31 results about "Scoring Engine" patented technology

The present invention relates to a system and method for deterministically generating reproducible evaluative scores for a subject of analysis (e.g., a security). The system comprises a processor and memory storing instructions to: receive verified data describing the subject; store this data in a fixed and version-controlled corpus to define a static analytical context; execute a large-language model (LLM) under a structured prompt framework that directs a controlled scratch-pad reasoning process for preliminary interpretations and evidence extraction; perform a multi-pass deterministic analysis of the fixed corpus to produce structured, synthesized statements as reproducible evidentiary outputs; and finally, apply a rubric-based scoring engine that converts these statements into calibrated alignment scores and aggregates them to generate a composite deterministic score. This architecture ensures reproducibility, transparency, and auditability by anchoring the flexible analysis of the LLM and the final scoring logic to a known, unchanging evidence corpus.

The invention discloses a circular region quantum communication channel redundancy configuration system based on AI-block chain collaboration. The system comprises a quantum transmission layer, an AI optimization layer and a block chain layer. The quantum transmission layer is based on a circular area sensor network topology, and comprises a central node, a quantum relay ring and a sensor node; the quantum transmission layer adopts ring topology, a center node is located at the center of a circle, and relay nodes are uniformly distributed according to polar coordinates; bidirectional quantum communication is realized between the sensor node and the central node through a quantum relay ring; the AI optimization layer comprises a channel state sensing module, a quantum graph neural network module, a QoS analysis module, a service priority mapping module, a dynamic scoring engine and a resource arbitration module; the block chain layer constructs a dynamic trust center to form an evaluation-evidence storage-control closed loop. According to the invention, the problems of limited transmission distance, high equipment cost, low key distribution efficiency and low resource utilization rate of quantum communication in a circular area scene are solved.

The invention relates to a medical quality control dynamic scoring system, device and equipment based on multi-modal perception and federal evolution, and the method comprises the steps: obtaining original scene information through multi-modal input based on a medical equipment recognition model, carrying out the scene analysis and semantic fusion, generating structured medical scene data, and carrying out the dynamic scoring of the medical quality control according to the structured medical scene data. Multi-source evidence verification is carried out through a medical scoring rule base, a quality evaluation result is generated through weight dynamic adjustment, difference analysis is carried out through comparison with a manual correction result according to the quality evaluation result, and model parameters and scoring rule updating are generated. Multi-modal input is fused based on a medical special perception layer, anti-interference scene analysis and equipment semantic binding are achieved, multi-source evidences are verified in real time through a dynamic scoring engine, deduction weights are dynamically adjusted, subjective item scoring floating is compressed, an identification model is updated through federation, and a rule base is thermally loaded in real time, so that the anti-interference scene analysis and equipment semantic binding is achieved. And automatic closed-loop processing of problem items is realized.

A systems and methods for determining a risk score for lookalike domains are disclosed. A plurality of candidate domains are generated from a seed domain using a genetic algorithm that applies deception techniques. Registered candidate domains are analyzed by multiple independent scoring engines that produce an internal deception score, a reputation score, a visual similarity score based on rendered webpage analysis, and a favicon similarity score. The individual scores are dynamically weighted and combined using conditional logic to calculate a final risk score. The weighting adapts based on signal strength, including prioritizing visual cloning indicators or malicious infrastructure signals. Unregistered domains are assigned reduced priority. When the final risk score exceeds a predefined threshold, an alert is generated. The disclosed approach integrates lexical, infrastructure, and content-based signals to reduce false positives and improve detection of sophisticated phishing domains.

The invention discloses an intelligent question answering and examination integration method, system and device based on retrieval enhancement generation. According to the method, two major functions of question answering and examination are deeply fused under the same technical framework, and accurate knowledge question answering and automatic examination evaluation are synchronously realized through a shared vectorization knowledge base by utilizing a large language model and a retrieval enhancement technology. According to the intelligent examination process, a question type planning algorithm, knowledge point diversity control, a question type professional scoring engine and a streaming output interaction mechanism are creatively integrated, examination paper with balanced questions and wide knowledge point coverage can be automatically generated according to preset distribution, and high-accuracy automatic scoring is achieved for complex question types such as multi-choice questions and simple answers. According to the method, the problems of information lag and answer non-traceability of a traditional large model and one-sided question setting, rough score, low efficiency and the like of an existing examination system are effectively solved, and the scientificity, efficiency and user experience of evaluation are remarkably improved.

The invention relates to the technical field of business evaluation and the field of smart medical and financial science and technology, and provides a business compliance risk evaluation method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining business compliance risk evaluation data from a business operation system based on an evaluation matrix; performing multi-dimensional scoring processing on the business compliance risk evaluation data based on an automatic scoring engine to obtain a compliance risk evaluation result; under the condition that the compliance risk assessment score is lower than a score threshold value, the compliance risk assessment item information is analyzed and processed based on a risk analysis module, and compliance problem root information is obtained; generating compliance risk optimization suggestion information according to the compliance problem root information and the historical compliance problem case database; and adjusting the business operation system according to the rectification step information and the supervision basis information. According to the technical scheme, the compliance of the business system can be evaluated and processed intelligently.

Some examples described herein related to automated functional-safety evaluation and deployment of a software package. As one example, a system can execute an automated scoring engine configured to analyze data associated with different phases of a software development process for a software package. Based on the data, the automated scoring engine can generate a score indicating compliance of the software package with a functional safety standard issued by a standard-setting organization. The system can then automatically control deployment of the software package to an entity based on the score.

The present invention relates to a method and system for automatically sanctioning users in an online community based on risk scores derived from user-generated content and behavior data. The system includes a machine learning risk score engine that processes segmented sessions of user activity, assigning risk scores according to predefined violation categories.These risk scores are aggregated and compared to configurable thresholds specific to each violation category. If the aggregated risk score exceeds the threshold, the system automatically triggers a sanction, such as a warning, muting, temporary suspension, or permanent ban. The system continuously monitors user activity, recalculating risk scores with new data, ensuring ongoing evaluation and appropriate sanctioning. The invention offers flexibility through customizable thresholds and violation categories, enhancing the effectiveness and precision of automated moderation in online communities.

A machine learning-driven system for pattern deviation detection and autonomous correction of data integrity errors in digital networks, comprising: (a) a data acquisition interface configured to receive digital data elements from a variety of network-connected sources and to associate the digital data elements with source metadata; (b) a preprocessing and feature extraction engine configured to transform the digital data elements into feature representations that include at least structural features, temporal features, protocol compliance features, and origin features; (c) a pattern modeling engine configured to maintain a reference behavior representation derived from baseline-consistent feature representations;(d) an anomaly scoring engine configured to calculate a composite anomaly score for an incoming feature representation based on at least one reconstruction deviation and one consistency deviation relative to the reference behavior representation; (e) a fault classification engine configured to assign an integrity fault category and an affected area, which may include a field, record, packet, block, or stream segment; (f) a corrective orchestration engine configured to generate and evaluate a variety of corrective candidates and select a corrective action according to a confidence score derived from the agreement of the fault category, the origin state, the source trustworthiness, and the expected validation success;(g) a validation engine configured to check a corrected data element against integrity constraints; (h) a rollback control configured to restore a saved previous state if the check fails; and (i) an audit log generator configured to record artifacts relating to anomalies, corrections, validations, and rollbacks.

The application provides a kind of enterprise tax data visualization method and system based on blockchain, belong to the technical field of enterprise financial monitoring based on blockchain, including: graph construction and data preprocessing unit, for obtaining maximum semantic matching score and rule index;Risk quantitative evaluation unit, for by risk scoring engine, rule conflict factor, historical mode anomaly factor and financial impact factor are aggregated into compliance risk degree;Chain before decision control unit, for obtaining approved chain signal, marked chain signal or rejected chain signal, and generating corresponding state mark;Risk perception visualization unit, for receiving compliance risk degree and state mark, dynamically rendering the transaction data that has been chained.The application ensures the compliance of chained data, solves the problem of permanent solidification of garbage data.

The invention relates to the technical field of machining and intelligent manufacturing, and discloses a heat treatment center hole quality detection method and system for a radial drilling machine machining shaft, and the method comprises the steps: S1, obtaining the processing data of the shaft on a radial drilling machine, and carrying out the cleaning; s2, extracting multi-dimensional features of the cleaned data to generate a quality feature vector; s3, inputting the feature vectors into a quality scoring model to output a comprehensive score and a risk coefficient, and dynamically calculating the scoring model by adopting a decision tree algorithm in combination with a weight factor; and S4, executing response operation by the quality control engine according to the scoring risk mapping rule. The system comprises a data acquisition module, a feature extraction module, a scoring engine, a quality control engine, a context sensing module and a response execution module. According to the method, the quality risk of subsequent processing is reduced, the defect identification accuracy is improved, and the production efficiency and the product percent of pass are improved.

Aspects related to an amalgamation platform providing concealed detection of code-passing using steganography are provided. An amalgamation platform may train a scoring engine to generate suspicion scores for code snippets and an amalgamation engine to generate execution scenarios for the code snippets. The platform may embed code in base code of a network using steganography. The platform may use the steganographic code to detect transmission of a code snippet. The platform may generate a suspicion score for the code snippet. The platform may update the scoring engine based on identifying the suspicion score satisfies a threshold. The platform may generate execution scenarios for the code snippet. The platform may determine a match between an execution scenario and a malicious code scenario. The platform may identify the code snippet as malicious and update the amalgamation engine based on the match. The platform may initiate security actions based on the match.

The invention relates to the technical field of network security, and discloses an SSL / TLS digital certificate health degree intelligent scoring system based on a multi-dimensional weighting model, and the system comprises a scoring engine layer which is used for calculating a health degree score based on the data of each dimension and the weight of the data, and judging a health degree grade; and the intelligent analysis layer is used for diagnosing safety problems and generating improvement suggestions. According to the SSL / TLS certificate security state evaluation method, a weighting model covering five dimensions of encryption strength, validity period, configuration, deployment and compliance is constructed, so that comprehensive and quantitative evaluation of the SSL / TLS certificate security state is realized; a 0-100 score credit score mechanism and a four-level grading system are adopted, so that the security situation is clear at a glance; through a grading alarm mechanism based on comprehensive health degree changes and key indexes, risks can be actively found; and the root cause of the safety problem can be automatically diagnosed, and specific and operable improvement suggestions with priorities are provided, so that the operation and maintenance difficulty and the dependence on professionals are remarkably reduced.

A method and system for location optimization for running application code include a system resource utilization engine collecting mainframe monitoring data. A workload operational key performance indicator (KPI) engine collects the mainframe system monitoring data, application code identifying information, and a workload definition. The system resource utilization engine and the workload operational KPI engine generate a computing workload model for the workload definition. A performance analyzer performs a first analysis of the computing workload model. An overhead analyzer performs a second analysis of the computing workload model. A target venue score engine generates a target venue score for each of one or more target venues using the first analysis and the second analysis.

An artificial-intelligence-based decision-assisting system and method are disclosed for generating ranked recommendations through adaptive multi-source analysis. The system includes a processor and a non-transitory computer-readable storage medium storing executable instructions that implement a scoring engine and a ranking engine. Decision parameters and user-defined weighting factors are received from user devices and combined with system-defined weighting factors retrieved from behavioral, historical, external-context, and scoring-criteria databases. Composite decision scores are computed and used to rank candidate options. The system iteratively updates weighting factors or rankings based on feedback data and dynamically restricts data retrieval to relevant parameters to reduce latency and improve throughput. Results are displayed via a graphical user interface, and anonymization procedures protect user identity. The method supports concurrent processing and adaptive learning to refine decision predictions.

The invention belongs to the technical field of computers, particularly relates to a software vulnerability availability evaluation system fusing multiple features, and aims to solve the problems of single evaluation dimension, high false alarm rate, lack of dynamic context perception and the like. The system comprises a vulnerability feature extraction module, an environment context perception module, an attack path modeling module, an availability scoring engine and a dynamic feedback optimization module, constructs an attack path model of environment perception by fusing static code features and a dynamic operation environment, and optimizes an attack path based on multi-dimensional quantitative scoring and online weight optimization. High-precision and self-adaptive vulnerability availability evaluation is realized, the false report and missing report rate is remarkably reduced, and the safety operation and maintenance efficiency is improved.

The application discloses a tumor multidimensional evaluation and report generation system and method based on a unified workflow, relates to the technical field of clinical auxiliary decision and medical workflow optimization, and comprises a unified workflow entrance module, a tumor type identification module, a special evaluation engine scheduling module, a special evaluation engine library module, an evaluation result collection module, an intelligent report generation module, a decision support module and a workflow state visualization module, wherein the modules are connected in series. The unified workflow entrance module provides unified data management; the special evaluation engine library module is internally provided with multiple scoring engines for different tumor types and different diagnosis and treatment scenes; and the intelligent report generation module automatically generates a complete evaluation report. The application can effectively solve the problem of fragmentation of the tumor multidimensional evaluation workflow, effectively realize report integration, and enhance the clinical decision support capability.

The application discloses a kind of self-adapting risk protection method and system of smart home equipment control, and relates to the technical field of smart home security.The method comprises the following steps: receiving the device control request of control end and extracting context information;Determine the risk analysis module to be called through decision scheduling module;Each risk analysis module carries out risk scoring;Comprehensive scoring engine carries out weighted aggregation and risk amplification to each risk score, and gives comprehensive risk score;According to the comprehensive risk score, the safety policy is executed on the device control request, including release, secondary authentication or interception operation;Finally, the device control request released by terminal is executed.The application realizes multi-dimensional dynamic risk assessment, and improves the security of smart home equipment control.

An artificial intelligence-based system for safe and fair online reviews. The system includes: an investigation processing device comprising at least one high-resolution camera, a directional microphone array, a secure processor and a non-volatile storage medium; a facial recognition module running on the secure processor and configured to continuously authenticate a test subject by comparing live facial features with pre-stored biometric templates; an eye-tracking module that determines the angular displacement of the subject's head and eyes in relation to the display screen and generates deviation signals when thresholds are exceeded; a control module for object recognition that is configured to analyze video images captured by the camera in order to identify unauthorized devices, printed materials, or other people in the field of view; an audio anomaly detection module that processes input signals from the microphone array and detects unauthorized speech activity, secondary human voices, or irregular background noise patterns; a probability-based misbehavior detection engine that calculates a probability score for misbehavior by assigning weighted coefficients to the outputs of the face detection module, the eye tracking module, the object detection control module, and the audio anomaly detection module; and an automated scoring engine configured to evaluate both objective and subjective responses from candidates, with subjective responses being processed using semantic and syntactic natural language processing models to generate a score, and with all results and alerts being transmitted to a remote examiner dashboard via an encrypted communication channel.

A system and method for providing real-time narrative analysis and scoring within a structured screenwriting environment, integrates directly with a script editor to continuously evaluate screenplay content as it is written, using a modular AI scoring engine based on predefined rubrics that assess multiple narrative and commercial dimensions, including pacing, genre alignment, demographic appeal, artistic merit, cultural volatility, ideological alignment, and formatting compliance.

The invention relates to an air-space integrated network-oriented unmanned aerial vehicle cluster head dynamic pushing method and system, and the method comprises the steps: obtaining the link stability, resource margin and topology centrality of all unmanned aerial vehicle nodes in an unmanned aerial vehicle group through a satellite-borne scoring engine, and enabling the satellite-borne scoring engine to be disposed on a stationary orbit satellite; based on the link stability, the resource margin and the topology centrality, performing cluster first title scoring on all unmanned aerial vehicle nodes through a multi-dimensional dynamic scoring model to obtain a cluster first title score; and selecting the cluster head node of the unmanned aerial vehicle group based on the cluster head title score. The method can solve the problems that the existing unmanned aerial vehicle cluster head election cannot adapt to a high-speed moving scene, the election communication overhead is large, and the survivability is poor.

A computer-implemented system for automated Name, Image, and Likeness (NIL) content creation, compliance verification, and sponsorship analytics is disclosed. The system includes an automated compliance and interpretation and visual system) module configured to generate personalized NIL digital assets, and a Retrieval-Augmented Generation (RAG) module configured to retrieve and apply NIL regulatory rules to verify compliance of the generated assets., while a Firestore data architecture stores athlete, sponsor, policy, and compliance records. A compliance scoring engine calculates normalized compliance risk scores, and an explainable AI visualization layer produces provenance-linked visual and textual outputs. A predictive scoring module forecasts NIL valuations and opportunity outcomes, and an AI security governance layer encrypts, audits, and monitors all data and models. Together, these components enable automated, transparent, and verifiable NIL content management within a unified cloud-based platform.

The invention relates to a learning situation large model construction method based on a double-engine collaborative framework and an academic early warning system, and the method comprises the steps: S1, carrying out the preprocessing and dynamic triple filling of multi-source heterogeneous data, and constructing a high-fidelity student learning behavior holographic data cube; s2, constructing a scoring engine based on causal enhancement; s3, constructing a report generation engine of knowledge distillation and DPO, and generating a structured diagnosis report with temperature; and S4, closed-loop feedback and adaptive evolution are carried out. Through a full-chain technical framework of multi-source heterogeneous data construction, a causal scoring engine, a semantic report engine and closed-loop feedback, the method has the following advantages of high-fidelity data reduction capability, accurate early risk capture capability, semantic alignment and interpretability conforming to educational ethics, and a vitality self-evolution mechanism.

The invention relates to the technical field of intelligent decision-making, and discloses an enterprise multi-agent collaborative decision-making scoring system based on a relay chain technology. The system comprises an agent behavior acquisition module, a relay chain network construction module, a collaborative decision feature fusion module, a machine learning scoring engine module and a dynamic weight distribution module. The acquisition module acquires dynamic behavior data of the intelligent agent in real time and extracts a decision feature vector; the network construction module is used for constructing a distributed relay chain network according to organization levels and deploying relay nodes; the fusion module realizes cross-level feature transmission fusion through relay nodes, and generates a global decision feature matrix; the scoring engine is used for training a model based on historical data and inputting a matrix to output collaborative decision scores; and the weight distribution module calculates the decision weight of each agent according to the score and updates the decision weight to the relay node. According to the system, through combination of a relay chain and machine learning, collaborative decision-making full-process optimization is realized, and dynamic decision-making requirements of enterprises are met.

Systems and methods for capturing and analyzing sports swing data use a single monocular camera and artificial intelligence. The swing capture system records video of athletic swings, detects key body points using pose estimation, and normalizes the data across varying camera angles and conditions. The swing analysis system classifies the swing type and context, selects a pre-trained analysis model, and evaluates biomechanical performance using joint trajectories and ball motion parameters. Scoring engines generate structured metrics and fault probabilities, which are presented to users via graphical overlays and dashboards.

Techniques for data security transactions using software container machine readable configuration data are described, including receiving at a platform in response to a query, an artifact associated with a software container, parsing the artifact to identify source code of the software container and a supply chain, invoking a scoring engine configured to generate a score associated with a component of the software container and an aggregate score of the software container, the aggregate score being based on a security tool being included with the software container, and generating a report from the platform, the report being associated with the software container and the report including an attestation comprising the score and the aggregate score.

A computer-implemented system for evaluating small business acquisitions comprises a core scoring engine employs a hierarchical weighted scoring algorithm with dynamic factor adjustment and an adaptive correlation matrix. The system has a data processing pipeline for real-time ingestion, validation, and normalisation of data from multiple sources. A machine learning module automates feature extraction, implements continuous learning with drift detection, and coordinates an ensemble of specialised models. A risk assessment engine evaluates operational, financial, and market risks through correlation mapping and stress scenario simulations. An output module generates viability scores, sub-scores, reports and data visualisations. Also disclosed is a data processing framework for business analysis, a machine learning feature engineering system and a risk correlation matrix.

Aspects related to an amalgamation platform providing concealed detection of code-passing using steganography are provided. An amalgamation platform may train a scoring engine to generate suspicion scores for code snippets and an amalgamation engine to generate execution scenarios for the code snippets. The platform may embed code in base code of a network using steganography. The platform may use the steganographic code to detect transmission of a code snippet. The platform may generate a suspicion score for the code snippet. The platform may update the scoring engine based on identifying the suspicion score satisfies a threshold. The platform may generate execution scenarios for the code snippet. The platform may determine a match between an execution scenario and a malicious code scenario. The platform may identify the code snippet as malicious and update the amalgamation engine based on the match. The platform may initiate security actions based on the match.

Disclosed herein are systems and methods for automated grading of written response tests. The method includes obtaining course material, test materials for a plurality of courses offered by an academic institution, a standardized scoring rubric for a plurality of tests and different courses, and grading guidelines comprising test scoring rules for the written response test or course. The method further includes analyzing the course materials and test materials using a trained rubric customizer LLM. The method also includes obtaining a written response from a learner for a test in a course taken by the learner. The method further includes analyzing the written response using two or more trained test grading LLM agents executing in parallel with each other. The method further includes combining, by a scoring engine, a plurality of criteria scores from the one or more trained test grading LLM agents. The method further includes generating a grading report.

Embodiments of the invention relate to systems, methods, and computer program products for optimized retention of container images in a cloud server, the invention including: identifying a plurality of container images, where the plurality of container images is stored in the cloud server; generating a non-fungible token (NFT) associated with each container image; using an NFT scoring engine to calculate a score associated with each container image of the plurality of container images; detecting a system failure associated with the cloud server; selecting a first container image of the plurality of container images; and moving the first container image from the cloud server to a local memory device.