Specific examples

[0368] For example, if a user enters the search query "AI ideas," the device sends this query to the server. The server then sends a request to the Google search engine to retrieve relevant search results. The server then analyzes the search results and evaluates each result's topic, relevance, and credibility. The server then lists the positives (e.g., introduction of new ideas, detailed examples, information about the latest technology) and negatives (e.g., outdated information, excessive commercial advertising, overly technical content). Finally, the server generates a report, sends it to the device, and displays it to the user.

[0369] The above is an embodiment of the search result evaluation system, which allows users to efficiently access high-quality information.

[0370] The processing flow will be explained below.

[0371] Step 1:

[0372] A user enters a search query "AI ideas" on a terminal and clicks the search button. Here, the user interface receives the user's input from the search field and monitors the click event of the search button.

[0373] Step 2:

[0374] The terminal sends the search query entered by the user to the server. Specifically, it generates an HTTP request including the search query and sends it to the specified endpoint of the server.

[0375] Step 3:

[0376] The server sends a request to a web search engine based on the received search query, using Google's search engine API and scraping techniques to retrieve results corresponding to the search query.

[0377] Step 4:

[0378] The server receives the response from the search engine and retrieves the search results, which are often returned in the form of links, titles, snippets, etc.

[0379] Step 5:

[0380] The server analyzes the search results and extracts the content of each page by visiting each linked page and parsing the HTML document to extract the body of the text and important metadata.

[0381] Step 6:

[0382] The server calculates metrics such as topic, relevance, and credibility for each page, and assigns a score to each page through keyword frequency analysis, external link evaluation, and metadata checks.

[0383] Step 7:

[0384] The server then uses the analytics data to rate the results on their merits and demerits, with the merits including freshness, uniqueness, depth, and accuracy of the information, and the demerits including the presence or absence of commercial advertising, the reliability of the information, and the excessive technical content of the article.

[0385] Step 8:

[0386] The server lists the pros and cons and embeds these ratings in a report template, which is then generated in a user-friendly format.

[0387] Step 9:

[0388] The server sends the generated report to the terminal by returning an HTTP response containing the report data in HTML or PDF format.

[0389] Step 10:

[0390] The user receives the report on their device and displays it through a user interface, which allows the user to view a visually arranged report that allows them to see at a glance the pros and cons of the search results.

[0391] Example 1

[0392] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0393] Current search systems provide search results as they are, forcing users to review each of the numerous results and evaluate their reliability and relevance. This takes time and effort, making it difficult for users to efficiently obtain high-quality information. Furthermore, the lack of a function for comprehensively evaluating the positive and negative aspects of search results makes it difficult for users to quickly find the information they need.

[0394] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0395] In this invention, the server includes means for receiving a search query and transmitting a request to a data processing device to obtain search results, means for analyzing the search results and extracting indicators such as the content, relevance, and reliability of each result, and means for evaluating the advantages and disadvantages of each search result based on the extracted indicators, thereby enabling users to quickly and efficiently obtain highly reliable and relevant information.

[0396] A "search query" is a keyword or phrase that a user enters to search for information.

[0397] "User interface" refers to the interface through which a user interacts with the system, including the search field and search button.

[0398] A "data processing device" is a device that receives a search query, sends a request to a search engine, and retrieves results.

[0399] A "search result" is a list of information returned by a search engine based on a search query, with each item including the title, URL, snippet, etc. of a web page.

[0400] "Analysis" is the process of examining the search results in detail and extracting indicators such as the content, relevance, and reliability of each result.

[0401] An "indicator" is an element extracted from analyzed data, and serves as a standard for evaluating relevance, reliability, advantages, disadvantages, etc.

[0402] "Good points" are positive elements that are evaluated in search results, including freshness, uniqueness, depth, and accuracy.

[0403] "Bad points" are negative elements that are evaluated in search results, including commercial advertising, unreliable information, and overly technical content.

[0404] "Evaluation" is the process of determining the good and bad points of a search result based on analyzed metrics.

[0405] A "report" is a document summarizing the evaluation results, and provides the user with the evaluation of the search results.

[0406] The search result evaluation system of the present invention analyzes search queries, evaluates the results, and generates reports. This system operates through a terminal, a server, and a user interface. Each component and its specific processing method are described in detail below.

[0407] First, a user enters a search query through the device's user interface. The user interface includes a search field and a search button, allowing for easy input and submission. For example, a user enters the query "AI ideas" and presses the search button.

[0408] The device then takes the entered search query and sends it to the server using an HTTP request, packaging the query data in JSON format.

[0409] The server processes the received search query and sends an API request to the specified web search engine (e.g., Google Search) using an official API such as the Google Search API. As a result of this request, the search engine returns search results in JSON format.

[0410] To analyze the received search results, the server first extracts the necessary text from the HTML document, then uses a natural language processing library (e.g., NLTK or spaCy) to evaluate the content, relevance, and credibility of each search result. This analysis extracts metrics such as each page's topic, keyword frequency, metadata, and external links.

[0411] The server then evaluates each search result based on its analysis, scoring it on its pros and cons: pros include freshness, uniqueness, depth, and accuracy of the information, while cons include the presence or absence of commercial advertising, reliability of the information, and excessive technical content.

[0412] Based on these evaluation results, the server generates a detailed report that clearly lists the pros and cons of each search result and is provided in a user-friendly format (e.g., HTML or PDF).The generated report is then sent back to the device.

[0413] Finally, the user can receive the report on their terminal and view it through a user interface, allowing the user to quickly obtain useful information and efficiently evaluate the quality of search results.

[0414] Specific examples

[0415] For example, if a user enters the search query "AI ideas," the following prompt will be generated:

[0416] "Find information about the following search query 'AI ideas', analyze the results, evaluate their pros and cons, and generate a report."

[0417] Based on this prompt, a series of processes are executed between the terminal and the server, and highly rated information is provided to the user.

[0418] The above is an embodiment of the present invention. This system allows users to quickly and accurately obtain the information they need.

[0419] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0420] Step 1:

[0421] A user inputs a search query through a user interface of a terminal, for example, by inputting "AI ideas" and pressing a search button, and the query is submitted. At this time, the user interface provides a search field and a search button.

[0422] Input: Search query (e.g. "AI ideas")

[0423] Output: The action to which the search query is sent

[0424] Step 2:

[0425] The device receives the search query entered in the user interface and sends it to the server using an HTTP request, packaging the query data in JSON format.

[0426] Input: The search query submitted by the user

[0427] Output: HTTP POST request containing a search query

[0428] Step 3:

[0429] The server processes the received search query and sends an API request to the specified web search engine, for example, sending the search query "AI ideas" as a request to the Google Search API.

[0430] Input: Search query sent from the device

[0431] Output: API request to search engine

[0432] Step 4:

[0433] The server receives search results returned by the search engine. The results are returned in JSON format and include information such as title, URL, and snippet.

[0434] Input: API response from search engine

[0435] Output: Search result data (JSON format)

[0436] Step 5:

[0437] The server analyzes the received search results by first extracting the required text from the HTML document and then analyzing the text using a natural language processing library (NLTK or spaCy).

[0438] Input: Search result data (JSON format)

[0439] Output: Analyzed data (e.g., keyword frequencies, topic modeling results)

[0440] Step 6:

[0441] The server then evaluates each search result based on its analysis, assessing its merits and demerits. For example, new ideas and detailed examples are evaluated as positive, while advertisements and outdated information are evaluated as negative.

[0442] Input: Parsed data

[0443] Output: Evaluation results (list of good and bad points)

[0444] Step 7:

[0445] The server generates a report based on the evaluation results, which is output in HTML or PDF format for easy viewing by the user.

[0446] Input: Evaluation results (list of good and bad points)

[0447] Output: Report (HTML or PDF format)

[0448] Step 8:

[0449] The server transmits the generated report to the terminal.

[0450] Input: Report

[0451] Output: Report sent to terminal

[0452] Step 9:

[0453] The terminal displays the report received from the server through a user interface, allowing the user to check the evaluation of search results and quickly obtain high-quality information.

[0454] Input: Report sent from server

[0455] Output: Report display on the user interface

[0456] (Application example 1)

[0457] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0458] Conventional search result systems make it difficult to evaluate the reliability and relevance of results based on a user's search query. Furthermore, especially on online shopping sites, search results are provided without considering factors such as product freshness or the reliability of reviews, making it difficult for users to select the optimal product. Furthermore, excessive advertising and technical content often impair user convenience.

[0459] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0460] In this invention, the server includes means for providing a user interface for inputting a search query, means for receiving the search query and sending a request to a web search engine to obtain search results, and means for analyzing the search results and extracting indicators such as the topic, relevance, and reliability of each page. This makes it possible to analyze the freshness, rating, price, and reliability of reviews of products based on search results containing the content of the search query, and to suggest optimal options to the user.

[0461] A "user interface" is a mechanism that provides a screen and operating means for users to interact with a system or application.

[0462] A "search query" refers to a keyword or phrase that a user enters into a search engine.

[0463] A "web search engine" is a system that automatically collects and indexes information on the Internet and provides relevant web pages in response to a user's search query.

[0464] "Metrics" refers to the criteria used to evaluate the quality and reliability of search results.

[0465] "Freshness" is an indicator of how up-to-date the information is.

[0466] "Rating" refers to reviews or scores given by users that indicate the satisfaction or quality of a product or service.

[0467] "Price" refers to the amount paid to purchase a product or service.

[0468] A "review" refers to an evaluation or impression written by a user after using a product or service.

[0469] "Report" refers to a document or presentation that provides a summary of the analysis and evaluation of search results.

[0470] "Good points" refer to the parts of the search results that are particularly outstanding or highly rated.

[0471] "Bad points" refer to problematic or poorly rated parts of search results.

[0472] "Product freshness" is an index that evaluates how recent the product information provided is.

[0473] "Credibility" is an indicator of how reliable the information and reviews provided are.

[0474] "Best choice" refers to the search results or products that best meet the user's needs and evaluation criteria.

[0475] This invention is a system in which a user inputs a search query, a server evaluates results based on the search query, and provides optimal search results. The configuration and operation of this system will be described in detail below.

[0476] The server provides a user interface for inputting a search query. The user interface includes a search field and a search button, and is designed to allow a user to easily input and submit a search query. The user inputs a search query such as "smartphone reviews" using a smartphone application.

[0477] When a search query is entered, the device receives it and sends it to a server. The server then sends the received search query as a request to a web search engine to retrieve related search results. This can be done using the API provided by the search engine. An example of a specific API is the Google Custom Search JSON API.

[0478] The server analyzes the search results and extracts metrics such as the topic, relevance, and credibility of each page. This analysis uses HTML parsing libraries such as BeautifulSoup and text analysis algorithms. For example, the analysis is based on information such as each page's metadata, keyword frequency, and external links.

[0479] To rate the quality of search results, the server uses criteria such as freshness, uniqueness, depth, and accuracy, taking into account the recency of the ratings, the quality of the reviews, the price, and the trustworthiness of external links. This rating is typically achieved using text analysis algorithms.

[0480] A report is generated based on the evaluation results and provided to the user through a user interface. This report is often generated in HTML format and clearly shows the good and bad points of the search results in a user-friendly format.

[0481] (Example)

[0482] For example, if a user searches for "new smartphone," the server sends a request to the Google search engine to retrieve relevant search results. The server then analyzes the retrieved search results and evaluates each page's topic, relevance, and credibility. It then generates a list of highly rated products based on factors such as product freshness and review credibility, allowing users to easily select the best product.

[0483] (Example of a prompt)

[0484] The following Python code retrieves data from the API based on a search query, analyzes it, and evaluates it. This code evaluates the freshness, uniqueness, depth, and accuracy of search results, and provides them to the user in the form of a report.

[0485] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0486] Step 1:

[0487] A user inputs and submits a search query through a user interface, for example, using the search query "new smartphone." The user interface includes a search field and a search button.

[0488] Step 2:

[0489] The terminal receives the search query entered by the user and sends it to the server. The input is the search query entered by the user, and the output is the query sent to the server. The data is sent to the server in the form of a query.

[0490] Step 3:

[0491] The server sends the received search query to a web search engine and retrieves related search results. The input is the search query sent to the server, and the output is the search results. Specifically, the data is retrieved using a search engine API such as Google Custom Search JSON API.

[0492] Step 4:

[0493] The server analyzes the search results and extracts metrics such as the topic, relevance, and credibility of each page. The input is the search results, and the output is analytical data for each page. This analysis uses HTML analysis libraries such as BeautifulSoup to process the data based on information such as metadata, keyword frequency, and external links.

[0494] Step 5:

[0495] The server evaluates the pros and cons of each search result based on the extracted metrics. The input is the extracted metrics data, and the output is the evaluation results. Each page is evaluated using criteria such as freshness, uniqueness, depth, and accuracy.

[0496] Step 6:

[0497] The server generates a report based on the evaluation results and displays it in the user interface. The input is the evaluation results and the output is the generated report. The report is generated in HTML format and explicitly shows the pros and cons of each search result.

[0498] Step 7:

[0499] The user views the generated report through the user interface on the terminal. The input is the report data sent from the server, and the output is the report displayed to the user. This allows the user to select the most suitable products and information based on the analyzed search results.

[0500] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0501] The present invention is a system that analyzes search queries, evaluates search results in combination with an emotion engine, and adds recommendations based on the user's emotions to the generated report. The system operates through a terminal, a server, and a user interface, as well as the emotion engine.

[0502] User Interface

[0503] A user inputs a search query such as "AI ideas" through the device's user interface. The user interface includes a search field and a search button, allowing input and submission. The emotion engine also recognizes the user's emotion when inputting and captures that information.

[0504] Sending a search query and getting results

[0505] After entering a search query, the device sends it to the server. The server then sends the received search query as a request to a web search engine to retrieve related search results. The server also receives emotion data from the emotion engine and begins analyzing it together with the search results.

[0506] Parsing search results

[0507] The server analyzes the search results and extracts the content of each page. Specifically, it accesses each linked page, parses the HTML document, and extracts the main text and important metadata. It also evaluates the appropriateness of the search results based on the user's emotional data obtained from the emotion engine.

[0508] Evaluation of the good and bad points

[0509] The server evaluates the positive and negative aspects of the search results based on the analysis data. The positive aspects include the freshness, uniqueness, depth, and accuracy of the information, while the negative aspects include the presence or absence of commercial advertisements, the reliability of the information, and the excessive technical content of the article. The server also takes into account the user's emotional data provided by the emotion engine and evaluates the results according to the user's emotions.

[0510] Generate and view reports

[0511] The server generates a report summarizing the evaluation results. The report clearly lists the pros and cons of each search result and is presented in an easy-to-read format for the user. In addition, additional recommendations based on the user's sentiment are added using data from the sentiment engine. The report is generated in formats such as HTML or PDF and sent to the device.

[0512] Users can receive the report on their device and view it through the user interface, allowing them to quickly obtain useful information and efficiently evaluate the quality of search results. Customized recommendations based on the user's emotional state also promote more effective use of search results.

[0513] Specific examples

[0514] For example, if a user enters the search query "AI ideas," the device sends this query to the server. The emotion engine collects emotion data from the user's facial expressions and tone of voice, and sends it to the server. The server then sends a request to the Google search engine to retrieve relevant search results. The server then analyzes the search results, assessing each result's topic, relevance, and credibility, and providing a more detailed evaluation based on the emotion data from the emotion engine. The server then generates a report listing the positives (e.g., introduction of new ideas, detailed examples, and information on the latest technology) and negatives (e.g., outdated information, excessive commercial advertising, and overly technical content) and adding recommendations based on the user's emotion (e.g., encouraging messages and motivational information for a depressed user). Finally, the server sends the report to the device and displays it to the user.

[0515] The above is an embodiment of a search result evaluation system that combines an emotion engine. This system not only allows users to efficiently access high-quality information, but also provides recommendations that correspond to individual emotions.

[0516] The processing flow will be explained below.

[0517] Step 1:

[0518] A user enters the search query "AI ideas" on a device and clicks the search button. The user interface receives user input from the search field and monitors the click event of the search button. The emotion engine also analyzes the user's facial expressions and tone of voice to collect emotion data in real time.

[0519] Step 2:

[0520] The device sends the search query entered by the user and the emotion data acquired by the emotion engine to the server. Specifically, it generates an HTTP request including the search query and emotion data and sends it to the specified endpoint of the server.

[0521] Step 3:

[0522] The server sends a request to a web search engine based on the received search query, using the Google search engine API to retrieve search results corresponding to the entered search query.

[0523] Step 4:

[0524] The server receives the response from the search engine and retrieves the search results, which include links, titles, snippets, etc.

[0525] Step 5:

[0526] The server visits each page of the retrieved search results and parses the HTML document to extract the body of the text and important metadata, using web scraping techniques to analyze the page content.

[0527] Step 6:

[0528] The server calculates metrics such as topic, relevance, and credibility for each page, based on information such as the page's keyword frequency, link structure, and metadata.

[0529] Step 7:

[0530] The server re-evaluates the relevance of search results based on the emotional data from the emotion engine, adjusting the ranking and filtering of search results according to the user's emotional state.

[0531] Step 8:

[0532] The server rates each search result on its pros and cons: pros include freshness, uniqueness, depth, and accuracy of the information, while cons include the presence or absence of commercial advertising, the reliability of the information, and the excessive technical content of the article.

[0533] Step 9:

[0534] The server uses the emotion data to make recommendations based on the user's feelings, such as encouraging messages for positive points and alternatives for negative points.

[0535] Step 10:

[0536] The server generates a report summarizing the assessment results and recommendations, which can be generated in HTML or PDF format.

[0537] Step 11:

[0538] The server sends the generated report to the terminal. Specifically, the server returns an HTTP response including the generated report to the terminal.

[0539] Step 12:

[0540] Users receive the report on their device and view it through a user interface, which provides a visually organized report that gives users an at-a-glance view of the pros and cons of search results, as well as sentiment-based recommendations.

[0541] Example 2

[0542] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0543] Conventional search systems are unable to fully meet user needs because they are unable to take into account the user's emotional state when evaluating search results. Furthermore, the quality evaluation of search results is uniform, making it impossible to provide recommendations that are adapted to individual users' characteristics and emotions. Therefore, there is a need for a system that can improve the quality of search results and effectively provide customized feedback based on the user's emotions.

[0544] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0545] In this invention, the server includes means for providing a user interface for inputting a search query, means for receiving the search query and sending a request to a web search engine to obtain search results, means for analyzing the search results and extracting indicators such as the topic, relevance, and reliability of each page, means for evaluating the pros and cons of each search result based on the extracted indicators, means for acquiring emotional data from the user at the time of input and including this emotional data in the analysis, and means for generating a report based on the evaluation results and the emotional data and displaying it on the user interface. This allows users to efficiently evaluate the quality of search results and receive recommendations that correspond to their emotions.

[0546] A "search query" refers to text or a phrase that a user types into a search engine to retrieve specific information.

[0547] "User interface" refers to the interface through which a user provides input to a system and receives output from the system, including input fields and search buttons on a computer screen.

[0548] "Server" refers to a computing device over a computer network that receives search queries, transmits requests to web search engines, analyzes search results, and so on.

[0549] A "web search engine" refers to a system for searching for information on the Internet, such as the Google search engine, that provides relevant information based on a search query.

[0550] "Emotional data" refers to data that indicates the user's emotional state at the time of input. It is captured by the emotion engine and analyzed from facial expressions and vocal tone.

[0551] "Analysis" refers to the process of extracting useful information from search results and analyzing that information for topic, relevance, reliability, etc.

[0552] "Metrics" refer to the criteria used to evaluate search results, such as topicality, relevance, and credibility.

[0553] "Evaluation" refers to the process of determining the pros and cons of search results based on extracted metrics and sentiment data.

[0554] "Report" refers to a document containing the evaluation results and sentiment-based recommendations, presented to the user in a user-friendly format.

[0555] MODE FOR CARRYING OUT THE INVENTION

[0556] The present invention is a system that analyzes search queries, evaluates search results in combination with an emotion engine, and adds recommendations based on the user's emotions to the generated report. Specific embodiments of this system are described below.

[0557] Hardware and Software Configuration

[0558] A user enters a search query using a device such as a PC or smartphone. This device has a web browser that provides the user interface. The user interface is composed of HTML, CSS, and JavaScript. It includes a search field and a search button, allowing input and submission. The emotion engine also uses a camera and microphone to analyze the user's facial expressions and tone of voice.

[0559] The device receives a search query entered by the user and sends it to the server, where the emotion engine analyzes the emotion data entered by the user and also sends the emotion data to the server.

[0560] The server requires high computing power, but is often configured as a general-purpose cloud server. The server sends the search query as a request to a web search engine and retrieves relevant search results. The search query is sent using an existing web search engine API, such as the Google Search API.

[0561] After retrieving the search results, the server performs an analysis process. Here, it uses an HTML parsing library such as BeautifulSoup to parse the HTML document of each search result page and extract the body of the text and metadata. Furthermore, it uses the results of this analysis to extract metrics such as the topic, relevance, and credibility of each page.

[0562] Based on the acquired analytical data and emotion data, the server evaluates the positive and negative aspects of each search result. The positive aspects include the freshness, uniqueness, depth, and accuracy of the information, while the negative aspects include the presence or absence of commercial advertisements, the reliability of the information, and the excessive technical content of the article. The server also takes into account the user's emotion data provided by the emotion engine and evaluates the results according to the user's emotions.

[0563] Finally, the server generates a report summarizing the results, clearly listing the pros and cons of each search result, in a user-friendly format, and in formats such as HTML and PDF.

[0564] The terminal receives the generated report and displays it through a user interface, allowing the user to quickly obtain useful information and efficiently evaluate the quality of search results. In addition, customized recommendations based on the user's emotional state promote more effective use of search results.

[0565] Specific examples

[0566] For example, consider a user entering the search query "AI ideas." The device sends this query along with emotional data collected by the emotion engine from facial expressions and voice tone to the server. The server then sends a request to the Google search engine to retrieve relevant search results. The server then uses BeautifulSoup to analyze the search results and evaluate each result's topic, relevance, and trustworthiness. Based on the emotional data, the server generates a report, including optional recommendations, which is finally sent to the device and displayed to the user.

[0567] Example prompts for generative AI models

[0568] "Work with the sentiment engine to evaluate search results for AI ideas and generate reports that provide customized recommendations to users."

[0569] The above is an embodiment of a search result evaluation system that combines an emotion engine. This system allows users to efficiently obtain high-quality information and receive recommendations that correspond to their emotions.

[0570] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0571] Specific explanation of processing steps

[0572] Step 1: Entering a User Query

[0573] A user uses a terminal to enter a search query into an input field in the user interface, for example, "AI ideas," and clicks the search button, which sends the search query to the system.

[0574] Input: A search query such as "AI ideas"

[0575] Output: Sending a search query based on the search button click event

[0576] Step 2: Submitting a query and sentiment data

[0577] The device sends the search query entered by the user to the server. At the same time, the emotion engine also sends the user's emotion data, which is analyzed based on facial expressions, voice tone, etc.

[0578] Input: Search query and emotional data (facial expression, voice tone)

[0579] Output: Search query and sentiment data are sent to the server.

[0580] Step 3: Processing the search request

[0581] The server sends a request to a web search engine (e.g., Google Search API) using the received search query. Once the search results are retrieved, the server stores them.

[0582] Input: search query

[0583] Output: A list of search results

[0584] Step 4: Parse the search results

[0585] The server accesses the URLs of the search results and retrieves the HTML documents. Using an HTML parser library such as BeautifulSoup, the body of each page and important metadata are extracted. This analysis provides metrics such as topic, relevance, and authority.

[0586] Input: Search result URL list

[0587] Output: Extracted data such as the body of each page, metadata, topics, relevance, and credibility

[0588] Step 5: Evaluate the search results

[0589] The server evaluates each search result's pros and cons based on analytical and sentiment data. Pros include freshness, uniqueness, depth, and accuracy of the information, while cons include the presence or absence of commercial advertising, the reliability of the information, and excessive technical content of the article. The server also customizes the evaluation based on the user's sentiment using data from the sentiment engine.

[0590] Input: Analysis data, emotion data

[0591] Output: A list of pros and cons for each search result

[0592] Step 6: Generate reports

[0593] The server compiles the results and generates a user-friendly report, which includes a rating for each search result and recommendations based on user sentiment. The report is generated in HTML and PDF formats.

[0594] Input: Evaluation results, emotion data

[0595] Output: HTML or PDF report

[0596] Step 7: Send and view the report

[0597] The server sends the generated report to the terminal, where the user can view the report through the user interface.

[0598] Input: Report in HTML or PDF format

[0599] Output: Report display in the user interface

[0600] Specific actions

[0601] User types in "AI Ideas" and clicks the search button

[0602] The device sends the search query and emotion data to the server.

[0603] The server executes a search using the Google Search API and retrieves the results.

[0604] The server uses BeautifulSoup to parse the HTML.

[0605] The server evaluates the content based on criteria such as freshness and uniqueness, and also takes into account data from the emotion engine.

[0606] The server generates a report summarizing the evaluation results and sentiment-based recommendations.

[0607] The server sends the generated report to the terminal, where the user can check it.

[0608] The above is the specific process flow that combines the search result evaluation system and emotion engine. This system allows users to easily evaluate the quality of search results and receive recommendations that correspond to their emotions.

[0609] (Application example 2)

[0610] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0611] Conventional food delivery services only provide uniform search results for user queries, and have the problem of being unable to make optimal suggestions based on the user's emotions or mood. In particular, they are unable to suggest dishes that correspond to a user's specific emotional state, such as when the user is stressed or tired, which limits the user experience and makes it difficult to improve satisfaction.

[0612] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for providing a user interface for inputting a search query, means for receiving the search query and sending a request to a network search engine to obtain search results, means for analyzing the search results and extracting indicators such as the topic, relevance, and reliability of each page, an emotion engine for recognizing the user's emotional state and obtaining that data, means for considering the user's emotional data in evaluating the search results, and means for generating a report based on the evaluation results and the emotional data, adding additional recommendations, and displaying the report on the user interface. This makes it possible to suggest optimal dishes based on the user's emotional state, thereby improving the food delivery experience.

[0613] A "search query" is the text or keywords a user enters to search for specific information.

[0614] A "user interface" refers to a screen or device that allows a user to perform operations or input data into a system.

[0615] A "network search engine" is a system that searches for information on the Internet and provides relevant results.

[0616] "Search Results" means information obtained by a network search engine based on a search query.

[0617] An "emotion engine" is a system that analyzes a user's emotions and provides data based on that.

[0618] "Indicators" are information that serves as a basis for evaluating search results.

[0619] A "report" is a document or data generated based on evaluation information and sentiment data of search results.

[0620] "Recommendations" are advice or suggestions provided based on a user's emotional state and evaluation of search results.

[0621] "User emotional state" refers to the current psychological and physiological state of the user performing a search or operation.

[0622] The present invention provides a system for providing search results that take into account a user's emotional state when using a food delivery app. The system includes a user interface, a server, and an emotion engine.

[0623] User Interface

[0624] The user interface is a screen where users can enter search queries through their devices. Users enter keywords such as cuisine or restaurant and press the search button. At this time, emotional data such as the user's facial expression and tone of voice are also acquired.

[0625] Emotion Engine

[0626] The emotion engine is a system for analyzing the user's emotional state. It uses the Google Cloud Vision API and Google Cloud Speech-to-Text API to recognize the user's emotional state from image and audio data. For example, if the user's stress level is determined to be high, that information is fed back to the system.

[0627] Sending a search query and getting results

[0628] When a user enters a search query, the device sends it to the server, which then sends a request to a network search engine to retrieve relevant search results.

[0629] Parsing search results

[0630] The server analyzes the search results and extracts indicators such as the topic, relevance, and reliability of each page. It also combines this with emotional data obtained from the emotion engine to evaluate the search results from multiple angles.

[0631] Evaluation of the good and bad points

[0632] The server evaluates the search results based on the analysis data. The evaluation of positive and negative aspects includes freshness, uniqueness, depth, and accuracy of the information, while the evaluation of negative aspects includes the presence or absence of commercial advertisements, the reliability of the information, and excessive technical content of the article. The server also evaluates the results based on the user's emotional state using emotional data.

[0633] Generate and view reports

[0634] The server generates a report summarizing the evaluation results. The report clearly lists the good and bad points of the search results and is presented to the user in an easy-to-read format. It also adds additional recommendations based on the emotional data, corresponding to the user's emotional state. The report is generated in a format such as HTML or PDF and sent to the device. The user can receive the report on their device and view it through the user interface.

[0635] Specific examples

[0636] For example, if a user enters the search query "relaxing food" and the emotion engine recognizes that the user is feeling stressed based on their facial expression and voice, the server will prioritize suggesting dishes with a relaxing effect (e.g., dishes using herbs and lemon).If the user is tired, the server will recommend dishes that will replenish energy (e.g., high-protein dishes).

[0637] Prompt Sentence Examples

[0638] "Show relaxing dishes. If a user is feeling stressed, prioritize dishes that will help relieve stress and provide recipes."

[0639] This makes it possible to provide optimal information based on the user's emotions, improving the food delivery experience.

[0640] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0641] Step 1:

[0642] A user enters a search query through the user interface of a food delivery app. The search query is a keyword such as "relaxing food." Emotional data, such as the user's facial expressions and voice tone, is also captured. The input consists of a text query and emotional data in the form of images and audio. The emotional data is sent in preparation for sending the image and audio input to the emotion engine.

[0643] Step 2:

[0644] The device sends a search query and emotion data to the server. The input is a text search query and emotion data in the form of images or audio. The output is the search query and emotion data sent to the server. The device performs this process asynchronously and notifies the user of the response.

[0645] Step 3:

[0646] The server receives a search query, sends a request to a network search engine, and retrieves relevant search results. The server stores the sentiment data separately. The input is a text search query, and the output is a list of search results in JSON format.

[0647] Step 4:

[0648] The server analyzes the image and audio data sent to the emotion engine and recognizes the user's emotional state. The input is emotional data in the form of images and audio. The output is emotional information returned by the emotion engine (e.g., stress level, high).

[0649] Step 5:

[0650] The server analyzes the search results and extracts metrics such as the topic, relevance, and credibility of each page. The input is a list of search results in JSON format, and the output is data containing evaluation metrics for each search result. This analysis includes text mining of the page content using natural language processing techniques.

[0651] Step 6:

[0652] The server evaluates the pros and cons of each search result based on the evaluation index and emotion data. The input is data including the evaluation index and emotion information from the emotion engine. The output is report data evaluating the pros and cons of each search result. The evaluation criteria used include the freshness of the information, uniqueness, depth, accuracy, the presence or absence of commercial advertisements, the reliability of the information, and whether the article contains excessive technical content.

[0653] Step 7:

[0654] The server generates a report based on the evaluation results and emotion data, and adds additional recommendations. The input is the evaluation data and emotion data. The output is a report file in HTML or PDF format. Specifically, if the user is feeling stressed, the report will include recipes that will help relieve stress.

[0655] Step 8:

[0656] The server sends the generated report to the terminal, which displays it on the user interface. The input is a report file in HTML or PDF format, and the output is a report that is displayed to the user. The user receives the report on the terminal and can choose the best dish based on the displayed information.

[0657] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0658] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0659] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0660] [Third embodiment]

[0661] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0662] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0663] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0664] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0665] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0666] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0667] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0668] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0669] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0670] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0671] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0672] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0673] The search result evaluation system of the present invention is for analyzing search queries, evaluating the results, and generating reports, and operates through a terminal, a server, and a user interface.

[0674] User Interface

[0675] A user enters a search query, such as "AI ideas," through a user interface on the device, which includes a search field and a search button for input and submission.

[0676] Sending a search query and getting results

[0677] After entering a search query, the device sends it to a server, which then sends the received search query as a request to a web search engine to retrieve relevant search results, using APIs provided by the search engine or web scraping technology.

[0678] Parsing search results

[0679] The server analyzes the search results and extracts metrics such as topic, relevance, and credibility for each page. The page content is then processed using text analysis algorithms, extracting the required text from the HTML document. Metrics used include keyword frequency, page metadata, and external links.

[0680] Evaluation of the good and bad points

[0681] Based on the analysis, the server evaluates each search result's pros and cons. Pros are assessed based on factors such as the freshness, uniqueness, depth, and accuracy of the information. Cons include the presence or absence of commercial advertising, the reliability of the information, and the excessive technical content of the article.

[0682] Generate and view reports

[0683] The server generates a report summarizing the evaluation results. The report clearly lists the pros and cons of each search result and is presented in a user-friendly format. The report is generated in HTML, PDF, or other formats and sent to the device.

[0684] The user can receive the report on their device and view it through the user interface, allowing the user to quickly obtain useful information and efficiently evaluate the quality of search results.

[0685] Specific examples

[0686] For example, if a user enters the search query "AI ideas," the device sends this query to the server. The server then sends a request to the Google search engine to retrieve relevant search results. The server then analyzes the search results and evaluates each result's topic, relevance, and credibility. The server then lists the positives (e.g., introduction of new ideas, detailed examples, information about the latest technology) and negatives (e.g., outdated information, excessive commercial advertising, overly technical content). Finally, the server generates a report, sends it to the device, and displays it to the user.

[0687] The above is an embodiment of the search result evaluation system, which allows users to efficiently access high-quality information.

[0688] The processing flow will be explained below.

[0689] Step 1:

[0690] A user enters a search query "AI ideas" on a terminal and clicks the search button. Here, the user interface receives the user's input from the search field and monitors the click event of the search button.

[0691] Step 2:

[0692] The terminal sends the search query entered by the user to the server. Specifically, it generates an HTTP request including the search query and sends it to the specified endpoint of the server.

[0693] Step 3:

[0694] The server sends a request to a web search engine based on the received search query, using Google's search engine API and scraping techniques to retrieve results corresponding to the search query.

[0695] Step 4:

[0696] The server receives the response from the search engine and retrieves the search results, which are often returned in the form of links, titles, snippets, etc.

[0697] Step 5:

[0698] The server analyzes the search results and extracts the content of each page by visiting each linked page and parsing the HTML document to extract the body of the text and important metadata.

[0699] Step 6:

[0700] The server calculates metrics such as topic, relevance, and credibility for each page, and assigns a score to each page through keyword frequency analysis, external link evaluation, and metadata checks.

[0701] Step 7:

[0702] The server then uses the analytics data to rate the results on their merits and demerits, with the merits including freshness, uniqueness, depth, and accuracy of the information, and the demerits including the presence or absence of commercial advertising, the reliability of the information, and the excessive technical content of the article.

[0703] Step 8:

[0704] The server lists the pros and cons and embeds these ratings in a report template, which is then generated in a user-friendly format.

[0705] Step 9:

[0706] The server sends the generated report to the terminal by returning an HTTP response containing the report data in HTML or PDF format.

[0707] Step 10:

[0708] The user receives the report on their device and displays it through a user interface, which allows the user to view a visually arranged report that allows them to see at a glance the pros and cons of the search results.

[0709] Example 1

[0710] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0711] Current search systems provide search results as they are, forcing users to review each of the numerous results and evaluate their reliability and relevance. This takes time and effort, making it difficult for users to efficiently obtain high-quality information. Furthermore, the lack of a function for comprehensively evaluating the positive and negative aspects of search results makes it difficult for users to quickly find the information they need.

[0712] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0713] In this invention, the server includes means for receiving a search query and transmitting a request to a data processing device to obtain search results, means for analyzing the search results and extracting indicators such as the content, relevance, and reliability of each result, and means for evaluating the advantages and disadvantages of each search result based on the extracted indicators, thereby enabling users to quickly and efficiently obtain highly reliable and relevant information.

[0714] A "search query" is a keyword or phrase that a user enters to search for information.

[0715] "User interface" refers to the interface through which a user interacts with the system, including the search field and search button.

[0716] A "data processing device" is a device that receives a search query, sends a request to a search engine, and retrieves results.

[0717] A "search result" is a list of information returned by a search engine based on a search query, with each item including the title, URL, snippet, etc. of a web page.

[0718] "Analysis" is the process of examining the search results in detail and extracting indicators such as the content, relevance, and reliability of each result.

[0719] An "indicator" is an element extracted from analyzed data, and serves as a standard for evaluating relevance, reliability, advantages, disadvantages, etc.

[0720] "Good points" are positive elements that are evaluated in search results, including freshness, uniqueness, depth, and accuracy.

[0721] "Bad points" are negative elements that are evaluated in search results, including commercial advertising, unreliable information, and overly technical content.

[0722] "Evaluation" is the process of determining the good and bad points of a search result based on analyzed metrics.

[0723] A "report" is a document summarizing the evaluation results, and provides the user with the evaluation of the search results.

[0724] The search result evaluation system of the present invention analyzes search queries, evaluates the results, and generates reports. This system operates through a terminal, a server, and a user interface. Each component and its specific processing method are described in detail below.

[0725] First, a user enters a search query through the device's user interface. The user interface includes a search field and a search button, allowing for easy input and submission. For example, a user enters the query "AI ideas" and presses the search button.

[0726] The device then takes the entered search query and sends it to the server using an HTTP request, packaging the query data in JSON format.

[0727] The server processes the received search query and sends an API request to the specified web search engine (e.g., Google Search) using an official API such as the Google Search API. As a result of this request, the search engine returns search results in JSON format.

[0728] To analyze the received search results, the server first extracts the necessary text from the HTML document, then uses a natural language processing library (e.g., NLTK or spaCy) to evaluate the content, relevance, and credibility of each search result. This analysis extracts metrics such as each page's topic, keyword frequency, metadata, and external links.

[0729] The server then evaluates each search result based on its analysis, scoring it on its pros and cons: pros include freshness, uniqueness, depth, and accuracy of the information, while cons include the presence or absence of commercial advertising, reliability of the information, and excessive technical content.

[0730] Based on these evaluation results, the server generates a detailed report that clearly lists the pros and cons of each search result and is provided in a user-friendly format (e.g., HTML or PDF).The generated report is then sent back to the device.

[0731] Finally, the user can receive the report on their terminal and view it through a user interface, allowing the user to quickly obtain useful information and efficiently evaluate the quality of search results.

[0732] Specific examples

[0733] For example, if a user enters the search query "AI ideas," the following prompt will be generated:

[0734] "Find information about the following search query 'AI ideas', analyze the results, evaluate their pros and cons, and generate a report."

[0735] Based on this prompt, a series of processes are executed between the terminal and the server, and highly rated information is provided to the user.

[0736] The above is an embodiment of the present invention. This system allows users to quickly and accurately obtain the information they need.

[0737] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0738] Step 1:

[0739] A user inputs a search query through a user interface of a terminal, for example, by inputting "AI ideas" and pressing a search button, and the query is submitted. At this time, the user interface provides a search field and a search button.

[0740] Input: Search query (e.g. "AI ideas")

[0741] Output: The action to which the search query is sent

[0742] Step 2:

[0743] The device receives the search query entered in the user interface and sends it to the server using an HTTP request, packaging the query data in JSON format.

[0744] Input: The search query submitted by the user

[0745] Output: HTTP POST request containing a search query

[0746] Step 3:

[0747] The server processes the received search query and sends an API request to the specified web search engine, for example, sending the search query "AI ideas" as a request to the Google Search API.

[0748] Input: Search query sent from the device

[0749] Output: API request to search engine

[0750] Step 4:

[0751] The server receives search results returned by the search engine. The results are returned in JSON format and include information such as title, URL, and snippet.

[0752] Input: API response from search engine

[0753] Output: Search result data (JSON format)

[0754] Step 5:

[0755] The server analyzes the received search results by first extracting the required text from the HTML document and then analyzing the text using a natural language processing library (NLTK or spaCy).

[0756] Input: Search result data (JSON format)

[0757] Output: Analyzed data (e.g., keyword frequencies, topic modeling results)

[0758] Step 6:

[0759] The server then evaluates each search result based on its analysis, assessing its merits and demerits. For example, new ideas and detailed examples are evaluated as positive, while advertisements and outdated information are evaluated as negative.

[0760] Input: Parsed data

[0761] Output: Evaluation results (list of good and bad points)

[0762] Step 7:

[0763] The server generates a report based on the evaluation results, which is output in HTML or PDF format for easy viewing by the user.

[0764] Input: Evaluation results (list of good and bad points)

[0765] Output: Report (HTML or PDF format)

[0766] Step 8:

[0767] The server transmits the generated report to the terminal.

[0768] Input: Report

[0769] Output: Report sent to terminal

[0770] Step 9:

[0771] The terminal displays the report received from the server through a user interface, allowing the user to check the evaluation of search results and quickly obtain high-quality information.

[0772] Input: Report sent from server

[0773] Output: Report display on the user interface

[0774] (Application example 1)

[0775] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0776] Conventional search result systems make it difficult to evaluate the reliability and relevance of results based on a user's search query. Furthermore, especially on online shopping sites, search results are provided without considering factors such as product freshness or the reliability of reviews, making it difficult for users to select the optimal product. Furthermore, excessive advertising and technical content often impair user convenience.

[0777] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0778] In this invention, the server includes means for providing a user interface for inputting a search query, means for receiving the search query and sending a request to a web search engine to obtain search results, and means for analyzing the search results and extracting indicators such as the topic, relevance, and reliability of each page. This makes it possible to analyze the freshness, rating, price, and reliability of reviews of products based on search results containing the content of the search query, and to suggest optimal options to the user.

[0779] A "user interface" is a mechanism that provides a screen and operating means for users to interact with a system or application.

[0780] A "search query" refers to a keyword or phrase that a user enters into a search engine.

[0781] A "web search engine" is a system that automatically collects and indexes information on the Internet and provides relevant web pages in response to a user's search query.

[0782] "Metrics" refers to the criteria used to evaluate the quality and reliability of search results.

[0783] "Freshness" is an indicator of how up-to-date the information is.

[0784] "Rating" refers to reviews or scores given by users that indicate the satisfaction or quality of a product or service.

[0785] "Price" refers to the amount paid to purchase a product or service.

[0786] A "review" refers to an evaluation or impression written by a user after using a product or service.

[0787] "Report" refers to a document or presentation that provides a summary of the analysis and evaluation of search results.

[0788] "Good points" refer to the parts of the search results that are particularly outstanding or highly rated.

[0789] "Bad points" refer to problematic or poorly rated parts of search results.

[0790] "Product freshness" is an index that evaluates how recent the product information provided is.

[0791] "Credibility" is an indicator of how reliable the information and reviews provided are.

[0792] "Best choice" refers to the search results or products that best meet the user's needs and evaluation criteria.

[0793] This invention is a system in which a user inputs a search query, a server evaluates results based on the search query, and provides optimal search results. The configuration and operation of this system will be described in detail below.

[0794] The server provides a user interface for inputting a search query. The user interface includes a search field and a search button, and is designed to allow a user to easily input and submit a search query. The user inputs a search query such as "smartphone reviews" using a smartphone application.

[0795] When a search query is entered, the device receives it and sends it to a server. The server then sends the received search query as a request to a web search engine to retrieve related search results. This can be done using the API provided by the search engine. An example of a specific API is the Google Custom Search JSON API.

[0796] The server analyzes the search results and extracts metrics such as the topic, relevance, and credibility of each page. This analysis uses HTML parsing libraries such as BeautifulSoup and text analysis algorithms. For example, the analysis is based on information such as each page's metadata, keyword frequency, and external links.

[0797] To rate the quality of search results, the server uses criteria such as freshness, uniqueness, depth, and accuracy, taking into account the recency of the ratings, the quality of the reviews, the price, and the trustworthiness of external links. This rating is typically achieved using text analysis algorithms.

[0798] A report is generated based on the evaluation results and provided to the user through a user interface. This report is often generated in HTML format and clearly shows the good and bad points of the search results in a user-friendly format.

[0799] (Example)

[0800] For example, if a user searches for "new smartphone," the server sends a request to the Google search engine to retrieve relevant search results. The server then analyzes the retrieved search results and evaluates each page's topic, relevance, and credibility. It then generates a list of highly rated products based on factors such as product freshness and review credibility, allowing users to easily select the best product.

[0801] (Example of a prompt)

[0802] The following Python code retrieves data from the API based on a search query, analyzes it, and evaluates it. This code evaluates the freshness, uniqueness, depth, and accuracy of search results, and provides them to the user in the form of a report.

[0803] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0804] Step 1:

[0805] A user inputs and submits a search query through a user interface, for example, using the search query "new smartphone." The user interface includes a search field and a search button.

[0806] Step 2:

[0807] The terminal receives the search query entered by the user and sends it to the server. The input is the search query entered by the user, and the output is the query sent to the server. The data is sent to the server in the form of a query.

[0808] Step 3:

[0809] The server sends the received search query to a web search engine and retrieves related search results. The input is the search query sent to the server, and the output is the search results. Specifically, the data is retrieved using a search engine API such as Google Custom Search JSON API.

[0810] Step 4:

[0811] The server analyzes the search results and extracts metrics such as the topic, relevance, and credibility of each page. The input is the search results, and the output is analytical data for each page. This analysis uses HTML analysis libraries such as BeautifulSoup to process the data based on information such as metadata, keyword frequency, and external links.

[0812] Step 5:

[0813] The server evaluates the pros and cons of each search result based on the extracted metrics. The input is the extracted metrics data, and the output is the evaluation results. Each page is evaluated using criteria such as freshness, uniqueness, depth, and accuracy.

[0814] Step 6:

[0815] The server generates a report based on the evaluation results and displays it in the user interface. The input is the evaluation results and the output is the generated report. The report is generated in HTML format and explicitly shows the pros and cons of each search result.

[0816] Step 7:

[0817] The user views the generated report through the user interface on the terminal. The input is the report data sent from the server, and the output is the report displayed to the user. This allows the user to select the most suitable products and information based on the analyzed search results.

[0818] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0819] The present invention is a system that analyzes search queries, evaluates search results in combination with an emotion engine, and adds recommendations based on the user's emotions to the generated report. The system operates through a terminal, a server, and a user interface, as well as the emotion engine.

[0820] User Interface

[0821] A user inputs a search query such as "AI ideas" through the device's user interface. The user interface includes a search field and a search button, allowing input and submission. The emotion engine also recognizes the user's emotion when inputting and captures that information.

[0822] Sending a search query and getting results

[0823] After entering a search query, the device sends it to the server. The server then sends the received search query as a request to a web search engine to retrieve related search results. The server also receives emotion data from the emotion engine and begins analyzing it together with the search results.

[0824] Parsing search results

[0825] The server analyzes the search results and extracts the content of each page. Specifically, it accesses each linked page, parses the HTML document, and extracts the main text and important metadata. It also evaluates the appropriateness of the search results based on the user's emotional data obtained from the emotion engine.

[0826] Evaluation of the good and bad points

[0827] The server evaluates the positive and negative aspects of the search results based on the analysis data. The positive aspects include the freshness, uniqueness, depth, and accuracy of the information, while the negative aspects include the presence or absence of commercial advertisements, the reliability of the information, and the excessive technical content of the article. The server also takes into account the user's emotional data provided by the emotion engine and evaluates the results according to the user's emotions.

[0828] Generate and view reports

[0829] The server generates a report summarizing the evaluation results. The report clearly lists the pros and cons of each search result and is presented in an easy-to-read format for the user. In addition, additional recommendations based on the user's sentiment are added using data from the sentiment engine. The report is generated in formats such as HTML or PDF and sent to the device.

[0830] Users can receive the report on their device and view it through the user interface, allowing them to quickly obtain useful information and efficiently evaluate the quality of search results. Customized recommendations based on the user's emotional state also promote more effective use of search results.

[0831] Specific examples

[0832] For example, if a user enters the search query "AI ideas," the device sends this query to the server. The emotion engine collects emotion data from the user's facial expressions and tone of voice, and sends it to the server. The server then sends a request to the Google search engine to retrieve relevant search results. The server then analyzes the search results, assessing each result's topic, relevance, and credibility, and providing a more detailed evaluation based on the emotion data from the emotion engine. The server then generates a report listing the positives (e.g., introduction of new ideas, detailed examples, and information on the latest technology) and negatives (e.g., outdated information, excessive commercial advertising, and overly technical content) and adding recommendations based on the user's emotion (e.g., encouraging messages and motivational information for a depressed user). Finally, the server sends the report to the device and displays it to the user.

[0833] The above is an embodiment of a search result evaluation system that combines an emotion engine. This system not only allows users to efficiently access high-quality information, but also provides recommendations that correspond to individual emotions.

[0834] The processing flow will be explained below.

[0835] Step 1:

[0836] A user enters the search query "AI ideas" on a device and clicks the search button. The user interface receives user input from the search field and monitors the click event of the search button. The emotion engine also analyzes the user's facial expressions and tone of voice to collect emotion data in real time.

[0837] Step 2:

[0838] The device sends the search query entered by the user and the emotion data acquired by the emotion engine to the server. Specifically, it generates an HTTP request including the search query and emotion data and sends it to the specified endpoint of the server.

[0839] Step 3:

[0840] The server sends a request to a web search engine based on the received search query, using the Google search engine API to retrieve search results corresponding to the entered search query.

[0841] Step 4:

[0842] The server receives the response from the search engine and retrieves the search results, which include links, titles, snippets, etc.

[0843] Step 5:

[0844] The server visits each page of the retrieved search results and parses the HTML document to extract the body of the text and important metadata, using web scraping techniques to analyze the page content.

[0845] Step 6:

[0846] The server calculates metrics such as topic, relevance, and credibility for each page, based on information such as the page's keyword frequency, link structure, and metadata.

[0847] Step 7:

[0848] The server re-evaluates the relevance of search results based on the emotional data from the emotion engine, adjusting the ranking and filtering of search results according to the user's emotional state.

[0849] Step 8:

[0850] The server rates each search result on its pros and cons: pros include freshness, uniqueness, depth, and accuracy of the information, while cons include the presence or absence of commercial advertising, the reliability of the information, and the excessive technical content of the article.

[0851] Step 9:

[0852] The server uses the emotion data to make recommendations based on the user's feelings, such as encouraging messages for positive points and alternatives for negative points.

[0853] Step 10:

[0854] The server generates a report summarizing the assessment results and recommendations, which can be generated in HTML or PDF format.

[0855] Step 11:

[0856] The server sends the generated report to the terminal. Specifically, the server returns an HTTP response including the generated report to the terminal.

[0857] Step 12:

[0858] Users receive the report on their device and view it through a user interface, which provides a visually organized report that gives users an at-a-glance view of the pros and cons of search results, as well as sentiment-based recommendations.

[0859] Example 2

[0860] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0861] Conventional search systems are unable to fully meet user needs because they are unable to take into account the user's emotional state when evaluating search results. Furthermore, the quality evaluation of search results is uniform, making it impossible to provide recommendations that are adapted to individual users' characteristics and emotions. Therefore, there is a need for a system that can improve the quality of search results and effectively provide customized feedback based on the user's emotions.

[0862] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0863] In this invention, the server includes means for providing a user interface for inputting a search query, means for receiving the search query and sending a request to a web search engine to obtain search results, means for analyzing the search results and extracting indicators such as the topic, relevance, and reliability of each page, means for evaluating the pros and cons of each search result based on the extracted indicators, means for acquiring emotional data from the user at the time of input and including this emotional data in the analysis, and means for generating a report based on the evaluation results and the emotional data and displaying it on the user interface. This allows users to efficiently evaluate the quality of search results and receive recommendations that correspond to their emotions.

[0864] A "search query" refers to text or a phrase that a user types into a search engine to retrieve specific information.

[0865] "User interface" refers to the interface through which a user provides input to a system and receives output from the system, including input fields and search buttons on a computer screen.

[0866] "Server" refers to a computing device over a computer network that receives search queries, transmits requests to web search engines, analyzes search results, and so on.

[0867] A "web search engine" refers to a system for searching for information on the Internet, such as the Google search engine, that provides relevant information based on a search query.

[0868] "Emotional data" refers to data that indicates the user's emotional state at the time of input. It is captured by the emotion engine and analyzed from facial expressions and vocal tone.

[0869] "Analysis" refers to the process of extracting useful information from search results and analyzing that information for topic, relevance, reliability, etc.

[0870] "Metrics" refer to the criteria used to evaluate search results, such as topicality, relevance, and credibility.

[0871] "Evaluation" refers to the process of determining the pros and cons of search results based on extracted metrics and sentiment data.

[0872] "Report" refers to a document containing the evaluation results and sentiment-based recommendations, presented to the user in a user-friendly format.

[0873] MODE FOR CARRYING OUT THE INVENTION

[0874] The present invention is a system that analyzes search queries, evaluates search results in combination with an emotion engine, and adds recommendations based on the user's emotions to the generated report. Specific embodiments of this system are described below.

[0875] Hardware and Software Configuration

[0876] A user enters a search query using a device such as a PC or smartphone. This device has a web browser that provides the user interface. The user interface is composed of HTML, CSS, and JavaScript. It includes a search field and a search button, allowing input and submission. The emotion engine also uses a camera and microphone to analyze the user's facial expressions and tone of voice.

[0877] The device receives a search query entered by the user and sends it to the server, where the emotion engine analyzes the emotion data entered by the user and also sends the emotion data to the server.

[0878] The server requires high computing power, but is often configured as a general-purpose cloud server. The server sends the search query as a request to a web search engine and retrieves relevant search results. The search query is sent using an existing web search engine API, such as the Google Search API.

[0879] After retrieving the search results, the server performs an analysis process. Here, it uses an HTML parsing library such as BeautifulSoup to parse the HTML document of each search result page and extract the body of the text and metadata. Furthermore, it uses the results of this analysis to extract metrics such as the topic, relevance, and credibility of each page.

[0880] Based on the acquired analytical data and emotion data, the server evaluates the positive and negative aspects of each search result. The positive aspects include the freshness, uniqueness, depth, and accuracy of the information, while the negative aspects include the presence or absence of commercial advertisements, the reliability of the information, and the excessive technical content of the article. The server also takes into account the user's emotion data provided by the emotion engine and evaluates the results according to the user's emotions.

[0881] Finally, the server generates a report summarizing the results, clearly listing the pros and cons of each search result, in a user-friendly format, and in formats such as HTML and PDF.

[0882] The terminal receives the generated report and displays it through a user interface, allowing the user to quickly obtain useful information and efficiently evaluate the quality of search results. In addition, customized recommendations based on the user's emotional state promote more effective use of search results.

[0883] Specific examples

[0884] For example, consider a user entering the search query "AI ideas." The device sends this query along with emotional data collected by the emotion engine from facial expressions and voice tone to the server. The server then sends a request to the Google search engine to retrieve relevant search results. The server then uses BeautifulSoup to analyze the search results and evaluate each result's topic, relevance, and trustworthiness. Based on the emotional data, the server generates a report, including optional recommendations, which is finally sent to the device and displayed to the user.

[0885] Example prompts for generative AI models

[0886] "Work with the sentiment engine to evaluate search results for AI ideas and generate reports that provide customized recommendations to users."

[0887] The above is an embodiment of a search result evaluation system that combines an emotion engine. This system allows users to efficiently obtain high-quality information and receive recommendations that correspond to their emotions.

[0888] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0889] Specific explanation of processing steps

[0890] Step 1: Entering a User Query

[0891] A user uses a terminal to enter a search query into an input field in the user interface, for example, "AI ideas," and clicks the search button, which sends the search query to the system.

[0892] Input: A search query such as "AI ideas"

[0893] Output: Sending a search query based on the search button click event

[0894] Step 2: Submitting a query and sentiment data

[0895] The device sends the search query entered by the user to the server. At the same time, the emotion engine also sends the user's emotion data, which is analyzed based on facial expressions, voice tone, etc.

[0896] Input: Search query and emotional data (facial expression, voice tone)

[0897] Output: Search query and sentiment data are sent to the server.

[0898] Step 3: Processing the search request

[0899] The server sends a request to a web search engine (e.g., Google Search API) using the received search query. Once the search results are retrieved, the server stores them.

[0900] Input: search query

[0901] Output: A list of search results

[0902] Step 4: Parse the search results

[0903] The server accesses the URLs of the search results and retrieves the HTML documents. Using an HTML parser library such as BeautifulSoup, the body of each page and important metadata are extracted. This analysis provides metrics such as topic, relevance, and authority.

[0904] Input: Search result URL list

[0905] Output: Extracted data such as the body of each page, metadata, topics, relevance, and credibility

[0906] Step 5: Evaluate the search results

[0907] The server evaluates each search result's pros and cons based on analytical and sentiment data. Pros include freshness, uniqueness, depth, and accuracy of the information, while cons include the presence or absence of commercial advertising, the reliability of the information, and excessive technical content of the article. The server also customizes the evaluation based on the user's sentiment using data from the sentiment engine.

[0908] Input: Analysis data, emotion data

[0909] Output: A list of pros and cons for each search result

[0910] Step 6: Generate reports

[0911] The server compiles the results and generates a user-friendly report, which includes a rating for each search result and recommendations based on user sentiment. The report is generated in HTML and PDF formats.

[0912] Input: Evaluation results, emotion data

[0913] Output: HTML or PDF report

[0914] Step 7: Send and view the report

[0915] The server sends the generated report to the terminal, where the user can view the report through the user interface.

[0916] Input: Report in HTML or PDF format

[0917] Output: Report display in the user interface

[0918] Specific actions

[0919] User types in "AI Ideas" and clicks the search button

[0920] The device sends the search query and emotion data to the server.

[0921] The server executes a search using the Google Search API and retrieves the results.

[0922] The server uses BeautifulSoup to parse the HTML.

[0923] The server evaluates the content based on criteria such as freshness and uniqueness, and also takes into account data from the emotion engine.

[0924] The server generates a report summarizing the evaluation results and sentiment-based recommendations.

[0925] The server sends the generated report to the terminal, where the user can check it.

[0926] The above is the specific process flow that combines the search result evaluation system and emotion engine. This system allows users to easily evaluate the quality of search results and receive recommendations that correspond to their emotions.

[0927] (Application example 2)

[0928] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0929] Conventional food delivery services only provide uniform search results for user queries, and have the problem of being unable to make optimal suggestions based on the user's emotions or mood. In particular, they are unable to suggest dishes that correspond to a user's specific emotional state, such as when the user is stressed or tired, which limits the user experience and makes it difficult to improve satisfaction.

[0930] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for providing a user interface for inputting a search query, means for receiving the search query and sending a request to a network search engine to obtain search results, means for analyzing the search results and extracting indicators such as the topic, relevance, and reliability of each page, an emotion engine for recognizing the user's emotional state and obtaining that data, means for considering the user's emotional data in evaluating the search results, and means for generating a report based on the evaluation results and the emotional data, adding additional recommendations, and displaying the report on the user interface. This makes it possible to suggest optimal dishes based on the user's emotional state, thereby improving the food delivery experience.

[0931] A "search query" is the text or keywords a user enters to search for specific information.

[0932] A "user interface" refers to a screen or device that allows a user to perform operations or input data into a system.

[0933] A "network search engine" is a system that searches for information on the Internet and provides relevant results.

[0934] "Search Results" means information obtained by a network search engine based on a search query.

[0935] An "emotion engine" is a system that analyzes a user's emotions and provides data based on that.

[0936] "Indicators" are information that serves as a basis for evaluating search results.

[0937] A "report" is a document or data generated based on evaluation information and sentiment data of search results.

[0938] "Recommendations" are advice or suggestions provided based on a user's emotional state and evaluation of search results.

[0939] "User emotional state" refers to the current psychological and physiological state of the user performing a search or operation.

[0940] The present invention provides a system for providing search results that take into account a user's emotional state when using a food delivery app. The system includes a user interface, a server, and an emotion engine.

[0941] User Interface

[0942] The user interface is a screen where users can enter search queries through their devices. Users enter keywords such as cuisine or restaurant and press the search button. At this time, emotional data such as the user's facial expression and tone of voice are also acquired.

[0943] Emotion Engine

[0944] The emotion engine is a system for analyzing the user's emotional state. It uses the Google Cloud Vision API and Google Cloud Speech-to-Text API to recognize the user's emotional state from image and audio data. For example, if the user's stress level is determined to be high, that information is fed back to the system.

[0945] Sending a search query and getting results

[0946] When a user enters a search query, the device sends it to the server, which then sends a request to a network search engine to retrieve relevant search results.

[0947] Parsing search results

[0948] The server analyzes the search results and extracts indicators such as the topic, relevance, and reliability of each page. It also combines this with emotional data obtained from the emotion engine to evaluate the search results from multiple angles.

[0949] Evaluation of the good and bad points

[0950] The server evaluates the search results based on the analysis data. The evaluation of positive and negative aspects includes freshness, uniqueness, depth, and accuracy of the information, while the evaluation of negative aspects includes the presence or absence of commercial advertisements, the reliability of the information, and excessive technical content of the article. The server also evaluates the results based on the user's emotional state using emotional data.

[0951] Generate and view reports

[0952] The server generates a report summarizing the evaluation results. The report clearly lists the good and bad points of the search results and is presented to the user in an easy-to-read format. It also adds additional recommendations based on the emotional data, corresponding to the user's emotional state. The report is generated in a format such as HTML or PDF and sent to the device. The user can receive the report on their device and view it through the user interface.

[0953] Specific examples

[0954] For example, if a user enters the search query "relaxing food" and the emotion engine recognizes that the user is feeling stressed based on their facial expression and voice, the server will prioritize suggesting dishes with a relaxing effect (e.g., dishes using herbs and lemon).If the user is tired, the server will recommend dishes that will replenish energy (e.g., high-protein dishes).

[0955] Prompt Sentence Examples

[0956] "Show relaxing dishes. If a user is feeling stressed, prioritize dishes that will help relieve stress and provide recipes."

[0957] This makes it possible to provide optimal information based on the user's emotions, improving the food delivery experience.

[0958] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0959] Step 1:

[0960] A user enters a search query through the user interface of a food delivery app. The search query is a keyword such as "relaxing food." Emotional data, such as the user's facial expressions and voice tone, is also captured. The input consists of a text query and emotional data in the form of images and audio. The emotional data is sent in preparation for sending the image and audio input to the emotion engine.

[0961] Step 2:

[0962] The device sends a search query and emotion data to the server. The input is a text search query and emotion data in the form of images or audio. The output is the search query and emotion data sent to the server. The device performs this process asynchronously and notifies the user of the response.

[0963] Step 3:

[0964] The server receives a search query, sends a request to a network search engine, and retrieves relevant search results. The server stores the sentiment data separately. The input is a text search query, and the output is a list of search results in JSON format.

[0965] Step 4:

[0966] The server analyzes the image and audio data sent to the emotion engine and recognizes the user's emotional state. The input is emotional data in the form of images and audio. The output is emotional information returned by the emotion engine (e.g., stress level, high).

[0967] Step 5:

[0968] The server analyzes the search results and extracts metrics such as the topic, relevance, and credibility of each page. The input is a list of search results in JSON format, and the output is data containing evaluation metrics for each search result. This analysis includes text mining of the page content using natural language processing techniques.

[0969] Step 6:

[0970] The server evaluates the pros and cons of each search result based on the evaluation index and emotion data. The input is data including the evaluation index and emotion information from the emotion engine. The output is report data evaluating the pros and cons of each search result. The evaluation criteria used include the freshness of the information, uniqueness, depth, accuracy, the presence or absence of commercial advertisements, the reliability of the information, and whether the article contains excessive technical content.

[0971] Step 7:

[0972] The server generates a report based on the evaluation results and emotion data, and adds additional recommendations. The input is the evaluation data and emotion data. The output is a report file in HTML or PDF format. Specifically, if the user is feeling stressed, the report will include recipes that will help relieve stress.

[0973] Step 8:

[0974] The server sends the generated report to the terminal, which displays it on the user interface. The input is a report file in HTML or PDF format, and the output is a report that is displayed to the user. The user receives the report on the terminal and can choose the best dish based on the displayed information.

[0975] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0976] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0977] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0978] [Fourth embodiment]

[0979] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0980] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0981] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0982] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0983] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0984] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0985] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0986] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0987] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0988] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0989] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0990] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0991] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0992] The search result evaluation system of the present invention is for analyzing search queries, evaluating the results, and generating reports, and operates through a terminal, a server, and a user interface.

[0993] User Interface

[0994] A user enters a search query, such as "AI ideas," through a user interface on the device, which includes a search field and a search button for input and submission.

[0995] Sending a search query and getting results

[0996] After entering a search query, the device sends it to a server, which then sends the received search query as a request to a web search engine to retrieve relevant search results, using APIs provided by the search engine or web scraping technology.

[0997] Parsing search results

[0998] The server analyzes the search results and extracts metrics such as topic, relevance, and credibility for each page. The page content is then processed using text analysis algorithms, extracting the required text from the HTML document. Metrics used include keyword frequency, page metadata, and external links.

[0999] Evaluation of the good and bad points

[1000] Based on the analysis, the server evaluates each search result's pros and cons. Pros are assessed based on factors such as the freshness, uniqueness, depth, and accuracy of the information. Cons include the presence or absence of commercial advertising, the reliability of the information, and the excessive technical content of the article.

[1001] Generate and view reports

[1002] The server generates a report summarizing the evaluation results. The report clearly lists the pros and cons of each search result and is presented in a user-friendly format. The report is generated in HTML, PDF, or other formats and sent to the device.

[1003] The user can receive the report on their device and view it through the user interface, allowing the user to quickly obtain useful information and efficiently evaluate the quality of search results.

[1004] Specific examples

[1005] For example, if a user enters the search query "AI ideas," the device sends this query to the server. The server then sends a request to the Google search engine to retrieve relevant search results. The server then analyzes the search results and evaluates each result's topic, relevance, and credibility. The server then lists the positives (e.g., introduction of new ideas, detailed examples, information about the latest technology) and negatives (e.g., outdated information, excessive commercial advertising, overly technical content). Finally, the server generates a report, sends it to the device, and displays it to the user.

[1006] The above is an embodiment of the search result evaluation system, which allows users to efficiently access high-quality information.

[1007] The processing flow will be explained below.

[1008] Step 1:

[1009] A user enters a search query "AI ideas" on a terminal and clicks the search button. Here, the user interface receives the user's input from the search field and monitors the click event of the search button.

[1010] Step 2:

[1011] The terminal sends the search query entered by the user to the server. Specifically, it generates an HTTP request including the search query and sends it to the specified endpoint of the server.

[1012] Step 3:

[1013] The server sends a request to a web search engine based on the received search query, using Google's search engine API and scraping techniques to retrieve results corresponding to the search query.

[1014] Step 4:

[1015] The server receives the response from the search engine and retrieves the search results, which are often returned in the form of links, titles, snippets, etc.

[1016] Step 5:

[1017] The server analyzes the search results and extracts the content of each page by visiting each linked page and parsing the HTML document to extract the body of the text and important metadata.

[1018] Step 6:

[1019] The server calculates metrics such as topic, relevance, and credibility for each page, and assigns a score to each page through keyword frequency analysis, external link evaluation, and metadata checks.

[1020] Step 7:

[1021] The server then uses the analytics data to rate the results on their merits and demerits, with the merits including freshness, uniqueness, depth, and accuracy of the information, and the demerits including the presence or absence of commercial advertising, the reliability of the information, and the excessive technical content of the article.

[1022] Step 8:

[1023] The server lists the pros and cons and embeds these ratings in a report template, which is then generated in a user-friendly format.

[1024] Step 9:

[1025] The server sends the generated report to the terminal by returning an HTTP response containing the report data in HTML or PDF format.

[1026] Step 10:

[1027] The user receives the report on their device and displays it through a user interface, which allows the user to view a visually arranged report that allows them to see at a glance the pros and cons of the search results.

[1028] Example 1

[1029] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1030] Current search systems provide search results as they are, forcing users to review each of the numerous results and evaluate their reliability and relevance. This takes time and effort, making it difficult for users to efficiently obtain high-quality information. Furthermore, the lack of a function for comprehensively evaluating the positive and negative aspects of search results makes it difficult for users to quickly find the information they need.

[1031] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1032] In this invention, the server includes means for receiving a search query and transmitting a request to a data processing device to obtain search results, means for analyzing the search results and extracting indicators such as the content, relevance, and reliability of each result, and means for evaluating the advantages and disadvantages of each search result based on the extracted indicators, thereby enabling users to quickly and efficiently obtain highly reliable and relevant information.

[1033] A "search query" is a keyword or phrase that a user enters to search for information.

[1034] "User interface" refers to the interface through which a user interacts with the system, including the search field and search button.

[1035] A "data processing device" is a device that receives a search query, sends a request to a search engine, and retrieves results.

[1036] A "search result" is a list of information returned by a search engine based on a search query, with each item including the title, URL, snippet, etc. of a web page.

[1037] "Analysis" is the process of examining the search results in detail and extracting indicators such as the content, relevance, and reliability of each result.

[1038] An "indicator" is an element extracted from analyzed data, and serves as a standard for evaluating relevance, reliability, advantages, disadvantages, etc.

[1039] "Good points" are positive elements that are evaluated in search results, including freshness, uniqueness, depth, and accuracy.

[1040] "Bad points" are negative elements that are evaluated in search results, including commercial advertising, unreliable information, and overly technical content.

[1041] "Evaluation" is the process of determining the good and bad points of a search result based on analyzed metrics.

[1042] A "report" is a document summarizing the evaluation results, and provides the user with the evaluation of the search results.

[1043] The search result evaluation system of the present invention analyzes search queries, evaluates the results, and generates reports. This system operates through a terminal, a server, and a user interface. Each component and its specific processing method are described in detail below.

[1044] First, a user enters a search query through the device's user interface. The user interface includes a search field and a search button, allowing for easy input and submission. For example, a user enters the query "AI ideas" and presses the search button.

[1045] The device then takes the entered search query and sends it to the server using an HTTP request, packaging the query data in JSON format.

[1046] The server processes the received search query and sends an API request to the specified web search engine (e.g., Google Search) using an official API such as the Google Search API. As a result of this request, the search engine returns search results in JSON format.

[1047] To analyze the received search results, the server first extracts the necessary text from the HTML document, then uses a natural language processing library (e.g., NLTK or spaCy) to evaluate the content, relevance, and credibility of each search result. This analysis extracts metrics such as each page's topic, keyword frequency, metadata, and external links.

[1048] The server then evaluates each search result based on its analysis, scoring it on its pros and cons: pros include freshness, uniqueness, depth, and accuracy of the information, while cons include the presence or absence of commercial advertising, reliability of the information, and excessive technical content.

[1049] Based on these evaluation results, the server generates a detailed report that clearly lists the pros and cons of each search result and is provided in a user-friendly format (e.g., HTML or PDF).The generated report is then sent back to the device.

[1050] Finally, the user can receive the report on their terminal and view it through a user interface, allowing the user to quickly obtain useful information and efficiently evaluate the quality of search results.

[1051] Specific examples

[1052] For example, if a user enters the search query "AI ideas," the following prompt will be generated:

[1053] "Find information about the following search query 'AI ideas', analyze the results, evaluate their pros and cons, and generate a report."

[1054] Based on this prompt, a series of processes are executed between the terminal and the server, and highly rated information is provided to the user.

[1055] The above is an embodiment of the present invention. This system allows users to quickly and accurately obtain the information they need.

[1056] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1057] Step 1:

[1058] A user inputs a search query through a user interface of a terminal, for example, by inputting "AI ideas" and pressing a search button, and the query is submitted. At this time, the user interface provides a search field and a search button.

[1059] Input: Search query (e.g. "AI ideas")

[1060] Output: The action to which the search query is sent

[1061] Step 2:

[1062] The device receives the search query entered in the user interface and sends it to the server using an HTTP request, packaging the query data in JSON format.

[1063] Input: The search query submitted by the user

[1064] Output: HTTP POST request containing a search query

[1065] Step 3:

[1066] The server processes the received search query and sends an API request to the specified web search engine, for example, sending the search query "AI ideas" as a request to the Google Search API.

[1067] Input: Search query sent from the device

[1068] Output: API request to search engine

[1069] Step 4:

[1070] The server receives search results returned by the search engine. The results are returned in JSON format and include information such as title, URL, and snippet.

[1071] Input: API response from search engine

[1072] Output: Search result data (JSON format)

[1073] Step 5:

[1074] The server analyzes the received search results by first extracting the required text from the HTML document and then analyzing the text using a natural language processing library (NLTK or spaCy).

[1075] Input: Search result data (JSON format)

[1076] Output: Analyzed data (e.g., keyword frequencies, topic modeling results)

[1077] Step 6:

[1078] The server then evaluates each search result based on its analysis, assessing its merits and demerits. For example, new ideas and detailed examples are evaluated as positive, while advertisements and outdated information are evaluated as negative.

[1079] Input: Parsed data

[1080] Output: Evaluation results (list of good and bad points)

[1081] Step 7:

[1082] The server generates a report based on the evaluation results, which is output in HTML or PDF format for easy viewing by the user.

[1083] Input: Evaluation results (list of good and bad points)

[1084] Output: Report (HTML or PDF format)

[1085] Step 8:

[1086] The server transmits the generated report to the terminal.

[1087] Input: Report

[1088] Output: Report sent to terminal

[1089] Step 9:

[1090] The terminal displays the report received from the server through a user interface, allowing the user to check the evaluation of search results and quickly obtain high-quality information.

[1091] Input: Report sent from server

[1092] Output: Report display on the user interface

[1093] (Application example 1)

[1094] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1095] Conventional search result systems make it difficult to evaluate the reliability and relevance of results based on a user's search query. Furthermore, especially on online shopping sites, search results are provided without considering factors such as product freshness or the reliability of reviews, making it difficult for users to select the optimal product. Furthermore, excessive advertising and technical content often impair user convenience.

[1096] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1097] In this invention, the server includes means for providing a user interface for inputting a search query, means for receiving the search query and sending a request to a web search engine to obtain search results, and means for analyzing the search results and extracting indicators such as the topic, relevance, and reliability of each page. This makes it possible to analyze the freshness, rating, price, and reliability of reviews of products based on search results containing the content of the search query, and to suggest optimal options to the user.

[1098] A "user interface" is a mechanism that provides a screen and operating means for users to interact with a system or application.

[1099] A "search query" refers to a keyword or phrase that a user enters into a search engine.

[1100] A "web search engine" is a system that automatically collects and indexes information on the Internet and provides relevant web pages in response to a user's search query.

[1101] "Metrics" refers to the criteria used to evaluate the quality and reliability of search results.

[1102] "Freshness" is an indicator of how up-to-date the information is.

[1103] "Rating" refers to reviews or scores given by users that indicate the satisfaction or quality of a product or service.

[1104] "Price" refers to the amount paid to purchase a product or service.

[1105] A "review" refers to an evaluation or impression written by a user after using a product or service.

[1106] "Report" refers to a document or presentation that provides a summary of the analysis and evaluation of search results.

[1107] "Good points" refer to the parts of the search results that are particularly outstanding or highly rated.

[1108] "Bad points" refer to problematic or poorly rated parts of search results.

[1109] "Product freshness" is an index that evaluates how recent the product information provided is.

[1110] "Credibility" is an indicator of how reliable the information and reviews provided are.

[1111] "Best choice" refers to the search results or products that best meet the user's needs and evaluation criteria.

[1112] This invention is a system in which a user inputs a search query, a server evaluates results based on the search query, and provides optimal search results. The configuration and operation of this system will be described in detail below.

[1113] The server provides a user interface for inputting a search query. The user interface includes a search field and a search button, and is designed to allow a user to easily input and submit a search query. The user inputs a search query such as "smartphone reviews" using a smartphone application.

[1114] When a search query is entered, the device receives it and sends it to a server. The server then sends the received search query as a request to a web search engine to retrieve related search results. This can be done using the API provided by the search engine. An example of a specific API is the Google Custom Search JSON API.

[1115] The server analyzes the search results and extracts metrics such as the topic, relevance, and credibility of each page. This analysis uses HTML parsing libraries such as BeautifulSoup and text analysis algorithms. For example, the analysis is based on information such as each page's metadata, keyword frequency, and external links.

[1116] To rate the quality of search results, the server uses criteria such as freshness, uniqueness, depth, and accuracy, taking into account the recency of the ratings, the quality of the reviews, the price, and the trustworthiness of external links. This rating is typically achieved using text analysis algorithms.

[1117] A report is generated based on the evaluation results and provided to the user through a user interface. This report is often generated in HTML format and clearly shows the good and bad points of the search results in a user-friendly format.

[1118] (Example)

[1119] For example, if a user searches for "new smartphone," the server sends a request to the Google search engine to retrieve relevant search results. The server then analyzes the retrieved search results and evaluates each page's topic, relevance, and credibility. It then generates a list of highly rated products based on factors such as product freshness and review credibility, allowing users to easily select the best product.

[1120] (Example of a prompt)

[1121] The following Python code retrieves data from the API based on a search query, analyzes it, and evaluates it. This code evaluates the freshness, uniqueness, depth, and accuracy of search results, and provides them to the user in the form of a report.

[1122] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1123] Step 1:

[1124] A user inputs and submits a search query through a user interface, for example, using the search query "new smartphone." The user interface includes a search field and a search button.

[1125] Step 2:

[1126] The terminal receives the search query entered by the user and sends it to the server. The input is the search query entered by the user, and the output is the query sent to the server. The data is sent to the server in the form of a query.

[1127] Step 3:

[1128] The server sends the received search query to a web search engine and retrieves related search results. The input is the search query sent to the server, and the output is the search results. Specifically, the data is retrieved using a search engine API such as Google Custom Search JSON API.

[1129] Step 4:

[1130] The server analyzes the search results and extracts metrics such as the topic, relevance, and credibility of each page. The input is the search results, and the output is analytical data for each page. This analysis uses HTML analysis libraries such as BeautifulSoup to process the data based on information such as metadata, keyword frequency, and external links.

[1131] Step 5:

[1132] The server evaluates the pros and cons of each search result based on the extracted metrics. The input is the extracted metrics data, and the output is the evaluation results. Each page is evaluated using criteria such as freshness, uniqueness, depth, and accuracy.

[1133] Step 6:

[1134] The server generates a report based on the evaluation results and displays it in the user interface. The input is the evaluation results and the output is the generated report. The report is generated in HTML format and explicitly shows the pros and cons of each search result.

[1135] Step 7:

[1136] The user views the generated report through the user interface on the terminal. The input is the report data sent from the server, and the output is the report displayed to the user. This allows the user to select the most suitable products and information based on the analyzed search results.

[1137] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1138] The present invention is a system that analyzes search queries, evaluates search results in combination with an emotion engine, and adds recommendations based on the user's emotions to the generated report. The system operates through a terminal, a server, and a user interface, as well as the emotion engine.

[1139] User Interface

[1140] A user inputs a search query such as "AI ideas" through the device's user interface. The user interface includes a search field and a search button, allowing input and submission. The emotion engine also recognizes the user's emotion when inputting and captures that information.

[1141] Sending a search query and getting results

[1142] After entering a search query, the device sends it to the server. The server then sends the received search query as a request to a web search engine to retrieve related search results. The server also receives emotion data from the emotion engine and begins analyzing it together with the search results.

[1143] Parsing search results

[1144] The server analyzes the search results and extracts the content of each page. Specifically, it accesses each linked page, parses the HTML document, and extracts the main text and important metadata. It also evaluates the appropriateness of the search results based on the user's emotional data obtained from the emotion engine.

[1145] Evaluation of the good and bad points

[1146] The server evaluates the positive and negative aspects of the search results based on the analysis data. The positive aspects include the freshness, uniqueness, depth, and accuracy of the information, while the negative aspects include the presence or absence of commercial advertisements, the reliability of the information, and the excessive technical content of the article. The server also takes into account the user's emotional data provided by the emotion engine and evaluates the results according to the user's emotions.

[1147] Generate and view reports

[1148] The server generates a report summarizing the evaluation results. The report clearly lists the pros and cons of each search result and is presented in an easy-to-read format for the user. In addition, additional recommendations based on the user's sentiment are added using data from the sentiment engine. The report is generated in formats such as HTML or PDF and sent to the device.

[1149] Users can receive the report on their device and view it through the user interface, allowing them to quickly obtain useful information and efficiently evaluate the quality of search results. Customized recommendations based on the user's emotional state also promote more effective use of search results.

[1150] Specific examples

[1151] For example, if a user enters the search query "AI ideas," the device sends this query to the server. The emotion engine collects emotion data from the user's facial expressions and tone of voice, and sends it to the server. The server then sends a request to the Google search engine to retrieve relevant search results. The server then analyzes the search results, assessing each result's topic, relevance, and credibility, and providing a more detailed evaluation based on the emotion data from the emotion engine. The server then generates a report listing the positives (e.g., introduction of new ideas, detailed examples, and information on the latest technology) and negatives (e.g., outdated information, excessive commercial advertising, and overly technical content) and adding recommendations based on the user's emotion (e.g., encouraging messages and motivational information for a depressed user). Finally, the server sends the report to the device and displays it to the user.

[1152] The above is an embodiment of a search result evaluation system that combines an emotion engine. This system not only allows users to efficiently access high-quality information, but also provides recommendations that correspond to individual emotions.

[1153] The processing flow will be explained below.

[1154] Step 1:

[1155] A user enters the search query "AI ideas" on a device and clicks the search button. The user interface receives user input from the search field and monitors the click event of the search button. The emotion engine also analyzes the user's facial expressions and tone of voice to collect emotion data in real time.

[1156] Step 2:

[1157] The device sends the search query entered by the user and the emotion data acquired by the emotion engine to the server. Specifically, it generates an HTTP request including the search query and emotion data and sends it to the specified endpoint of the server.

[1158] Step 3:

[1159] The server sends a request to a web search engine based on the received search query, using the Google search engine API to retrieve search results corresponding to the entered search query.

[1160] Step 4:

[1161] The server receives the response from the search engine and retrieves the search results, which include links, titles, snippets, etc.

[1162] Step 5:

[1163] The server visits each page of the retrieved search results and parses the HTML document to extract the body of the text and important metadata, using web scraping techniques to analyze the page content.

[1164] Step 6:

[1165] The server calculates metrics such as topic, relevance, and credibility for each page, based on information such as the page's keyword frequency, link structure, and metadata.

[1166] Step 7:

[1167] The server re-evaluates the relevance of search results based on the emotional data from the emotion engine, adjusting the ranking and filtering of search results according to the user's emotional state.

[1168] Step 8:

[1169] The server rates each search result on its pros and cons: pros include freshness, uniqueness, depth, and accuracy of the information, while cons include the presence or absence of commercial advertising, the reliability of the information, and the excessive technical content of the article.

[1170] Step 9:

[1171] The server uses the emotion data to make recommendations based on the user's feelings, such as encouraging messages for positive points and alternatives for negative points.

[1172] Step 10:

[1173] The server generates a report summarizing the assessment results and recommendations, which can be generated in HTML or PDF format.

[1174] Step 11:

[1175] The server sends the generated report to the terminal. Specifically, the server returns an HTTP response including the generated report to the terminal.

[1176] Step 12:

[1177] Users receive the report on their device and view it through a user interface, which provides a visually organized report that gives users an at-a-glance view of the pros and cons of search results, as well as sentiment-based recommendations.

[1178] Example 2

[1179] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1180] Conventional search systems are unable to fully meet user needs because they are unable to take into account the user's emotional state when evaluating search results. Furthermore, the quality evaluation of search results is uniform, making it impossible to provide recommendations that are adapted to individual users' characteristics and emotions. Therefore, there is a need for a system that can improve the quality of search results and effectively provide customized feedback based on the user's emotions.

[1181] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1182] In this invention, the server includes means for providing a user interface for inputting a search query, means for receiving the search query and sending a request to a web search engine to obtain search results, means for analyzing the search results and extracting indicators such as the topic, relevance, and reliability of each page, means for evaluating the pros and cons of each search result based on the extracted indicators, means for acquiring emotional data from the user at the time of input and including this emotional data in the analysis, and means for generating a report based on the evaluation results and the emotional data and displaying it on the user interface. This allows users to efficiently evaluate the quality of search results and receive recommendations that correspond to their emotions.

[1183] A "search query" refers to text or a phrase that a user types into a search engine to retrieve specific information.

[1184] "User interface" refers to the interface through which a user provides input to a system and receives output from the system, including input fields and search buttons on a computer screen.

[1185] "Server" refers to a computing device over a computer network that receives search queries, transmits requests to web search engines, analyzes search results, and so on.

[1186] A "web search engine" refers to a system for searching for information on the Internet, such as the Google search engine, that provides relevant information based on a search query.

[1187] "Emotional data" refers to data that indicates the user's emotional state at the time of input. It is captured by the emotion engine and analyzed from facial expressions and vocal tone.

[1188] "Analysis" refers to the process of extracting useful information from search results and analyzing that information for topic, relevance, reliability, etc.

[1189] "Metrics" refer to the criteria used to evaluate search results, such as topicality, relevance, and credibility.

[1190] "Evaluation" refers to the process of determining the pros and cons of search results based on extracted metrics and sentiment data.

[1191] "Report" refers to a document containing the evaluation results and sentiment-based recommendations, presented to the user in a user-friendly format.

[1192] MODE FOR CARRYING OUT THE INVENTION

[1193] The present invention is a system that analyzes search queries, evaluates search results in combination with an emotion engine, and adds recommendations based on the user's emotions to the generated report. Specific embodiments of this system are described below.

[1194] Hardware and Software Configuration

[1195] A user enters a search query using a device such as a PC or smartphone. This device has a web browser that provides the user interface. The user interface is composed of HTML, CSS, and JavaScript. It includes a search field and a search button, allowing input and submission. The emotion engine also uses a camera and microphone to analyze the user's facial expressions and tone of voice.

[1196] The device receives a search query entered by the user and sends it to the server, where the emotion engine analyzes the emotion data entered by the user and also sends the emotion data to the server.

[1197] The server requires high computing power, but is often configured as a general-purpose cloud server. The server sends the search query as a request to a web search engine and retrieves relevant search results. The search query is sent using an existing web search engine API, such as the Google Search API.

[1198] After retrieving the search results, the server performs an analysis process. Here, it uses an HTML parsing library such as BeautifulSoup to parse the HTML document of each search result page and extract the body of the text and metadata. Furthermore, it uses the results of this analysis to extract metrics such as the topic, relevance, and credibility of each page.

[1199] Based on the acquired analytical data and emotion data, the server evaluates the positive and negative aspects of each search result. The positive aspects include the freshness, uniqueness, depth, and accuracy of the information, while the negative aspects include the presence or absence of commercial advertisements, the reliability of the information, and the excessive technical content of the article. The server also takes into account the user's emotion data provided by the emotion engine and evaluates the results according to the user's emotions.

[1200] Finally, the server generates a report summarizing the results, clearly listing the pros and cons of each search result, in a user-friendly format, and in formats such as HTML and PDF.

[1201] The terminal receives the generated report and displays it through a user interface, allowing the user to quickly obtain useful information and efficiently evaluate the quality of search results. In addition, customized recommendations based on the user's emotional state promote more effective use of search results.

[1202] Specific examples

[1203] For example, consider a user entering the search query "AI ideas." The device sends this query along with emotional data collected by the emotion engine from facial expressions and voice tone to the server. The server then sends a request to the Google search engine to retrieve relevant search results. The server then uses BeautifulSoup to analyze the search results and evaluate each result's topic, relevance, and trustworthiness. Based on the emotional data, the server generates a report, including optional recommendations, which is finally sent to the device and displayed to the user.

[1204] Example prompts for generative AI models

[1205] "Work with the sentiment engine to evaluate search results for AI ideas and generate reports that provide customized recommendations to users."

[1206] The above is an embodiment of a search result evaluation system that combines an emotion engine. This system allows users to efficiently obtain high-quality information and receive recommendations that correspond to their emotions.

[1207] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1208] Specific explanation of processing steps

[1209] Step 1: Entering a User Query

[1210] A user uses a terminal to enter a search query into an input field in the user interface, for example, "AI ideas," and clicks the search button, which sends the search query to the system.

[1211] Input: A search query such as "AI ideas"

[1212] Output: Sending a search query based on the search button click event

[1213] Step 2: Submitting a query and sentiment data

[1214] The device sends the search query entered by the user to the server. At the same time, the emotion engine also sends the user's emotion data, which is analyzed based on facial expressions, voice tone, etc.

[1215] Input: Search query and emotional data (facial expression, voice tone)

[1216] Output: Search query and sentiment data are sent to the server.

[1217] Step 3: Processing the search request

[1218] The server sends a request to a web search engine (e.g., Google Search API) using the received search query. Once the search results are retrieved, the server stores them.

[1219] Input: search query

[1220] Output: A list of search results

[1221] Step 4: Parse the search results

[1222] The server accesses the URLs of the search results and retrieves the HTML documents. Using an HTML parser library such as BeautifulSoup, the body of each page and important metadata are extracted. This analysis provides metrics such as topic, relevance, and authority.

[1223] Input: Search result URL list

[1224] Output: Extracted data such as the body of each page, metadata, topics, relevance, and credibility

[1225] Step 5: Evaluate the search results

[1226] The server evaluates each search result's pros and cons based on analytical and sentiment data. Pros include freshness, uniqueness, depth, and accuracy of the information, while cons include the presence or absence of commercial advertising, the reliability of the information, and excessive technical content of the article. The server also customizes the evaluation based on the user's sentiment using data from the sentiment engine.

[1227] Input: Analysis data, emotion data

[1228] Output: A list of pros and cons for each search result

[1229] Step 6: Generate reports

[1230] The server compiles the results and generates a user-friendly report, which includes a rating for each search result and recommendations based on user sentiment. The report is generated in HTML and PDF formats.

[1231] Input: Evaluation results, emotion data

[1232] Output: HTML or PDF report

[1233] Step 7: Send and view the report

[1234] The server sends the generated report to the terminal, where the user can view the report through the user interface.

[1235] Input: Report in HTML or PDF format

[1236] Output: Report display in the user interface

[1237] Specific actions

[1238] User types in "AI Ideas" and clicks the search button

[1239] The device sends the search query and emotion data to the server.

[1240] The server executes a search using the Google Search API and retrieves the results.

[1241] The server uses BeautifulSoup to parse the HTML.

[1242] The server evaluates the content based on criteria such as freshness and uniqueness, and also takes into account data from the emotion engine.

[1243] The server generates a report summarizing the evaluation results and sentiment-based recommendations.

[1244] The server sends the generated report to the terminal, where the user can check it.

[1245] The above is the specific process flow that combines the search result evaluation system and emotion engine. This system allows users to easily evaluate the quality of search results and receive recommendations that correspond to their emotions.

[1246] (Application example 2)

[1247] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1248] Conventional food delivery services only provide uniform search results for user queries, and have the problem of being unable to make optimal suggestions based on the user's emotions or mood. In particular, they are unable to suggest dishes that correspond to a user's specific emotional state, such as when the user is stressed or tired, which limits the user experience and makes it difficult to improve satisfaction.

[1249] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for providing a user interface for inputting a search query, means for receiving the search query and sending a request to a network search engine to obtain search results, means for analyzing the search results and extracting indicators such as the topic, relevance, and reliability of each page, an emotion engine for recognizing the user's emotional state and obtaining that data, means for considering the user's emotional data in evaluating the search results, and means for generating a report based on the evaluation results and the emotional data, adding additional recommendations, and displaying the report on the user interface. This makes it possible to suggest optimal dishes based on the user's emotional state, thereby improving the food delivery experience.

[1250] A "search query" is the text or keywords a user enters to search for specific information.

[1251] A "user interface" refers to a screen or device that allows a user to perform operations or input data into a system.

[1252] A "network search engine" is a system that searches for information on the Internet and provides relevant results.

[1253] "Search Results" means information obtained by a network search engine based on a search query.

[1254] An "emotion engine" is a system that analyzes a user's emotions and provides data based on that.

[1255] "Indicators" are information that serves as a basis for evaluating search results.

[1256] A "report" is a document or data generated based on evaluation information and sentiment data of search results.

[1257] "Recommendations" are advice or suggestions provided based on a user's emotional state and evaluation of search results.

[1258] "User emotional state" refers to the current psychological and physiological state of the user performing a search or operation.

[1259] The present invention provides a system for providing search results that take into account a user's emotional state when using a food delivery app. The system includes a user interface, a server, and an emotion engine.

[1260] User Interface

[1261] The user interface is a screen where users can enter search queries through their devices. Users enter keywords such as cuisine or restaurant and press the search button. At this time, emotional data such as the user's facial expression and tone of voice are also acquired.

[1262] Emotion Engine

[1263] The emotion engine is a system for analyzing the user's emotional state. It uses the Google Cloud Vision API and Google Cloud Speech-to-Text API to recognize the user's emotional state from image and audio data. For example, if the user's stress level is determined to be high, that information is fed back to the system.

[1264] Sending a search query and getting results

[1265] When a user enters a search query, the device sends it to the server, which then sends a request to a network search engine to retrieve relevant search results.

[1266] Parsing search results

[1267] The server analyzes the search results and extracts indicators such as the topic, relevance, and reliability of each page. It also combines this with emotional data obtained from the emotion engine to evaluate the search results from multiple angles.

[1268] Evaluation of the good and bad points

[1269] The server evaluates the search results based on the analysis data. The evaluation of positive and negative aspects includes freshness, uniqueness, depth, and accuracy of the information, while the evaluation of negative aspects includes the presence or absence of commercial advertisements, the reliability of the information, and excessive technical content of the article. The server also evaluates the results based on the user's emotional state using emotional data.

[1270] Generate and view reports

[1271] The server generates a report summarizing the evaluation results. The report clearly lists the good and bad points of the search results and is presented to the user in an easy-to-read format. It also adds additional recommendations based on the emotional data, corresponding to the user's emotional state. The report is generated in a format such as HTML or PDF and sent to the device. The user can receive the report on their device and view it through the user interface.

[1272] Specific examples

[1273] For example, if a user enters the search query "relaxing food" and the emotion engine recognizes that the user is feeling stressed based on their facial expression and voice, the server will prioritize suggesting dishes with a relaxing effect (e.g., dishes using herbs and lemon).If the user is tired, the server will recommend dishes that will replenish energy (e.g., high-protein dishes).

[1274] Prompt Sentence Examples

[1275] "Show relaxing dishes. If a user is feeling stressed, prioritize dishes that will help relieve stress and provide recipes."

[1276] This makes it possible to provide optimal information based on the user's emotions, improving the food delivery experience.

[1277] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1278] Step 1:

[1279] A user enters a search query through the user interface of a food delivery app. The search query is a keyword such as "relaxing food." Emotional data, such as the user's facial expressions and voice tone, is also captured. The input consists of a text query and emotional data in the form of images and audio. The emotional data is sent in preparation for sending the image and audio input to the emotion engine.

[1280] Step 2:

[1281] The device sends a search query and emotion data to the server. The input is a text search query and emotion data in the form of images or audio. The output is the search query and emotion data sent to the server. The device performs this process asynchronously and notifies the user of the response.

[1282] Step 3:

[1283] The server receives a search query, sends a request to a network search engine, and retrieves relevant search results. The server stores the sentiment data separately. The input is a text search query, and the output is a list of search results in JSON format.

[1284] Step 4:

[1285] The server analyzes the image and audio data sent to the emotion engine and recognizes the user's emotional state. The input is emotional data in the form of images and audio. The output is emotional information returned by the emotion engine (e.g., stress level, high).

[1286] Step 5:

[1287] The server analyzes the search results and extracts metrics such as the topic, relevance, and credibility of each page. The input is a list of search results in JSON format, and the output is data containing evaluation metrics for each search result. This analysis includes text mining of the page content using natural language processing techniques.

[1288] Step 6:

[1289] The server evaluates the pros and cons of each search result based on the evaluation index and emotion data. The input is data including the evaluation index and emotion information from the emotion engine. The output is report data evaluating the pros and cons of each search result. The evaluation criteria used include the freshness of the information, uniqueness, depth, accuracy, the presence or absence of commercial advertisements, the reliability of the information, and whether the article contains excessive technical content.

[1290] Step 7:

[1291] The server generates a report based on the evaluation results and emotion data, and adds additional recommendations. The input is the evaluation data and emotion data. The output is a report file in HTML or PDF format. Specifically, if the user is feeling stressed, the report will include recipes that will help relieve stress.

[1292] Step 8:

[1293] The server sends the generated report to the terminal, which displays it on the user interface. The input is a report file in HTML or PDF format, and the output is a report that is displayed to the user. The user receives the report on the terminal and can choose the best dish based on the displayed information.

[1294] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1295] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1296] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1297] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1298] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1299] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1300] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1301] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1302] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1303] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1304] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1305] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1306] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1307] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1308] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1309] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1310] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1311] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1312] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1313] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1314] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1315] The following is further disclosed regarding the above embodiment.

[1316] (Claim 1)

[1317] means for providing a user interface for entering a search query;

[1318] means for receiving a search query and sending a request to a web search engine to retrieve search results;

[1319] A means of analyzing search results and extracting metrics such as topic, relevance, and credibility for each page;

[1320] A means of evaluating the pros and cons of each search result based on the extracted metrics;

[1321] The system includes means for generating a report based on the evaluation results and displaying the report on a user interface.

[1322] (Claim 2)

[1323] The system according to claim 1, wherein the evaluation criteria for the merits are freshness, originality, depth, and accuracy of the information.

[1324] (Claim 3)

[1325] The system according to claim 1 uses the following criteria to evaluate the negative aspects: the presence or absence of commercial advertisements, the reliability of the information, and the excessively technical content of the article.

[1326] "Example 1"

[1327] (Claim 1)

[1328] means for providing a user interface for entering a search query;

[1329] means for receiving a search query and transmitting a request to a data processing device to obtain search results;

[1330] A means of analyzing search results and extracting metrics such as the content, relevance, and reliability of each result;

[1331] A means of evaluating the advantages and disadvantages of each search result based on the extracted metrics; and

[1332] The system includes means for generating a report based on the evaluation results and displaying the report on a user interface.

[1333] (Claim 2)

[1334] 2. The system according to claim 1, wherein the merits are evaluated using criteria such as freshness, uniqueness, depth, and accuracy of the information.

[1335] (Claim 3)

[1336] The system of claim 1 evaluates negative points using criteria such as the presence or absence of commercial advertising, the reliability of the information, and the excessive technical content of the article.

[1337] "Application Example 1"

[1338] (Claim 1)

[1339] means for providing a user interface for entering a search query;

[1340] means for receiving a search query and sending a request to a web search engine to retrieve search results;

[1341] A means of analyzing search results and extracting metrics such as topic, relevance, and credibility for each page;

[1342] A means of evaluating the pros and cons of each search result based on the extracted metrics;

[1343] means for generating a report based on the evaluation results and displaying the report on a user interface;

[1344] A system that includes a means for analyzing product freshness, ratings, prices, and review reliability based on search results for products containing the content of a search query, and suggests optimal options to users.

[1345] (Claim 2)

[1346] The system according to claim 1, wherein the evaluation criteria for the merits are freshness, originality, depth, and accuracy of the information.

[1347] (Claim 3)

[1348] The system according to claim 1 uses the following criteria to evaluate the negative aspects: the presence or absence of commercial advertisements, the reliability of the information, and the excessively technical content of the article.

[1349] "Example 2: Combining Emotion Engines"

[1350] (Claim 1)

[1351] means for providing a user interface for entering a search query;

[1352] means for receiving a search query and sending a request to a web search engine to retrieve search results;

[1353] A means of analyzing search results and extracting metrics such as topic, relevance, and credibility for each page;

[1354] A means of evaluating the pros and cons of each search result based on the extracted metrics;

[1355] means for acquiring emotional data from a user at the time of input and including the emotional data in the analysis;

[1356] The system includes means for generating a report based on the evaluation results and the emotion data and displaying the report in a user interface.

[1357] (Claim 2)

[1358] The system according to claim 1, wherein the evaluation criteria for the merits are freshness, originality, depth, and accuracy of the information.

[1359] (Claim 3)

[1360] The system according to claim 1 uses the following criteria to evaluate the negative aspects: the presence or absence of commercial advertisements, the reliability of the information, and the excessively technical content of the article.

[1361] "Application example 2 when combining emotion engines"

[1362] (Claim 1)

[1363] means for providing a user interface for entering a search query;

[1364] means for receiving a search query and transmitting a request to a network search engine to obtain search results;

[1365] A means of analyzing search results and extracting metrics such as topic, relevance, and credibility for each page;

[1366] A means of evaluating the pros and cons of each search result based on the extracted metrics;

[1367] an emotion engine that recognizes the user's emotional state and captures the data;

[1368] means for taking user emotion data into account in evaluating the search results;

[1369] The system includes means for generating a report based on the evaluation results and sentiment data, and displaying the report in a user interface with additional recommendations.

[1370] (Claim 2)

[1371] The system according to claim 1, wherein the evaluation criteria for the merits are freshness, originality, depth, and accuracy of the information.

[1372] (Claim 3)

[1373] The system according to claim 1 uses the following criteria to evaluate the negative aspects: the presence or absence of commercial advertisements, the reliability of the information, and the excessively technical content of the article. [Explanation of symbols]

[1374] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for providing a user interface for entering a search query; means for receiving a search query and sending a request to a web search engine to retrieve search results; A means of analyzing search results and extracting metrics such as topic, relevance, and credibility for each page; A means of evaluating the pros and cons of each search result based on the extracted metrics; The system includes means for generating a report based on the evaluation results and displaying the report on a user interface.

2. 2. The system according to claim 1, wherein the evaluation criteria for the merits are freshness, originality, depth, and accuracy of the information.

3. 2. The system according to claim 1, wherein the evaluation of negative points is based on the evaluation criteria of the presence or absence of commercial advertisements, the reliability of the information, and the excessively technical content of the article.

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