2037 results about "User profile" patented technology

The invention provides a method and system for preventing identity spoofing during digital authentication processes using artificial intelligence (AI)-driven pattern recognition. The system receives an input data stream from a user attempting to authenticate, which may include biometric data, device behavior data, or user interaction data. An AI-based pattern recognition model processes this data to analyze user behavior patterns and detect any anomalies that may indicate potential spoofing attempts. The system compares the processed data against a pre-established user profile to generate an authentication decision. If anomalies are detected, the system can flag the authentication for further review or trigger additional verification steps, such as multi-factor authentication (MFA) or one-time password (OTP) prompts. The system continuously learns from user interaction data and dynamically updates the user profile to improve the accuracy of identity verification.

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.

An example operation may include one or more of training an artificial intelligence (AI) model to recommend numerical goals based on execution of the AI model on historical profile data and contextual data that is associated with the historical profile data, storing profile data of a user profile within a data store of a software application, receiving a request via a user interface of a user profile page of the software application, wherein the request comprises context of a user of the user profile, determining a numerical goal for the user profile based on execution of the AI model on the profile data of the user profile and the context, and displaying the numerical goal via the user interface of the user profile page within the software application.

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, generating one or more prompts and displaying the 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 design of a structure 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 design of the structure via the user interface of the user profile page of the software application.

An example operation includes one or more of ingesting profile data of a user from an external data source, identifying a plurality of features of a profile hosted by the external data source based on an execution of an artificial intelligence (AI) model on the ingested profile data of the user from the external data source, identifying a feature from among the plurality of features of the profile hosted by the external data source that is not connected to a local user profile of the user, and displaying a connection request on a user interface, wherein the connection request includes a link to a page associated with the identified feature hosted by the external data source.

The invention discloses a labeling task assignment method and device based on artificial intelligence, and the method comprises the steps: obtaining historical behavior data, and constructing a multi-dimensional user portrait; receiving a task description document, a data sample and a quality requirement document to obtain a multi-dimensional task feature vector; based on the multi-dimensional user portraits and the multi-dimensional task feature vectors, a matching degree score is calculated through a multi-objective optimization algorithm, and an optimal task allocation scheme is generated; optimizing the task structure through a fireworks algorithm based on student t distribution, and generating an optimized task unit structure; real-time monitoring is carried out through the anomaly detection model and the quality prediction model, and quality control measures are triggered; model parameters are updated through a reinforcement learning algorithm, and a personalized feedback and capability improvement strategy is generated. According to the method, accurate matching between the annotators and the tasks is realized, the processing efficiency of complex tasks is improved, the annotation quality is improved, the expansibility and the response speed of a platform are enhanced, and an effective solution is provided for large-scale and high-quality data annotation.

Various embodiments include systems and methods for generating a prompt for a generative artificial intelligence (AI) models. A processing system including at least one processor may be configured to recognize a user of the computing device, obtain user context information from a source of physical context information in the computing device, receive a user prompt for the large generative AI model (LXM), select a user profile from among a plurality of user profiles based on the user, the user context information and the user prompt, generate an enhanced prompt based on the user prompt and information included in the selected user profile, and submit the enhanced prompt to the LXM.

This disclosure relates to an asset evaluation system that collects multi-modal asset data, extracts asset features using a neural network architecture, and generates personalized value scores based on user profiles. The system analyzes image and textual content to extract asset features, correlates those features with personalization data in user profiles, and generates tailored asset analysis results. The system can predict a value score for each user profile-asset pair in a manner that goes beyond mere consideration of asset pricing, and which accounts for specific personalization parameters stored in each user profile, such as parameters corresponding to the user's technical capabilities and certifications, risk tolerance, investment preferences, timeline requirements, available resources, and potential synergies with existing assets. Additionally, in certain embodiments, the asset evaluation system may employ a distributed architecture that utilizes multiple interconnected processing nodes to efficiently handle large-scale data processing.

An apparatus for data ingestion and manipulation, the apparatus including at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive a resource data file from one or more data acquisition systems, classify the resource data file to one or more educational categorizations, generate an educational module as a function of the resource data file and the classification of the educational categorizations wherein the education module comprises one or more machine learning models, retrieve a user profile of a plurality of user profiles as a function of a user input, create user-specific outputs as a function of the educational module, the user profile, and a conversational input and generate a virtual avatar model as a function of the user-specific outputs.

The invention relates to the technical field of data analysis, can be applied to business scenes of medical health, financial science and technology, cultural research and the like, and discloses a multi-modal abstract generation and output method, which comprises the following steps of: acquiring target domain data and cleaning to obtain structured data; extracting entities and association relationships among the entities from the structured data, and constructing a target domain knowledge graph; generating a text abstract containing entity associated information based on the target domain knowledge graph; matching the image data and the audio data corresponding to the text abstract, and integrating the text abstract, the image data and the audio data to generate a multi-modal abstract; and constructing a user portrait according to the user behavior data, and outputting the multi-modal abstract based on the user portrait. According to the method, extraction and analysis of entities and association relationships thereof are optimized through knowledge graph construction; through multi-modal fusion, associated display of texts, images and voices is realized; and through user portrait optimization output, the efficiency and adaptability of knowledge summarization are improved.

A machine learning-based fraud detection system (100) for financial transactions, comprising the following: a data collection and preprocessing module configured to capture transaction data from financial institutions, payment gateways and bank modules, and to perform data cleansing, normalization and format conversion; a module for creating user profiles and behavioral analyses, configured to create dynamic user profiles based on historical transaction patterns, spending habits, device usage, and geographic location; an anomaly detection and risk assessment module configured to use machine learning algorithms to detect deviations from normal transaction patterns and assign a dynamic risk rating to each transaction; a fraud detection module configured to classify transactions as legitimate or fraudulent based on supervised and unsupervised learning techniques, including decision trees, support vector machines (SVMs), and deep learning models; a rule-based and hybrid fraud detection module configured to integrate predefined rule-based fraud detection mechanisms with AI-driven anomaly detection to improve fraud detection accuracy; a real-time fraud detection and warning module configured to monitor transactions in real time and generate security alerts via SMS, email or mobile app when suspicious activity is detected; a transaction verification and multi-factor authentication (MFA) module configured to implement adaptive authentication measures, including biometric verification, one-time passwords (OTP), and challenge-response authentication for high-risk transactions; a self-learning and model-updating module configured to dynamically update fraud detection models using reinforcement learning and federated learning techniques; a data encryption and security module configured to protect transaction data using encryption techniques, including AES-256 and RSA, to prevent unauthorized access and data breaches; and A dashboard and reporting module configured to provide real-time analytics, visualizations, and fraud detection reports for financial institutions to monitor, track, and mitigate fraudulent activity.

The invention discloses a personalized interaction enhancement method and system based on a big language model, and the method and system can accurately capture and understand the demands, interests and preferences of a user through the personalized interaction enhancement based on the big language model, thereby providing a more personalized interaction service, and greatly improving the user experience. Particularly, a self-iterative user portrait mechanism is innovatively introduced, and the behavior pattern and interest change of the user are tracked in real time by dynamically analyzing historical dialogue information, so that the dialogue strategy can be adjusted at any time, and the response content is ensured to be highly consistent with the current demand of the user. Besides, by utilizing a topic segmentation and keyword matching technology, the system can quickly identify the intention of the user, ensure the context correlation of the dialogue, and provide deep domain knowledge support through knowledge graph extension. Generally speaking, the interactive quality is improved, the user portrait is more accurate and dynamic, the personalized service capability is effectively enhanced, and the intelligent response level of the system is improved.

In some embodiments, an electronic device presents representations of items of content available for playback on the electronic device. In some embodiments, an electronic device presents selectable options for playing an item of content in accordance with the manners in which the item of content is available to be played on the electronic device. In some embodiments, an electronic device presents representations of episodes in a series of episodic content. In some embodiments, an electronic device presents an enhanced preview of content. In some embodiments, an electronic device presents a control panel. In some embodiments, an electronic device switches the active user profile of a device. In some embodiments, an electronic device enters into a picture-in-picture mode.

An embodiment includes receiving, from a device, at an authentication service, a dynamic user identifier having a one-time password in an authentication message constructed to carry the dynamic user identifier in place of a pre-determined user identifier of a user. The embodiment locates in a profiles database, using a customized search query with a code based on the dynamic user identifier, a user profile. The embodiment receives at the authentication service, a secondary identification data of the user including a biometric information of the user. The embodiment validates the biometric information using the user profile and enables, when the validating is successful, the device to perform an operation. The enabling is not based on the authentication service validating an entirely static user identifier and is not based on the authentication service validating a manually typed password.

The invention relates to the technical field of art education data analysis, and discloses an AI-enabled data-driven art education resource recommendation engine, which is characterized in that user behaviors, resource metadata and context environment data are acquired through a multi-source data acquisition module; the user portrait construction module constructs a multi-dimensional user portrait by using a deep clustering algorithm and an attention mechanism; the resource feature extraction module extracts resource features by means of a graph neural network; the privacy protection processing module uses differential privacy and federated learning to protect data privacy; and the dynamic recommendation generation module generates a personalized recommendation list based on reinforcement learning. The recommendation engine can accurately recommend art education resources meeting user requirements, data privacy is guaranteed, resource association is mined, recommendation strategies are optimized, the quality and effect of art education resource recommendation are effectively improved, and personalized learning requirements of users are met.

The invention belongs to the field of artificial intelligence, and discloses a candidate question recommendation method for an intelligent dialogue system and a related device.Firstly, a deep learning model is adopted for conducting semantic coding and intention classification on an original question of a user, a semantic vector and an intention label are generated, and the semantic limitation of traditional keyword matching is broken through; screening the candidate question database by using the intention label to form a primary screening set, and narrowing the retrieval range; semantic matching of problem levels is achieved through semantic vector similarity calculation; and finally, performing dynamic weighted sorting by integrating multi-dimensional features such as semantic similarity, user portrait matching degree, question popularity and type adjustment factors to form a personalized recommendation list. By adopting the method, the accuracy of question recommendation and the scene generalization ability are effectively improved, so that the recommendation result not only meets the real-time semantic demand of the user, but also gives consideration to personalized preference and business scene characteristics.

The invention relates to the technical field of big data, in particular to an e-commerce marketing propaganda system based on behavior analysis, which comprises a global user portrait module, an intelligent recommendation engine, a marketing fatigue management module, a supply chain collaboration module, a lightweight terminal module and a budget and value management module. In the prior art, user portraits are constructed only depending on single channel data such as online clicking or purchase records, so that user interest modeling is incomplete and lagged; according to the method, all-channel behavior data such as APP, Web, offline POS and social media are integrated, a user-commodity-scene heterogeneous graph is constructed by using a graph neural network, and an interest attenuation period (for example, the weight is reduced by 50% after the interest of mother and infant users lasts for 18 months) is dynamically captured through an LSTM model; for example, after a user tries on a certain style of clothes offline, the system associates online behaviors in real time and recommends commodities of the same style, the cross-scene conversion rate is improved by 32%, and the user portrait coverage degree is improved by 60%.

An apparatus for data fault detection and repair is disclosed. The apparatus comprises at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a user profile relating to a user, wherein the user profile comprises at least provider data of a user. The memory instructs the processor to generate practitioner data as a function of the user profile. The memory additionally instructs the processor to retrieve remittance data as a function of the practitioner data. The memory then instructs the processor to identify a data fault in at least one of the practitioner data and the remittance data. The memory instructs the processor to initiate a data correction action based on the identified data fault.

Systems and methods are described for dynamic execution of artificial intelligence (“AI”) agents. A server can receive, from a client device, an input associated with an AI agent. Based on a manifest file or user profile, the server can identify a management policy that applies to the AI agent. The server then dynamically configures access to the agent objects based on applying the management policy. The management policy is applied to a device status of the client device, a user profile of a user of the client device, and / or a network configuration of the client device. The server then executes a modified workflow based on the dynamically configured access, wherein the modified workflow bypasses or changes operation of at least one of the agent objects. Based on the modified workflow, the server transmits an output to the client device.

A vehicle communication system facilitates hands-free interaction with a mobile device m a vehicle or elsewhere. Users interact with the system by speaking to it. The system processes text and processes commands. The system supports Bluetooth wireless technology for hands-free use. The system handles telephone calls, email, and SMS text messages. The user can customize the device via a user profile stored on an Internet web server.

An AI-based training recommendation system (100) for personalized employee development, which includes the following: a module for creating user profiles that is configured to collect and analyze employee data, including job role, skills, learning history, performance indicators, and career aspirations; a module for real-time skills gap analysis that uses artificial intelligence to assess employee skills and identify areas for improvement; a training recommendation engine that uses machine learning algorithms to create personalized training suggestions based on employee data, company goals, and industry trends; a learning path optimization module configured to adaptively modify training recommendations based on employee progress, feedback, and evolving qualification requirements; an integration module for connecting to existing enterprise systems, including Learning Management Systems (LMS), HR platforms and performance management tools; a module for monitoring engagement and progress, designed to track employee participation in training, assess knowledge retention, and provide managers with insights; and an automated training planner configured to assign and prioritize training sessions based on employee availability and business requirements.

The invention discloses an electronic product personalized marketing method based on a big data accurate portrait, and relates to the technical field of big data processing, and the method comprises the following steps: multi-source data collection and cross-domain fusion processing: obtaining a multi-source data set related to an electronic product from each platform of the Internet by using a web crawler and a streaming processing engine, the multi-source data set is subjected to cleaning classification and alignment fusion processing to generate multi-modal data, and then the multi-modal data is updated in real time to dynamically reflect data changes, so that a foundation is laid for subsequent data analysis; according to the method, multi-source data is collected by using a web crawler and a streaming processing engine, quantitative analysis is performed on the data by using a Hofscode culture dimension model and a Maslow demand level theory, a user portrait is constructed by using a distributed data flow engine Apache Flink to dynamically update a label, and a GAN (GAN Adversarial Generative Network) anomaly detection and evaluation analysis method is applied, so that the user portrait is obtained. And comprehensive and accurate data acquisition and processing are realized.

A system and method for real-time or near real-time multilingual language transformation in telecommunications environments using distributed artificial intelligence (Al). The system includes at least one processor configured to instantiate a plurality of Al agents, each corresponding to a user in a communication session, and to manage their operation via an Al-agent runtime adapter comprising a microkernel agent manager and a virtual machine layer. A language model abstraction layer enables access to language models and Al algorithm libraries through publish-subscribe interfaces. The system supports contextual transformation of speech based on user profile data such as sentiment, emotional tone, and cultural background. Execution can occur on telecommunication-class switches, edge devices, or general-purpose computing hardware. Al agents operate asynchronously, access shared or private memory, and produce personalized, bidirectional linguistic output. The architecture supports secure, scalable deployment across heterogeneous devices and networks.

One embodiment provides a method comprising collecting explicit user data relating to a user associated with an in-person event, and extrapolating implicit user data relating to the user from user interactions and content engagement patterns of the user. The method further comprises generating a user profile for the user by integrating the explicit user data with the implicit user data, and dynamically updating the user profile based on real-time data. The method further comprises generating, via a matchmaking algorithm utilizing one or more machine learning models, a professional match recommendation for the user based on the user profile and one or more additional user profiles of one or more additional users. The professional match recommendation suggests the user professionally network with a different user having one or more attributes that are complementary to the user.

Systems and methods for automated blockchain-based recommendation generation, advertising and promotion in accordance with various embodiments of the invention are described. A user device in accordance with an embodiment of the invention includes: a network interface; memory; and a processor. In addition, the processor is configured to implement an execution environment that enables: initiation of transactions via an immutable ledger; recordation of events; updating a user profile, where the user profile comprises at least one characterization associated with the user profile; encrypting the updated user profile and securely storing the encrypted user profile; receiving a request to access the encrypted user profile from a process; determining access permissions of the process; and when the process has sufficient access permissions, decrypting the user profile and providing user profile data to the process.

The invention discloses a high-low voltage linkage line loss comprehensive intelligent diagnosis system and method. The system comprises a distributed storage and high-performance calculation module, a data fusion and processing module, a multi-dimensional feature construction module, a high-low voltage linkage analysis module and a line loss intelligent diagnosis module. The method is used for carrying out line loss comprehensive intelligent diagnosis based on the system, and comprises the following steps: processing multi-source data in real time through the distributed storage and high-performance calculation module; bus-line-user archive data are fused, and a line loss index is calculated; extracting time, space and electrical three-dimensional characteristic indexes; performing high-low voltage linkage analysis based on the Pearson's correlation coefficient and the DTW distance; and fusing the system state model and the isolated forest anomaly detection model to output an anomaly diagnosis result. The method is suitable for accurate analysis and treatment of line loss in an intelligent power grid environment, and the efficiency and quality of line loss management can be improved.

Aspects of the present application help to alter searches in non-linear media. In some embodiments, a server may receive a part of a user search query related to non-linear content. The server may access progress data that is associated with the user profile and perform a search on a database using the progress data and the search query to produce a number of candidate content items (e.g., search results). The server may access a data structure that defines possible paths through the non-linear media, and may filter the candidate content items based on the progress data and the data structure that defines the possible paths through the non-linear media to generate output of a filtered candidate content item.

A system and a device for active threat monitoring in a user worn helmet is provided. The system comprises an Internet of Things (IoT) cloud server having an IoT database. The IoT database is configured to generate virtual data feed. The system comprises a head mounted wearable device. The head mounted wearable device comprises a plurality of physical sensors configured to sense and generate at least one of visual feed and radar feed and a multimodal communication interface. The system comprises a user database designated to store user profile data. The system comprises an environment monitoring unit configured to map an environment based on at least of one of the visual feed, the radar feed, and the virtual data. The system comprises an awareness unit configured to generate an environmental context based on position of a user. The system comprises a controller. The controller is configured to receive the user profile data from the user database, the mapped environment from the environment monitoring unit, the environment context from the awareness unit, and the virtual data feed from the IoT database; analyse the user profile data, the visual feed and the radar feed, the environment context, and the virtual data feed to predict occurrence of threat based on the analysed data; and warn the user about a threat based on the predicted occurrence of the threat via the multimodal communication interface.

This disclosure relates generally to system and method for providing personalized explainable response by generating personalized context-aware prompt using contextual information. Agriculture faces critical challenges such as adverse climate, pest / disease outbreak, soil, degradation and the like due to which personalized, contextual and explainable advisories are needed to achieve sustainable and optimal agricultural productivity. The method receives a multimedia user query to provide personalized explainable response using initial validation and a personalized context generation. Further, a personalized context aware prompt is generated to obtain response from a plurality of LLM's. Simultaneously, a contextually relevant user profile is composed to generate a personalized explainable response. Additionally, the method is capable of providing the personalized explainable response in multilingual and multimodal content. Further, feedback form enables the user to provide feedback which enables adaptive improvement and storing the context, personalized explainable response, and feedback in a blockchain platform and a prompt library.