1476 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.

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%.

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 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.

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 for the real-time delivery of personalized motivational content based on biometric information; the system includes: a biometric acquisition module configured to capture a variety of physiological signals from a user, wherein the physiological signals include at least heart rate variability, electrodermal activity, facial expressions and electroencephalographic (EEG) signals; a preprocessing module that is operationally coupled with the biometric acquisition module, wherein the preprocessing module is configured to remove noise, normalize and extract signal features from the physiological signals in real time; a multimodal biometric fusion engine configured to temporally align and synchronize the extracted features across signal modalities using dynamic time distortion and confidence-weighted interpolation; a motivational state inference model with a hybrid neural architecture comprising a Convolutional Neural Network (CNN) for spatial pattern recognition and a Recurrent Neural Network (RNN) for temporal sequence modeling, wherein the inference model is configured to output a motivational input score and an affective state classification; an engine for recommending motivational content, configured to select and prioritize content from a content repository based on motivational uptake score, user profile metadata, contextual signals including time of day and geolocation, and historical content effectiveness profiles; and a content delivery subsystem comprising one or more output modalities selected from an acoustic actuator, a visual display, a haptic actuator or an environmental controller, wherein the content delivery subsystem is capable of presenting the selected motivational content in a modality that is dynamically adapted to the user's current psychophysiological state.

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 recommendation system is described, which identifies engaged fans for an artist and requests input from the engaged fans regarding a collaboration by the artist with at least one different artist. In implementations, engaged fans are identified as having user profiles on a media content platform that satisfy at least one threshold engagement criteria based on consumption of at least one media content item associated with the artist. The recommendation system presents a user interface that includes at least one prompt for feedback that enables engaged fans to recommend how the artist collaborate with others. In some implementations, the user interface includes controls that are selectable to define artist characteristics to feature in a collaboration and the recommendation system is configured to generate a synthesized collaboration by automatically combining different artists' characteristics using a trained machine learning model. Recommendations based on engaged fan feedback are then provided to artists.

The invention provides an intelligent customer service dynamic intention recognition system based on a semantic analysis model, and the system collects the multi-dimensional data of a user through a data collection module, constructs a user portrait through a feature extraction module, extracts the portrait features, carries out the semantic analysis of multiple rounds of historical dialogue data, and extracts preference features. And constructing a dynamic user-entity association graph to extract GNN node features. Multi-modal data of a user is analyzed through an emotion recognition module, emotion features are recognized, portrait features, preference features and emotion features are fused and analyzed based on an MLP model to obtain a final emotion state, and the portrait features, the preference features, GNN node features and the emotion features are fused through an intention recognition module to obtain a final emotion state. According to the method, dynamic intention analysis is carried out on the basis of the Transform sequence-to-sequence model, the current intention of the user is recognized, the recognition accuracy is high, the dynamically changing intention is adjusted in real time, and more accurate and targeted answers or services are provided for the user.

The invention discloses a game level generation recommendation method, system, device and medium, and the method specifically comprises the steps: carrying out the combined modeling of multi-modal data through a multi-task learning network, and generating a dynamic user portrait vector, the main task of the multi-task learning network is used for predicting the skill level of a player, and the auxiliary task of the multi-task learning network is used for predicting the skill level of the player; auxiliary tasks of the multi-task learning network are used for classifying interest labels and analyzing emotional tendencies; inputting the dynamic user portrait vector into a dual-channel generative adversarial network, generating a candidate level set, and screening initial recommended levels through a playability evaluation algorithm; and calculating the matching degree between the skill score of the user and the level challenge degree in real time according to a difficulty-skill dynamic balance model, and dynamically adjusting the level parameters of the initial recommendation level through the matching degree to obtain a first recommendation level. According to the method, key problems in traditional game level design and recommendation are effectively solved, and efficient, personalized and real-time game level generation and recommendation with optimized user experience are realized.

The embodiment of the invention provides a financial credit intelligent marketing system and method. Comprising a multi-source data acquisition and integration module, a data preprocessing and fusion module, a multi-modal data fusion and feature engineering module, a user portrait construction and subdivision module, an intelligent marketing strategy making module, a marketing execution and real-time monitoring module and an effect evaluation and strategy iteration module. The financial credit intelligent marketing system comprises a plurality of functional modules such as data acquisition, data processing, model training, strategy making, real-time marketing, batch marketing, execution monitoring and the like. A comprehensive and multi-dimensional user portrait is formed by integrating enterprise internal data and an external third-party authorization data source. And machine learning algorithms such as XGBoost, LightGBM and the like are utilized to carry out deep mining and analysis on user behaviors, and purchase intention and demand change of the user are predicted. Meanwhile, based on the Flink real-time data processing technology, quick response and dynamic adjustment of marketing activities are achieved.

Systems and methods for generating user-specific textual and image-based outputs, in response to a user query, for provision of a matching item are disclosed herein. For example, the system may receive a query that includes a textual description. The system may retrieve a user profile for a user associated with the query. Based on the query and the user profile, the system may generate outputs and images using a machine learning model. Based on the outputs, the system may generate graphical representations. The system may receive a selection of a graphical representation. Based on the selection of the graphical representation, the system may enable access to an item that corresponds to the graphical representation.

Systems and methods for automating workflows, improving operating efficiency, and / or improving security in a multi-party document management platform are disclosed. The systems and methods utilize artificial intelligence (AI) models to analyze user profiles, generate risk scores for multi-party agreements, and determine risk mitigation actions. User interactions with modified agreements are monitored, and the AI models are updated based on these interactions. The system automatically adjusts operating parameters to improve operating efficiency and / or security. The methods and systems also provide for generating recommendations, utilizing user feedback, analyzing user action patterns, performing portfolio-level risk assessments, and identifying workflow inefficiencies. These features enable a dynamic, adaptive platform that continuously improves its performance in managing multi-party agreements while also enhancing security and operational efficiency.

The invention relates to the field of artificial intelligence, and discloses an E-commerce platform commodity recommendation method based on AI intelligence, and the method comprises the steps: obtaining user environment and behavior data through multi-modal perception, generating a three-dimensional intention vector through space-time convolution network coding, and driving a dynamic strategy selector to configure a recommendation weight; generating an initial recommendation list in combination with the commodity feature library and the user portrait; collecting user interaction feedback in real time, analyzing and outputting a correction vector through a lightweight loop network, dynamically adjusting a strategy, and reordering recommendation results; after a user places an order, a scene complementation generator is started, a complementation commodity sub-list adaptive to a current transaction scene is generated based on knowledge graph multi-hop reasoning, and visual fusion presentation is carried out. According to the technical scheme, accurate intention recognition, millisecond-level strategy correction and dynamic complementary recommendation are achieved, and recommendation real-time performance, adaptability and diversity are improved.

The invention relates to the field of artificial intelligence and personalized recommendation, and discloses a sensing data intelligent recommendation system. The system comprises a perception data acquisition module, a multi-dimensional feature analysis module, a user portrait construction module, an intelligent recommendation decision module and an enhanced feedback optimization module. The method comprises the following steps: acquiring original data through a multi-source sensing device, and extracting multi-dimensional statistical characteristics after standardization processing; a user portrait model is constructed in combination with user historical behaviors, candidate recommendation vectors are generated, a recommendation strategy is dynamically optimized based on user feedback, efficient, accurate and personalized recommendation results are achieved, and the method is suitable for complex application environments such as intelligent interaction, mobile terminals and intelligent scenes.

The invention discloses a digital cultural tourism management system based on multi-source data analysis, and the system comprises a multi-source heterogeneous data collection module which is used for collecting tourist behavior data, environment data, cultural resource data and third-party platform data in real time; the user demand intelligent analysis module is used for constructing a dynamic user portrait through the multi-modal interaction data and identifying dominant and implicit demands; the culture knowledge graph construction module is used for generating a reasonable multi-dimensional knowledge network based on culture resource attributes and historical data; the travel route dynamic generation module is used for generating a personalized touring route in combination with the user portrait, the real-time environment and the resource state; according to the digital cultural tourism management system based on multi-source data analysis disclosed by the invention, the utilization rate of cultural resources is greatly improved, and the decision response speed is increased; the cultural cognition depth of tourists is improved, and the residence time of the tourists is prolonged; the damage rate of high-sensitivity cultural relics is reduced; and the special group culture acquisition efficiency is improved in a breakthrough manner.

A display improvement support method and device for improving a display in accordance with the user's conscious reaction is provided. [Solution] In the display object improvement support device, the generation AI control unit executes the following steps: a reaction prediction prompt generation step of generating a reaction prediction prompt that instructs the generation AI to predict the reaction that will occur in the mind of a virtual user having user profile information of at least one of age, sex, occupation, height, and weight when the virtual user uses the display object; a reaction acquisition step of inputting the reaction prediction prompt into the generation AI and acquiring reaction information, which is information related to the reaction, from the generation AI; a screen operation prediction prompt generation step of generating a screen operation prediction prompt that instructs the generation AI to predict the screen operations that the user will perform on the screen of an information device based on the reaction information; and a screen operation acquisition step of inputting the screen operation prediction prompt into the generation AI and acquiring screen operation information, which is information related to screen operations, from the generation AI.

The invention relates to the technical field of block chains, in particular to a block chain multi-platform data credible management method and system based on a user portrait, and the method comprises the steps: carrying out the multi-platform data collection, obtaining a plurality of original user data sets, carrying out the encryption processing of the original user data sets, obtaining an encrypted user data set, carrying out the block packaging processing of the encrypted user data set, and carrying out the block packaging processing of the encrypted user data set. Performing consensus verification on the block to be subjected to consensus encryption to obtain an on-chain encryption block; performing feature extraction on the on-chain encryption block to obtain a user feature data set; performing feature division on the user feature data set to obtain a user feature type set; performing portrait construction based on the user feature type set to obtain a user portrait; and obtaining a portrait access standard, and according to the portrait access standard, performing authorization judgment by using the data access authorization unit. According to the method and the device, the problem of low data credibility in the current management process of the multi-platform data of the block chain can be solved.

An apparatus comprises a memory communicatively coupled to a processor. The memory is configured to store network interchange parameters and user profiles. The processor is configured to receive a data exchange request triggered by a user device. Further, the processor is configured to request, to a decentralized network, a token indicating that a user profile associated with the user device is entitled to access an entitlement, determine whether a data exchange network is available to exchange data in accordance with the network interchange parameters, identify a second data exchange network that is available in accordance with second network interchange parameters in response to determine that the data exchange network is unavailable, and request a data exchange output from the second data exchange network via the token. The processor is configured to receive the data exchange output from the second data exchange network.

The invention discloses a marketing decision analysis method based on artificial intelligence, and the method comprises the steps: S1, obtaining the marketing data of a plurality of channels, carrying out the preprocessing of the marketing data, and carrying out the classification of the preprocessed marketing data according to the data type; s2, obtaining a marketing scene demand, obtaining a corresponding analysis model according to the marketing scene demand, and generating a marketing decision-making scene through the intelligent decision-making model based on the analysis model; s3, acquiring user behavior data, then collecting behavior feedback of the user on the marketing decision scene, merging user behavior characteristics into user portrait data, and analyzing the user portrait data; and S4, extracting feature information in the marketing decision scene, constructing a marketing effect evaluation index according to the extracted feature information, and predicting a marketing effect suitable for the marketing decision scene from the historical marketing data based on the marketing effect evaluation index. According to the method, the problem of data islands when the multi-source heterogeneous marketing data is processed by a traditional method is solved.

The invention discloses a social group evaluation method and system based on double-space fusion calibration, and the method comprises the steps: firstly, extracting user preference features based on user-news interaction data and news type tags, constructing a fixed user portrait through employing a statistical analysis and maximum preference strategy, and completing the division of the user portrait; then, starting from the matching space, sorting the users in the matching space through joint sorting of the behavior activeness and the behavior coupling degree; meanwhile, starting from a semantic space, generating a portrait semantic expression by utilizing a large language model, generating a semantic center point in combination with user behaviors, and sorting the users in the semantic space based on a vector distance; on this basis, the sorting information of the matching space and the semantic space is fused, the semantic consistency and the behavioral representativeness of the users are balanced, and finally the user groups with comprehensive representativeness under each type of portraits are screened out; further, based on the high-frequency behavior record and portrait features of the representative user, constructing an injection simulation behavior sequence, selecting low-interaction news items, and generating an evaluation sample with interference features; and finally, retraining a recommendation model, evaluating recommendation response change of each portrait group on low-interaction information, and quantifying anti-interference scores of the system under different portrait consistency conditions, thereby revealing robustness difference of the recommendation system, and providing support for security enhancement and portrait recognition. According to the method, the performance difference and the anti-interference capability of different user portrait groups in a recommendation system can be described.

An AI-driven matchmaking system designed around narrative-based user profiles is disclosed. Each user is represented as a digital book comprising thematic categories and expressive chapters. A dual-embedding compatibility engine analyzes self-expressed and preferred traits, enabling bidirectional scoring. Users interact through chapter-level swiping and engage in milestone-triggered emotional messaging. The system promotes reflective authenticity and dynamic compatibility modeling beyond visual-first paradigms.

Provided is a system for robotic collaboration. A first robotic device executes a first action and a second robotic device executes a second action upon the first robotic device completing the first action. An application of a communication device is paired with the first robotic device and the second robotic device. The application is configured to receive at least one input designating, for example, a hitting speed of a tennis ball, a hitting direction of a tennis ball, a unique tag of a subarea in a map of an environment, a task schedule of the first robotic or second robotic device, a driving speed, a selection of a particular user profile, an instruction to drive to a user-identified location, an instruction to turn in a left or right direction, an instruction to drive forwards or backwards, an instruction to stop moving, etc.