system

Abstract

We provide the system. [Solution] A means of receiving search queries from users, A means of obtaining relevant information based on a search query, A means of analyzing acquired information and using a generative model to evaluate its credibility and safety, A means for calculating the credibility score and safety score of information and assigning them to the information, A means for displaying credibility scores and safety scores to users, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In information retrieval, since it is necessary for the user to judge the accuracy and safety of the acquired information by himself / herself, there is a problem that there is a possibility of erroneously selecting misinformation or information with a high security risk. Especially in modern times when a vast amount of information is easily accessible online, it is required to efficiently find a reliable information source. However, conventional search engines only provide highly relevant information and do not involve an evaluation of the reliability and safety of the information, thus imposing a burden on the user in information selection.

Means for Solving the Problems

[0005] This invention relates to a system that, when a user performs a search, analyzes the information obtained using a generative model and evaluates its credibility and safety. Specifically, it receives a search query from the user and retrieves relevant information based on that query. Next, the generative model analyzes the retrieved information using natural language processing and provides means for calculating a credibility score and a safety score. The calculated scores are assigned to the information, and by presenting these scores to the user when displaying the information, it enables the user to quickly select reliable information. Furthermore, by prioritizing the provision of information with credibility and safety scores within a specific range to the user, it further facilitates information selection and reduces the risk of misinformation.

[0006] A "user" is a person who searches for and receives information, and is the entity that enters the search query.

[0007] A "search query" is text data that a user enters into the search bar to obtain specific information.

[0008] "Related information" refers to a collection of web pages and data retrieved based on a search query. This information may be collected through search engines.

[0009] A "generative model" is an algorithm or system that uses natural language processing techniques to analyze information and is responsible for calculating a specific evaluation score.

[0010] A "credibility score" is a numerical representation of the accuracy and reliability of information, and is an indicator of how trustworthy the information obtained is.

[0011] A "security score" is a numerical representation of the security risks associated with information, and serves as an indicator of how secure the information is.

[0012] "Natural language processing" is a technology that analyzes human language so that machines can understand and process it, and in this invention, it is a technology used for information analysis. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a signed storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] As an embodiment of this invention, an information retrieval system using a generative model is constructed. When a user performs a search using a terminal, the terminal receives the search query from the user and sends it to the server. Based on the received search query, the server uses a search engine API to retrieve relevant information.

[0035] The acquired information exists in the form of links and web pages, and the server passes this information to a generative model. The generative model uses natural language processing techniques to analyze the information. In this analysis process, a credibility score is calculated based on factors such as the number of citations and the reliability of the authors. A security score is also calculated by referring to the page's security certificate and past security history.

[0036] The server associates this credibility score and security score with each piece of information. The information with the assigned score is returned from the server to the terminal, which then presents it to the user. Information with high credibility and security is visually highlighted, allowing the user to easily verify and select reliable information.

[0037] A concrete example would be a search for "COVID-19 vaccine effectiveness." Websites containing medical-related data and papers would receive a high credibility score. On the other hand, blogs with unknown sources and unsubstantiated content would receive a low score. This system of information filtering significantly reduces the risk of users choosing incorrect information.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user enters a search query into the device's search bar and presses the "Search" button. This action is the first step the user takes to obtain specific information.

[0041] Step 2:

[0042] The terminal sends the user's search query to the server. This process is performed to ensure that the user's request is properly forwarded to the server.

[0043] Step 3:

[0044] The server receives a search query and calls a search engine API to retrieve relevant information. The server then collects the information obtained from this API in a list format.

[0045] Step 4:

[0046] The server provides the acquired information to the generative model, which then begins the analysis. The generative model analyzes the content of the information and generates scores regarding its credibility and safety.

[0047] Step 5:

[0048] The generative model analyzes the information using natural language processing techniques and calculates a credibility score and a safety score while considering the source of the information, the reliability of the author, and security-related matters.

[0049] Step 6:

[0050] The server associates the calculated score with each information item and assigns a corresponding evaluation to each item. This makes the reliability and security of the information immediately apparent.

[0051] Step 7:

[0052] The server sends the scored information results to the terminal. This data is presented to the user as the final search results for viewing.

[0053] Step 8:

[0054] The device displays the received results to the user. Credibility and security scores are presented visually to the user, allowing them to easily select and use appropriate information.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] When users gather information via the internet, it is difficult to find reliable and secure information from the vast amount of data available. In particular, there is a growing risk of accidentally accessing unreliable information or pages with security concerns. Therefore, there is a need to create an environment where users can access accurate information with peace of mind.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes a computer system means for receiving search requests from users, a computer system means for acquiring relevant information from a database based on the search requests, and a computer system means for analyzing the acquired information and using a generative model to evaluate its reliability and security. This makes it possible for users to prioritize the acquisition of highly reliable and secure information, enabling them to use information with peace of mind.

[0060] A "user" refers to an individual or group that seeks to search for and obtain information.

[0061] A "search request" refers to a query or question that a user enters into their device to identify the information they want to obtain.

[0062] A "computer system" refers to the totality of digital devices, including the hardware and software necessary for processing information.

[0063] A "database" refers to a collection of data that is structured to manage information and allow for quick access.

[0064] A "generative model" refers to an algorithm or computer program that performs natural language processing or machine learning based on a large amount of data.

[0065] A "reliability score" refers to an indicator that shows, using numerical values ​​or evaluations, whether the acquired information is trustworthy.

[0066] A "safety score" refers to an index that evaluates whether the information or webpage obtained is safe.

[0067] "Visual emphasis" refers to methods of indicating importance to users through the use of text color, boldness, and order when presenting information.

[0068] This invention is an information retrieval system that utilizes the internet, aiming to enable users to efficiently acquire information while ensuring reliability and security. This system is primarily constructed using a server, user terminals, and a generative AI model.

[0069] The user enters a search query using a terminal. The terminal has a browser or dedicated application installed, and the user enters information through this interface. For example, if the user enters the search request "latest research on climate change," this information is sent to the server.

[0070] The server uses the received search request to access external databases and search engine APIs (e.g., general-purpose search engine platforms) and collect relevant information. The collected information is returned to the server in the form of links or text.

[0071] The server then processes the acquired information using a generative AI model (e.g., a general-purpose language processing algorithm). This model uses natural language processing to analyze the reliability and security of the information. Specifically, it evaluates based on factors such as the number of citations, the information provider's profile, and the page's security certificate. This results in the calculation of reliability and security scores.

[0072] The server assigns a calculated score to the information and sends the result to the terminal. The terminal uses visual emphasis to indicate highly reliable and secure information to the user. This visual emphasis includes the use of font weight and color. This allows the user to easily select trustworthy information.

[0073] As a concrete example, consider a scenario where a user requests information about the "health benefits of plant-based diets." The server prioritizes displaying articles from reliable medical journals and visually highlights them on the user's device. In this case, the AI ​​model is instructed with a prompt in the form of "Analyze reliable information on the query 'health benefits of plant-based diets' and visualize the results." This allows the user to access the necessary information with a high degree of confidence.

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

[0075] Step 1:

[0076] The user enters a search query using their device. Specifically, they launch a browser or search app on their device and enter a search request, such as "latest climate change research," into a text box. The device temporarily stores this input and prepares to send it to the server.

[0077] Step 2:

[0078] The terminal sends the entered search query to the server. The terminal packages the search request as an HTTP request and sends it to the server using a secure communication protocol (HTTPS). When the server receives this request, it parses the query and decides which database or API to connect to.

[0079] Step 3:

[0080] The server retrieves relevant information based on the received search query. The server accesses an external search API (e.g., a common search engine API) and sends a request to find information matching the query. This process retrieves data containing relevant links and text from the network and stores it within the server.

[0081] Step 4:

[0082] The server passes the acquired information to a generative AI model for analysis. In this process, the server inputs the acquired data into a generative AI model (e.g., a language model) and uses natural language processing techniques to analyze the information. Specifically, it evaluates the reliability of the information source and the author, and calculates a reliability score. It also checks the site's security certificate and derives a safety score.

[0083] Step 5:

[0084] The server associates the reliability and safety scores obtained through analysis with each information item. The scored information is organized into a newly constructed dataset, ready for transmission to the terminal. This dataset includes the title, link, and score information for each piece of information.

[0085] Step 6:

[0086] The server sends the organized information to the terminal. The server packages the response as an HTTP response and sends it to the terminal. The terminal interprets this and converts it into a format that the user can view.

[0087] Step 7:

[0088] The device visually highlights and presents highly reliable and secure information to the user. The device displays received data on the screen, highlighting information with high reliability and security scores using bold text and color coding. This allows users to intuitively recognize important information and select accurate and secure information.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] In today's world, phishing scams and misinformation are rampant, making it difficult for users to easily judge the reliability and safety of information obtained online. Links received via email or messages, in particular, pose a risk of users unknowingly accessing malicious content, thus requiring a high level of security. Therefore, a system is needed that can evaluate the reliability and safety of information received by users and present it in a visually easy-to-understand manner.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes means for receiving search requests from users, means for obtaining relevant information based on the search requests, and means for using a generative model to analyze the obtained information and evaluate its reliability and security. This makes it possible to evaluate the security and reliability of link information received by the user and present it visually.

[0094] A "user" is an entity that uses an information processing system to send search requests or receive results.

[0095] A "search request" is a query that a user enters into a system to retrieve specific information.

[0096] "Relevant information" refers to data related to the information the user is seeking, retrieved from a search engine or database based on a search request.

[0097] A "generative model" is a model that includes artificial intelligence techniques used to analyze received information and evaluate its reliability and safety.

[0098] A "reliability score" is an index that evaluates and quantifies the reliability of acquired information.

[0099] A "safety score" is an index that evaluates and quantifies the safety of acquired information.

[0100] "Security analysis" is the process of analyzing link information to determine whether that information is malicious.

[0101] An "information processing system" is a set of mechanisms that receive requests from users, acquire, analyze, and evaluate information, and then provide the results.

[0102] The system for realizing this invention includes a terminal including a smartphone and a server for processing information. The server receives search requests from users and retrieves information using a search engine API based on those requests. The retrieved information is evaluated for reliability and security using a generative AI model. In this process, the server utilizes natural language processing technology to calculate a score based on factors such as the number of citations, author reliability, and page security certificates.

[0103] Hardware used includes smartphones and computer servers. Software includes search engine APIs and natural language processing libraries (e.g., TENSORFLOW®, PyTorch). The server associates reliability and safety scores with information, sends them to the user's device, and the device displays them visually.

[0104] As a concrete example, consider a scenario where this system analyzes links in emails received by users. The system collects information about the links, and if the reliability score is low, it displays a warning to the user, urging them not to open the link. In this way, the security of information can be enhanced.

[0105] An example of a prompt for a generative AI model is, "Evaluate and score the reliability of this link." Based on this prompt, the generative model can analyze the information and calculate the required score.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server accepts search requests received from terminals. These requests include queries entered by the user and form the basis for subsequent information retrieval. Using these queries as input, the server prepares to retrieve relevant information.

[0109] Step 2:

[0110] The server uses a search engine API to collect relevant information based on the received search request. At this stage, the query is passed to the API to retrieve relevant links and literature. The input is the search query, and the output is links to the relevant information.

[0111] Step 3:

[0112] The server passes the acquired link information to a generative AI model for analysis. The generative AI model uses natural language processing techniques to evaluate the reliability and security-related information of the literature. The input here is link information, and the output is a reliability score and a security score. Specifically, it evaluates the number of citations and the security certificate of the page.

[0113] Step 4:

[0114] The server associates the reliability and safety scores obtained from the generative AI model with each piece of information. This assigns a specific evaluation to each link. The input is the evaluation result of the generative AI model, and the output is link information with scores.

[0115] Step 5:

[0116] The server sends the score-associated information to the terminal. The terminal visually presents the received information to the user. Highly reliable information is highlighted. In this process, the scored information is the input, and the visual display for the user is the output.

[0117] Step 6:

[0118] Users select necessary links based on the visually presented information, verifying their reliability and safety. Specifically, users can prioritize reviewing highlighted links.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] As an embodiment of this invention, an information retrieval system incorporating an emotion engine that recognizes user emotions will be described. The terminal receives a search query entered by the user and sends it to the server. At this time, the terminal collects the user's facial expressions and voice, and the emotion engine analyzes the user's emotions in real time.

[0121] The server retrieves relevant information based on the search query and analyzes it using a generative model. Natural language processing is applied to score the credibility and safety of the retrieved information. These scores are assigned to the information, and the display format is adjusted according to the results of the sentiment engine's analysis. For example, if the user is stressed, the information is presented in a visually calming way.

[0122] Furthermore, the emotion engine collects user emotional data, which the server uses to filter information according to the user's preferences. From the relevant information, information that matches the user's emotional state is prioritized, and results based on that are displayed.

[0123] For example, when a user searches for "weather forecast," if the user is in a hurry, the emotion engine will detect tension and impatience. In this case, the server will display the most important and specific information at the top, reducing the time it takes to retrieve the information. In this way, the system can provide a more appropriate and comfortable information retrieval experience based on the user's emotional state.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The user enters a search query into the device's search bar and presses the "Search" button. This action starts the search, and simultaneously, the device's camera and microphone collect the user's facial expressions and voice.

[0127] Step 2:

[0128] An emotion engine operates on the device, analyzing collected user facial and voice data in real time. This identifies the user's emotional state (e.g., joy, sadness, tension, etc.).

[0129] Step 3:

[0130] The terminal sends the user's search query along with sentiment data analyzed by the sentiment engine to the server. The server receives this data and prepares it for analysis.

[0131] Step 4:

[0132] The server uses search queries to retrieve relevant information via search engine APIs. This information is then compiled on the server as web pages and datasets.

[0133] Step 5:

[0134] A generative model within the server analyzes the collected information using natural language processing techniques. As a result of the analysis, each piece of information is assigned a credibility score and a safety score.

[0135] Step 6:

[0136] The server adjusts the display order and method of information based on user emotion data from the emotion engine. For example, if the user is feeling anxious, the server will adjust the display to show the most important information at the top and in a highly visible format.

[0137] Step 7:

[0138] The server sends the refined search results to the device. The device receives these results and displays the information in a user-optimized format, along with credibility and safety scores.

[0139] Step 8:

[0140] Users review the search results displayed on their devices and obtain the necessary information. The user's search experience is comfortable and efficient because it is tailored to their emotional state.

[0141] (Example 2)

[0142] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0143] Current information retrieval systems provide information uniformly without considering the user's emotions or state of mind, meaning the information a user receives may not be appropriate to their current mood or situation. In particular, the lack of evaluation of the credibility and safety of the information poses a risk of making decisions based on inaccurate information. Furthermore, for users experiencing stress, the way information is presented may be inappropriate, leading to a negative user experience.

[0144] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0145] In this invention, the server includes a device means for receiving search data from the user, a device means for collecting and analyzing the user's emotional information using an emotional analysis device installed in the terminal, and a device means for adjusting the display format of items based on the emotional information. This makes it possible to provide information that is adapted to the user's emotional state.

[0146] A "user" is someone who attempts to obtain information using an information retrieval system.

[0147] "Search data" refers to keywords and phrases that users enter with the aim of obtaining information.

[0148] A "device" is a component that performs functions such as information collection, analysis, and display.

[0149] An "emotion analysis device" is a device equipped with the function to analyze a user's facial expressions and voice data to determine their emotional state.

[0150] A "generation device" is a device that analyzes acquired information using a generation AI model to evaluate its credibility and safety.

[0151] "Language processing" is the technology of analyzing natural language and understanding and processing the meaning and context of the information.

[0152] A "credibility score" is an indicator that numerically represents the reliability of the information obtained.

[0153] A "safety score" is an index that numerically represents how safe the acquired information is.

[0154] "Display format" refers to the method or style in which information is provided to the user visually or audibly.

[0155] This invention relates to an information retrieval system that combines an emotion analysis device that recognizes user emotions with an information analysis device. This system consists of a user, a terminal, and a server.

[0156] First, the user enters search data into the device. The device uses its camera and microphone to collect the user's facial expressions and voice data. This data is analyzed by an emotion analysis device to identify the user's emotional state. The analysis is performed in real time and reflects the user's current emotions.

[0157] Next, the device sends search data and sentiment information to the server. The server retrieves relevant information from the internet based on the search data. Generative AI models are used for information analysis, such as models with natural language processing technology. The server analyzes the retrieved information, evaluates its credibility and safety, and assigns a score. This score is used to prioritize the information presented to the user.

[0158] Furthermore, the server adjusts the display format of the information based on the user's emotional information provided by the emotion analysis device. For example, if the user is feeling stressed, the server will support the user's information acquisition by selecting a calm interface with muted colors.

[0159] For example, if a user searches for "weather forecast" and sentiment analysis reveals they are in a hurry, the server can display the most important information at the top, making access to the information more efficient.

[0160] An example of a prompt might be: "Prioritize displaying the information the user wants to know most based on their current emotional state. For example, if the user is feeling anxious, display it in a visually appealing way to help alleviate that anxiety." Such prompts allow the generative AI model to derive an appropriate information presentation method.

[0161] This system enables the delivery of information in a more comfortable and efficient manner, adapted to the user's emotional state.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The user enters search data into the terminal. The terminal uses its camera and microphone to collect the user's facial expressions and voice, and sends this data to an emotion analysis device. The input includes the user's search data, facial expressions, and voice, and the output prepares these for transfer to the emotion analysis device.

[0165] Step 2:

[0166] The device uses an emotion analysis device to analyze the user's facial expressions and voice data to identify the user's emotional state. Specifically, emotion classification is performed using a machine learning algorithm. The input is facial expressions and voice data, and the output is digital data of the user's emotional state.

[0167] Step 3:

[0168] The terminal sends search data and emotional state data to the server. The input data consists of search data and emotional state, which are transferred to the server via the network. The output is all the data passed to the server.

[0169] Step 4:

[0170] The server retrieves relevant information from the internet based on the received search data. A generative AI model is used to analyze the credibility and safety of the information. For data processing, a web crawler collects information, and the AI ​​model evaluates its credibility and safety. The input is search data, and the output is a set of evaluated information and its score.

[0171] Step 5:

[0172] The server assigns credibility and safety scores to the acquired information and determines the display format according to the emotional state. Specifically, the information is visually and structurally adjusted based on the results of the sentiment analysis. The input is information and emotional state data, and the output is an adjusted set of information.

[0173] Step 6:

[0174] The server returns prioritized information to the terminal and presents it to the user. Through the terminal, the user can receive emotionally appropriate information. The input for this step is a pre-configured set of information, and the output is the information displayed on the user's screen.

[0175] (Application Example 2)

[0176] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0177] When users acquire information, they are required to quickly and accurately obtain the necessary information from a vast amount of data. However, there is no established method for displaying information optimally according to the user's emotional state. Especially in commercial environments, suggesting products and services according to the customer's emotions is crucial for improving customer satisfaction. Therefore, a system is needed that can adjust the information display format according to the user's emotional state and suggest appropriate products and services.

[0178] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0179] In this invention, the server includes means for recognizing the user's emotions and adjusting the information display format according to those emotions, means for suggesting the most suitable product or service based on the observer's emotional state in a commercial environment, and means for receiving search queries from the user. This enables the provision of information and product suggestions based on the user's emotions.

[0180] "Means of recognizing user emotions" refers to technologies that analyze the user's facial expressions and voice to determine their emotional state at that time.

[0181] "Means for adjusting the display format of information" refers to technologies that change the way information is displayed and its layout according to the user's emotions, presenting information in a more appropriate and preferable format.

[0182] "Means of proposing goods or services in a commercial environment" refers to technologies that select and propose relevant goods or services based on the emotional state of customers in commercial facilities and similar settings.

[0183] A "means for receiving search queries" refers to the technology that receives and processes requests and questions from users regarding information retrieval.

[0184] A "generative model" is an artificial intelligence model used to analyze acquired information and evaluate its credibility and safety.

[0185] "Natural language processing" is a technology that enables computers to understand and process human language.

[0186] The "credibility score and safety score" are indicators used to evaluate how reliable and safe the acquired information is.

[0187] As an embodiment of this invention, an information suggestion system equipped with emotion recognition capabilities will be described. It consists of a server, a terminal, and a device used in a commercial environment.

[0188] The device receives the user's search query and simultaneously uses its built-in camera and microphone to collect the user's facial expressions and voice in real time. This data is analyzed by an emotion recognition engine to obtain the user's emotional state. The OpenCV image processing library is used for emotion recognition, and Google's Cloud Speech-to-Text API is used for voice analysis. The analysis results are sent to a server via the internet.

[0189] The server retrieves relevant information based on the received search query. During this process, the information is analyzed using a generative AI model to calculate a credibility score and a safety score. Natural language processing techniques are used for the information analysis, specifically utilizing Python and machine learning libraries.

[0190] After assigning scores to the acquired information, the server sends the information to the terminal in a display format tailored to the user's emotional state. This includes adjusting visual elements. If the user is wearing smart glasses, product suggestions tailored to their emotional state are displayed in real time. This allows for the suggestion of optimal products and services, improving customer satisfaction in commercial environments.

[0191] For example, if a user enters a store and displays a relaxed expression, the emotion recognition engine detects this state, and the server suggests products with a relaxing effect. The display order and colors of the information shown on the terminal are then adjusted to a calmer tone.

[0192] As an example of a prompt, the AI ​​model might be input with a request such as, "Generate suggestions for the most suitable relaxation products when the customer is showing signs of relaxation." This allows for the optimization of the user experience.

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The device receives search queries from the user. It collects the queries entered by the user and simultaneously uses the camera and microphone to acquire the user's facial expression data and voice data. This data becomes the input.

[0196] Step 2:

[0197] The device passes the acquired facial expressions and audio data to an emotion recognition engine. Image processing is performed using OpenCV, and the audio data is converted to text using the Google Cloud Speech-to-Text API. This allows the user's emotional state to be analyzed, and the results are obtained as output.

[0198] Step 3:

[0199] The device sends search queries and analyzed sentiment data to the server. This transmitted data serves as input for information retrieval.

[0200] Step 4:

[0201] The server collects relevant information based on the submitted search query. It uses a generative AI model to evaluate the credibility and safety of the information and calculates scores using natural language processing techniques. This results in an output that assigns a credibility score and a safety score to the information.

[0202] Step 5:

[0203] The server filters the generated scored information based on the user's emotional state. It selects the display format for the information according to the emotional state, structuring the information to suit the user. This filtered information becomes the output.

[0204] Step 6:

[0205] The server sends the configured information to the terminal. The terminal receives this information and displays it in a format appropriate to the user's emotional state. Specifically, in smart glasses, product and service suggestions are presented using both video and text.

[0206] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0207] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0208] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0212] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0213] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0214] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0215] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0216] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0217] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0218] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0219] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0220] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0221] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0222] As an embodiment of this invention, an information retrieval system using a generative model is constructed. When a user performs a search using a terminal, the terminal receives the search query from the user and sends it to the server. Based on the received search query, the server uses a search engine API to retrieve relevant information.

[0223] The acquired information exists in the form of links and web pages, and the server passes this information to a generative model. The generative model uses natural language processing techniques to analyze the information. In this analysis process, a credibility score is calculated based on factors such as the number of citations and the reliability of the authors. A security score is also calculated by referring to the page's security certificate and past security history.

[0224] The server associates this credibility score and security score with each piece of information. The information with the assigned score is returned from the server to the terminal, which then presents it to the user. Information with high credibility and security is visually highlighted, allowing the user to easily verify and select reliable information.

[0225] A concrete example would be a search for "COVID-19 vaccine effectiveness." Websites containing medical-related data and papers would receive a high credibility score. On the other hand, blogs with unknown sources and unsubstantiated content would receive a low score. This system of information filtering significantly reduces the risk of users choosing incorrect information.

[0226] The following describes the processing flow.

[0227] Step 1:

[0228] The user enters a search query into the device's search bar and presses the "Search" button. This action is the first step the user takes to obtain specific information.

[0229] Step 2:

[0230] The terminal sends the user's search query to the server. This process is performed to ensure that the user's request is properly forwarded to the server.

[0231] Step 3:

[0232] The server receives a search query and calls a search engine API to retrieve relevant information. The server then collects the information obtained from this API in a list format.

[0233] Step 4:

[0234] The server provides the acquired information to the generative model, which then begins the analysis. The generative model analyzes the content of the information and generates scores regarding its credibility and safety.

[0235] Step 5:

[0236] The generative model analyzes the information using natural language processing techniques and calculates a credibility score and a safety score while considering the source of the information, the reliability of the author, and security-related matters.

[0237] Step 6:

[0238] The server associates the calculated score with each information item and assigns a corresponding evaluation to each item. This makes the reliability and security of the information immediately apparent.

[0239] Step 7:

[0240] The server sends the scored information results to the terminal. This data is presented to the user as the final search results for viewing.

[0241] Step 8:

[0242] The device displays the received results to the user. Credibility and security scores are presented visually to the user, allowing them to easily select and use appropriate information.

[0243] (Example 1)

[0244] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0245] When users gather information via the internet, it is difficult to find reliable and secure information from the vast amount of data available. In particular, there is a growing risk of accidentally accessing unreliable information or pages with security concerns. Therefore, there is a need to create an environment where users can access accurate information with peace of mind.

[0246] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0247] In this invention, the server includes a computer system means for receiving search requests from users, a computer system means for acquiring relevant information from a database based on the search requests, and a computer system means for analyzing the acquired information and using a generative model to evaluate its reliability and security. This makes it possible for users to prioritize the acquisition of highly reliable and secure information, enabling them to use information with peace of mind.

[0248] A "user" refers to an individual or group that seeks to search for and obtain information.

[0249] A "search request" refers to a query or question that a user enters into their device to identify the information they want to obtain.

[0250] A "computer system" refers to the totality of digital devices, including the hardware and software necessary for processing information.

[0251] A "database" refers to a collection of data that is structured to manage information and allow for quick access.

[0252] A "generative model" refers to an algorithm or computer program that performs natural language processing or machine learning based on a large amount of data.

[0253] A "reliability score" refers to an indicator that shows, using numerical values ​​or evaluations, whether the acquired information is trustworthy.

[0254] A "safety score" refers to an index that evaluates whether the information or webpage obtained is safe.

[0255] "Visual emphasis" refers to methods of indicating importance to users through the use of text color, boldness, and order when presenting information.

[0256] This invention is an information retrieval system that utilizes the internet, aiming to enable users to efficiently acquire information while ensuring reliability and security. This system is primarily constructed using a server, user terminals, and a generative AI model.

[0257] The user enters a search query using a terminal. The terminal has a browser or dedicated application installed, and the user enters information through this interface. For example, if the user enters the search request "latest research on climate change," this information is sent to the server.

[0258] The server uses the received search request to access external databases and search engine APIs (e.g., general-purpose search engine platforms) and collect relevant information. The collected information is returned to the server in the form of links or text.

[0259] The server then processes the acquired information using a generative AI model (e.g., a general-purpose language processing algorithm). This model uses natural language processing to analyze the reliability and security of the information. Specifically, it evaluates based on factors such as the number of citations, the information provider's profile, and the page's security certificate. This results in the calculation of reliability and security scores.

[0260] The server assigns a calculated score to the information and sends the result to the terminal. The terminal uses visual emphasis to indicate highly reliable and secure information to the user. This visual emphasis includes the use of font weight and color. This allows the user to easily select trustworthy information.

[0261] As a concrete example, consider a scenario where a user requests information about the "health benefits of plant-based diets." The server prioritizes displaying articles from reliable medical journals and visually highlights them on the user's device. In this case, the AI ​​model is instructed with a prompt in the form of "Analyze reliable information on the query 'health benefits of plant-based diets' and visualize the results." This allows the user to access the necessary information with a high degree of confidence.

[0262] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0263] Step 1:

[0264] The user enters a search query using their device. Specifically, they launch a browser or search app on their device and enter a search request, such as "latest climate change research," into a text box. The device temporarily stores this input and prepares to send it to the server.

[0265] Step 2:

[0266] The terminal sends the entered search query to the server. The terminal packages the search request as an HTTP request and sends it to the server using a secure communication protocol (HTTPS). When the server receives this request, it parses the query and decides which database or API to connect to.

[0267] Step 3:

[0268] The server retrieves relevant information based on the received search query. The server accesses an external search API (e.g., a common search engine API) and sends a request to find information matching the query. This process retrieves data containing relevant links and text from the network and stores it within the server.

[0269] Step 4:

[0270] The server passes the acquired information to a generative AI model for analysis. In this process, the server inputs the acquired data into a generative AI model (e.g., a language model) and uses natural language processing techniques to analyze the information. Specifically, it evaluates the reliability of the information source and the author, and calculates a reliability score. It also checks the site's security certificate and derives a safety score.

[0271] Step 5:

[0272] The server associates the reliability and safety scores obtained through analysis with each information item. The scored information is organized into a newly constructed dataset, ready for transmission to the terminal. This dataset includes the title, link, and score information for each piece of information.

[0273] Step 6:

[0274] The server sends the organized information to the terminal. The server packages the response as an HTTP response and sends it to the terminal. The terminal interprets this and converts it into a format that the user can view.

[0275] Step 7:

[0276] The device visually highlights and presents highly reliable and secure information to the user. The device displays received data on the screen, highlighting information with high reliability and security scores using bold text and color coding. This allows users to intuitively recognize important information and select accurate and secure information.

[0277] (Application Example 1)

[0278] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0279] In modern times, phishing fraud and false information are rampant, and it is difficult for users to easily judge the reliability and security of the information obtained on the Internet. In particular, link information received via email or messages has a risk that users may access malicious content without knowing it, so security is required. Therefore, there is a need for a system that can evaluate the reliability and security of the information received by users and provide it in an easily visually understandable manner.

[0280] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0281] In this invention, the server includes means for receiving a search request from a user, means for obtaining related information based on the search request, and means for using a generation model that analyzes the obtained information and evaluates its reliability and security. As a result, it becomes possible to evaluate the safety and reliability of the link information received by the user and present it visually.

[0282] A "user" is a subject that sends a search request or receives a result using an information processing system.

[0283] [[ID=1,5]] A "search request" is a query that a user inputs into the system to obtain specific information.

[0284] s "Related information" is data related to the information required by the user, obtained from a search engine or database based on the search request.

[0285] A "generation model" is a model that includes artificial intelligence technology used to analyze the received information and evaluate its reliability and security.

[0286] A "reliability score" is an index obtained by evaluating and quantifying the reliability of the obtained information.

[0287] A "security score" is an index obtained by evaluating and quantifying the security of the obtained information.

[0288] "Security analysis" is the process of analyzing link information to determine whether that information is malicious.

[0289] An "information processing system" is a set of mechanisms that receive requests from users, acquire, analyze, and evaluate information, and then provide the results.

[0290] The system for realizing this invention includes a terminal including a smartphone and a server for processing information. The server receives search requests from users and retrieves information using a search engine API based on those requests. The retrieved information is evaluated for reliability and security using a generative AI model. In this process, the server utilizes natural language processing technology to calculate a score based on factors such as the number of citations, author reliability, and page security certificates.

[0291] Hardware used includes smartphones and computer servers. Software includes search engine APIs and natural language processing libraries (e.g., TensorFlow, PyTorch). The server associates reliability and safety scores with information, sends them to the user's device, and the device displays them visually.

[0292] As a concrete example, consider a scenario where this system analyzes links in emails received by users. The system collects information about the links, and if the reliability score is low, it displays a warning to the user, urging them not to open the link. In this way, the security of information can be enhanced.

[0293] An example of a prompt for a generative AI model is, "Evaluate and score the reliability of this link." Based on this prompt, the generative model can analyze the information and calculate the required score.

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The server accepts search requests received from terminals. These requests include queries entered by the user and form the basis for subsequent information retrieval. Using these queries as input, the server prepares to retrieve relevant information.

[0297] Step 2:

[0298] The server uses a search engine API to collect relevant information based on the received search request. At this stage, the query is passed to the API to retrieve relevant links and literature. The input is the search query, and the output is links to the relevant information.

[0299] Step 3:

[0300] The server passes the acquired link information to a generative AI model for analysis. The generative AI model uses natural language processing techniques to evaluate the reliability and security-related information of the literature. The input here is link information, and the output is a reliability score and a security score. Specifically, it evaluates the number of citations and the security certificate of the page.

[0301] Step 4:

[0302] The server associates the reliability and safety scores obtained from the generative AI model with each piece of information. This assigns a specific evaluation to each link. The input is the evaluation result of the generative AI model, and the output is link information with scores.

[0303] Step 5:

[0304] The server sends the score-associated information to the terminal. The terminal visually presents the received information to the user. Highly reliable information is highlighted. In this process, the scored information is the input, and the visual display for the user is the output.

[0305] Step 6:

[0306] Based on the visually presented information, the user selects the necessary links while confirming reliability and safety. As a specific action, the user can prioritize checking the highlighted links.

[0307] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion identification model 59 and perform specific processing using the user's emotions.

[0308] As a form for implementing this invention, an information search system combined with an emotion engine for recognizing the user's emotions will be described. The terminal receives the search query input by the user and transmits it to the server. At this time, the terminal collects the user's facial expressions and voice, and the emotion engine analyzes the user's emotions in real time.

[0309] The server obtains relevant information based on the search query and analyzes the information using a generation model. At this time, natural language processing is applied to score the reliability and safety of the obtained information. These scores are attached to the information, and the display form of the information is adjusted according to the analysis result of the emotion engine. For example, when the user is feeling stressed, the information is presented in a visually calming way.

[0310] Also, the emotion engine collects the user's emotion data, and the server uses this to perform information filtering according to the user's preferences. Among the relevant information, information that matches the user's emotional state is preferentially selected, and the results based on it are displayed.

[0311] For example, when a user searches for "weather forecast," if the user is in a hurry, the emotion engine will detect tension and impatience. In this case, the server will display the most important and specific information at the top, reducing the time it takes to retrieve the information. In this way, the system can provide a more appropriate and comfortable information retrieval experience based on the user's emotional state.

[0312] The following describes the processing flow.

[0313] Step 1:

[0314] The user enters a search query into the device's search bar and presses the "Search" button. This action starts the search, and simultaneously, the device's camera and microphone collect the user's facial expressions and voice.

[0315] Step 2:

[0316] An emotion engine operates on the device, analyzing collected user facial and voice data in real time. This identifies the user's emotional state (e.g., joy, sadness, tension, etc.).

[0317] Step 3:

[0318] The terminal sends the user's search query along with sentiment data analyzed by the sentiment engine to the server. The server receives this data and prepares it for analysis.

[0319] Step 4:

[0320] The server uses search queries to retrieve relevant information via search engine APIs. This information is then compiled on the server as web pages and datasets.

[0321] Step 5:

[0322] A generative model within the server analyzes the collected information using natural language processing techniques. As a result of the analysis, each piece of information is assigned a credibility score and a safety score.

[0323] Step 6:

[0324] The server adjusts the display order and method of information based on user emotion data from the emotion engine. For example, if the user is feeling anxious, the server will adjust the display to show the most important information at the top and in a highly visible format.

[0325] Step 7:

[0326] The server sends the refined search results to the device. The device receives these results and displays the information in a user-optimized format, along with credibility and safety scores.

[0327] Step 8:

[0328] Users review the search results displayed on their devices and obtain the necessary information. The user's search experience is comfortable and efficient because it is tailored to their emotional state.

[0329] (Example 2)

[0330] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0331] Current information retrieval systems provide information uniformly without considering the user's emotions or state of mind, meaning the information a user receives may not be appropriate to their current mood or situation. In particular, the lack of evaluation of the credibility and safety of the information poses a risk of making decisions based on inaccurate information. Furthermore, for users experiencing stress, the way information is presented may be inappropriate, leading to a negative user experience.

[0332] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0333] In this invention, the server includes a device means for receiving search data from the user, a device means for collecting and analyzing the user's emotional information using an emotional analysis device installed in the terminal, and a device means for adjusting the display format of items based on the emotional information. This makes it possible to provide information that is adapted to the user's emotional state.

[0334] A "user" is someone who attempts to obtain information using an information retrieval system.

[0335] "Search data" refers to keywords and phrases that users enter with the aim of obtaining information.

[0336] A "device" is a component that performs functions such as information collection, analysis, and display.

[0337] An "emotion analysis device" is a device equipped with the function to analyze a user's facial expressions and voice data to determine their emotional state.

[0338] A "generation device" is a device that analyzes acquired information using a generation AI model to evaluate its credibility and safety.

[0339] "Language processing" is the technology of analyzing natural language and understanding and processing the meaning and context of the information.

[0340] A "credibility score" is an indicator that numerically represents the reliability of the information obtained.

[0341] A "safety score" is an index that numerically represents how safe the acquired information is.

[0342] "Display format" refers to the method or style in which information is provided to the user visually or audibly.

[0343] This invention relates to an information retrieval system that combines an emotion analysis device that recognizes user emotions with an information analysis device. This system consists of a user, a terminal, and a server.

[0344] First, the user enters search data into the device. The device uses its camera and microphone to collect the user's facial expressions and voice data. This data is analyzed by an emotion analysis device to identify the user's emotional state. The analysis is performed in real time and reflects the user's current emotions.

[0345] Next, the device sends search data and sentiment information to the server. The server retrieves relevant information from the internet based on the search data. Generative AI models are used for information analysis, such as models with natural language processing technology. The server analyzes the retrieved information, evaluates its credibility and safety, and assigns a score. This score is used to prioritize the information presented to the user.

[0346] Furthermore, the server adjusts the display format of the information based on the user's emotional information provided by the emotion analysis device. For example, if the user is feeling stressed, the server will support the user's information acquisition by selecting a calm interface with muted colors.

[0347] For example, if a user searches for "weather forecast" and sentiment analysis reveals they are in a hurry, the server can display the most important information at the top, making access to the information more efficient.

[0348] An example of a prompt might be: "Prioritize displaying the information the user wants to know most based on their current emotional state. For example, if the user is feeling anxious, display it in a visually appealing way to help alleviate that anxiety." Such prompts allow the generative AI model to derive an appropriate information presentation method.

[0349] This system enables the delivery of information in a more comfortable and efficient manner, adapted to the user's emotional state.

[0350] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0351] Step 1:

[0352] The user enters search data into the terminal. The terminal uses its camera and microphone to collect the user's facial expressions and voice, and sends this data to an emotion analysis device. The input includes the user's search data, facial expressions, and voice, and the output prepares these for transfer to the emotion analysis device.

[0353] Step 2:

[0354] The device uses an emotion analysis device to analyze the user's facial expressions and voice data to identify the user's emotional state. Specifically, emotion classification is performed using a machine learning algorithm. The input is facial expressions and voice data, and the output is digital data of the user's emotional state.

[0355] Step 3:

[0356] The terminal sends search data and emotional state data to the server. The input data consists of search data and emotional state, which are transferred to the server via the network. The output is all the data passed to the server.

[0357] Step 4:

[0358] The server retrieves relevant information from the internet based on the received search data. A generative AI model is used to analyze the credibility and safety of the information. For data processing, a web crawler collects information, and the AI ​​model evaluates its credibility and safety. The input is search data, and the output is a set of evaluated information and its score.

[0359] Step 5:

[0360] The server assigns credibility and safety scores to the acquired information and determines the display format according to the emotional state. Specifically, the information is visually and structurally adjusted based on the results of the sentiment analysis. The input is information and emotional state data, and the output is an adjusted set of information.

[0361] Step 6:

[0362] The server returns prioritized information to the terminal and presents it to the user. Through the terminal, the user can receive emotionally appropriate information. The input for this step is a pre-configured set of information, and the output is the information displayed on the user's screen.

[0363] (Application Example 2)

[0364] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0365] When users acquire information, they are required to quickly and accurately obtain the necessary information from a vast amount of data. However, there is no established method for displaying information optimally according to the user's emotional state. Especially in commercial environments, suggesting products and services according to the customer's emotions is crucial for improving customer satisfaction. Therefore, a system is needed that can adjust the information display format according to the user's emotional state and suggest appropriate products and services.

[0366] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0367] In this invention, the server includes means for recognizing the user's emotions and adjusting the information display format according to those emotions, means for suggesting the most suitable product or service based on the observer's emotional state in a commercial environment, and means for receiving search queries from the user. This enables the provision of information and product suggestions based on the user's emotions.

[0368] "Means of recognizing user emotions" refers to technologies that analyze the user's facial expressions and voice to determine their emotional state at that time.

[0369] "Means for adjusting the display format of information" refers to technologies that change the way information is displayed and its layout according to the user's emotions, presenting information in a more appropriate and preferable format.

[0370] "Means of proposing goods or services in a commercial environment" refers to technologies that select and propose relevant goods or services based on the emotional state of customers in commercial facilities and similar settings.

[0371] A "means for receiving search queries" refers to the technology that receives and processes requests and questions from users regarding information retrieval.

[0372] A "generative model" is an artificial intelligence model used to analyze acquired information and evaluate its credibility and safety.

[0373] "Natural language processing" is a technology that enables computers to understand and process human language.

[0374] The "credibility score and safety score" are indicators used to evaluate how reliable and safe the acquired information is.

[0375] As an embodiment of this invention, an information suggestion system equipped with emotion recognition capabilities will be described. It consists of a server, a terminal, and a device used in a commercial environment.

[0376] The device receives the user's search query and simultaneously uses its built-in camera and microphone to collect the user's facial expressions and voice in real time. This data is analyzed by an emotion recognition engine to obtain the user's emotional state. The OpenCV image processing library is used for emotion recognition, and the Google Cloud Speech-to-Text API is used for voice analysis. The analysis results are sent to a server via the internet.

[0377] The server retrieves relevant information based on the received search query. During this process, the information is analyzed using a generative AI model to calculate a credibility score and a safety score. Natural language processing techniques are used for the information analysis, specifically utilizing Python and machine learning libraries.

[0378] After assigning scores to the acquired information, the server sends the information to the terminal in a display format tailored to the user's emotional state. This includes adjusting visual elements. If the user is wearing smart glasses, product suggestions tailored to their emotional state are displayed in real time. This allows for the suggestion of optimal products and services, improving customer satisfaction in commercial environments.

[0379] For example, if a user enters a store and displays a relaxed expression, the emotion recognition engine detects this state, and the server suggests products with a relaxing effect. The display order and colors of the information shown on the terminal are then adjusted to a calmer tone.

[0380] As an example of a prompt, the AI ​​model might be input with a request such as, "Generate suggestions for the most suitable relaxation products when the customer is showing signs of relaxation." This allows for the optimization of the user experience.

[0381] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0382] Step 1:

[0383] The device receives search queries from the user. It collects the queries entered by the user and simultaneously uses the camera and microphone to acquire the user's facial expression data and voice data. This data becomes the input.

[0384] Step 2:

[0385] The device passes the acquired facial expressions and audio data to an emotion recognition engine. Image processing is performed using OpenCV, and the audio data is converted to text using the Google Cloud Speech-to-Text API. This allows the user's emotional state to be analyzed, and the results are obtained as output.

[0386] Step 3:

[0387] The device sends search queries and analyzed sentiment data to the server. This transmitted data serves as input for information retrieval.

[0388] Step 4:

[0389] The server collects relevant information based on the submitted search query. It uses a generative AI model to evaluate the credibility and safety of the information and calculates scores using natural language processing techniques. This results in an output that assigns a credibility score and a safety score to the information.

[0390] Step 5:

[0391] The server filters the generated scored information based on the user's emotional state. It selects the display format for the information according to the emotional state, structuring the information to suit the user. This filtered information becomes the output.

[0392] Step 6:

[0393] The server sends the configured information to the terminal. The terminal receives this information and displays it in a format appropriate to the user's emotional state. Specifically, in smart glasses, product and service suggestions are presented using both video and text.

[0394] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0395] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0396] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0397] [Third Embodiment]

[0398] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0399] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0400] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0401] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0402] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0403] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0404] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0405] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0406] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0407] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0408] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0409] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0410] As an embodiment of this invention, an information retrieval system using a generative model is constructed. When a user performs a search using a terminal, the terminal receives the search query from the user and sends it to the server. Based on the received search query, the server uses a search engine API to retrieve relevant information.

[0411] The acquired information exists in the form of links and web pages, and the server passes this information to a generative model. The generative model uses natural language processing techniques to analyze the information. In this analysis process, a credibility score is calculated based on factors such as the number of citations and the reliability of the authors. A security score is also calculated by referring to the page's security certificate and past security history.

[0412] The server associates this credibility score and security score with each piece of information. The information with the assigned score is returned from the server to the terminal, which then presents it to the user. Information with high credibility and security is visually highlighted, allowing the user to easily verify and select reliable information.

[0413] A concrete example would be a search for "COVID-19 vaccine effectiveness." Websites containing medical-related data and papers would receive a high credibility score. On the other hand, blogs with unknown sources and unsubstantiated content would receive a low score. This system of information filtering significantly reduces the risk of users choosing incorrect information.

[0414] The following describes the processing flow.

[0415] Step 1:

[0416] The user enters a search query into the device's search bar and presses the "Search" button. This action is the first step the user takes to obtain specific information.

[0417] Step 2:

[0418] The terminal sends the user's search query to the server. This process is performed to ensure that the user's request is properly forwarded to the server.

[0419] Step 3:

[0420] The server receives a search query and calls a search engine API to retrieve relevant information. The server then collects the information obtained from this API in a list format.

[0421] Step 4:

[0422] The server provides the acquired information to the generative model, which then begins the analysis. The generative model analyzes the content of the information and generates scores regarding its credibility and safety.

[0423] Step 5:

[0424] The generative model analyzes the information using natural language processing techniques and calculates a credibility score and a safety score while considering the source of the information, the reliability of the author, and security-related matters.

[0425] Step 6:

[0426] The server associates the calculated score with each information item and assigns a corresponding evaluation to each item. This makes the reliability and security of the information immediately apparent.

[0427] Step 7:

[0428] The server sends the scored information results to the terminal. This data is presented to the user as the final search results for viewing.

[0429] Step 8:

[0430] The device displays the received results to the user. Credibility and security scores are presented visually to the user, allowing them to easily select and use appropriate information.

[0431] (Example 1)

[0432] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0433] When users gather information via the internet, it is difficult to find reliable and secure information from the vast amount of data available. In particular, there is a growing risk of accidentally accessing unreliable information or pages with security concerns. Therefore, there is a need to create an environment where users can access accurate information with peace of mind.

[0434] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0435] In this invention, the server includes a computer system means for receiving search requests from users, a computer system means for acquiring relevant information from a database based on the search requests, and a computer system means for analyzing the acquired information and using a generative model to evaluate its reliability and security. This makes it possible for users to prioritize the acquisition of highly reliable and secure information, enabling them to use information with peace of mind.

[0436] A "user" refers to an individual or group that seeks to search for and obtain information.

[0437] A "search request" refers to a query or question that a user enters into their device to identify the information they want to obtain.

[0438] A "computer system" refers to the totality of digital devices, including the hardware and software necessary for processing information.

[0439] A "database" refers to a collection of data that is structured to manage information and allow for quick access.

[0440] A "generative model" refers to an algorithm or computer program that performs natural language processing or machine learning based on a large amount of data.

[0441] A "reliability score" refers to an indicator that shows, using numerical values ​​or evaluations, whether the acquired information is trustworthy.

[0442] A "safety score" refers to an index that evaluates whether the information or webpage obtained is safe.

[0443] "Visual emphasis" refers to methods of indicating importance to users through the use of text color, boldness, and order when presenting information.

[0444] This invention is an information retrieval system that utilizes the internet, aiming to enable users to efficiently acquire information while ensuring reliability and security. This system is primarily constructed using a server, user terminals, and a generative AI model.

[0445] The user enters a search query using a terminal. The terminal has a browser or dedicated application installed, and the user enters information through this interface. For example, if the user enters the search request "latest research on climate change," this information is sent to the server.

[0446] The server uses the received search request to access external databases and search engine APIs (e.g., general-purpose search engine platforms) and collect relevant information. The collected information is returned to the server in the form of links or text.

[0447] The server then processes the acquired information using a generative AI model (e.g., a general-purpose language processing algorithm). This model uses natural language processing to analyze the reliability and security of the information. Specifically, it evaluates based on factors such as the number of citations, the information provider's profile, and the page's security certificate. This results in the calculation of reliability and security scores.

[0448] The server assigns a calculated score to the information and sends the result to the terminal. The terminal uses visual emphasis to indicate highly reliable and secure information to the user. This visual emphasis includes the use of font weight and color. This allows the user to easily select trustworthy information.

[0449] As a concrete example, consider a scenario where a user requests information about the "health benefits of plant-based diets." The server prioritizes displaying articles from reliable medical journals and visually highlights them on the user's device. In this case, the AI ​​model is instructed with a prompt in the form of "Analyze reliable information on the query 'health benefits of plant-based diets' and visualize the results." This allows the user to access the necessary information with a high degree of confidence.

[0450] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0451] Step 1:

[0452] The user enters a search query using their device. Specifically, they launch a browser or search app on their device and enter a search request, such as "latest research on climate change," into a text box. The device temporarily stores this input and prepares to send it to the server.

[0453] Step 2:

[0454] The terminal sends the entered search query to the server. The terminal packages the search request as an HTTP request and sends it to the server using a secure communication protocol (HTTPS). When the server receives this request, it parses the query and decides which database or API to connect to.

[0455] Step 3:

[0456] The server retrieves relevant information based on the received search query. The server accesses an external search API (e.g., a common search engine API) and sends a request to find information matching the query. This process retrieves data containing relevant links and text from the network and stores it within the server.

[0457] Step 4:

[0458] The server passes the acquired information to a generative AI model for analysis. In this process, the server inputs the acquired data into a generative AI model (e.g., a language model) and uses natural language processing techniques to analyze the information. Specifically, it evaluates the reliability of the information source and the author, and calculates a reliability score. It also checks the site's security certificate and derives a safety score.

[0459] Step 5:

[0460] The server associates the reliability and safety scores obtained through analysis with each information item. The scored information is organized into a newly constructed dataset, ready for transmission to the terminal. This dataset includes the title, link, and score information for each piece of information.

[0461] Step 6:

[0462] The server sends the organized information to the terminal. The server packages the response as an HTTP response and sends it to the terminal. The terminal interprets this and converts it into a format that the user can view.

[0463] Step 7:

[0464] The device visually highlights and presents highly reliable and secure information to the user. The device displays received data on the screen, highlighting information with high reliability and security scores using bold text and color coding. This allows users to intuitively recognize important information and select accurate and secure information.

[0465] (Application Example 1)

[0466] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0467] In today's world, phishing scams and misinformation are rampant, making it difficult for users to easily judge the reliability and safety of information obtained online. Links received via email or messages, in particular, pose a risk of users unknowingly accessing malicious content, thus requiring a high level of security. Therefore, a system is needed that can evaluate the reliability and safety of information received by users and present it in a visually easy-to-understand manner.

[0468] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0469] In this invention, the server includes means for receiving search requests from users, means for obtaining relevant information based on the search requests, and means for using a generative model to analyze the obtained information and evaluate its reliability and security. This makes it possible to evaluate the security and reliability of link information received by the user and present it visually.

[0470] A "user" is an entity that uses an information processing system to send search requests or receive results.

[0471] A "search request" is a query that a user enters into a system to retrieve specific information.

[0472] "Relevant information" refers to data related to the information the user is seeking, retrieved from a search engine or database based on a search request.

[0473] A "generative model" is a model that includes artificial intelligence techniques used to analyze received information and evaluate its reliability and safety.

[0474] A "reliability score" is an index that evaluates and quantifies the reliability of acquired information.

[0475] A "safety score" is an index that evaluates and quantifies the safety of acquired information.

[0476] "Security analysis" is the process of analyzing link information to determine whether that information is malicious.

[0477] An "information processing system" is a set of mechanisms that receive requests from users, acquire, analyze, and evaluate information, and then provide the results.

[0478] The system for realizing this invention includes a terminal including a smartphone and a server for processing information. The server receives search requests from users and retrieves information using a search engine API based on those requests. The retrieved information is evaluated for reliability and security using a generative AI model. In this process, the server utilizes natural language processing technology to calculate a score based on factors such as the number of citations, author reliability, and page security certificates.

[0479] Hardware used includes smartphones and computer servers. Software includes search engine APIs and natural language processing libraries (e.g., TensorFlow, PyTorch). The server associates reliability and safety scores with information, sends them to the user's device, and the device displays them visually.

[0480] As a concrete example, consider a scenario where this system analyzes links in emails received by users. The system collects information about the links, and if the reliability score is low, it displays a warning to the user, urging them not to open the link. In this way, the security of information can be enhanced.

[0481] An example of a prompt for a generative AI model is, "Evaluate and score the reliability of this link." Based on this prompt, the generative model can analyze the information and calculate the required score.

[0482] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0483] Step 1:

[0484] The server accepts search requests received from terminals. These requests include queries entered by the user and form the basis for subsequent information retrieval. Using these queries as input, the server prepares to retrieve relevant information.

[0485] Step 2:

[0486] The server uses a search engine API to collect relevant information based on the received search request. At this stage, the query is passed to the API to retrieve relevant links and literature. The input is the search query, and the output is links to the relevant information.

[0487] Step 3:

[0488] The server passes the acquired link information to a generative AI model for analysis. The generative AI model uses natural language processing techniques to evaluate the reliability and security-related information of the literature. The input here is link information, and the output is a reliability score and a security score. Specifically, it evaluates the number of citations and the security certificate of the page.

[0489] Step 4:

[0490] The server associates the reliability and safety scores obtained from the generative AI model with each piece of information. This assigns a specific evaluation to each link. The input is the evaluation result of the generative AI model, and the output is link information with scores.

[0491] Step 5:

[0492] The server sends the score-associated information to the terminal. The terminal visually presents the received information to the user. Highly reliable information is highlighted. In this process, the scored information is the input, and the visual display for the user is the output.

[0493] Step 6:

[0494] Users select necessary links based on the visually presented information, verifying their reliability and safety. Specifically, users can prioritize reviewing highlighted links.

[0495] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0496] As an embodiment of this invention, an information retrieval system incorporating an emotion engine that recognizes user emotions will be described. The terminal receives a search query entered by the user and sends it to the server. At this time, the terminal collects the user's facial expressions and voice, and the emotion engine analyzes the user's emotions in real time.

[0497] The server retrieves relevant information based on the search query and analyzes it using a generative model. Natural language processing is applied to score the credibility and safety of the retrieved information. These scores are assigned to the information, and the display format is adjusted according to the results of the sentiment engine's analysis. For example, if the user is stressed, the information is presented in a visually calming way.

[0498] Furthermore, the emotion engine collects user emotional data, which the server uses to filter information according to the user's preferences. From the relevant information, information that matches the user's emotional state is prioritized, and results based on that are displayed.

[0499] For example, when a user searches for "weather forecast," if the user is in a hurry, the emotion engine will detect tension and impatience. In this case, the server will display the most important and specific information at the top, reducing the time it takes to retrieve the information. In this way, the system can provide a more appropriate and comfortable information retrieval experience based on the user's emotional state.

[0500] The following describes the processing flow.

[0501] Step 1:

[0502] The user enters a search query into the device's search bar and presses the "Search" button. This action starts the search, and simultaneously, the device's camera and microphone collect the user's facial expressions and voice.

[0503] Step 2:

[0504] An emotion engine operates on the device, analyzing collected user facial and voice data in real time. This identifies the user's emotional state (e.g., joy, sadness, tension, etc.).

[0505] Step 3:

[0506] The terminal sends the user's search query along with sentiment data analyzed by the sentiment engine to the server. The server receives this data and prepares it for analysis.

[0507] Step 4:

[0508] The server uses search queries to retrieve relevant information via search engine APIs. This information is then compiled on the server as web pages and datasets.

[0509] Step 5:

[0510] A generative model within the server analyzes the collected information using natural language processing techniques. As a result of the analysis, each piece of information is assigned a credibility score and a safety score.

[0511] Step 6:

[0512] The server adjusts the display order and method of information based on user emotion data from the emotion engine. For example, if the user is feeling anxious, the server will adjust the display to show the most important information at the top and in a highly visible format.

[0513] Step 7:

[0514] The server sends the refined search results to the device. The device receives these results and displays the information in a user-optimized format, along with credibility and safety scores.

[0515] Step 8:

[0516] Users review the search results displayed on their devices and obtain the necessary information. The user's search experience is comfortable and efficient because it is tailored to their emotional state.

[0517] (Example 2)

[0518] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0519] Current information retrieval systems provide information uniformly without considering the user's emotions or state of mind, meaning the information a user receives may not be appropriate to their current mood or situation. In particular, the lack of evaluation of the credibility and safety of the information poses a risk of making decisions based on inaccurate information. Furthermore, for users experiencing stress, the way information is presented may be inappropriate, leading to a negative user experience.

[0520] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0521] In this invention, the server includes a device means for receiving search data from the user, a device means for collecting and analyzing the user's emotional information using an emotional analysis device installed in the terminal, and a device means for adjusting the display format of items based on the emotional information. This makes it possible to provide information that is adapted to the user's emotional state.

[0522] A "user" is someone who attempts to obtain information using an information retrieval system.

[0523] "Search data" refers to keywords and phrases that users enter with the aim of obtaining information.

[0524] A "device" is a component that performs functions such as information collection, analysis, and display.

[0525] An "emotion analysis device" is a device equipped with the function to analyze a user's facial expressions and voice data to determine their emotional state.

[0526] A "generation device" is a device that analyzes acquired information using a generation AI model to evaluate its credibility and safety.

[0527] "Language processing" is the technology of analyzing natural language and understanding and processing the meaning and context of the information.

[0528] A "credibility score" is an indicator that numerically represents the reliability of the information obtained.

[0529] A "safety score" is an index that numerically represents how safe the acquired information is.

[0530] "Display format" refers to the method or style in which information is provided to the user visually or audibly.

[0531] This invention relates to an information retrieval system that combines an emotion analysis device that recognizes user emotions with an information analysis device. This system consists of a user, a terminal, and a server.

[0532] First, the user enters search data into the device. The device uses its camera and microphone to collect the user's facial expressions and voice data. This data is analyzed by an emotion analysis device to identify the user's emotional state. The analysis is performed in real time and reflects the user's current emotions.

[0533] Next, the device sends search data and sentiment information to the server. The server retrieves relevant information from the internet based on the search data. Generative AI models are used for information analysis, such as models with natural language processing technology. The server analyzes the retrieved information, evaluates its credibility and safety, and assigns a score. This score is used to prioritize the information presented to the user.

[0534] Furthermore, the server adjusts the display format of the information based on the user's emotional information provided by the emotion analysis device. For example, if the user is feeling stressed, the server will support the user's information acquisition by selecting a calm interface with muted colors.

[0535] For example, if a user searches for "weather forecast" and sentiment analysis reveals they are in a hurry, the server can display the most important information at the top, making access to the information more efficient.

[0536] An example of a prompt might be: "Prioritize displaying the information the user wants to know most based on their current emotional state. For example, if the user is feeling anxious, display it in a visually appealing way to help alleviate that anxiety." Such prompts allow the generative AI model to derive an appropriate information presentation method.

[0537] This system enables the delivery of information in a more comfortable and efficient manner, adapted to the user's emotional state.

[0538] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0539] Step 1:

[0540] The user enters search data into the terminal. The terminal uses its camera and microphone to collect the user's facial expressions and voice, and sends this data to an emotion analysis device. The input includes the user's search data, facial expressions, and voice, and the output prepares these for transfer to the emotion analysis device.

[0541] Step 2:

[0542] The device uses an emotion analysis device to analyze the user's facial expressions and voice data to identify the user's emotional state. Specifically, emotion classification is performed using a machine learning algorithm. The input is facial expressions and voice data, and the output is digital data of the user's emotional state.

[0543] Step 3:

[0544] The terminal sends search data and emotional state data to the server. The input data consists of search data and emotional state, which are transferred to the server via the network. The output is all the data passed to the server.

[0545] Step 4:

[0546] The server retrieves relevant information from the internet based on the received search data. A generative AI model is used to analyze the credibility and safety of the information. For data processing, a web crawler collects information, and the AI ​​model evaluates its credibility and safety. The input is search data, and the output is a set of evaluated information and its score.

[0547] Step 5:

[0548] The server assigns credibility and safety scores to the acquired information and determines the display format according to the emotional state. Specifically, the information is visually and structurally adjusted based on the results of the sentiment analysis. The input is information and emotional state data, and the output is an adjusted set of information.

[0549] Step 6:

[0550] The server returns prioritized information to the terminal and presents it to the user. Through the terminal, the user can receive emotionally appropriate information. The input for this step is a pre-configured set of information, and the output is the information displayed on the user's screen.

[0551] (Application Example 2)

[0552] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0553] When users acquire information, they are required to quickly and accurately obtain the necessary information from a vast amount of data. However, there is no established method for displaying information optimally according to the user's emotional state. Especially in commercial environments, suggesting products and services according to the customer's emotions is crucial for improving customer satisfaction. Therefore, a system is needed that can adjust the information display format according to the user's emotional state and suggest appropriate products and services.

[0554] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0555] In this invention, the server includes means for recognizing the user's emotions and adjusting the information display format according to those emotions, means for suggesting the most suitable product or service based on the observer's emotional state in a commercial environment, and means for receiving search queries from the user. This enables the provision of information and product suggestions based on the user's emotions.

[0556] "Means of recognizing user emotions" refers to technologies that analyze the user's facial expressions and voice to determine their emotional state at that time.

[0557] "Means for adjusting the display format of information" refers to technologies that change the way information is displayed and its layout according to the user's emotions, presenting information in a more appropriate and preferable format.

[0558] "Means of proposing goods or services in a commercial environment" refers to technologies that select and propose relevant goods or services based on the emotional state of customers in commercial facilities and similar settings.

[0559] A "means for receiving search queries" refers to the technology that receives and processes requests and questions from users regarding information retrieval.

[0560] A "generative model" is an artificial intelligence model used to analyze acquired information and evaluate its credibility and safety.

[0561] "Natural language processing" is a technology that enables computers to understand and process human language.

[0562] The "credibility score and safety score" are indicators used to evaluate how reliable and safe the acquired information is.

[0563] As an embodiment of this invention, an information suggestion system equipped with emotion recognition capabilities will be described. It consists of a server, a terminal, and a device used in a commercial environment.

[0564] The device receives the user's search query and simultaneously uses its built-in camera and microphone to collect the user's facial expressions and voice in real time. This data is analyzed by an emotion recognition engine to obtain the user's emotional state. The OpenCV image processing library is used for emotion recognition, and the Google Cloud Speech-to-Text API is used for voice analysis. The analysis results are sent to a server via the internet.

[0565] The server retrieves relevant information based on the received search query. During this process, the information is analyzed using a generative AI model to calculate a credibility score and a safety score. Natural language processing techniques are used for the information analysis, specifically utilizing Python and machine learning libraries.

[0566] After assigning scores to the acquired information, the server sends the information to the terminal in a display format tailored to the user's emotional state. This includes adjusting visual elements. If the user is wearing smart glasses, product suggestions tailored to their emotional state are displayed in real time. This allows for the suggestion of optimal products and services, improving customer satisfaction in commercial environments.

[0567] For example, if a user enters a store and displays a relaxed expression, the emotion recognition engine detects this state, and the server suggests products with a relaxing effect. The display order and colors of the information shown on the terminal are then adjusted to a calmer tone.

[0568] As an example of a prompt, the AI ​​model might be input with a request such as, "Generate suggestions for the most suitable relaxation products when the customer is showing signs of relaxation." This allows for the optimization of the user experience.

[0569] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0570] Step 1:

[0571] The device receives search queries from the user. It collects the queries entered by the user and simultaneously uses the camera and microphone to acquire the user's facial expression data and voice data. This data becomes the input.

[0572] Step 2:

[0573] The device passes the acquired facial expressions and audio data to an emotion recognition engine. Image processing is performed using OpenCV, and the audio data is converted to text using the Google Cloud Speech-to-Text API. This allows the user's emotional state to be analyzed, and the results are obtained as output.

[0574] Step 3:

[0575] The device sends search queries and analyzed sentiment data to the server. This transmitted data serves as input for information retrieval.

[0576] Step 4:

[0577] The server collects relevant information based on the submitted search query. It uses a generative AI model to evaluate the credibility and safety of the information and calculates scores using natural language processing techniques. This results in an output that assigns a credibility score and a safety score to the information.

[0578] Step 5:

[0579] The server filters the generated scored information based on the user's emotional state. It selects the display format for the information according to the emotional state, structuring the information to suit the user. This filtered information becomes the output.

[0580] Step 6:

[0581] The server sends the configured information to the terminal. The terminal receives this information and displays it in a format appropriate to the user's emotional state. Specifically, in smart glasses, product and service suggestions are presented using both video and text.

[0582] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0583] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0584] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0585] [Fourth Embodiment]

[0586] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0587] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0588] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0589] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0590] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0591] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0592] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0593] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0594] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0595] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0596] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0597] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0598] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0599] As an embodiment of this invention, an information retrieval system using a generative model is constructed. When a user performs a search using a terminal, the terminal receives the search query from the user and sends it to the server. Based on the received search query, the server uses a search engine API to retrieve relevant information.

[0600] The acquired information exists in the form of links and web pages, and the server passes this information to a generative model. The generative model uses natural language processing techniques to analyze the information. In this analysis process, a credibility score is calculated based on factors such as the number of citations and the reliability of the authors. A security score is also calculated by referring to the page's security certificate and past security history.

[0601] The server associates this credibility score and security score with each piece of information. The information with the assigned score is returned from the server to the terminal, which then presents it to the user. Information with high credibility and security is visually highlighted, allowing the user to easily verify and select reliable information.

[0602] A concrete example would be a search for "COVID-19 vaccine effectiveness." Websites containing medical-related data and papers would receive a high credibility score. On the other hand, blogs with unknown sources and unsubstantiated content would receive a low score. This system of information filtering significantly reduces the risk of users choosing incorrect information.

[0603] The following describes the processing flow.

[0604] Step 1:

[0605] The user enters a search query into the device's search bar and presses the "Search" button. This action is the first step the user takes to obtain specific information.

[0606] Step 2:

[0607] The terminal sends the user's search query to the server. This process is performed to ensure that the user's request is properly forwarded to the server.

[0608] Step 3:

[0609] The server receives a search query and calls a search engine API to retrieve relevant information. The server then collects the information obtained from this API in a list format.

[0610] Step 4:

[0611] The server provides the acquired information to the generative model, which then begins the analysis. The generative model analyzes the content of the information and generates scores regarding its credibility and safety.

[0612] Step 5:

[0613] The generative model analyzes the information using natural language processing techniques and calculates a credibility score and a safety score while considering the source of the information, the reliability of the author, and security-related matters.

[0614] Step 6:

[0615] The server associates the calculated score with each information item and assigns a corresponding evaluation to each item. This makes the reliability and security of the information immediately apparent.

[0616] Step 7:

[0617] The server sends the scored information results to the terminal. This data is presented to the user as the final search results for viewing.

[0618] Step 8:

[0619] The device displays the received results to the user. Credibility and security scores are presented visually to the user, allowing them to easily select and use appropriate information.

[0620] (Example 1)

[0621] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0622] When users gather information via the internet, it is difficult to find reliable and secure information from the vast amount of data available. In particular, there is a growing risk of accidentally accessing unreliable information or pages with security concerns. Therefore, there is a need to create an environment where users can access accurate information with peace of mind.

[0623] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0624] In this invention, the server includes a computer system means for receiving search requests from users, a computer system means for acquiring relevant information from a database based on the search requests, and a computer system means for analyzing the acquired information and using a generative model to evaluate its reliability and security. This makes it possible for users to prioritize the acquisition of highly reliable and secure information, enabling them to use information with peace of mind.

[0625] A "user" refers to an individual or group that seeks to search for and obtain information.

[0626] A "search request" refers to a query or question that a user enters into their device to identify the information they want to obtain.

[0627] A "computer system" refers to the totality of digital devices, including the hardware and software necessary for processing information.

[0628] A "database" refers to a collection of data that is structured to manage information and allow for quick access.

[0629] A "generative model" refers to an algorithm or computer program that performs natural language processing or machine learning based on a large amount of data.

[0630] A "reliability score" refers to an indicator that shows, using numerical values ​​or evaluations, whether the acquired information is trustworthy.

[0631] A "safety score" refers to an index that evaluates whether the information or webpage obtained is safe.

[0632] "Visual emphasis" refers to methods of indicating importance to users through the use of text color, boldness, and order when presenting information.

[0633] This invention is an information retrieval system that utilizes the internet, aiming to enable users to efficiently acquire information while ensuring reliability and security. This system is primarily constructed using a server, user terminals, and a generative AI model.

[0634] The user enters a search query using a terminal. The terminal has a browser or dedicated application installed, and the user enters information through this interface. For example, if the user enters the search request "latest research on climate change," this information is sent to the server.

[0635] The server uses the received search request to access external databases and search engine APIs (e.g., general-purpose search engine platforms) and collect relevant information. The collected information is returned to the server in the form of links or text.

[0636] The server then processes the acquired information using a generative AI model (e.g., a general-purpose language processing algorithm). This model uses natural language processing to analyze the reliability and security of the information. Specifically, it evaluates based on factors such as the number of citations, the information provider's profile, and the page's security certificate. This results in the calculation of reliability and security scores.

[0637] The server assigns a calculated score to the information and sends the result to the terminal. The terminal uses visual emphasis to indicate highly reliable and secure information to the user. This visual emphasis includes the use of font weight and color. This allows the user to easily select trustworthy information.

[0638] As a concrete example, consider a scenario where a user requests information about the "health benefits of plant-based diets." The server prioritizes displaying articles from reliable medical journals and visually highlights them on the user's device. In this case, the AI ​​model is instructed with a prompt in the form of "Analyze reliable information on the query 'health benefits of plant-based diets' and visualize the results." This allows the user to access the necessary information with a high degree of confidence.

[0639] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0640] Step 1:

[0641] The user enters a search query using their device. Specifically, they launch a browser or search app on their device and enter a search request, such as "latest research on climate change," into a text box. The device temporarily stores this input and prepares to send it to the server.

[0642] Step 2:

[0643] The terminal sends the entered search query to the server. The terminal packages the search request as an HTTP request and sends it to the server using a secure communication protocol (HTTPS). When the server receives this request, it parses the query and decides which database or API to connect to.

[0644] Step 3:

[0645] The server retrieves relevant information based on the received search query. The server accesses an external search API (e.g., a common search engine API) and sends a request to find information matching the query. This process retrieves data containing relevant links and text from the network and stores it within the server.

[0646] Step 4:

[0647] The server passes the acquired information to a generative AI model for analysis. In this process, the server inputs the acquired data into a generative AI model (e.g., a language model) and uses natural language processing techniques to analyze the information. Specifically, it evaluates the reliability of the information source and the author, and calculates a reliability score. It also checks the site's security certificate and derives a safety score.

[0648] Step 5:

[0649] The server associates the reliability and safety scores obtained through analysis with each information item. The scored information is organized into a newly constructed dataset, ready for transmission to the terminal. This dataset includes the title, link, and score information for each piece of information.

[0650] Step 6:

[0651] The server sends the organized information to the terminal. The server packages the response as an HTTP response and sends it to the terminal. The terminal interprets this and converts it into a format that the user can view.

[0652] Step 7:

[0653] The device visually highlights and presents highly reliable and secure information to the user. The device displays received data on the screen, highlighting information with high reliability and security scores using bold text and color coding. This allows users to intuitively recognize important information and select accurate and secure information.

[0654] (Application Example 1)

[0655] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0656] In today's world, phishing scams and misinformation are rampant, making it difficult for users to easily judge the reliability and safety of information obtained online. Links received via email or messages, in particular, pose a risk of users unknowingly accessing malicious content, thus requiring a high level of security. Therefore, a system is needed that can evaluate the reliability and safety of information received by users and present it in a visually easy-to-understand manner.

[0657] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0658] In this invention, the server includes means for receiving search requests from users, means for obtaining relevant information based on the search requests, and means for using a generative model to analyze the obtained information and evaluate its reliability and security. This makes it possible to evaluate the security and reliability of link information received by the user and present it visually.

[0659] A "user" is an entity that uses an information processing system to send search requests or receive results.

[0660] A "search request" is a query that a user enters into a system to retrieve specific information.

[0661] "Relevant information" refers to data related to the information the user is seeking, retrieved from a search engine or database based on a search request.

[0662] A "generative model" is a model that includes artificial intelligence techniques used to analyze received information and evaluate its reliability and safety.

[0663] A "reliability score" is an index that evaluates and quantifies the reliability of acquired information.

[0664] A "safety score" is an index that evaluates and quantifies the safety of acquired information.

[0665] "Security analysis" is the process of analyzing link information to determine whether that information is malicious.

[0666] An "information processing system" is a set of mechanisms that receive requests from users, acquire, analyze, and evaluate information, and then provide the results.

[0667] The system for realizing this invention includes a terminal including a smartphone and a server for processing information. The server receives search requests from users and retrieves information using a search engine API based on those requests. The retrieved information is evaluated for reliability and security using a generative AI model. In this process, the server utilizes natural language processing technology to calculate a score based on factors such as the number of citations, author reliability, and page security certificates.

[0668] Hardware used includes smartphones and computer servers. Software includes search engine APIs and natural language processing libraries (e.g., TensorFlow, PyTorch). The server associates reliability and safety scores with information, sends them to the user's device, and the device displays them visually.

[0669] As a concrete example, consider a scenario where this system analyzes links in emails received by users. The system collects information about the links, and if the reliability score is low, it displays a warning to the user, urging them not to open the link. In this way, the security of information can be enhanced.

[0670] An example of a prompt for a generative AI model is, "Evaluate and score the reliability of this link." Based on this prompt, the generative model can analyze the information and calculate the required score.

[0671] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0672] Step 1:

[0673] The server accepts search requests received from terminals. These requests include queries entered by the user and form the basis for subsequent information retrieval. Using these queries as input, the server prepares to retrieve relevant information.

[0674] Step 2:

[0675] The server uses a search engine API to collect relevant information based on the received search request. At this stage, the query is passed to the API to retrieve relevant links and literature. The input is the search query, and the output is links to the relevant information.

[0676] Step 3:

[0677] The server passes the acquired link information to a generative AI model for analysis. The generative AI model uses natural language processing techniques to evaluate the reliability and security-related information of the literature. The input here is link information, and the output is a reliability score and a security score. Specifically, it evaluates the number of citations and the security certificate of the page.

[0678] Step 4:

[0679] The server associates the reliability and safety scores obtained from the generative AI model with each piece of information. This assigns a specific evaluation to each link. The input is the evaluation result of the generative AI model, and the output is link information with scores.

[0680] Step 5:

[0681] The server sends the score-associated information to the terminal. The terminal visually presents the received information to the user. Highly reliable information is highlighted. In this process, the scored information is the input, and the visual display for the user is the output.

[0682] Step 6:

[0683] Users select necessary links based on the visually presented information, verifying their reliability and safety. Specifically, users can prioritize reviewing highlighted links.

[0684] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0685] As an embodiment of this invention, an information retrieval system incorporating an emotion engine that recognizes user emotions will be described. The terminal receives a search query entered by the user and sends it to the server. At this time, the terminal collects the user's facial expressions and voice, and the emotion engine analyzes the user's emotions in real time.

[0686] The server retrieves relevant information based on the search query and analyzes it using a generative model. Natural language processing is applied to score the credibility and safety of the retrieved information. These scores are assigned to the information, and the display format is adjusted according to the results of the sentiment engine's analysis. For example, if the user is stressed, the information is presented in a visually calming way.

[0687] Furthermore, the emotion engine collects user emotional data, which the server uses to filter information according to the user's preferences. From the relevant information, information that matches the user's emotional state is prioritized, and results based on that are displayed.

[0688] For example, when a user searches for "weather forecast," if the user is in a hurry, the emotion engine will detect tension and impatience. In this case, the server will display the most important and specific information at the top, reducing the time it takes to retrieve the information. In this way, the system can provide a more appropriate and comfortable information retrieval experience based on the user's emotional state.

[0689] The following describes the processing flow.

[0690] Step 1:

[0691] The user enters a search query into the device's search bar and presses the "Search" button. This action starts the search, and simultaneously, the device's camera and microphone collect the user's facial expressions and voice.

[0692] Step 2:

[0693] An emotion engine operates on the device, analyzing collected user facial and voice data in real time. This identifies the user's emotional state (e.g., joy, sadness, tension, etc.).

[0694] Step 3:

[0695] The terminal sends the user's search query along with sentiment data analyzed by the sentiment engine to the server. The server receives this data and prepares it for analysis.

[0696] Step 4:

[0697] The server uses search queries to retrieve relevant information via search engine APIs. This information is then compiled on the server as web pages and datasets.

[0698] Step 5:

[0699] A generative model within the server analyzes the collected information using natural language processing techniques. As a result of the analysis, each piece of information is assigned a credibility score and a safety score.

[0700] Step 6:

[0701] The server adjusts the display order and method of information based on user emotion data from the emotion engine. For example, if the user is feeling anxious, the server will adjust the display to show the most important information at the top and in a highly visible format.

[0702] Step 7:

[0703] The server sends the refined search results to the device. The device receives these results and displays the information in a user-optimized format, along with credibility and safety scores.

[0704] Step 8:

[0705] Users review the search results displayed on their devices and obtain the necessary information. The user's search experience is comfortable and efficient because it is tailored to their emotional state.

[0706] (Example 2)

[0707] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0708] Current information retrieval systems provide information uniformly without considering the user's emotions or state of mind, meaning the information a user receives may not be appropriate to their current mood or situation. In particular, the lack of evaluation of the credibility and safety of the information poses a risk of making decisions based on inaccurate information. Furthermore, for users experiencing stress, the way information is presented may be inappropriate, leading to a negative user experience.

[0709] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0710] In this invention, the server includes a device means for receiving search data from the user, a device means for collecting and analyzing the user's emotional information using an emotional analysis device installed in the terminal, and a device means for adjusting the display format of items based on the emotional information. This makes it possible to provide information that is adapted to the user's emotional state.

[0711] A "user" is someone who attempts to obtain information using an information retrieval system.

[0712] "Search data" refers to keywords and phrases that users enter with the aim of obtaining information.

[0713] A "device" is a component that performs functions such as information collection, analysis, and display.

[0714] An "emotion analysis device" is a device equipped with the function to analyze a user's facial expressions and voice data to determine their emotional state.

[0715] A "generation device" is a device that analyzes acquired information using a generation AI model to evaluate its credibility and safety.

[0716] "Language processing" is the technology of analyzing natural language and understanding and processing the meaning and context of the information.

[0717] A "credibility score" is an indicator that numerically represents the reliability of the information obtained.

[0718] A "safety score" is an index that numerically represents how safe the acquired information is.

[0719] "Display format" refers to the method or style in which information is provided to the user visually or audibly.

[0720] This invention relates to an information retrieval system that combines an emotion analysis device that recognizes user emotions with an information analysis device. This system consists of a user, a terminal, and a server.

[0721] First, the user enters search data into the device. The device uses its camera and microphone to collect the user's facial expressions and voice data. This data is analyzed by an emotion analysis device to identify the user's emotional state. The analysis is performed in real time and reflects the user's current emotions.

[0722] Next, the device sends search data and sentiment information to the server. The server retrieves relevant information from the internet based on the search data. Generative AI models are used for information analysis, such as models with natural language processing technology. The server analyzes the retrieved information, evaluates its credibility and safety, and assigns a score. This score is used to prioritize the information presented to the user.

[0723] Furthermore, the server adjusts the display format of the information based on the user's emotional information provided by the emotion analysis device. For example, if the user is feeling stressed, the server will support the user's information acquisition by selecting a calm interface with muted colors.

[0724] For example, if a user searches for "weather forecast" and sentiment analysis reveals they are in a hurry, the server can display the most important information at the top, making access to the information more efficient.

[0725] An example of a prompt might be: "Prioritize displaying the information the user wants to know most based on their current emotional state. For example, if the user is feeling anxious, display it in a visually appealing way to help alleviate that anxiety." Such prompts allow the generative AI model to derive an appropriate information presentation method.

[0726] This system enables the delivery of information in a more comfortable and efficient manner, adapted to the user's emotional state.

[0727] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0728] Step 1:

[0729] The user enters search data into the terminal. The terminal uses its camera and microphone to collect the user's facial expressions and voice, and sends this data to an emotion analysis device. The input includes the user's search data, facial expressions, and voice, and the output prepares these for transfer to the emotion analysis device.

[0730] Step 2:

[0731] The device uses an emotion analysis device to analyze the user's facial expressions and voice data to identify the user's emotional state. Specifically, emotion classification is performed using a machine learning algorithm. The input is facial expressions and voice data, and the output is digital data of the user's emotional state.

[0732] Step 3:

[0733] The terminal sends search data and emotional state data to the server. The input data consists of search data and emotional state, which are transferred to the server via the network. The output is all the data passed to the server.

[0734] Step 4:

[0735] The server retrieves relevant information from the internet based on the received search data. A generative AI model is used to analyze the credibility and safety of the information. For data processing, a web crawler collects information, and the AI ​​model evaluates its credibility and safety. The input is search data, and the output is a set of evaluated information and its score.

[0736] Step 5:

[0737] The server assigns credibility and safety scores to the acquired information and determines the display format according to the emotional state. Specifically, the information is visually and structurally adjusted based on the results of the sentiment analysis. The input is information and emotional state data, and the output is an adjusted set of information.

[0738] Step 6:

[0739] The server returns prioritized information to the terminal and presents it to the user. Through the terminal, the user can receive emotionally appropriate information. The input for this step is a pre-configured set of information, and the output is the information displayed on the user's screen.

[0740] (Application Example 2)

[0741] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0742] When users acquire information, they are required to quickly and accurately obtain the necessary information from a vast amount of data. However, there is no established method for displaying information optimally according to the user's emotional state. Especially in commercial environments, suggesting products and services according to the customer's emotions is crucial for improving customer satisfaction. Therefore, a system is needed that can adjust the information display format according to the user's emotional state and suggest appropriate products and services.

[0743] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0744] In this invention, the server includes means for recognizing the user's emotions and adjusting the information display format according to those emotions, means for suggesting the most suitable product or service based on the observer's emotional state in a commercial environment, and means for receiving search queries from the user. This enables the provision of information and product suggestions based on the user's emotions.

[0745] "Means of recognizing user emotions" refers to technologies that analyze the user's facial expressions and voice to determine their emotional state at that time.

[0746] "Means for adjusting the display format of information" refers to technologies that change the way information is displayed and its layout according to the user's emotions, presenting information in a more appropriate and preferable format.

[0747] "Means of proposing goods or services in a commercial environment" refers to technologies that select and propose relevant goods or services based on the emotional state of customers in commercial facilities and similar settings.

[0748] A "means for receiving search queries" refers to the technology that receives and processes requests and questions from users regarding information retrieval.

[0749] A "generative model" is an artificial intelligence model used to analyze acquired information and evaluate its credibility and safety.

[0750] "Natural language processing" is a technology that enables computers to understand and process human language.

[0751] The "credibility score and safety score" are indicators used to evaluate how reliable and safe the acquired information is.

[0752] As an embodiment of this invention, an information suggestion system equipped with emotion recognition capabilities will be described. It consists of a server, a terminal, and a device used in a commercial environment.

[0753] The device receives the user's search query and simultaneously uses its built-in camera and microphone to collect the user's facial expressions and voice in real time. This data is analyzed by an emotion recognition engine to obtain the user's emotional state. The OpenCV image processing library is used for emotion recognition, and the Google Cloud Speech-to-Text API is used for voice analysis. The analysis results are sent to a server via the internet.

[0754] The server retrieves relevant information based on the received search query. During this process, the information is analyzed using a generative AI model to calculate a credibility score and a safety score. Natural language processing techniques are used for information analysis, specifically leveraging Python and machine learning libraries.

[0755] After assigning scores to the acquired information, the server sends the information to the terminal in a display format tailored to the user's emotional state. This includes adjusting visual elements. If the user is wearing smart glasses, product suggestions tailored to their emotional state are displayed in real time. This allows for the suggestion of optimal products and services, improving customer satisfaction in commercial environments.

[0756] For example, if a user enters a store and displays a relaxed expression, the emotion recognition engine detects this state, and the server suggests products with a relaxing effect. The display order and colors of the information shown on the terminal are then adjusted to a calmer tone.

[0757] As an example of a prompt, the AI ​​model might be input with a request such as, "Generate suggestions for the most suitable relaxation products when the customer is showing signs of relaxation." This allows for the optimization of the user experience.

[0758] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0759] Step 1:

[0760] The device receives search queries from the user. It collects the queries entered by the user and simultaneously uses the camera and microphone to acquire the user's facial expression data and voice data. This data becomes the input.

[0761] Step 2:

[0762] The device passes the acquired facial expressions and audio data to an emotion recognition engine. Image processing is performed using OpenCV, and the audio data is converted to text using the Google Cloud Speech-to-Text API. This allows the user's emotional state to be analyzed, and the results are obtained as output.

[0763] Step 3:

[0764] The device sends search queries and analyzed sentiment data to the server. This transmitted data serves as input for information retrieval.

[0765] Step 4:

[0766] The server collects relevant information based on the submitted search query. It uses a generative AI model to evaluate the credibility and safety of the information and calculates scores using natural language processing techniques. This results in an output that assigns a credibility score and a safety score to the information.

[0767] Step 5:

[0768] The server filters the generated scored information based on the user's emotional state. It selects the display format for the information according to the emotional state, structuring the information to suit the user. This filtered information becomes the output.

[0769] Step 6:

[0770] The server sends the configured information to the terminal. The terminal receives this information and displays it in a format appropriate to the user's emotional state. Specifically, in smart glasses, product and service suggestions are presented using both video and text.

[0771] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0772] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0773] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0774] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0775] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0776] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0777] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0778] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0779] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0780] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0781] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0782] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0783] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0784] 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.

[0785] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0786] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0787] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0788] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0789] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0790] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0791] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0792] The following is further disclosed regarding the embodiments described above.

[0793] (Claim 1)

[0794] A means of receiving search queries from users,

[0795] A means of obtaining relevant information based on a search query,

[0796] A means of analyzing acquired information and using a generative model to evaluate its credibility and safety,

[0797] A means for calculating the credibility score and safety score of information and assigning them to the information,

[0798] A means for displaying credibility scores and safety scores to users,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, wherein the generative model analyzes the content of information using natural language processing.

[0802] (Claim 3)

[0803] The system according to claim 1, which prioritizes providing users with information whose credibility score and safety score fall within a specific range.

[0804] "Example 1"

[0805] (Claim 1)

[0806] A computer system means for receiving search requests from users,

[0807] A computer system means for obtaining relevant information from a database based on a search request,

[0808] A computer system means that uses a generative model to analyze acquired information and evaluate reliability and safety,

[0809] A computer system means for calculating the reliability score and safety score of information and associating them with the information,

[0810] A computer system means for visually highlighting and presenting reliability scores and safety scores to the user,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, wherein the generative model analyzes the details of the information using language processing techniques.

[0814] (Claim 3)

[0815] The system according to claim 1, which prioritizes providing the user with information in which the reliability score and safety score are within a predetermined range.

[0816] "Application Example 1"

[0817] (Claim 1)

[0818] A means of receiving search requests from users,

[0819] Means for obtaining relevant information based on a search request,

[0820] A means of using a generative model to analyze acquired information and evaluate reliability and safety,

[0821] A means for calculating the reliability score and safety score of information and associating them with the information,

[0822] A means of presenting reliability scores and safety scores to users,

[0823] A security analysis means for analyzing link information and evaluating its reliability and safety,

[0824] An information processing system that includes this.

[0825] (Claim 2)

[0826] The information processing system according to claim 1, wherein the generative model analyzes the content of information using natural language processing techniques.

[0827] (Claim 3)

[0828] The information processing system according to claim 1, which prioritizes providing users with information in which the reliability score and safety score fall within a specific range.

[0829] "Example 2 of combining an emotion engine"

[0830] (Claim 1)

[0831] A device that receives search data from users,

[0832] A device means for acquiring related items based on search data,

[0833] A device means for collecting and analyzing user emotional information using an emotion analysis device installed in a terminal,

[0834] A device means that uses a generating device to analyze the acquired items and evaluate their reliability and safety,

[0835] A device for calculating the credibility score and safety score of an item and assigning these to the item,

[0836] A device means for adjusting the display format of items based on emotional information,

[0837] A device means for displaying a credibility score and a safety score to the user,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, wherein the generating device analyzes the content of an item using language processing.

[0841] (Claim 3)

[0842] The system according to claim 1, which preferentially provides the user with items whose credibility score and safety score fall within a specific range.

[0843] "Application example 2 when combining with an emotional engine"

[0844] (Claim 1)

[0845] A means of receiving search queries from users,

[0846] A means of obtaining relevant information based on a search query,

[0847] A means of analyzing acquired information and using a generative model to evaluate its credibility and safety,

[0848] A means for calculating the credibility score and safety score of information and assigning them to the information,

[0849] A means for displaying credibility scores and safety scores to users,

[0850] A means of recognizing the user's emotions and adjusting the display format of information accordingly,

[0851] A means of suggesting the optimal product or service based on the emotional state of the observer in a commercial environment,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, wherein the generative model analyzes the content of information using natural language processing.

[0855] (Claim 3)

[0856] The system according to claim 1, which prioritizes providing users with information whose credibility score and safety score fall within a specific range, and further selects and provides information in accordance with the user's emotions. [Explanation of Symbols]

[0857] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving search queries from users, A means of obtaining relevant information based on a search query, A means of analyzing acquired information and using a generative model to evaluate its credibility and safety, A means for calculating the credibility score and safety score of information and assigning them to the information, A means for displaying credibility scores and safety scores to users, A system that includes this.

2. The system according to claim 1, wherein the generative model analyzes the content of information using natural language processing.

3. The system according to claim 1, which prioritizes providing users with information whose credibility score and safety score fall within a specific range.

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