3391 results about "Biometric trait" 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.

A computer-implemented method and system for privacy-preserving location verification in distributed networks comprises initializing a multi-modal biometric authentication system on a user device, generating cryptographic keys using a distributed key generation protocol, binding the cryptographic keys to biometric templates using a fuzzy vault scheme, constructing and broadcasting encrypted location beacons, and generating zero-knowledge proofs of location claims. The system includes user devices equipped with biometric sensors and verifier devices configured to validate location claims and maintain consensus in a blockchain network. The method implements real-time liveness detection for multiple biometric input types, executes fault-tolerant consensus algorithms with privacy preservation, and maintains a dual-scoring mechanism comprising device trust scores and user reputation scores. The system enables secure location verification while preserving user privacy through cryptographic protocols and biometric authentication in decentralized environments.

The invention relates to an immersive VR psychological detection system and method based on multi-modal AI. The system comprises a data acquisition and processing module which is used for acquiring a multi-modal data set of a user in a virtual reality scene based on unified clock synchronization, performing time-space alignment and noise reduction standardization processing on the multi-modal data set, and extracting key biological characteristics. And the correlation model construction module performs space-time correlation mapping through a spatial transformation network, constructs a three-dimensional space attention model, and generates a real-time fluctuation curve after inputting the key biological characteristics into the trained model. And the state report generation module identifies a real-time fluctuation curve by using a time sequence analysis model, performs backtracking analysis in combination with the psychological state conversion node and a multi-modal cross validation result, and finally generates a three-dimensional interactive report. By adopting the method, multi-modal data fusion can be realized, the dynamic change of the psychological state of the user can be effectively captured, the psychological state of the user can be comprehensively and deeply analyzed, and a scientific basis is provided for psychological health assessment and intervention.

The invention relates to the technical field of intelligent decision making, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-modal data-based event analysis method, which comprises the following steps of: obtaining attribute data, monitoring data, environment data and biological characteristic data of a user, and performing standardization processing on the attribute data, the monitoring data, the environment data and the biological characteristic data; combining semantic analysis and structured fusion of the text records to form a unified input data set; and performing multi-modal fusion modeling on the unified input data set by using an intelligent decision model, generating a potential event analysis result of an individual state, generating a visual report containing potential event levels, main influence factors and intervention suggestions based on the result, and finally sending the report to a user terminal. According to the method, the multi-source structured data and the multi-source unstructured data are integrated, and the intelligent decision-making model is introduced to realize feature level fusion and semantic enhancement modeling, so that the accuracy and timeliness of individual state analysis are effectively improved, and the personalized expression of an analysis result is enhanced.

The invention provides an intelligent lock control method and system based on dynamic identity verification, and relates to the technical field of artificial intelligence, and the method comprises the steps: building a multi-dimensional risk assessment model comprising an operation time interval, a biological characteristic change rate and a spatial position parameter, and obtaining a risk assessment score; according to the risk assessment score, performing preliminary adjustment on the biological characteristic data, and performing encryption processing and characteristic enhancement on the preliminarily adjusted biological characteristic data in combination with a dynamic key to obtain processed biological characteristic data; performing collaborative calibration on the processed biological characteristic data according to the risk assessment score to obtain calibrated biological characteristic data; and encrypting the calibrated biological characteristic data through an anti-quantum computing encryption protocol, and carrying out weighted fusion on the encrypted characteristic data, the dynamic key, the environmental adaptability score and the risk assessment score to generate a final unlocking instruction. According to the invention, the safety and robustness of the intelligent lock are improved.

A programmatically generated AI avatar includes a customizable personality module, acting as the embodied interface for a powerful AI “mind” that delivers personalized coaching to improve user health, well-being, and longevity. The system uses machine learning, large language models, and biometric modeling to synthesize real-time, multi-modal health data—including sleep, nutrition, glucose, mood, and activity—and generate forward-prescribed KHAs. Unlike human coaches, it continuously adapts based on context and behavior, targeting the root cause: metabolic dysfunction—namely by restoring healthy, sustainable body composition through the preservation or building of lean muscle mass and reduction of excess fat. KHAs can also be shared with friends or programmatically generated AI avatars, allowing for coordinated action, emotional support, and accountability through social connection—further reinforcing positive behavior and adherence. The system's reinforcement learning engine incorporates both individual response data and anonymized population-level insights to optimize recommendations over time, learning which interventions are most effective for users with similar physiological and behavioral profiles. First validated with Olympic athletes—resulting in measurable improvements and medal-winning outcomes—this system offers a scalable, emotionally intelligent coaching engine that exceeds human capability, designed for the ultimate purpose of supporting sustainable health, resilience, and human thriving.

The invention relates to the technical field of cloud computing financial security, and provides a financial data encryption transmission and storage method. The method is characterized by comprising the following steps of: executing sensitivity-driven data grading fragmentation on a user terminal; dynamic elliptic curve encryption is carried out on transmission data through a two-channel encryption engine, and fully homomorphic encryption is carried out on storage data; dynamically distributing the fragments to heterogeneous cloud nodes by the multi-cloud routing based on reinforcement learning; a distributed key management matrix is constructed, key fragments are dispersed and stored in a block chain and a hardware security module, and reconstruction is activated through biological characteristics. The method has the advantages that full-link ciphertext operation is realized, and the plaintext exposure risk is eliminated; ciphertext state financial calculation is supported; single point failure is resisted; the APT attack is defended dynamically; and the quantum security evolution capability is realized. The method is suitable for mobile banks, cross-border payment and other scenes.

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 an intelligent dialogue system and method based on an AI multi-mode large model. The method comprises the following steps: collecting biological characteristic data of a user in real time; cloning a personalized expression mode of the user by using the generative adversarial network; driving the audio avatar to perform multi-round dialogue interaction with the user, capturing a real-time physiological signal of the user through an expression recognition module, and generating a dynamic response content suggestion in combination with a dialogue context; and analyzing the interaction process based on the reinforcement learning model. The multi-modal deep fusion of the voice rhythm features, the language structure features and the facial dynamic features is realized, so that the limitation of single-modal or simple feature splicing in the prior art is broken through, the emotional state of the user can be accurately and comprehensively captured, the intention is expressed and the fine physiological reaction is expressed, and the user experience is improved. And a solid foundation is laid for constructing high-fidelity user representation.

The invention discloses an intelligent security and protection monitoring system and method fusing multi-modal data analysis, and relates to the field of intelligent monitoring, and the method comprises the following steps: collecting a video stream, infrared thermal imaging data and an environment audio signal in real time through a heterogeneous sensor array; operating a lightweight neural network at an edge computing node to carry out space-time alignment and feature level fusion on the multi-source data, and generating an enhanced environment sensing matrix; a dual-channel anomaly detection mechanism is adopted, a first channel identifies short-term sudden abnormal events through a dynamic threshold self-adaptive model, and a second channel early warns potential risk behaviors through a time sequence prediction model; according to the system and the method, a dynamic privacy protection mechanism is adopted, behavior analysis is completed on the premise of ensuring biological feature safety, and transformation from passive monitoring to active early warning is realized in combination with a space-time prediction model; the problem of multi-view target tracking is solved through a distributed consensus algorithm, and the monitoring reliability in a complex scene is remarkably improved.

The invention relates to the technical field of Internet of Things, and discloses a multi-factor dynamic authentication Internet of Things network security access platform, which adopts a multi-factor authentication mechanism, combines biological characteristics, behavior characteristics and environment characteristics of equipment, performs identity verification through biological recognition, equipment fingerprints and behavior analysis, and utilizes a dynamic authentication strategy. Detecting an abnormal IP behavior and triggering a security policy by adopting a dynamic IP binding technology, combining with equipment fingerprint identification and learning and analyzing an equipment network behavior mode; a dynamic key management mechanism is adopted, and keys are automatically generated, distributed and updated according to different devices, application scenes and communication requirements; machine learning and artificial intelligence technologies are utilized to comprehensively analyze historical access data, abnormal behavior modes, environment security states and the like of equipment, and the security risk of equipment access is automatically evaluated if suspicious equipment is detected. The method has the advantage of improving the security of the Internet of Things.

A system for real-time analysis of emotional feedback during motivational speeches, consisting of: a series of multimodal sensors, including at least one visual sensor configured to capture facial expressions of spectators, at least one directional microphone configured to capture the audio responses of the audience, and optionally one or more physiological sensors configured to capture biometric signals from spectators; an edge-based processing unit that is communicatively coupled to the arrangement of multimodal sensors, wherein the edge-based processing unit comprises the following: (a) a feature extraction module configured to extract visual features from captured facial images, acoustic features from voice responses, and physiological features from biometric signals; (b) an emotion inference machine configured to process the features using a deep learning-based emotion recognition model comprising a convolutional neural network (CNN) for classifying facial expressions, a recurrent neural network (RNN) for classifying voice emotions, and a multimodal late fusion layer configured to compute a composite emotion state vector representing the aggregated emotions of the audience; (c) a timestamp and speech alignment module configured to correlate the calculated composite emotion state vector with segmented portions of a live motivational speech based on real-time speech-to-text transcription and semantic analysis; and (d) a session-based storage unit configured to log time-indexed emotional state vectors and corresponding speech segments for post-event analysis; A speaker feedback interface comprising a portable display or a podium-mounted visualization panel, wherein the interface is configured to display visual indicators of emotional feedback in real time, the indicators being derived from the emotional state vector and including at least emotional trend graphs, threshold alerts, or engagement indices.

A system and method for person identification from video data by disentangling biometric identity features from non-biometric appearance and activity features are disclosed. The system processes RGB video sequences depicting individuals performing various activities to extract spatio-temporal features. These features are separated into distinct biometric identity representations and non-biometric features related to appearance and performed activities. To achieve this separation and minimize appearance bias, the system utilizes an auxiliary supervisory model. At least two implementations of this supervisory model are disclosed: one using semantic supervision via structured embeddings processed through a vision-language model, and another employing silhouette-based feature distillation from a silhouette-trained neural network. Joint training for biometric identification and activity classification ensures accurate identification of individuals independently of facial visibility, clothing differences, or activity variations.

The invention relates to the technical field of information security, provides an intelligent warehouse login verification method and system based on dynamic living body detection, and is used for realizing accurate generation of an identity label vector, enhanced authentication of a dynamic password and automatic matching of warehouse authority while accurately performing living body detection. And the security and convenience of warehousing system login are comprehensively improved. The method comprises the following steps: collecting real-time interaction action data of a warehousing system login user, calling a preset living body detection algorithm to carry out dynamic biological characteristic analysis on the real-time interaction action data, and generating a living body verification confidence coefficient and a user identity identification vector; performing dynamic password enhanced authentication according to the living body verification confidence coefficient and a preset security authentication threshold value, and generating a dynamic access token containing multiple layers of encryption identifiers; and based on the user identity identification vector and the dynamic access token, executing storage permission automatic matching processing, and outputting a login verification result associated with the user permission level to the storage system according to the permission matching label.

According to various embodiments, a system and method for verifying a user's identity using both document-based and biometric data is disclosed. The system may prompt a user to upload an image of a government-issued identification document and extract user information from the image using optical character recognition (OCR). The system may also extract the embedded face image from the ID and prompt the user to take a real-time selfie while performing one or more randomized actions or poses. A facial recognition engine may compare the extracted ID image to the live selfie to determine a similarity score. Based on this match, along with optional document authenticity checks, the system may confirm the user's identity in real-time for use cases such as account access, onboarding, and regulatory KYC compliance.

A digital healthcare architecture including a cloud-based virtual clinic platform configured to facilitate remote therapy of one or more patients based on biometric authentication systems and methods. In an example arrangement, one or more biometric indicia of a user (e.g., a clinician and / or a patient) as well as one or more non-biometric input factors may be used in authenticating the user prior to granting access to a protected resource or application (e.g., a therapy application executing on a UE device) configured for effectuating remote programming of an implantable medical device (IMD) operative to provide therapy to the patient.

The invention discloses an intelligent system and method for fusing mental health evaluation in an interest scene based on an education robot. The system realizes non-intrusive mental health assessment by constructing a technical architecture of'multi-modal biological feature anonymization-quantum hybrid encryption-federated learning privacy protection-dynamic risk assessment '. The method is characterized by comprising the following steps of: (1) generating irreversible biological characteristic codes by adopting a chaos random number generator, and realizing anonymized association by combining a double hash chain technology; (2) designing a quantum enhanced hybrid encryption system, fusing an SM4 algorithm, NTRU post quantum cryptography and quantum key distribution; (3) developing a dynamic scene generator, and naturally fusing psychological test questions into interest topics by using a generative adversarial network (GAN) and a curiosity engine; (4) constructing a multi-modal emotion analysis system, and combining an LSTM + CNN hybrid model to realize physiology-behavior-voice data fusion analysis; and (5) deploying a federated learning framework, and protecting model training data through differential privacy noise injection (epsilon = 0.5).

The invention relates to the technical field of biological feature recognition, and discloses a neural reflex motion feature extraction and quantitative evaluation method, which comprises the following steps that: multi-modal behavior data of a premature infant are synchronously acquired through a sensor array, and the multi-modal behavior data comprise a motion inertia signal, optical tracking data and plantar pressure distribution data; preprocessing the multi-modal behavior data, and performing dynamic feature fusion on the preprocessed multi-modal data to generate a joint feature vector; extracting a motion symmetry index and a reflection intensity grade from the joint feature vector; and inputting the joint feature vector, the motion symmetry index and the reflection intensity level into a hierarchical rule engine, and executing risk judgment of a behavior pattern. The invention also provides a neural reflex motion feature extraction and quantitative evaluation system. According to the invention, the accuracy of neural reflex motion feature extraction and quantitative evaluation can be improved.

The invention relates to the technical field of computers, in particular to a multi-modal model fusion-based water decision-making system, which comprises a virtual digital human interaction module, an intelligent agent business middle table and a large model technology base, and is characterized in that the virtual digital human interaction module is used for monitoring a voice instruction of a user; the method comprises the following steps: acquiring a voice instruction of a user, acquiring biological characteristic data of the user by using a multi-modal sensor, identifying relevance characteristics between the voice instruction and the biological characteristic data through a water conservancy emergency scene emotion identification model, acquiring indication information, and sending the indication information to an intelligent agent service middle station; the agent business middle platform performs task chain analysis on the indication information based on a water conservancy professional term intention recognition model to obtain different task requirements, and calls a large model technology base to make a decision based on the different task requirements to obtain decision information; a water conservancy large model trained based on a domain mechanism model library is integrated on the large model technology base. According to the invention, more efficient and intelligent services can be provided.

Systems, methods, and non-transitory computer-readable media containing instructions are disclosed for detecting and utilizing facial skin micro-motion. In some non-limiting embodiments, detection of facial skin micro-motion occurs using a speech detection system that may include a wearable housing, a light source (coherent light source or incoherent light source), a light detector, and at least one processor. The one or more processors may be configured to analyze light reflections received from the facial region to determine facial skin micromotion, and extract meanings from the determined facial skin micromotion. Examples of meanings that may be extracted from the determined facial skin micromotion may include words spoken by the individual (silent or voiced), an identity of the individual, an emotional state of the individual, a heart rate of the individual, a respiratory rate of the individual, or any other biometric, emotion or speech related indicator.

The invention discloses a shortest-time-based bird flock repelling effect evaluation method for an airport intelligent bird repelling system, and the method comprises the steps: S1, collecting the multi-dimensional monitoring data of an airport bird flock repelling process through a biological feature sensing system, and enabling the multi-dimensional monitoring data to comprise a repelling duration parameter, a region clearing index, and bird flock adaptability response data; s2, based on the multi-dimensional monitoring data, analyzing the spatio-temporal behavior characteristics of the bird flock through an intelligent analysis terminal, and constructing a multi-dimensional bird flock expelling effect characteristic matrix; s3, based on the feature matrix, performing dynamic weight optimization of bird flock expelling effect evaluation indexes with the shortest expelling time as a target, and generating a self-adaptive weight coefficient group; and S4, based on the self-adaptive weight coefficient group, performing panoramic evaluation on the bird flock repelling effect by adopting a time sequence perception memory network, and outputting a repelling efficiency analysis result taking the repelling timeliness as a core. While the flight safety is guaranteed, efficient utilization of bird repelling resources is realized.

The invention provides a PPG big data driven biological feature recognition method, which comprises the following steps: acquiring PPG signal data of trainees, and performing high-pass filtering and low-pass filtering preprocessing on the PPG signal data to obtain preprocessed PPG signals; framing is carried out on the preprocessed PPG signal, a Mel frequency cepstral coefficient feature and a Gammatone frequency cepstral coefficient feature are extracted respectively, the Mel frequency cepstral coefficient feature and the Gammatone frequency cepstral coefficient feature are fused, then principal component analysis dimension reduction is carried out, and a fusion feature is obtained; a universal background model UBM of a Gaussian mixture model is constructed based on the fusion features, zero-order, first-order and second-order Baum-Welch statistics are calculated, a global difference space matrix is estimated from the Baum-Welch statistics, and i-vector identity authentication vectors are extracted; and inputting the i-vector identity authentication vector into a long short-term memory network for training and classification to obtain an identity recognition result of the trainees. According to the invention, the influence of motion artifacts can be eliminated, the overall variability of PPG signals is captured, and an identity authentication scheme is provided for wearable equipment.

The invention discloses an MES (Manufacturing Execution System) login authentication method based on a face recognition technology, which relates to the technical field of industrial information security, and comprises the following steps of: constructing an authentication data acquiring a face image of an authorized user of an MES, extracting a reference face feature template after preprocessing, and constructing the authentication database; performing associated storage on the reference feature template, user identity information, preset operation authority and affiliated production station information to form an authentication database; according to the method, spoofing attacks are prevented through multi-mode living body detection, and the problem that a traditional password / key is easy to leak and borrow is solved in combination with uniqueness of human face biological characteristics; a person-post-terminal binding mechanism further limits permission abuse, and internal operation risks are reduced; the non-contact authentication does not need to memorize a password or carry a physical key, and is suitable for a working scene that a workshop operator wears gloves and holds tools; and the recognition passing rate in a complex industrial environment is improved through dynamic threshold optimization.

Systems and methods are provided to enable a user to conduct a transaction using their credentials stored on a secure server computer (e.g., a computer associated with a partner such as another merchant) by merely presenting their authentication data at a physical location via an auxiliary device. An auxiliary device may be provided for interfacing with a partner's backend server (e.g., the secure server computer). In some embodiments, biometric authentication may provide a mechanism for a true seamless and potentially frictionless (in the case of modalities that do not require physical contact) interaction. Payment can occur without any need for a card, phone, wearable, or any other user device as long as the auxiliary device is able to recognize the user and retrieve a credential that can be linked to that user.

The invention provides a face image recognition method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the fusion of a topological relation feature vector and a biological feature contour, calculating the curvature manifold of the concave region of five sense organs, and generating a local curvature feature vector through differential geometric transformation; inputting the topological relation feature vector, the local curvature feature vector and the micro feature coding sequence into an evolvable biological feature library, and fusing a space-time weight to update a comparison template; fusing the micro-feature coding sequence, the topological relation feature vector and the local curvature feature vector to obtain a biological feature set, and calculating the multi-scale similarity between the biological feature set and the updated comparison template; and if the multi-scale similarity exceeds an adaptive threshold, generating a face recognition result. According to the invention, the stability and accuracy of face recognition in a complex illumination environment are improved.

A system for serving personalized advertisements based on biometric and identifying data captured by sensors. The system employs pre-processing, feature extraction, and recognition modules to identify individuals. A context-aware processing module adapts recognition strategies based on environmental data and analyzes personal information to confirm the identity of the targeted individual and send targeted advertisements. The system includes display and speaker modules for visual and audio advertisements, a predictive interaction modeling module for generating personalized advertisements, and a cross-platform behavior analysis module for analyzing user behavior across offline and online platforms.

A system and method for processing genomic data using dynamic latent manifolds that transforms multi-modal genomic datasets into geometric representations within a curved manifold space. The system receives genomic datasets including DNA sequences, genetic variants, and expression data, then extracts biological features and assesses importance using trained neural networks. Manifold curvature values are computed based on biological significance, and genomic data is embedded as geometric structures where semantic relationships are represented through distance and curvature properties. The system generates compression pressure fields that influence processing decisions and computes optimal geodesic paths through the manifold to minimize cognitive action functionals. Adaptive compression rates are determined for different genomic regions based on geometric properties and biological importance. The manifold structure evolves through use, strengthening frequently accessed pathways while applying thermodynamic decay to unused concepts. The system supports hierarchical organization across biological scales, reversible navigation, and federated learning capabilities that enable privacy-preserving collaboration.

The invention discloses a dynamic network access control method and system based on a zero-trust architecture, and the method comprises the steps: integrating equipment health degree evaluation through the triple dynamic binding of biological feature dynamic binding, equipment fingerprint salt value hash verification and environmental state perception, and constructing a real-time trust basis; dynamic risk quantification is realized based on a multi-source heterogeneous data fusion machine learning model, real-time upgrading and degrading self-adaptive adjustment of authority is realized through an AI driving strategy generation module according to a real-time risk score, a zero-trust sandbox limitation sensitive operation is triggered for high-risk access, and a minimum authority channel is started for low-risk access; performing fine-grained access control and intercepting an unauthorized request in real time by adopting an agent-free API gateway technology, and monitoring an operation behavior in combination with a block chain non-tampering storage access log and an anomaly detection algorithm; finally, a continuous self-adaptive evolutionary cycle is formed through a risk assessment-policy execution-abnormal feedback closed loop mechanism, and the static lag problem of traditional network access control is systematically solved.

Examples relate to a fatigue detection system to monitor and assess pilot fatigue levels during flight operations. An example system includes a wearable biometric sensor (WBS) integrated into a wristband that captures biometric data from the pilot. A processor analyzes the captured data to determine the pilot's fatigue level. When this level exceeds a predetermined threshold, the system provides an alert to the pilot through a haptic feedback mechanism in the wristband. Additionally, the system generates personalized fatigue mitigation advice, which is displayed on an electronic flight bag (EFB) application accessible to the pilot. The advice may include recommendations for taking a controlled rest, consuming caffeine, or engaging in physical activity. The system enhances flight safety by providing real-time alerts and actionable advice to combat pilot fatigue.