267 results about "Individual data" patented technology

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.

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.

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.

Approaches are described for secure identity verification using blockchain technology. A configuration record for a user's identity attributes is obtained. The configuration record is used to evaluate and verify various identity elements through cross-referencing with authoritative sources. Based on the configuration record, identity tokens (KYbits) are generated using cryptographic techniques and stored on a blockchain ledger. These KYbits encapsulate verified identity information and can be utilized in verification processes without disclosing sensitive personal data. The identity tokens enable secure and privacy-preserving transactions across various platforms, ensuring compliance with data protection regulations and enhancing user control over personal identity information.

An autonomous machine learning (ML) system and methods are provided that are configured to intelligently cluster training data into separate training data sets for customized ML model training. The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform model training operations which include accessing training data, determining a set of features used for the customized ML model training, clustering the training data into the separate training data sets according to the set of features, outputting the separate training data sets, training the plurality of ML models, packaging the plurality of ML models in individual data containers, and configuring the ML data processing platform with the individual data containers.

The present disclosure involves systems, software, and computer implemented methods for data privacy. One example method includes receiving a first request to correct personal data or restrict processing of personal data of a data subject. Response data is identified that is provided by applications in a multiple-application landscape in response to a second request for access to personal data processed by respective applications in a multiple-application landscape. Relevant applications for the first request are identified based on the response data. A data correction or data restriction work package is sent to each relevant application and data correction or data restriction work package responses are received from relevant applications. An overall data correction or data restriction result is determined based on the data correction or data restriction work package responses and is provided in response to the first request.

The invention discloses a personal data value management system and method. The system comprises a main body collaboration layer which is used for defining roles and responsibilities of multiple main bodies and constructing a multi-main-body collaboration framework; the process management and control layer is used for constructing a full-process closed-loop design and establishing linkage rules of all links; the rule guarantee layer is used for constructing a rule system and dynamically adjusting the rule system according to scenes, policies and markets; the value distribution layer is used for carrying out quantitative processing on the individual contribution degree and the enterprise contribution degree and carrying out income distribution according to the individual contribution degree and the enterprise contribution degree; and the personal participation layer is used for constructing a personal participation mechanism so as to obtain personal data dynamics and rights and interests in real time. By adopting the system provided by the embodiment of the invention, a five-layer cooperative personal data valued management system is constructed, each layer not only defines the core function, but also realizes cooperation through data synchronization, rule linkage and a subject counterbalance mechanism, and a management closed loop covering the full life cycle of personal data is formed.

A computer-implemented method for deleting an individual's personal data is provided. A request to delete the individual's personal data may be received from a communication device associated with the individual. The individual's identity may be verified. Responsive to verifying the individual's identity, the request may be compared with a data retention policy, wherein the data retention policy may comprise a data retention end date. If a date of the request is earlier than the data retention end date, a denial notification may be generated and transmitted to the communication device to notify the individual that the request is denied based on the data retention end date. Responsive to a determination that the date of the request is later than the data retention end date, the request to delete the individual's personal data from one or more associated databases may be processed.

The invention discloses a brain development characteristic evolution analysis system and method based on multi-mode brain image data. The system comprises a brain development map construction module, a data acquisition module, a data processing module and an evolution matching evaluation module. The method comprises the following steps: collecting demographic information and multi-modal brain image data of multi-age healthy people, constructing a structure, and connecting and activating a three-dimensional group brain development map; the method comprises the following steps: processing individual data by adopting the same process, obtaining a multi-modal brain feature vector in a unified brain region space, mapping the multi-modal brain feature vector to a corresponding group map space, calculating a deviation amount and combining a stability weight to obtain a fusion deviation index, identifying an abnormal brain region according to the fusion deviation index, calculating an abnormal proportion and a risk change rate, and realizing brain development feature evolution analysis. According to the method, group evolution priori and individual multi-modal data are fused, brain development can be dynamically and accurately evaluated, and the problems of static detection, group individual disjunction, insufficient multi-modal fusion and the like are solved.

The invention discloses a health management early warning system based on big data and physical condition fusion, and relates to the technical field of health management and data processing, the early warning system comprises a multi-modal data acquisition module, a pathology and rhythm two-dimensional reference module, a time sequence causal analysis module, a dynamic comprehensive early warning module and a response execution module; all the modules cooperatively construct a dynamic reference system, individual pathological attributes and physiological rhythm characteristics are fused to form a three-dimensional reference structure, deviation quantification and variation trend analysis are performed on health data through the reference system, and an evaluation result is output. According to the health management and early warning system based on big data and physical condition fusion, three types of personal data including the physiological state, the health file and the behavior characteristics are synchronously obtained, and an individual health basic portrait is constructed; and then three types of global data of regional health dynamics, environmental health association and medical resource adaptation are accessed, external influence dimensions are supplemented, the comprehensiveness of data acquisition is ensured, and meanwhile, the accuracy of a subsequent structure is improved.

The invention discloses a multi-dimensional night urination behavior monitoring system based on rhythm features, and relates to the technical field of biomedical signal processing, and the system specifically comprises a data acquisition module, a rhythm feature extraction module, a urine volume dynamics modeling module, a clustering monitoring module and a real-time feedback module; collecting individual data in real time through IoT equipment and preprocessing the individual data; calculating a nocturnal urination frequency index, a nocturnal urination time concentration ratio, a urination interval rhythm variation coefficient and a deviation index based on the individual data, and performing rhythm feature extraction; performing quadratic polynomial least square fitting on the night accumulated urine volume, and calculating a urine volume acceleration index by using an obtained second derivative to quantify a urine volume generation trend; the method comprises the following steps: constructing and preprocessing a night urine multi-dimensional digital phenotypic vector matrix, fitting a Gaussian mixture model based on an expectation maximization algorithm of a Bayesian information criterion, automatically mapping individuals into four types of subtypes according to cluster center features, and outputting individual subtype labels and confidence coefficients; and obtaining a comprehensive risk score through normalized risk assessment.

The invention discloses a human body feature sensing method and system based on a large model and a sensing terminal, and a medium, and relates to the technical field of large model sensing. According to the scheme, the message channel state information is acquired through the multi-source sensing terminal, and the pre-processed message channel state information is sensed by using the fine-tuned large model, so that human body feature sensing can be performed by effectively utilizing the wireless network channel state information, and cross-individual generalization is realized; a traditional human body feature sensing method based on wireless network channel state information generally depends on data of a specific individual for training, so that the performance of a model on a new individual is poor; according to the method, by pre-training a large model, common features can be extracted from a large number of wireless network channel state information data of different individuals, so that good generalization performance is achieved on new individuals, and dependence on specific data of the individuals is reduced.

Systems and methods are disclosed for mitigating oversampling of data points collected by a medical device. In some aspects, a system is configured to receive, during an examination of an anatomical structure, data points of a sampled examination point cloud detected by a sensor of a medical device; determine, during the examination, at least one parameter associated with (i) the medical device and / or (ii) the received data points detected by the sensor, including a change in translational and / or rotational motion of the medical device, a distance from a data point to a nearest neighbor within the sampled examination point cloud, or a density of data points of a subset of the sampled examination point cloud corresponding to a sub-region of the anatomical structure; analyze the one or more parameters by comparing the one or more parameters to a threshold value; and record individual data points in a registered point cloud when the analyzed one or more parameters satisfy the respective threshold value.

Artificial-intelligence computer-implemented processes and machines predict whether personal data may be present in structured software based on metadata field(s) contained therein. Natural language processing preprocesses input strings corresponding to the metadata field(s) into normalized input sequence(s). Individual characters in the sequence(s) are embedded into fixed-dimension vectors of real numbers. Bidirectional LSTM(s) or other machine-learning algorithm(s) are utilized to generate forward and backward contextualization(s). Neural network output(s) are provided based on element-wise averaging or feed forwarding based on the contextualization(s) in order to predict whether one or more value fields corresponding to the metadata field(s) may contain personal data.

The utility model discloses a steering wheel angle sensor environment endurance test device comprising a pedestal, a control system box body and a computer, the top end of the pedestal is fixedly provided with a rack, the bottom end of the rack is fixedly provided with a servo motor, and the top end of the rack is movably embedded with a rotating shaft; a weather-proof belt is arranged on the outer sides of the rotating shaft and the output end of the servo motor in a surrounding mode, and a belt tensioner is movably embedded in the top end of the rack. When the detection device is used, the steering wheel angle sensor to be detected is installed on the product carrier, then the servo motor is started to drive all the rotating shafts to rotate through the weather-proof belt, so that the steering wheel angle sensor is detected, and detection data is transmitted to the interior of the computer for analysis. Meanwhile, the control system box body is used for controlling the detection process, a plurality of steering wheel angle sensors can be tested through a plurality of rotating shafts and a product carrier during use, and differences of individual data can be compared through a computer.

The system according to the embodiment aims to provide criteria for identifying high performers during interviews. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a determination unit. The collection unit collects personal data from the first few years of employment. The collection unit collects data on interviewers. The analysis unit analyzes the data collected by the collection unit and extracts characteristics of high performers. The generation unit generates questions or evaluation criteria to be used during interviews based on the characteristics extracted by the analysis unit. The determination unit analyzes the candidate's answers in real time based on the questions or evaluation criteria generated by the generation unit and determines whether the candidate is a high performer.

The application discloses an end-to-end communication record synchronization method and system, relates to the technical field of communication, and saves user personal communication record data in a private device, satisfies the convenient and safe use of the user in multiple terminals, and thus guarantees that the personal communication record data can be recovered, synchronized, conveniently saved and found, and accessed and used by the user at any time and in any place. The data of the user communication record does not need to be saved in a platform, is completely stored in the private device, and finally realizes the functions of the synchronization, recovery and storage of the user communication record data in multiple terminals, avoids data leakage to a larger extent compared with previous methods, guarantees the safety of the data, and realizes the convenient personal data management.

Systems, computer program products, and methods are described herein managing data retrieval flow rates using artificial intelligence engines. The present disclosure is configured to assign priority of individual data pipelines within an overseer data pipeline via a data pipeline controller, wherein the overseer data pipeline comprises a set of data pipelines, wherein the data pipeline controller utilizes an artificial intelligence engine; regulate a data flow rate for individual data pipelines within the overseer data pipeline via the data pipeline controller; compress data within the set of data pipelines within the overseer data pipeline; encrypt data within the set of data pipelines within the overseer data pipeline; pause individual data pipelines within the set of data pipelines upon receiving a signal; filter data within the overseer data pipeline; and validate data within the overseer data pipeline.

To support the recording of accident data, instead of the isolated recording of data by the accident participants, a data exchange takes place between two mobile communication devices (110, 210), which in particular allows a user to release individual data, thus improving the data situation later.

Described herein are systems and methods that related to techniques for managing individual data (such as patient data). In examples, systems can be configured to obtain data associated with individual profiles; obtain data associated with individual samples indexed in accordance with a period of time; and for each individual, link one or more entries of the individual profile corresponding to the individual with individual samples corresponding to the individual based on the period of time and the profile for each individual. In some examples, the system can generate workflow profiles corresponding to each individual of the plurality of individuals based on linking the one or more entries of the individual profile with individual samples. The system can then de-identify the workflow profiles corresponding to each individual of the plurality of individuals to generate limited workflow profiles that are used to test one or more models in a development environment.

A personal data protection method of the present disclosure includes sending, by a user process configured to communicate with a secure process via a remote procedure call (RPC), a first RPC request related to a first operation and a second RPC request related to a second operation to the secure process. The first operation is an operation of reading personal data or an operation of transforming the personal data. The second operation is an operation of applying differential privacy to statistical information based on the personal data. The personal data protection method further includes, by the secure process, executing the first operation based on the first RPC request, returning, to the user process, a handler corresponding to data obtained through the first operation, executing the second operation based on the second RPC request, and returning, to the user process, the statistical information to which the differential privacy has been applied.

The invention provides a big data simulation method, simulation platform and device, and relates to the technical field of simulation computation.The big data simulation method is characterized by comprising the following steps that 1, a reusable operator component library is constructed; step 2, constructing a simulation task process through the graphical interface, receiving operation of a user on the operator component through the graphical interface, and constructing the simulation task process; 3, realizing individualized data association of a multi-data-source product based on the product ID, editing a simulation task process, and realizing association loading and unified output of multi-data-source data; 4, performing parallel task scheduling and distributed calculation execution; and 5, aggregating calculation results and carrying out post-processing. The method has the advantages that the technical threshold and the use cost can be greatly reduced, precise batch modeling simulation of large-batch individuals of products oriented to multiple data sources is realized, meanwhile, efficient cloud parallel computing capability can be provided, and the data simulation modeling and computing efficiency is improved.

This invention discloses a method for calculating stress age based on saliva extraction, belonging to the field of data processing technology. It includes collecting saliva samples and personal data from individuals, including individual characteristics, physiological data, and behavioral data; obtaining telomere detection data from the saliva samples; analyzing the telomere detection data, physiological data, and behavioral data to obtain telomere-related data; and generating a test report based on the telomere-related data. This method achieves the scientific quantification of emotional and psychological stress, and compared to traditional scales, the test results are more scientific and accurate.

A method for tagging and organizing data is provided. In one example, physiological data detected from a wearer of a wearable device is received and associated with a tag based, at least in art, on an input by the wearer. The input may be a state of the wearer, such as physical or mental state, or a rule. The collected physiological data may be organized based on the tag and, in some examples, on other types of received data, such as a wearer's personal data. In other example methods, data may be stored in a database based on one or more tags associated with the data.

The invention provides a client grading processing method and system fusing multi-source data. The method comprises the following steps: in response to a received voice service request of a client, obtaining audio data and text data in the voice service request; based on the audio data and the text data, identifying the emotion and intention of the customer in the voice service request to obtain an emotion type and a target demand of the customer; obtaining personal data, historical power consumption data, payment data and historical complaint feedback data of a customer; according to the personal data, the historical power consumption data, the payment data and the historical complaint feedback data, performing grading processing on the clients by adopting a preset client grading model to obtain target grades of the clients; and processing the voice service request according to the emotion type, the target demand and the target level. The method is used for performing customer service in a targeted manner, so as to achieve the effects of improving the service efficiency and further improving the customer satisfaction.

The invention discloses an alteration information extraction method based on data fusion, which relates to the technical field of remote sensing image processing and mineral exploration, and comprises the following steps: generating a pixel-level fusion weight map by using an attention network, dynamically adjusting the contribution degree of optical and radar data in combination with auxiliary data such as vegetation indexes, terrains and sensor geometry, and extracting the alteration information. The limitation of traditional fixed weight fusion is effectively overcome; in a vegetation dense area, the method can preferentially allocate high weight to radar data, and the shielding problem of optical data is compensated by using the penetrability of the radar data, so that false detection of a shadow area is reduced; meanwhile, in an exposed or low vegetation area, the advantage of high resolution of optical data is emphasized, and the extraction precision of alteration signals is improved; the adaptive mechanism ensures the environmental sensitivity of the fusion process and significantly reduces the background noise interference. And secondly, the reasonability and the stability of weight distribution are ensured by introducing normalization constraint, fusion deviation caused by abnormity of individual data sources is avoided, and the reliability of a result is enhanced.

The invention discloses a human resource management method and system based on artificial intelligence, and relates to the technical field of human resource management, and the method comprises the following steps: guaranteeing personal data sovereignty through an employee side personal data insurance library and homomorphic encryption; the method comprises the following steps: constructing double agents which belong to employees and organizations and represent respective interests, and generating and signing an automatically executed initial bidirectional contract through anti-fact simulation and game theory negotiation; during the duration of the contract, the employee intelligent agent monitors the multivariate contribution in real time, and adjusts the contract terms according to a dynamic formula; dynamic value measurement and distribution of multi-element contribution are realized by combining causal reasoning interpretable analysis and a Shapley value algorithm through a federated learning cooperative training model integrating differential privacy and homomorphic encryption; by constructing a full-link privacy architecture, a bidirectional agent negotiation system and alliance chain cross-organization ecology, privacy and data utilization and organization and employee benefit balance are realized, talent ecology is constructed, and management intelligence and fairness are improved.

A data anonymization pipeline system for managing holding and pooling data is disclosed. The data anonymization pipeline system transforms personal data at a source and then stores the transformed data in a safe environment. Furthermore, a re-identification risk assessment is performed before providing access to a user to fetch the de-identified data for secondary purposes.

The invention discloses a live pig feeding fine management device and system, and relates to the technical field of live pig breeding, the live pig feeding fine management device comprises a pig trough and a readable electronic ear tag, the pig trough is provided with a channel, and a first electronic weighing device is arranged below the channel; according to the invention, through readable electronic ear tag identification, automatic feeding control and double-scale synchronous metering, the identity, accurate feed intake and weight data of pigs are automatically collected when the pigs eat each time, a continuous individual data link is formed, and on the basis, real-time monitoring of the feed intake interval, automatic early warning of abnormal feed deprivation and early detection of diseases can be realized; according to long-term accurate feed conversion ratio and daily weight gain data, a quantitative basis is provided for scientific breeding of high-yield breeding pigs, the non-pregnant period is intervened and effectively shortened in time by continuously analyzing the weight change trend of breeding sows and intelligently assisting early pregnancy diagnosis, and finally, the system changes traditional empirical group management into traditional group management. And the system is upgraded to a data-driven and individualized intelligent management closed loop.