5016 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 discloses a network security verification method and system for a security system, and belongs to the technical field of network security, and the method comprises the steps: obtaining a biological feature recognition request triggered by a user on terminal equipment, synchronously collecting dynamic behavior track data, and generating a behavior feature code; inputting physiological feature data and behavior feature codes in the biological feature recognition request into a feature verification model, when an output biological behavior correlation index is lower than a first preset threshold value, triggering a dynamic verification strategy adjustment instruction, generating a multi-dimensional verification superposition strategy, and executing at least two verification operations in the multi-dimensional verification superposition strategy, the verification result is input into the distributed time synchronization node to generate an encryption token; and when the synchronous matching degree of the encryption token among different nodes exceeds a second preset threshold value, outputting a security verification passing instruction. According to the method, multi-modal feature fusion, dynamic hierarchical verification and time-space encryption technologies are adopted, replay attacks are prevented, an adaptive defense system is constructed, and reliable authentication of a high-security network scene is ensured.

The invention relates to the technical field of data processing, and provides an intelligent risk control method for carbon asset transaction, which is used for solving the problems of data island, response lag, low efficiency, safety risk and the like in traditional carbon transaction. According to the invention, standardized access of a carbon emission permit trading system, a registration system and enterprise internal data is realized by constructing a carbon asset data middle platform, integrating a block chain and an RPA technology, a price fluctuation rate and a quota gap risk are calculated in real time based on Apache Flink, a carbon price trend is predicted through an LSTM + Attention model, and an intelligent contract is triggered to generate a hedging instruction; a three-level risk prevention and control system is constructed by adopting an ABAC model and biological characteristic verification, a quota regulation path is optimized through a graph neural network, automatic performance is driven, strategy self-adaption is realized in combination with PPO reinforcement learning and BERT policy analysis, and the problems of data islands, response lag, low efficiency and security risks in traditional carbon transactions are systematically solved.

Devices, systems, and methods for providing a patient with a personalized digital healthcare assistant. A medical history and biometric information for a patient are received by a computing device. A text conversation is conducted with the patient using a large language model (LLM) through a user interface (UI) to obtain a plurality of responses. The medical history, the plurality of responses, and the biometric information are stored in the non-transitory computer-readable memory medium. An intervention proposal is determined using a machine learning software based at least in part on the medical history, the plurality of responses, and the biometric information, and the intervention proposal is displayed on a display.

The invention discloses a multi-factor dynamic identity verification and access control system, which comprises a user management module, a biological recognition module, a strategy-level authorization module, a dynamic evaluation module and an interface module, and is characterized in that the user management module is used for user registration, information management and logout; the biological recognition module adopts a biological recognition technology to collect, process and compare biological characteristics of a user; the strategy-level authorization module is used for accessing sensitivity levels of applications and terminal type factors according to user identities; the dynamic evaluation module is responsible for monitoring and evaluating the user access process in real time; and the interface module is used for providing an interactive interface between the system and other systems or applications. By integrating various advanced technologies, efficient, safe and flexible access control management is realized, and the safety and user experience of the whole system are improved.

The present disclosure provides a method for secure verification of identities. The method includes obtaining personal identifying information from an individual using a remote device and transmitting the information to an application service. The application service accesses multiple disparate data sources via an API, including public entity data sources, third party data sources, and services sources. The API normalizes data received from the disparate data sources. The personal identifying information is verified against the normalized data. Biometric identifying information is obtained from the individual using the remote device and transmitted to the application service. The biometric identifying information is compared with normalized biometric data from the disparate data sources. Based on the comparison, a determination is made whether to issue a digital identification credential to the individual. An authentication result is transmitted to the remote device.

The invention belongs to the technical field of water pollutant traceability, and particularly discloses a water quality fingerprint database analysis method based on feature recognition, which relates to the field of water pollutant traceability, and comprises the following steps: cross-comparing feature curved surface information of different upstream and downstream under feature peak whole database data and sorting to obtain pollutant features under different intensities; the pollution source type is identified according to the pollutant difference; sorting the characteristic curved surface information of the water body samples reflecting different nodes, calling a chemical characteristic analysis result, outputting a chemical characteristic sequence, and selecting a first sequence sampling point position of the chemical characteristic sequence corresponding to the spectral characteristics of the identified pollution source type as a pollution source; according to the method, whether the identification result is met or not is analyzed according to the biological characteristics of the pollution source, efficient source tracing is carried out on water pollutants, an accurate pollutant transmission path is output, meanwhile, the corresponding pollutant type is evaluated, the corresponding pollutant diffusion trend is given, and powerful data is provided for follow-up pollutant treatment.

A set of distance measurable encrypted feature vectors can be derived from any biometric data and / or physical or logical user behavioral data, and then using an associated deep neural network (“DNN”) on the output (i.e., biometric feature vector and / or behavioral feature vectors, etc.) an authentication system can determine matches or execute searches on encrypted data. Behavioral or biometric encrypted feature vectors can be stored and / or used in conjunction with respective classifications, or in subsequent comparisons without fear of compromising the original data. In various embodiments, the original behavioral and / or biometric data is discarded responsive to generating the encrypted vectors. In another embodiment, distance measurable or homomorphic encryption enables computations and comparisons on cypher-text without decryption of the encrypted feature vectors. Security of such privacy enabled embeddings can be increased by implementing an assurance factor (e.g., liveness) to establish a submitted credential has not been spoofed or faked.

Bending data is used to facilitate tracking operations of an extended reality (XR) device, such as hand tracking or other object tracking operations. The XR device obtains bending data indicative of bending of the XR device to accommodate a body part of a user wearing the XR device. The XR device determines, based on the bending data, whether to use previously identified biometric data in a tracking operation. A mode of the XR device is selected responsive to determining whether to use the previously identified biometric data. The selected mode is used to initialize the tracking operation. The selected mode may be a first mode in which the previously identified biometric data is used in the tracking operation or a second mode in which the previously identified biometric data is not used in the tracking operation.

A System for providing Interactive Transaction Frameworks, utilizing biometric authentication and semantic web technologies is disclosed. The system registers users with a unique biometric-based identifier, and employs ontologies, schemas, and knowledge graphs to create a semantic foundation. A Large Language Model integrated with the Knowledge Graph Module enables intelligent processing of Interactive Transaction data. The system composes Interactive Transactions with fields mapped to semantic triples, maintains them in an Interactive Transaction Library, and manages user permissions. A Transaction Publishing Platform facilitates Interactive Transaction distribution and submission-response cycles. The system refines inputs and responses, determining outcomes based on these interactions. The system offers a sophisticated, secure, and adaptable approach to Interactive Transaction management and processing across various domains.

A cellular communication network based non-fungible token creation system with integrated creator biometric authentication comprises an electronic device of the user; a passive biometric user identification technology coupled to the electronic device and configured for identifying the individual with the passive biometric user identification technology by passively obtaining biometric characteristics that are unique to each human; and a one click non-fungible token forming component which is configured for identifying digital data files on an electronic device with a single user click; wherein the digital data files include the biometric characteristics of the creator, and at least one of audio, video, data and image files, and wherein the system is configured for creating authentication data comprising the digital data files and for recording on a blockchain the authentication data forming the non-fungible token; and systems for managing and transferring ownership of the non-fungible token.

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.

It is disclosed a vehicle transportation safety monitoring system based on driver holographic management. In the system, a driver identity verification unit reads driver certificate information and / or collects driver biometric information, and compares the information with biometric information corresponding to authorized driver identity information to complete driver identity verification, a vehicle control unit determines, according to an identity verification result, whether a vehicle can be started; emotion recognition and / or behavioral intelligent analysis and / or sensor monitoring data analysis and recognition is performed, and when the result triggers a warning, an operation such as automatic speed control is performed to control the movement of the vehicle. This system can bind a driver identity with a driving permission of a designated operating vehicle, monitors a driver emotion, a driving behavior, and a fatigue status, and controls a vehicle driving status and / or sends a safety warning to a driver according to a monitoring result.

The invention relates to a DID-based block chain data sharing and security verification method. The method comprises the following steps: a user generates a unique real-name DID through multi-factor identity authentication, generates multiple groups of public and private key pairs in an application process, and stores private key fragments in a decentralized network by adopting a secret sharing technology; a real-name DID is used as a unique user account on the block chain, and data sharing and security verification are carried out in combination with a deep learning model and an adaptive zero-knowledge proof mechanism; when the private key is lost, a public and private key pair is regenerated through biological characteristics and equipment fingerprint verification; and cross-chain data sharing and security verification are realized by using an intelligent contract and an inter-chain communication protocol, and identity authentication and data operation processes of the user on different block chains are simplified.

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.

According to the face data privacy protection method and device provided by the embodiment of the invention, a credible authority management mechanism is constructed through a distributed network and a smart contract, and a multi-dimensional feature extraction scheme fusing geometric features and texture features is innovatively designed. The system adopts data fragmentation and homomorphic encryption technologies to carry out security processing on feature vectors, and dispersedly stores encrypted data in a block chain network, so that decentralized management of the feature data is realized. According to the method, feature comparison and identity verification are carried out in a ciphertext domain, the whole process is traceable in combination with a timestamp and an operation record, the problems of data security and privacy protection in a traditional face recognition system are effectively solved, and a safe and credible technical solution is provided for the field of biological feature recognition.

The invention provides a two-dimensional code recognition unlocking method suitable for an entrance machine and related equipment, and is applied to the technical field of data processing. The method comprises the following steps: acquiring target door lock information, a photoresistor value of a target entrance machine, biological characteristic information of a target user and access information of a target application program; processing the biological characteristic information of the target user to generate identity attribute information of the target user; processing the photoresistor value of the entrance machine to generate environment scene information of the entrance machine; processing the identity information of the target user and the access information of the target application program to generate initial entrance machine two-dimensional code information; generating target entrance machine two-dimensional code information according to the environment scene information of the entrance machine and the initial entrance machine two-dimensional code information; and processing the target door lock information based on the target entrance machine two-dimensional code information to generate an identification result of the target door lock.

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.

The invention discloses a server security anti-theft trigger type alarm system, which relates to the technical field of server security, and comprises a biological recognition module, a monitoring module, a judgment module, a warning module and a linkage module, the biological recognition module is used for verifying the identity of a user trying to access the server; the method comprises the following steps: acquiring a biological characteristic sample of a user, such as a fingerprint, an iris or a facial image, performing digital processing on the acquired biological characteristic sample through biological recognition equipment, and storing the processed biological characteristic sample in a safe database; according to the invention, by integrating high-precision biological recognition, real-time monitoring and intelligent analysis technologies, comprehensive security protection is provided for the server, and the system can accurately recognize and prevent unauthorized access, timely detect abnormal events and quickly judge potential threats through intelligent analysis, so that the security risk is reduced to the greatest extent.