104 results about "Risk profile" patented technology

A computerized system for dynamic cybersecurity policy using AI-based contextual adaptive learning includes an AI system that evaluates business contexts, risk tolerance, and productivity impact to generate threat intelligence assessments. The system includes a Contextual Adaptive Learning module that dynamically adjusts cybersecurity policies based on threat assessments to create security workflows. A Cybersecurity Mesh Development module that integrates policies across security frameworks. A Dynamic Scenario Catalog module that updates policy adjustments based on threat intelligence. An Automated Workflow Orchestration module that creates and refines security workflows for optimal efficiency. A Policy Recommendation and Automation module that generates prioritized security recommendations and automates policy changes based on organizational risk profiles and current security controls. This system harmonizes security policies while considering business context, risk, and productivity impacts.

Systems, devices and methods for optimising and managing distributed energy storage and flexibility resources on a localised and group aggregation basis, particularly around the determination, analysis and predictive learning of local data patterns, scoring availability for flexibility and risk profiles, to inform the optimisation of energy supply and behind the meter storage resources and local clusters of co-located or close resources within a community, low voltage network, feeder, neighbourhood or building. Said optimisation to involve scheduled, reactive and active management of data sources and local clusters of resources, for a range of goals such as price, energy supply, renewable leverage, asset value, constraint or risk management. Or where said optimisation achieves a local objective such as providing resources to off-set, aid local balancing or constraint management of larger local supplies and loads, or to aid active management of local energy demands and renewable supplies, storage resources, electric heat resources, electric vehicle charging resources or clusters of electric vehicle chargers, flexible loads in buildings.

The invention discloses an engineering building construction quality analysis system and method based on data processing, and belongs to the technical field of commercial data processing and intelligent decision, and the system comprises a causal relationship graph construction module which is used for fusing multi-source monitoring data to construct a causal relationship graph; the risk profile construction module is used for performing reverse deduction and forward simulation and constructing a prospective quality risk profile; the multi-target collaborative optimization module is used for solving a Pareto optimal equilibrium solution set; the quality pre-control instruction generation module is used for instantiating a dynamic quality pre-control instruction; the efficiency evaluation and attribution correction module is used for updating the causal relationship graph; and the evolution auditing and causal knowledge distillation module is used for auditing historical updating of the atlas and distilling a core causal rule set. According to the method, risk deduction and simulation are carried out by constructing the dynamic causal relationship graph, and multi-objective optimization and closed-loop feedback correction are combined, so that prospective identification and collaborative intelligent pre-control of the project quality risk can be realized.

A system and method may collect telematics and / or other data, and apply the data to insurance-based applications. From the data, an insurance provider may determine accurate vehicle usage information, including information regarding who is using a vehicle and under what conditions. An insurance provider may likewise determine risk levels or a risk profile for an insured driver (or other drivers), which may be used to adjust automobile or other insurance policies. The insurance provider may also use the data collected to adjust behavior based insurance using incentives, recommendations, or other means. For customers that option to the data collection program offered, the present embodiments present the opportunity to demonstrate a low or moderate risk lifestyle and the chance for insurance-related savings based upon that low or moderate risk.

A fraud detection system preemptively identifies and mitigates fraudulent financial transactions in real-time across a diverse range of digital and physical transaction sources. Transaction data may be acquired from various platforms and categorized into rule sets specific to transaction types. Categorized data is aligned with existing risk profiles to construct a dynamic hysteresis model. This model, evaluated by a decision tree algorithm, identifies potential fraud by integrating immediate transaction details with a comprehensive historical data analysis, thus enabling advanced trend analysis and pattern recognition. Key features identified by the decision tree are used to form a heuristics model, which is then analyzed by a transformer network risk model. A feedback loop enhances the system's effectiveness by incorporating decision outcomes back into the model training server, thus refining the training dataset and continuously improving the accuracy of the risk model.

The application belongs to the technical field of bidding risk assessment, and specifically provides a bidding risk assessment method and system based on big data, which mainly comprises the following steps: collecting basic identification data of bidders, historical bidding record data, real-time behavior log data and external environment index data, and generating an integrated initial data set through multi-source data integration processing; performing data cleaning and standardization processing on the integrated initial data set to obtain purified standard data; performing risk feature extraction on the purified standard data to form feature risk indexes; and performing risk linkage analysis by using the feature risk indexes to construct a comprehensive risk profile. The application can realize efficient integration and in-depth analysis of multi-dimensional data of bidders, accurately identify risk abnormalities, highlight key risk signals, reduce the probability of decision-making misjudgment, and provide intuitive and visual risk assessment results to assist decision-making.

The invention discloses a special gas holder safety control method and system based on multi-parameter monitoring, and the method comprises the steps: collecting the gas concentration, environmental parameters, equipment state and other multi-dimensional data inside and outside a special gas holder through a distributed sensor network, carrying out the adaptive filtering and time sequence feature extraction, and constructing a dynamic risk portrait in combination with a process situation. An LSTM-VAE hybrid model, a gas reaction knowledge graph, an improved D-S evidence theory and a pipeline topology propagation model are utilized to calculate an abnormal score, a gas interaction risk score and a space risk score respectively, a comprehensive risk value is generated through dynamic weighted fusion, risk grades are divided, and a hierarchical control strategy is triggered. When the risk is abnormal, leakage source positioning and gas mixing traceability are automatically started, and a diagnosis report is generated. The system realizes closed-loop management of monitoring, early warning, response and traceability, integrates a data driving method and a knowledge driving method, and improves the risk identification accuracy and the disposal intelligent level.

According to an embodiment, disclosed is a system comprising a processor configured to define, one or more risk categories for monitoring a risk associated with a clinical trial, wherein the risk categories comprise one or more risk elements; calculate, a first risk profile data of the risk categories based on a risk factor and a weighting assigned to the risk elements; generate, a machine learning (ML) model; train, the ML model; receive, a second risk profile data; analyse, the second risk profile data to identify a pattern based on the first risk profile data using a database; predict, an overall risk score; recommend, one or more of a type of monitoring, a level of monitoring, and the overall risk score; and wherein the ML model comprises a feed-back layer to enable continuous learning and improve the prediction of the overall risk score and monitoring decisions of the clinical trial.

The present invention discloses a quality verification method and system for silicon carbide purification processing based on a deep model, which relates to the field of silicon carbide inspection. The quality verification method for silicon carbide purification processing based on a deep model includes the following steps: classifying monitoring data to obtain classified data; obtaining association rules related to silicon carbide product defects; based on a deep learning model, combining the obtained association rules and parameter features in the classified data to predict the defect type and probability of the silicon carbide product; quantifying the predicted defect type and probability of the silicon carbide product to obtain a quantitative score, and combining the quantitative score with a risk index to calculate an overall risk score and construct a risk profile. The present invention combines the characteristics of the association rules and the characteristics of the classified data to improve the model's prediction accuracy for the defect type and probability of the silicon carbide product, quantifies the prediction results into a risk score, and combines them with other risk indicators to provide an intuitive quality verification evaluation indicator.

A system and a method for data protection assessment using threat modelling and adaptive optimization is disclosed. The system (100) comprising a processor (105) and memory (110) with instructions to receive a data access request (345) from entities (120) via authenticated digital interface (125) including structured and unstructured data categories (350), metadata parameters (355), initiating contextual analysis to establish baseline compliance parameters (360). The system analyses data category and metadata using adaptive classification logic and correlation metrics across stored data lineage, distinguishing privacy -critical from non- sensitive data. Threat modelling (130) evaluates correlations among privacy attributes (365), metadata, operational parameters, and regulatory requirements to identify vulnerabilities (375) and exposure points. A composite risk index (135) is derived, forming a structured risk profile (140) with quantified scores (380). Compliance recommendations (150) are generated, safeguard parameters (155) are derived, authorization validated by a compliance authority (160), and a compliance trace (388) is recorded.

A method includes: receiving a first message from a first agent, the first message comprising a request to transfer a virtual asset to a recipient identifier, associated with a second agent, from a sender identifier associated with a sender blockchain address; for the first agent, accessing a set of policies and a set of transaction characteristics of transactions associated with the first agent; characterizing a compliance state and a risk profile of the first agent based on the set of policies and the set of transaction characteristics; transmitting a second message to the second agent, the second message including the compliance state of the first agent and the risk profile of the first agent; receiving confirmation from the second agent to proceed with transfer based on the first compliance state and the first risk profile; and releasing a recipient blockchain address associated with the recipient identifier to the first agent.

There is provided a method of determining a risk profile for a dwelling. The method includes: determining whether the dwelling is being monitored, the type and extent of dwelling sensors within the dwelling, and the extent to which a security alarm system of the dwelling is activated; adjusting the risk profile in response to said determining; sending the risk profile so adjusted to an insurance provider or broker; and obtaining an insurance quote based on the same. There is also provided a security alarm assembly including dwelling sensors, a security alarm system in communication with the dwelling sensors, and a processor. The processor is configured to determine the number and type of said dwelling sensors, determine the extent to which the security alarm system is activated, determine whether the security alarm assembly and / or the dwelling sensors are monitored, and calculate a risk profile based on the same.

This invention provides a multinomial label generation method and apparatus for risk profile construction, characterized by the following steps: S1-S3, dividing all features into multiple sub-feature sets, and selecting features from each sub-feature set to construct a filtered feature set based on importance value, relevance value, and feature selection quantity; S4, performing multinomial cross-feature calculation on the features in the filtered feature set to obtain a multinomial feature set; S5, calculating the importance value, relevance value, and feature selection quantity of each feature in the multinomial feature set; S6, selecting multiple features from the multinomial feature set based on importance value, relevance value, and feature selection quantity to construct a multinomial filtered feature set; and S7, calculating the WOE value of each feature corresponding to each target in the multinomial filtered feature set. In summary, this method can describe the multidimensional risk status of a target with fine granularity.

A computer-implemented method, according to one embodiment, includes mapping hosts in communication with a storage system to compartment constructs that are logical partitions of the storage system, analyzing interoperability of the hosts and the compartment constructs and defining, based on the analysis, risk profiles for applications run on the hosts. Ownership of storage objects to the compartment constructs is assigned based on the risk profiles, where each of the storage objects define a logical partition of one of the hosts and a logical partition of a storage volume of the storage system.

The present application relates to the technical field of surface defect detection, and particularly relates to a surface detection method for shaft part machining, which screens a defect suspected area according to a cross-section diameter parameter obtained in each detection area along a characteristic direction, screens a risk profile according to a distribution coefficient of a texture profile, marks a first risk sub-profile or determines a characteristic risk profile based on a texture representation value of the risk profile, determines a second risk sub-profile according to a characteristic profile point and screens the characteristic risk profile, constructs a defect trend vector according to the risk profile to determine a profile trend representation coefficient, adjusts an illumination parameter for defect detection of the characteristic risk profile, and performs defect detection on the characteristic risk profile again based on the adjusted illumination parameter. The present application realizes rapid identification of a region with a risk of shadow obstruction, adaptively adjusts a detection mode according to actual defect characteristics of shaft parts, and improves the precision and reliability of surface defect detection.

The present invention provides a label generation method and device for risk profile construction, which has the following characteristics: Steps S1-S3: dividing all features into multiple sub-feature sets, selecting features from the sub-feature sets based on importance values, correlation values, and feature screening amounts, and constructing corresponding screening feature sets; Step S4: for each sub-feature set, inputting the values ​​of all features corresponding to each target in the sub-feature set into a machine learning model, and combining the risk judgment label corresponding to the target to obtain the predicted value corresponding to each target in the sub-feature set; Step S5: for each sub-feature set, merging the features of the corresponding screening feature set and the predicted features to obtain the corresponding model feature set; Step S6: calculating the woe value of each feature corresponding to each target in the model feature set. In short, this method can generate multiple labels and corresponding label values ​​for each target to improve the accuracy of risk profiles.

A digital system development lifecycle system processes approval requests for applications. The system generates a risk profile using a questionnaire and selects a set of deliverables based on the risk profile. The set of deliverables is tuned based on regulatory or enterprise policies. The system provides a user interface via which users can view progress towards the tuned set of deliverables.

A computer system for analyzing and mitigating risks associated with an event is provided. The computer system is configured to: (i) receive at least one of a city risk profile and a building risk profile from a database; (ii) receive city systems data from the city services computer system; (iii) utilize a trained machine learning model to determine at least one potential risk associated with the event; (iv) generate an event risk profile that includes the at least one potential risk associated with the event; and / or (v) generate a risk mitigation output based upon at least one of the city risk profile and the at least one potential risk, wherein the risk mitigation output includes at least one of a risk alert, a risk mitigation recommendation, and risk mitigation instructions. Computer systems for analyzing and mitigation risks associated with a city, a building, and a user are also provided.

A system and method for a risk management visualization system having a computer comprising a processor and memory and configured to model a supply chain network as a supply chain planning problem, one or more key process indicators (KPIs) of the supply chain planning problem is based, at least in part, on the one or more input variables, model an impact on the one or more KPIs from each of the one or more input variables at a selected confidence interval using a Bayesian optimization process comprising an exploration phase and a learning phase, and display a visualization of the risk profile for the one or more KPIs, the visualization indicating a probability that an actual KPI value differs from a predicted KPI value.

The invention relates to the technical field of artificial intelligence security and cloud computing resource management, and discloses an AI reasoning service elastic scaling method and system for an edge cluster, and the method comprises the steps: constructing a dynamic risk file for each hardware computing unit in a computing cluster; distributing the reasoning service request to a target hardware calculation unit of which the risk state is matched with the security demand level; instantiating an independent logic calculation container for each allocated reasoning service request, and destroying the logic calculation container after the reasoning task is executed; and monitoring the resource configuration change of the computing cluster, and updating the risk state of the computing cluster in the dynamic risk file. According to the method, a mixed protection mechanism with logic isolation as a main part and physical reset as an auxiliary part is adopted, and hardware-level state pollution attacks triggered by antagonistic input are effectively defended in a dynamic elastic edge AI reasoning scene on the premise that the elastic scalability is not obviously sacrificed.

The application discloses an import and export enterprise risk assessment method based on machine learning, comprising the following steps: constructing an enterprise risk profile database according to historical monitoring data, and counting the total number of each matter; generating the time weight and risk weight of each type of matter; calculating the risk value of each risk matter of the overall matter of the enterprise; dividing the risk level of the enterprise according to the corresponding risk value; calculating the membership degree of the enterprise risk in each risk level; and judging whether the enterprise is in the qualified range according to the membership degree matrix and feeding back. The application realizes risk analysis of the target enterprise through historical monitoring data, an enterprise risk profile database, a time risk double attention module, an enterprise risk assessment module, a risk level division module, a first-level fuzzy comprehensive evaluation model and a feedback module, so that whether the enterprise has risks can be judged according to the risk value of the target enterprise.

The application discloses a network abnormal data security early warning evaluation processing system, comprising a risk feature acquisition module, a first profiling module, a second profiling module and a distribution correlation module; the first profiling module can evaluate the abnormal risk of traffic through a network neural model to obtain a first risk profile of encrypted traffic; the second profiling module can obtain the frequency abnormality of distribution detection features through global frequency abnormal distribution and local frequency abnormal distribution of the distribution detection features; the distribution correlation module can obtain distribution correlation features associated with the distribution detection features of the frequency abnormality, evaluate the degree of frequency abnormality in combination with the deviation degree of the distribution correlation features, and obtain a second risk profile of encrypted traffic corresponding to the distribution detection features; and the first risk profile is adjusted according to the obtained second risk profile. The application increases the monitoring accuracy of traffic risk and improves network security.

A patient management system and method for providing a risk profile and care plan to a patient and provider. The patient management system includes a risk analysis module configured to convert unstructured data to structured data and includes a data acquisition module for collecting and transmitting unstructured data and a classifier module for analyzing unstructured data and converting the unstructured data into structured data. The data acquisition module receives the unstructured data set, and the classifier module converts the unstructured data to a structured data set. A provider interface system is in communication with the classifier module and is configured to collect a plurality of structured data sets, analyze the collected structured data sets and output a risk profile / group to a provider.

Different versions of a program are executed in a first mode of operation in accordance with normal operations with only expected behavior to generate different acceptable behavior models indicative of behavior of the program. A difference between acceptable behavior models indicates different risk profiles for the different versions of the program. Risk profiles can be generated by a model explainer. An event that attempts to execute remote code or a delayed event can be detected in a second mode of operation if the event or the delayed event is not included in the acceptable behavior model. Different sequences of events in the first or second mode can be instrumented at different levels of instrumentation to generate or use the acceptable behavior model. The sequences of events can be generated from a stacktrace in the first and second modes. The instrumentation can be performed in hardware by intercepting SYSCALLs.

A system and method for a risk management visualization system having a computer comprising a processor and memory and configured to model a supply chain network as a supply chain planning problem, one or more key process indicators (KPIs) of the supply chain planning problem is based, at least in part, on the one or more input variables, model an impact on the one or more KPIs from each of the one or more input variables at a selected confidence interval using a Bayesian optimization process comprising an exploration phase and a learning phase, and display a visualization of the risk profile for the one or more KPIs, the visualization indicating a probability that an actual KPI value differs from a predicted KPI value.

A cyber threat defense system can incorporate data from an instant messaging platform with multiple other platforms in a client system to identify cyber threats across the client system. The system can have one or more instant messaging modules to collect instant messaging data from one or more network entities that utilizes one or more instant messaging platforms. A user specific profile module can identify a user of the client system associated with the user account based on a composite user profile constructed from user context data collected across multiple platforms of the client system. A risk profile module can associate the user with a user risk profile based on the composite user profile. The risk profile module can apply one or more artificial intelligence classifiers to the instant message based on the user risk profile. A cyber threat module is configured to identify whether the instant messaging data corresponds to a cyber threat partially based on the user risk profile. An autonomous response module can execute an autonomous response in response to the cyber threat factoring in the user risk profile.

The present disclosure relates to a method and system for managing health risk for members of a population. The method includes receiving a query by a user on the user interface, identifying a patient via at least one AI agent in response to a query by a user having a high-risk profile for at least one medical condition based on a corresponding Electronic Health Record (EHR) and notifying the patient having the high-risk profile. The method further includes facilitating onboarding of the patient via one or more external devices, processing the multimedia data to extract an image corresponding to Regions of Interest (ROIs) associated with the at least one medical condition of the patient from the multimedia data. Further, the method includes analyzing the image corresponding to the one or more ROIs to generate a summarized report.

To provide a system for analyzing risk profiles of users by utilizing generated AI and proposing an optimum insurance plan.SOLUTION: A means for the user to input risk profile information, a means for the server to receive and store the risk profile information in a database, a means for the server to transmit the risk profile information to a generative artificial intelligence, a means for the generative artificial intelligence to analyze the risk profile information and select an appropriate insurance plan, a means for the generative artificial intelligence to return the selected insurance plan to the server, a means for the server to present the returned insurance plan to the user, a means for the user to customize the insurance plan and finalize the final insurance plan, a means for the server to receive and store the finalized insurance plan in the database, a means for the server to transmit the finalized insurance plan to the insurance company system, and a means for the server to notify the user of the completion of the insurance application.SELECTED DRAWING: Figure 1

A system for controlling over-the-air updates for a device (i) stores a plurality of version and setting information for plurality of devices; (ii) receives version and setting information for the first device of the plurality of devices; (iii) stores the version and setting information for the first device; (iv) determines a risk profile for the first device based upon the corresponding version and setting information; and (v) determines an insurance quote based upon the risk profile for the first device.

This invention relates to the field of CNC turning technology, specifically to a method and system for planning variable depth of cut turning paths to suppress chatter in thin-walled ring turning. The method includes: constructing a variable network path model integrating four modes: constant depth of cut, linear gradual change, sinusoidal variable depth of cut, and stiffness feedback adaptive variable depth of cut; generating vibration response using a digital twin of the thin-walled ring-tool system and extracting a risk profile containing a comprehensive chatter risk index and modal risk boundary parameters; optimizing the depth of cut parameters of key nodes using a path value function and a three-layer nested evolutionary game of macro-meso-micro levels; and performing local reconstruction iteration on brittle node groups to obtain a variable depth of cut path that balances agility and robustness. This achieves precise suppression of chatter in thin-walled ring turning, significant improvement in machining stability, and high efficiency.