581 results about "Individual data" patented technology

The invention discloses a personal data cross-border circulation method based on a block chain. The method comprises the steps of data acquisition and local storage, third-party neutral institution witness and data fingerprint chaining, data transmission and use authorization declaration, fingerprint verification and data verification on a receiver chain, and full-link tracing and dynamic auditing. Aiming at the problems that trust guarantee of personal data cross-border circulation depends on a single technical means such as centralized institution authentication or encrypted transmission or a static legal protocol, a trust transfer chain is lengthy, and cross-national legal mutual recognition is fragile, a data fingerprint anchoring system constructed based on a block chain is designed; aiming at the problems that traditional data cross-border authorization depends on a manual signing protocol or static strategy configuration, response lag exists, rule execution deviation exists, privacy protection and compliance auditing are difficult to consider and the like, a data use rule is dynamically constrained through an intelligent contract; a user defines a data use range, timeliness and an operation white list through a declarative intelligent contract, and contract codes are automatically executed after being subjected to consensus verification of nodes on a chain.

The invention relates to the technical field of campus management, and particularly discloses a smart campus management system based on big data, an event-driven data management architecture is used for dynamically collecting, integrating and associating multi-source heterogeneous data in a campus, and generating a standardized event stream; the dynamic resource scheduling engine is in communication connection with the event-driven data governance architecture, generates a resource allocation instruction based on event types and priorities in event streams, and dynamically deploys campus resources; the closed-loop evaluation optimization module receives a resource scheduling result of the dynamic resource scheduling engine and generates a multi-dimensional evaluation index, and the multi-dimensional evaluation index is fed back to the data governance architecture through root cause analysis so as to optimize a subsequent decision; the privacy enhancement processing unit integrates a federated learning framework and a differential privacy algorithm, performs collaborative analysis on cross-system data and ensures the anonymity of individual data; through three core technologies of dynamic data management, intelligent resource scheduling and closed-loop evaluation optimization, intelligent upgrading of the whole campus management process is realized.

Various systems, computer-readable media, and computer-implemented methods of providing improved data privacy, anonymity and security by enabling subjects to which data pertains to remain “dynamically anonymous,” i.e., anonymous for as long as is desired—and to the extent that is desired—are disclosed herein. Embodiments include systems that create, access, use, store and / or erase data with increased privacy, anonymity, and security—thereby facilitating the availability of more qualified and accurate information. When personal data is authorized by data subjects to be shared with third parties, embodiments described herein may facilitate the sharing of information in a dynamically-controlled manner that also enables the delivery of temporally-, geographically-, and / or purpose-limited information to the receiving party. In one example, the disclosed techniques may be used to functionally separate geospatial information, such that it remains “dynamically anonymous,” i.e., anonymous for as long as is desired—and to the extent or degree that is desired.

The invention belongs to the technical field of cross-border data security, and provides a cross-border personal data transmission security detection method and system based on differential privacy, and the method comprises the steps of multi-dimensional situation awareness, privacy budget allocation, interpretability and audit, and execution and response. By synchronously sensing technical environment risks, multi-national regulation constraints and multi-modal data features, comprehensive control of the cross-border transmission security situation is realized, and one-sidedness of single-dimensional sensing is avoided; privacy budget is dynamically allocated under multiple constraints based on a deep Q network algorithm, so that the protection intensity of high-sensitivity data is ensured, and excessive loss of effectiveness of low-sensitivity data is avoided; an interpretable report is generated in combination with causal analysis, full-process evidence storage is realized by relying on an alliance chain, the problems of decision black box and audit tracing are solved, cross-border supervision requirements are met, and the balance of cross-border transmission in the aspects of safety, compliance and availability is improved.

The invention discloses a medical clinical data quality analysis method and system based on a knowledge graph, and relates to the technical field of medical data analysis. The method comprises the following steps: constructing a dynamic medical field knowledge graph fusing medical ontology and semantic relationship, accessing multi-source heterogeneous clinical data into the graph through entity linking and standardized mapping, and generating a personal data sub-graph containing time sequence information for each patient; through combination of logical reasoning based on a semantic rule base and anomaly detection based on a graph neural network, deep discovery and accurate positioning of known contradictions and unknown mode quality problems are realized, and a targeted repair scheme is generated by utilizing the traceability of a graph; quality problems are quantitatively graded and fed back to the atlas, and closed-loop management is formed; according to the method, the limitations of isolated examination and lack of semantic understanding of a traditional method are overcome, the transformation from passive verification to active prevention is realized, and the control precision and efficiency of the medical data quality are remarkably improved.

A computer-implemented apparatus, system, and method is disclosed for protecting sensitive data. A cognoscible computing engine is multi-layered and multi-pathed. It includes features for handling different data formats, including structured, semi-structured, and unstructured data. Features are included to support near real-time processing at scale with high accuracy. Applications include redacting or masking sensitive data to comply with data privacy and security standards.

Systems, methods, and other embodiments associated with quasi-supervised clustering for activity pattern characterization and anomalous activity detection are described. In one embodiment, a method generates a first sparse similarity matrix for nearest neighbors of a plurality of data points. The data points each characterize a pattern of activity associated with an account. The method generates a second sparse similarity matrix for random neighbors of the plurality of data points. The method recursively clusters the plurality of data points based on the first sparse similarity matrix. The method quasi-supervises the recursive clustering based on the second sparse similarity matrix to stop the iterative clustering when the data points are split into N clusters. The value of N is not pre-determined. The method detects that the individual data point has changed clusters, indicating anomalous activity. And, the method generates an electronic alert that the anomalous activity is associated with the account.

The invention provides an electric vehicle cluster parameter prediction method and device and computer equipment, and the method comprises the steps: carrying out the pre-training of a bidirectional long-short-term memory network according to commercial charging pile data, and enabling the bidirectional long-short-term memory network to be used for predicting the charging behavior parameters of an electric vehicle in combination with a historical charging track and a future charging trend; based on authorized household charging pile data, a transfer learning algorithm is adopted to carry out fine tuning on the pre-trained bidirectional long-short-term memory network, and a charging behavior prediction model is obtained; inputting the historical charging data of each electric vehicle into a charging behavior prediction model to obtain predicted charging data of each electric vehicle; and according to each piece of predicted charging data, constructing a monomer feasible region of each electric vehicle, and according to each monomer feasible region, obtaining a cluster feasible region of the electric vehicle cluster through sino polyhedron modeling and Minkowski summation. Therefore, the problems of limited individual data and long time span are relieved, and the accuracy and practicability of electric vehicle cluster parameter prediction are further improved.

The embodiments of the present invention are related to automatically capturing, logging, rating, scoring, and tracing attributes of a product against known data sets and storing that information as a point-in-time capture that cannot be edited. The platform can capture, rate, score, trace, and distribute content for the health and sustainability factors / attributes of a product. A multifactor holistic scoring process is implemented that produces a reliable estimate of the sustainability of the product (in context). Related data and the score (in connection with timing information) are saved when the product (or product lot) is updated in content or manufacturing. A digital blockchain ledger is integrated by way of interaction with the system to store individual input and the score and configured to securely store the data, rules, or other items that resulted in the score. Individual data items are stored in the ledger and include an identifier.

A computing system is configured to generate a predictive model during training of a machine learning program using a training data set including a personal data set of a plurality of first users. The predictive model is configured to generate predicted survey data with respect to a second user by correlating a personal data set of the second user to the personal data set of at least one of the first users. The predicted survey data includes data regarding the predicted responses of the second user to a survey from which the survey data of each first user is derived, as well as one or more assessment scores calculated from the survey. The computing system is configured to take an action with respect to a user device of the second user in reaction to the generating of the predicted survey data regarding the second user.

The invention discloses a multi-layer heterogeneous sequential network feature alignment and clustering method and device based on tensor self-representation. The method comprises the steps that sequential feature matrix sequences {},..., {} of data views in a multi-view heterogeneous network in time windows are obtained; based on the matrix sequence of the first time window, constructing a Laplacian regular term by introducing a self-representation learning mechanism, modeling multiple views by adopting a tensor structure and introducing a Schatten p-norm regularization mode to construct an optimization objective function for optimization, and representing shared feature representation of each data view in the first time window; and solving the function and carrying out clustering processing on the obtained function, wherein the clustering result of each time window is used for carrying out characteristic analysis on nodes in the network. According to the method, on the premise that the original structure and semantics of the network are guaranteed, unified modeling can be carried out on the incomplete multi-view heterogeneous network, and feature expression is aligned.

The invention relates to the technical field of information processing, in particular to a medicine conflict automatic detection and prescription optimization system for old multi-disease patients. According to the system, a data management module is used for collecting individual data of patients and group data of co-diseased reference groups and receiving a planned medication scheme; the parameter calculation module is used for identifying a target medicine combination with time overlapping in the patient medication record and the planned medication scheme; determining a basic risk index according to the group medication response data, the patient medication record and the work and rest text data; determining a lag risk coefficient according to the physiological indexes and the drug metabolism data; determining group medication associated risk parameters according to atypical reactions and group medication records in the medication reaction data; the risk fusion module is used for determining a drug use conflict risk value based on the basic risk index, the lagging risk coefficient and the group drug use associated risk parameter; and the prescription optimization module is used for generating an optimal medication scheme, so that the generated optimal scheme is more suitable for the actual life of the patient.

An individual data unit for enhancing the security of a user data record is provided that includes a processor and a memory configured to store data. The individual data unit is associated with a network and the memory is in communication with the processor. The memory has instructions stored thereon which, when read and executed by the processor cause the individual data unit to perform basic operations only. The basic operations include communicating securely with computing devices, computer systems, and a central user data server. Moreover, the basic operations include receiving a user data record, storing the user data record, retrieving the user data record, and transmitting the user data record. The individual data unit can be located in a geographic location associated with the user which can be different than the geographic locations of the computer systems and the central user data server.

The Somatic Artificial Intelligence Data Systems (SAIDS) invention provides a method, system and apparatus for user-controlled data curation, privacy, and monetization. Leveraging a Generative Artificial Intelligence Agent (GAIA), SAIDS enables the collection and real-time processing of multimodal data, including biometric and environmental inputs, through wearable or standalone devices. GAIA anonymizes data, detects unauthorized use via integrity checks, and supports user decisions to either opt-in for economic benefits through data sharing or opt-out for enhanced privacy. This system democratizes data ownership by empowering users to retain control and derive value from their personal data in a privacy-preserving manner.

The invention belongs to the technical field of computer server management, and particularly discloses a laboratory collaborative environment-oriented user and data full-life-cycle server management system, which comprises a container virtualization layer, a server management layer and a server management layer, dynamic resource allocation is supported through a state saving-resource reallocation-environment recovery mechanism; the heterogeneous transaction coordination layer adopts a Saga transaction mode to coordinate a heterogeneous system so as to realize cross-system transaction consistency; the data collaboration ecological layer establishes typed management of a personal data volume, a shared software environment volume and a public data set volume, and supports software environment encapsulation sharing and versioning management; and the metadata management layer realizes real-time update and high-performance query of metadata based on a Redis hot update architecture. According to the method, the problems of inflexible resource allocation, incomplete environment isolation, dilemma in authority management and difficulty in data collaboration and recovery in the prior art are solved.

The invention relates to the technical field of artificial intelligence and legal information processing, in particular to a security legal system intelligent question-answering system based on an RAG architecture, and is used for solving the problems of insufficient real-time performance, poor speciality of generated content, computing power dependence, data quality deviation, weak knowledge association and the like of a general large model in a legal scene in traditional legal consultation. According to the technical scheme, the method comprises the following steps: dynamically partitioning a law knowledge base, and constructing a high-dimensional vector index based on a BERT model to support semantic retrieval; calculating by fusing TF-IDF content similarity and cosine semantic similarity, and optimizing a retrieval result in combination with a sorting learning model; a rigorous Prompt template constraint generation logic is built in, legal provisions are cited forcibly, an efficacy level is marked, and an optimal result is screened through a multi-candidate answer evaluation mechanism. The system realizes efficient matching and specialized output of legal clauses, judicial cases and law knowledge, provides intelligent and highly credible legal consultation services for national security, personal data protection and common law scenes, and promotes deep fusion of artificial intelligence and legal information processing technologies.

The invention relates to the technical field of data security, in particular to a data security agent system of a trusted data space, which comprises the following steps: dividing the trusted data space into a plurality of data systems, calculating privacy scores of single data sets in each data system, performing location arrangement of privacy levels on the single data sets through the privacy scores, and performing location arrangement of the privacy levels on the single data sets through the privacy scores; the method comprises the following steps: obtaining a privacy level sequence, identifying basic identity information of a target visitor, determining a reference visitor, analyzing a historical access record of the reference visitor, obtaining an access keyword of the target visitor, calculating a data association value between a system keyword and the access keyword, comparing the data association value with the privacy level sequence, determining open information, and displaying the open information to the target visitor. In the data opening process, the privacy risk of the data is fully considered, and data leakage caused by excessive opening is avoided.

An intelligent assistant incorporating a Large Language Model (LLM) can transform a transcript containing communications regarding tasks into structured data. The transcript can be a meeting transcript or a transcript of a chatbot chat that was autonomously initiated by the intelligent assistant to obtain additional information regarding a task. With LLM assistance, the intelligent assistant transforms unstructured communication data from the transcript into structured data which is then assigned to relevant task objects stored in a database. Prior to processing a transcript, personal data therein can be sanitized, and the intelligent assistant can divide the transcript into smaller segments which each encapsulate a discussion of a different topic.

System and method for managing interaction of data between Artificial Intelligent (AI) agents within a secure cloud-based enclave are disclosed. The method comprises initiating, by a single AI agent, an interaction request with a shared AI agent. The shared AI agent triggers a negotiation and authorization process to the single AI agent based on the interaction request. The negotiation and authorization process determines whether the single AI agent is eligible to interact with the shared AI agent. The shared AI agent receives user data from the single AI agent when the single AI agent is eligible to interact with the shared AI agent. The shared AI agent categorizes the user data into data sets based on a type of the user data. The shared AI agent generates personalized recommendations based on the data sets.

The invention provides a user data access method and device, equipment and a storage medium, and the method comprises the steps: analyzing an authorization code corresponding to a target user when a data access request is received, and obtaining the authorization information of the target user; matching data access meta-information corresponding to the data access request with the authorization information; and if the matching is successful, returning the target data associated with the data access meta-information to the data access request system. Through the implementation of the scheme, the authorization information can be obtained by analyzing the authorization code of the target user, whether the target data needed by the data access request is returned or not is judged according to the authorization information, the authorization code is designed and generated according to the personalized demand of the user, and therefore the user can have the control right to process the personal data of the user, and the user experience is improved. And the access request is ensured to accord with the self-defined authorization range, so that the user data security is effectively improved.

Systems, methods, and other embodiments associated with quasi-supervised clustering for activity pattern characterization and anomalous activity detection are described. In one embodiment, a method generates a first sparse similarity matrix for nearest neighbors of a plurality of data points. The data points each characterize a pattern of activity associated with an account. The method generates a second sparse similarity matrix for random neighbors of the plurality of data points. The method recursively clusters the plurality of data points based on the first sparse similarity matrix. The method quasi-supervises the recursive clustering based on the second sparse similarity matrix to stop the iterative clustering when the data points are split into N clusters. The value of N is not pre-determined. The method detects that the individual data point has changed clusters, indicating anomalous activity. And, the method generates an electronic alert that the anomalous activity is associated with the account.

The invention discloses a player training auxiliary system based on previous football match videos, and relates to the technical field of football auxiliary training, which comprises a video acquisition module, a video analysis module, a player evaluation module, a visualization module and a video marking module, the player evaluation module comprises a personal data analysis unit, a team cooperation data analysis unit and a comprehensive evaluation unit, the personal data analysis unit is used for evaluating the personal ability of players, team cooperation data is used for evaluating the team cooperation ability of the players, and the comprehensive evaluation unit is used for comprehensively evaluating the players; the system has the advantages that the training direction is judged by comprehensively analyzing the personal ability and team cooperation ability of players and setting a threshold value, evaluation stability is guaranteed in combination with multi-game data, then the comprehensive ability score of the players is calculated through the comprehensive evaluation unit, and the score at the critical moment and the game difficulty coefficient are introduced, so that a training plan can be accurately formulated, and the training efficiency is improved. And the auxiliary training quality of the system is improved.

An apparatus to facilitate capability-based memory access control for graphics processors and accelerators is disclosed. The apparatus includes one or more processing cores to: determine that a set of data accesses triggers a bulk access capability check; generate the bulk access capability check to combine with the set of data accesses; prior to performing a first data access of the set of data accesses, perform the bulk access capability check to check that the set of data accesses is allowed to be accessed in accordance with a capability; and responsive to the bulk access capability check passing, allow the set of data accesses to proceed without performing a capability check for each individual data access of the set of data accesses.

Method, system, device, and non-transitory computer-readable medium for data management. In some examples, a computer-implemented method includes: collecting a plurality of personal data sets and a plurality of sensor data sets; for each vehicle operator of the plurality of vehicle operators: generating and continually updating an operator profile, one or more telematics inferences, a data profile; and listing and continually updating the data profile onto a telematics marketplace; receiving a plurality of conditional bids for a target operator profile associated with a target data profile, each conditional bid of the plurality of conditional bids including one or more conditional payments and one or more payment conditions; determining, for each conditional bid of the plurality of conditional bids, a predicted bid-generated profit or a predicted bid-generated revenue; determining a winning bid and an associated winning bidder; and transmitting the target operator profile to the winning bidder.

The invention relates to a system and method for operating fraud prevention. The present invention aims to resolve those issues encountered when individuals are targeted by a potential fraudster, such as (i) enhance the security of accessing individuals digital assets / accounts, (ii) allow the onboarding of passbook users or account holder identification item users with an existing account onto a digital platform without the need to displace themselves yet without having to repeat their know-your-customer (KYC) process and by protecting the security of access to the digital assets strictly to the actual individual account holder(s) of the account assets, (iii) and reduce the unintended willing or un-willing provisioning of any personal data or account(s) access to malicious individuals or organizations contacting the unsuspecting user(s).

The invention discloses a pedestrian high-temperature health risk early warning method based on a thermal health risk index, and relates to the technical field of high-temperature risk early warning, and the method comprises the steps: collecting environment parameters, individual data and perception feedback data; obtaining spatial features and environmental features based on the environmental parameters, obtaining physiological features based on the individual data, and obtaining target variables based on the perception feedback data; inputting the spatial features, the environmental features, the physiological features and the target variables into a pre-training model to obtain a prediction probability; and obtaining a thermal health risk index based on the prediction probability and the target variable, and obtaining risk early warning information based on the thermal health risk index, thereby solving the problems of low early warning precision, high false alarm rate and low individuation degree caused by the adoption of a simple meteorological parameter or a fixed threshold value for early warning in a current urban high temperature early warning method.

The invention discloses a Holstein cattle production life estimation method and system based on a dynamic regression model, and relates to the technical field of dairy cow breeding, and the method comprises the steps: collecting data of a plurality of Holstein cattle individuals, screening the data of a plurality of Holstein cattle individuals with the life reaching 66 months, and constructing a data set; lactation-related variable data, physiological and environmental variable data and target character MIM66 data when the individuals survive to different age points are extracted from the data set, and the extracted data are coded to form a training set; establishing a linear regression model by taking lactation-related variables, physiology and environment variables as independent variables and taking a target character MIM66 as a dependent variable, training the model by utilizing a training set, and dynamically updating a regression coefficient; and obtaining individual data of the Holstein cattle to be estimated, and estimating the production life character MIM66 of the individual through the dynamic regression model. According to the method, the production life of the Holstein cattle at different age stages can be dynamically and accurately estimated.

The embodiment of the invention relates to a cognitive competence assessment processing method and device based on multi-modal data. The method comprises the following steps: setting two-stage data typing rules and typing feature coding rules of the multi-modal data; setting a corresponding score-classification relation table for the fluid intelligence scoring indexes of the two-stage data classification detailed rule; carrying out original data collection on the open biomedical database according to the two-stage data classification detailed rule, and carrying out training data arrangement according to the classification feature coding detailed rule, the score-classification relation table and the original data set; constructing a cognitive ability prediction model based on a LightGBM model; performing first-stage hyper-parameter optimization and full-data training on the cognitive competence prediction model, and then performing second-stage fault-tolerant training on the cognitive competence prediction model; and receiving current individual data after the two-stage training is finished, and performing cognitive ability evaluation and feature importance analysis based on the current individual data and the cognitive ability prediction model. According to the invention, the cognitive ability evaluation accuracy can be improved.

Various systems, computer-readable media, and computer-implemented methods of providing improved data privacy, anonymity and security by enabling subjects to which data pertains to remain “dynamically anonymous,” i.e., anonymous for as long as is desired—and to the extent that is desired—are disclosed herein. Embodiments include systems that create, access, use, store and / or erase data with increased privacy, anonymity, and security—thereby facilitating the availability of more qualified and accurate information. When personal data is authorized by data subjects to be shared with third parties, embodiments described herein may facilitate the sharing of information in a dynamically-controlled manner that also enables the delivery of temporally-, geographically-, and / or purpose-limited information to the receiving party. In one example, the disclosed techniques may be used to functionally separate geospatial information, such that it remains “dynamically anonymous,” i.e., anonymous for as long as is desired—and to the extent or degree that is desired.

The invention relates to the technical field of diesel engine fuel supply, in particular to a variable stiffness spring simulation design method for a high-pressure common rail pump. Comprising the steps of establishing a multi-physics field coupling model based on simulation software; establishing a thermodynamic module; simulating the movement of the plunger; simulating the movement of the valve core; establishing a fuel pressure dynamic change model; calculating the spring stiffness; calculating a target adjustment parameter; optimizing a spring stiffness coefficient: adjusting the spring stiffness coefficient through a genetic algorithm; a population is set and initialized, then an operation method is selected, crossover operation is carried out, and filial generation individual data is obtained; outputting an optimal parameter; optimal target parameters are calculated, specifically, the optimal parameters are introduced into simulation software, so that the maximum collision speed V of the valve element is reduced, and the system volume efficiency L is improved; by using the variable stiffness spring, the collision speed can be effectively reduced, the impact stress between the valve element and the valve seat is reduced, the fatigue damage between the valve element assemblies is reduced, and the service life of the pump valve assembly of the high-pressure common rail pump is prolonged.