4967 results about "Web site" patented technology

A system and method is described for internationalization of web pages by extracting translatable content from extensible mark-up language (XML), or similar data-centric meta-language representations of web pages or data used to build web pages. The extracted translatable content is stored in a translation task repository (TTR) accessible by the web developer and the translator. The XML representation is then modified to include selection control logic to select the appropriate translations for insertion into the final web page. The translator accesses the TTR to translate the appropriate content and saves the translations back to the TTR associated with the original translatable data. The translations are obtained from the TTR as selection cases for the selection control logic of the XML representation. As the XML is converted into the web source code, the selection logic and translations are embedded therein facilitating building the web site in multiple different languages.

An example operation may include one or more of ingesting data from one or more websites via one or more application programming interfaces (APIs) and storing the data within a data store of a host platform, displaying one or more prompts on a user interface of a user profile page within a software application hosted by the host platform, receiving one or more responses to the one or more prompts, generating a travel itinerary based on execution of an artificial intelligence (AI) model on the data from the one or more websites, the one or more prompts, and the one or more responses, and displaying the travel itinerary via the user interface of the user profile page of the software application.

An example operation may include one or more of ingesting profile data from one or more websites via one or more application programming interfaces (APIs) and storing the profile data within a data store, displaying one or more prompts on a user interface of a user profile page within a software application hosted by a host platform, receiving one or more responses to the one or more prompts, determining a goal for a user of the user profile page based on execution of an artificial intelligence (AI) model on the profile data, the one or more prompts, and the one or more responses to the one or more prompts, and displaying an identifier of the goal via the user interface of the user profile page of the software application.

Machine learning techniques are leveraged to provide personalized assistance on a computing device. In some configurations a timeline of a user's interactions with the computing device is generated. For example, screenshots and audio streams may be saved as entries in the timeline. Context—the state of the computing device when the entry is created, such as which documents and websites are open—is also stored. Entries in the timeline are processed by a model to generate embedding vectors. The timeline may be searched by finding the embedding vector that is closest to an embedding vector derived from a search query. The user may select a query result, causing the associated context to be restored. For example, if the query is “show me all documents related to my upcoming trip to Japan”, the query result may open documents and websites that were open when booking a flight to Japan.

The invention relates to the technical field of power distribution network source website collaborative planning, and particularly discloses a power distribution network source website collaborative planning method and system considering a V2G mode. The method comprises the following steps: acquiring an existing topological structure of a power distribution network, traffic road network structure data, electric vehicle number space-time distribution data, photovoltaic resource distribution data and a historical daily load power curve set; predicting to obtain a node load power set, a V2G charging and discharging station maximum schedulable power set and a PVG station maximum output power set in various typical day scenes in the future; establishing an upper-layer planning model by taking the minimum total economic cost of the system as a target, and establishing a lower-layer planning model by taking the minimum annual comprehensive operation cost as a target; and solving the bilayer planning model by adopting a nested iterative algorithm, and outputting a power distribution network source website collaborative planning scheme. According to the method, the economical efficiency and reliability of collaborative planning of the power distribution network, the PVG station and the V2G charging and discharging station are improved, the resource utilization efficiency is remarkably improved, and the total economic cost is reduced.

This invention provides a web application system that allows instructors to effectively use generative AI in classes to match learning tasks, reducing the risk of students asking questions that are inappropriate for the task or trusting false information from the generative AI, and encouraging students' creative thinking through dialogue using generative AI, with the aim of supporting creative activities. SOLUTION: The web application system of this invention creates prompts by integrating questions from learners with prompts preset by the instructor, and provides automatically generated answers through API connection. The app is built using HTML code with a website creation tool, so it can be managed without specialized knowledge. Prompts are edited using three items: Theme, Conditions, and Additional Instructions, to control the answers of the generating AI. In addition, restrictions on questions outside the topic can be set to prevent misuse.

This application relates to the field of home kitchens, and discloses a social public opinion risk intelligent prediction system, including: a data collection module, which is used to collect text, image and video data from multi-source heterogeneous data sources in real time, including social media, news websites, forums, blogs and self-media; a data preprocessing module, which is used to clean, normalize, segment and tag the collected data to form a structured data set; an advanced natural language processing module, which is used to use the pre-trained Transformer model and graph neural network to perform deep semantic analysis on the pre-processed text data, and extract entity, relationship and context semantic features. By integrating advanced natural language processing, deep learning and multimodal data fusion technology, the system can monitor and accurately predict social public opinion in real time, quickly capture emergencies and public opinion changes, and deeply analyze complex semantics and emotional tendencies.

The invention discloses an intelligent construction site safety monitoring method and system fused with a multi-modal large model, and relates to the technical field of building construction.The method comprises the steps that based on a Linux system platform, an Nginx server is used as a static website server to be combined with a camera to request video stream data, a client side and the Nginx server are connected, configuration is conducted according to a configuration file, and the Nginx server is connected with the client side; and sending response data according to an HTTP request of the client, and carrying out video online monitoring and image recognition real-time processing on the construction area. According to the invention, through detailed scene division and targeted safety guide formulation and in combination with a visual language model-based few-sample scene identification method, different scenes can be accurately identified, and it is ensured that monitoring meets actual requirements; through cooperation of the personnel article wearing detection module and the article attribute detection module, deep and comprehensive monitoring of personnel protection equipment and behaviors is realized; depth estimation and three-dimensional collision detection ensure full coverage of a monitoring scene.

An AI-driven system for personalized advertising in real time, where: ◯ an analytics unit to monitor and analyze user behavior in real time across multiple digital platforms such as websites, mobile applications, social media, and smart devices; the unit collects data on user engagement, browsing patterns, time spent, and content preferences to enable targeted, personalized advertising; ◯ a prediction module that predicts preferences and interests of a user, operatively connected to the user interaction data collection unit, wherein the prediction module uses machine learning models such as deep learning, recurrent neural networks (RNNs) and transformer-based architectures to predict interests of users based on historical interactions and inferred preferences; ◯ an emotion and sentiment analysis unit that assesses the user's mood in real time through computer vision, natural language processing (NLP) and voice analysis, whereby the analysis of facial expressions, voice pitch and linguistic mood is used to determine emotional states and receptivity to advertising content; ◯ an embodiment of a context awareness component in operational communication with the emotion analysis and mood unit, in some cases further augmented by various environmental and situational data such as the device type and its physical location, date and time, and the content processed in the device, processing methods, etc., in establishing adaptability and automatic ad placement to be as relevant as possible to the user and their status as prescribed; ◯ Use reinforcement learning algorithms and generative AI models to drive advertising with personalization engines. Creative elements, messaging, and presentations are dynamically adjusted in real time based on user responses to ensure advertising is personalized and always optimized for best performance; o a privacy-focused AI system with federated learning, differential privacy methods, and on-device AI processing to reduce targeted advertising while complying with international data protection laws such as the General Data Protection Regulation (GDPR) and California consumer privacy laws; o an operationally adapted ad delivery mechanism to engage with real-time bidding (RTB) networks, programmatic advertising exchanges and demand-side platforms (DSPs) and place advertisements through digital advertising networks, which guarantees the delivery of tailored advertising to the most relevant audience in real time; and o a contextual feedback loop in which the machine learning models used in the preference and interest prediction module and in advertising personalization The engine is continuously updated to reflect the latest user engagement data, improving personalization over time and optimizing advertising performance.

The method and systems for automatically identified currently viewing website domain name or a uniform resource locator (URL), receiving, by the server, said automatically identified website domain name or the uniform resource locator (URL), a request or an invitation for initiating a communication including live chat, puts the request for initiating a communication including live chat from the first user and the second user in a communication including live chat queue of the website or the uniform resource locator (URL) associated account, for available or identified or relevant agents to pick up, routing request or invitation for initiating a communication including live chat from the first user and the second user according to a pre-set rules and start a communication including live chat by available agent by selecting particular request for initiating a communication including live chat from the queued communication including live chats.

To provide a website creation support device for creating a high-quality website in a short time by proposing a combination of parts optimized for each individual user. [Solution] The website creation support device 1 has a web part management database 1212 in which web part information which is the elements that make up a website is stored, a profile database 1211 in which profile information of a user 4 creating a website is stored, a communication unit 13 which enables communication of site-related information regarding the website between the user terminal 2 operated by the user 4, and a website creation support unit 11 which predicts the website desired by the user 4 based on the profile information and the site-related information, extracts selection candidates for web part information suitable for creating the website from the web part management database 1212, and presents this to the user 4 as web creation support information to support the creation of the website.

The invention discloses a bad traffic monitoring method and system based on an LSTM and a clustering algorithm, belongs to the technical field of network security, and efficiently converges network traffic by using a BPF technology. The method comprises the following steps: accurately identifying fragmented traffic through an LSTM network, and capturing a complex dependency relationship by using a special memory unit structure of the LSTM network; performing session recombination by adopting an FCM clustering algorithm, restoring a complete network session, and quickly identifying malicious traffic through feature matching; and finally, timely treatment measures are taken for the confirmed malicious traffic. According to the method, rapid and accurate detection and identification of bad website traffic fragmentation behaviors are realized, and the technical problem that a traditional method is difficult to deal with is solved; through efficient caching and recombination mechanisms, the efficiency and accuracy of malicious traffic processing are improved; intelligent disposal measures are adopted, and it is ensured that malicious traffic is effectively restrained in time; the overall protection capability of network security is improved, and enterprise network resources and user data security are protected.

A platform for dynamically generating application experiences. The platform comprises a design management system, an agent orchestration system, an analytics system, a model management system, a user management system, and databases for storing design elements and templates. The design management system provides a portal for application owners / designers to create UX / UI designs, allowing them to select design elements from a set of categories or templates. The platform gathers existing websites / applications to identify common design patterns, stored in a design catalogue database, and suggests historical interfaces for design exploration. It enables the generation of templated applications that integrate with legacy systems. The agent orchestration system parses user specifications, selects generative AI systems, and generates UX / UI content based on the specifications. The analytics system collects and analyzes data to provide insights for improving UX / UI design and optimizing website performance. The model management system trains and maintains generative AI models used for content generation.

In certain examples, computer-implemented methods include assembling automatically generated content into a website structure that is optimal to a particular user and tailored to user goals and consistent with attributes of media assets of the user. The method further includes: analyzing, for the particular user (e.g., individual or entity), inputs and media assets; constructing a set of context-aware prompts, wherein each of the context-aware prompts is associated with one or more contexts discerned in response to user inputs and media assets being analyzed; and generating, via large language modeling and based on the constructed context-aware prompts, the website content inclusive of text, code, and media elements. In photography examples, such methods customize content based on responses to constructed context-aware prompts, and use a library of predefined website content (e.g., images) with configurable parameters and customized content created dynamically.

The invention relates to the technical field of phishing detection, in particular to a phishing detection method and system based on multi-modal fusion features, and the method comprises the following steps: S1, constructing a phishing sample based on multi-modal simulation and data generation; according to the method, the multi-modal simulation and data generation technology is utilized, phishing samples with high diversity and authenticity are created, a solid foundation is provided for the model, diversified interactive behavior data are generated through the user behavior simulator designed by the reinforcement learning model, the system can simulate and understand behaviors of different types of users, and the user experience is improved. The system improves the detection precision and efficiency through a non-traditionally designed multi-mode fusion deep detection model in combination with a multi-branch neural network and multi-task learning, realizes the real-time adaptive detection and defense capability through adversarial training and federated learning, greatly improves the accuracy and robustness of phishing detection, and improves the detection accuracy and efficiency. And comprehensive protection is provided for network security.

A system and method for chatbot and search engine integration comprising traversing a conversational flow of a chatbot, generating a data structure based on the conversational flow, embedding the data structure in a webpage, and directing a search engine to the webpage for the search engine to assign a webpage rank to the data structure. In some embodiments, this integration may allow search engine users to be redirected to a non-public website hosting the chatbot when that chatbot would not otherwise be identified by the search engine.

A system for the dynamic optimization of sponsored product advertising using real-time data and machine learning, which includes the following: a) a data collection and integration module configured to collect real-time data from advertising platforms, e-commerce websites and user interactions, and to integrate key performance indicators such as impressions, click-through rates (CTR), conversion rates and competitors' bidding strategies; b) a data preprocessing and filtering module configured to clean, normalize and structure the collected data by removing inconsistencies, duplicates and irrelevant information; c) a machine learning machine configured to analyze historical and real-time data using predictive models, including deep learning and regression algorithms, to identify patterns in user behavior and optimize bidding strategies; d) a real-time trend analysis module configured to detect emerging market trends, seasonal fluctuations and changes in consumer sentiment to enable adaptive adjustments to ad placement; e) a bid management and optimization module configured to dynamically adjust ad bids in real time based on auction data, competitor activity and performance metrics to ensure cost-effective ad placements; f) a module for targeting and segmenting audiences, configured to classify users based on demographic data, browsing behavior, purchase history and engagement patterns, thus ensuring optimal ad delivery to relevant consumers; (g) an ad creative optimization module configured to analyze ad performance using A / B testing and natural language processing (NLP) and automatically make recommendations or changes to ad creatives to improve engagement and conversion rates; h) a performance monitoring and analytics dashboard configured to provide real-time visual insights into ad performance metrics, optimization recommendations, and budget usage; (i) an automated decision and control system configured to make bid adjustments, budget reallocations, and target audience refinements based on machine learning insights without manual intervention; and j) a compliance and fraud detection module configured to monitor ad interactions, detect fraudulent activities such as click fraud and bot traffic, and ensure compliance with platform policies; with the system continuously optimizing sponsored product advertising by using real-time data and machine learning to maximize return on investment and improve advertising efficiency.

The invention discloses a multi-agent automatic penetration testing system fusing a large model and reinforcement learning, and relates to the technical field of network security, and the system comprises the steps: starting a mixed multi-agent automatic penetration testing process combined with the large model through inputting a URL (Uniform Resource Locator); multi-agent joint action is carried out to comprehensively collect and classify website information; multiple agents execute vulnerability mining in parallel, and a result is fed back to perform deep reinforcement learning optimization; the multi-agent formulates a specific utilization scheme for the mined vulnerabilities; the potential vulnerabilities are subjected to validity verification through multi-agent cooperation, results are recycled, and the decision accuracy is gradually improved through a deep learning feedback mechanism; the multi-agent generates a safety report according to a module result, and feeds back the safety report to the related agent to enhance the deep learning performance and improve the decision effectiveness. According to the method, large model analysis and reasoning capabilities are integrated, multi-agent collaborative scheduling is realized, automatic security scanning is executed on a target website, and potential security vulnerabilities are effectively detected and evaluated.

Embodiments provide for responsive optimization of websites. In some examples, a layout graph data structure is generated based on applying a trained layout model to extracted website elements from an input object including one or more website building tools. A webpage layout data structure is generated based on applying a trained structure optimization model to the layout graph data structure. An optimized webpage that is configured to render according to a plurality of screen parameters is generated based on applying a trained responsive optimization model to the webpage layout data structure and a plurality of screen parameters.

The invention belongs to the technical field of webpage data analysis, and particularly relates to a webpage data analysis method based on a large language model, which comprises the following steps: acquiring a target website URL (Uniform Resource Locator) input by a user; carrying out preliminary analysis on the input URL by utilizing a large language model; the DOM structure of each website is analyzed, and HTML elements and the hierarchical relation thereof are extracted; generating a corresponding data crawling script according to an analysis result; executing the data crawling script, and extracting required data from each website; the extracted data is preprocessed; performing multi-source fusion on the preprocessed data; extracting main information from the fused data; and storing the finally processed and extracted information in a database. According to the method, automatic analysis of complex webpage data, intelligent entity extraction and knowledge expansion and efficient data fusion and duplicate removal are realized.

The invention discloses an intelligent travel text system based on python deep learning, and relates to the technical field of python deep learning, and the system comprises the following components: a data collection module which is used for automatically collecting comment data about a specific travel destination from various channels of various travel websites, social media platforms and online travel forums, according to the method, massive comments and evaluation data about a specific tourism destination can be automatically collected from multiple channels, the accuracy and consistency of analysis data are ensured through fine data preprocessing, fine adjustment is performed by applying an advanced deep learning sentiment analysis algorithm in combination with a specific corpus in the tourism field, and the accuracy and consistency of data analysis are ensured. The system can accurately judge the emotional tendency of tourists and deeply mine key opinions and suggestions in comments, and the deep emotional mining ability not only helps tourism management departments and scenic spots to quickly understand real feedback of the tourists, but also can accurately identify satisfaction distribution and main concerns of the tourists on services.

The invention relates to a network attack threat trapping method, device and equipment. Preprocessing and prompt engineering processing can be sequentially carried out on a Web intrusion request; judging whether the processed Web intrusion request is an attack request or not by utilizing a detection model, and if yes, generating an attack type result; constructing an attack knowledge base corresponding to different network attack types according to existing website resources and related data; according to an attack type result and attack content of the Web intrusion request, similar data are retrieved from the attack knowledge base; and splicing the attack content and the similar data, and inputting the spliced attack content and similar data into a first large language model to obtain a simulation response aiming at the attack content. According to the technical scheme, the type of the attack request can be accurately identified by using the detection model, and the detection capability is improved. And the obtained simulation response can perform more complex and flexible interaction with an attacker, so that attack behaviors can be better cheated and trapped.

Systems and methods for private application access continuity include providing access to one or more private applications for users associated with a tenant of a cloud-based system; detecting one or more criteria suggesting an outage of the cloud-based system; and responsive to activation of a disaster recovery mode based on the one or more criteria, providing access to the one or more private applications via an on-site disaster recovery system including a site controller, wherein providing the access via the site controller does not require communication with the cloud-based system.

Embodiments are related to determining if an unverified web page is a phishing site of a verified web page. In one or more embodiments, a computing server detects a style sheet element of an unverified web page accessed by a user using a web browser extension. The computing server checks if the detected style sheet element contains a copy of a part of a style sheet element present in the verified web page. If the style sheet element of the unverified web page contains a copy of the part of a style sheet element of the verified web page, indicating that the unverified web page is a phishing site, the computing server takes a security action. In some embodiments, the computing server may check if the unverified web page is a known web page.

The invention discloses a method and a system for intelligently acquiring and analyzing webpage merchant information. Automatic acquisition of the merchant information is realized through five main steps of intelligent webpage capture, intelligent field identification, accurate address information analysis, intelligent merchant grouping and automatic page discovery. According to the method, a traditional rule-based extraction method is abandoned, information such as merchant names, phone numbers and addresses in webpages is automatically recognized by using context semantic analysis and mode recognition technologies, and accurate association grouping of fields is realized through a dynamic weight multi-dimensional similarity calculation method; and the merchant information can be continuously discovered and collected by using an automatic page discovery mechanism without manually specifying a collection path. The technical bottlenecks that specific rules need to be written for different websites, webpage structure changes are difficult to deal with, multi-merchant information grouping cannot be processed and the like in a traditional method are broken through, the method adapts to various webpage structure changes, and the automation degree, accuracy and efficiency of merchant information collection are greatly improved.

In particular embodiments, a consent notice configuration determination system may be configured to: (1) scan a particular website from a plurality of different locations; (2) identify one or more types of technologies available on the particular website for individuals accessing the particular website from each of the plurality of different locations; (3) determine, for each of the plurality of different locations, based on a global set of databases, legal / regulatory guidance, etc. and the one or more types of technologies, particular legal and industry requirements for each of the plurality of different locations; and (4) automatically configure, for each of the plurality of different locations, a consent interface for the particular website for each of the plurality of different locations based at least in part on the one or more types of technologies and the global set of databases.

Machine learning models for autonomously navigating the web and / or apps are provided that receive (i) visual input that includes one or more images representing the current, and optionally one or more prior, visual states of the website or app being navigated, and (ii) textual input that represents a user request to be implemented by the model, the current HTML or other code defining the website or app, and optionally additional text like prior model outputs that represent prior steps in the navigation of the website or app by the model. The visual input and textual input are transformed into respective sets of visual and textual tokens, and this multi-modal token set is applied to an encoder. The output of the encoder is applied to a decoder to generate textual output that represents an action to take, relative to the code defining the website or app, to pursue the user request.

A recommendation system identifies resale goods corresponding to retail goods being viewed by a user on a retail goods website. Retail goods metadata and images are extracted from the retail goods website using heuristics and LLM-based methods. The extracted retail goods data undergoes ML model-based product category classification, intelligent image cropping, and color detection. Vector embeddings are generated for images and text using ML models and compared to resale goods vector embeddings stored in a vector database. Multiple result sets are retrieved, fused, and re-ranked using LLM-based and preference-aware re-ranking. A data pipeline continuously loads, cleans, and processes resale goods inventory from resale goods websites.

A system and associated method for simplifying and enhancing a customer browsing experience on websites. In an embodiment, the method involves, upon determining that a customer has clicked on a clickable prompt button embedded at a website, in association with a specific product or article, displaying instantaneous product information to the user without requiring the user to type in lengthy requests. In another embodiment, upon determining that a customer has clicked on a clickable prompt button embedded at a website, providing means for enabling the customer to interact with a large language model that is capable of responding with specificity to user questions about products on the websites based on contextual information transparently provided by the clickable prompt buttons.

A website development system automatically generates text for a webpage. The system obtains a prompt template associated with a section of the webpage, where the prompt template includes one or more parameters. Based on the webpage, the prompt template determines a first value for a first one of the one or more parameters. A request to provide input for a second value of a second parameter is sent for display to a user. Using the prompt template, the first value, and the second value, the system generates a prompt to a large language model to generate text for the section of the webpage.