734 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.

Methods, systems, and techniques for processing natural language queries. A natural language query related to personal information of the user is obtained. Query vectors that include embeddings of the query are generated and matched with document vectors generated from chunks of reference documents related to the personal data. The document chunks are retrieved from a database and used as context for a prompt to a large language model that is used to respond to the natural language query. The query itself is also included in the prompt. The query may be in respect of a particular goal, and the large language model's response may include recommendations to help the user achieve that goal.

Techniques are provided for personal data and AI system integration. It is determined to respond to a user prompt. An AI system is provided a user prompt description, a database description describing a user database comprising user data associated with the user, and instructions to provide a query for relevant data in the user database that the AI system can use to answer to the user prompt. A query is received from the AI system and executed against the user database to generate a result set. The result set is submitted to the AI system. The answer is received from the AI system based on the user prompt description and the result set. A response is generated for the user based on the answer from the AI system.

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 discloses a tumor recurrence risk assessment and early warning system based on data analysis, and belongs to the technical field of medical information. The invention discloses a tumor recurrence risk assessment and early warning system based on data analysis. The system comprises a multi-data collection module, a big data processing platform, a model architecture system and an individual data processing system. According to the invention, the problems of lack of big data support, limited individualized prediction ability and insufficient real-time performance and continuous monitoring in the prior art are solved; according to the method, large-scale historical case data are integrated, the rich data sets are used for model training, the accuracy and generalization ability of a prediction model can be remarkably improved, multiple data sources such as genomics information, living habits and environmental factors are integrated, personalized risk assessment is provided for each patient, and the risk assessment efficiency is improved. The real-time monitoring interface is connected with various intelligent medical devices to collect the daily physiological parameter change condition of the patient, and continuous health management suggestions are provided for the tumor postoperative patient.

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 present disclosure involves systems, software, and computer implemented methods for data privacy protocols. One example method includes receiving information defining a purpose for processing personal data of a data category stored in an object. A first mapping is received of a processing action to the purpose. Input data to be obtained for the processing action is identified. A determination is made as to whether the input data is of the data category that has been mapped to the purpose. The processing action is executed using the input data as purpose-based processing of the input data, in response to determining that the input data can be used during execution of the processing action for the purpose. Processing of the input data by the processing action is prevented, in response to determining that the input data cannot be used during execution of the processing action for the purpose.

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 invention discloses an Internet of Things service management system based on regional digital economy, and relates to the technical field of data processing. The Internet of Things service management system based on regional digital economy comprises a data minimization and anonymization module, an end-to-end encryption module, an access control and identity authentication module, a data security and auditing module and a security management and response module, and the data minimization and anonymization module is used for collecting personal sensitive information data; the end-to-end encryption module is used for encrypting a personal sensitive information data transmission process; the access control and identity authentication module is used for multi-factor authentication; the data security and audit module is used for protecting personal sensitive information data; and the security management and corresponding module is used for dynamically adjusting security policies and measures according to the privacy security state evaluation value. The problem that in the prior art, as the number of devices increases, more and more personal data are collected, and once sensitive data are integrated and analyzed, personal privacy may be exposed is solved.

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.

Various aspects described herein relate to systems and methods for merging records from multiple database systems. A method may include comparing, by one or more processors, two or more records of a plurality of records. The plurality of records may be from a plurality of database systems. Each of the plurality of records may be a patient record or an employee record. The method may also include determining, by the one or more processors, that a threshold match exists between the two or more records based upon a set of threshold matching criteria. The method may also include merging, by the one or more processors, the two or more records, based upon determining that the threshold match exists, into a person database entity. A unique entity identifier may be associated with the person database entity.

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.

A data privacy system and method is disclosed. The method includes providing at least one processor, at least one memory device including computer-readable instructions, and at least one user device in communication with the at least one processor via a network connection. The at least one processor, upon execution of the computer-readable instructions, 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 one or more users. The predictive model is configured to predict at least one predicted data privacy and / or data portability measure of at least one of the users. At least one quick response code embedded with data or a link to the data based upon the at least one predicted data privacy and / or data portability measure is generated to provide enhanced data privacy protection and data portability.

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.

In various embodiments, an entity may provide a WebView where a transaction between an entity and a data subject may be performed. As described herein, the transaction may involve the collection or processing of personal data associated with the data subject by the entity as part of a processing activity undertaken by the entity that the data subject is consenting to as part of the transaction. Additionally, the entity may provide a native application where the transactions between the entity and a data subject may be performed. In some embodiments, the system may be configured to share consent data between the WebView and the native application so data subjects experience a seamless transition while using either the WebView or the native application, and the data subjects are not required to go through a consent workflow for each of the WebView and the native application.

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.