95 results about "Heuristic" patented technology

The present invention discloses a modular AI-based system for knowledge instantiation and evidence synthesis, unifying five modules: Knowledge Instantiation, ingesting anchor knowledge via retrieval- augmented generation and neuromorphic prompting; Case-Based Agent Generation, orchestrating domain constraints, performance criteria, and sub-task decomposition; Secondary Epistemogenesis, spawning sub-agents with inherited heuristics; Workflow Orchestration and Evidence Synthesis, merging outputs, tracking metadata, and storing final artifacts; and Reinforcement Learning from Human Refinement, capturing feedback and edits. The method for output generation initiates by defining knowledge requirements, indexing public and proprietary knowledge, validating output structure, drafting content, and refining outputs via sub-agent spawning and human oversight. The system ensures domain alignment, concurrency control, and persistent versioning. By combining specialized knowledge sources, multi-agent concurrency, and iterative human feedback loops, it dynamically adapts to evolving knowledge requirements. This invention emphasizes reliability, traceability, and AI-driven outputs in regulated industries, delivering trustworthy outcomes anchored in expert knowledge.

The invention relates to the field of automatic algorithm design, in particular to an automatic algorithm design method for solving a vehicle path planning problem by utilizing a large language model, which comprises the following steps of: S1, constructing an AutoDH framework, and defining a decision form of an intelligent agent; s2, defining heuristics in an LLM pool, an improvement pool and a disturbance pool; s3, acquiring state information of the CVRPs, and intelligently selecting a heuristic mode through two-stage MDP; s4, designing a reward mechanism fusing solution quality improvement, heuristic time cost and LLMs API calling cost; and S5, training the intelligent agent to optimize the solution of the CVRPs. The method can intelligently select the most cost-effective heuristic algorithm according to the state information of the current CVRPs, and improves the flexibility and adaptability of algorithm design.

A system, method, and device for adaptively limiting web requests is disclosed. The method includes (i) receiving a request at a proxy for an authentication service, (ii) determining whether the request is trusted based at least in part on a classification obtained from a classifier, and (iii) handling the request according to a determination of whether the request is trusted.

A neural network on-chip mapping method and apparatus based on a tabu search algorithm are provided. The method includes: constructing a tabu search table and using a heuristic-based iterative search process to select local computing cores of a network-on-chip as candidates, establishing an integer programming model and solving an optimal solution, continuously reducing an objective cost function of a mapping solution by loop iteration, and finally obtaining an approximately optimal deployment scheme.

Systems and methods for training and using large language models to respond to queries and cascading to new models when their performance falls below a threshold during the response generation cycle are described. The methods involve receiving and analyzing a query using heuristics to determine the query's categories and a level of granularity at which its response is to be evaluated. Selecting a language model based on the query analysis and during the generation of the response, evaluating the model's performance for its ability to predict the next segment in a response. The methods score the evaluation and use the score to determine whether the language model's performance exceeds a confidence threshold. If it does not, then during the response is being generated, e.g., at a point before the response generation is completed, cascading from the model to a different model that can perform at a higher confidence level.

Disclosed apparatuses and methods for trajectory and collision prediction comprising a processor operable to generate, using a machine-learning (ML) model, a ML predicted trajectory of a vehicle and a ML model confidence of the ML predicted trajectory, generate, using a heuristics model, a heuristic predicted trajectory of the vehicle, determine whether the ML model confidence satisfies a metric, in response to determining that the ML model confidence satisfies the metric, apply the ML predicted trajectory as a predicted trajectory, and in response to determining that the ML learning model confidence fails the metric, apply the heuristic predicted trajectory as the predicted trajectory.

A data platform monitors a compute environment by performing multi-stage heuristic analysis of event data representing a plurality of events occurring within the environment. The platform utilizes multiple event analyzers, each configured according to a distinct analysis heuristic, to evaluate different subsets of the event data and generate corresponding output signals. A higher-level event analyzer applies a further heuristic to the multiple output signals to generate a composite alert signal, indicating whether the combination of analyzed events collectively represents a security intrusion or other anomalous condition of sufficient severity to warrant alerting. Based on the composite alert signal, the platform performs an alert-based operation, such as generating a user-facing alert, initiating an automated mitigation, or updating a contextual model of system behavior. By combining the analytical outputs of heterogeneous heuristics, the disclosed architecture enhances the accuracy and contextual relevance of automated intrusion detection within complex computing environments.

This disclosure describes techniques and architecture provide automated contribution analysis for “why question” style NLQ answering, e.g., “why is revenue down in North America Q1 2022.” In particular, the techniques described herein combine multiple signals together including, for example, frequency of use of combinations of dimensions in previous NLQs (warm-start), statistical information about columns (e.g., entropy), correlation / co-occurrence between pairs of dimension columns, and correlation between dimensions and dates. This information is used with a set of heuristics and rules to pick the best set of dimensions as contributing factors for a particular metric over a particular time period and present an automatic contribution analysis to the users to give them insights into their data.

Systems and Methods for Automatically Detecting Root Cause Failures Described herein are techniques for automatically detecting root cause failures in a computing environment. A data center may experience a critical system failure that renders the data center inoperable. An administrator may utilize a root cause detector to analyze alerts generated from the data center to automatically detect the root cause of the critical system failure. Once detected, the administrator may investigate the device with the root cause failure to repair the data center. In some examples, the root cause detector may compare alerts received from the data center with root cause failures in a failure repository that it is familiar with to determine whether the present sequence of alerts is similar to alert patterns it has seen before. The root cause detector may be implemented with a classifier model, a large language model, a rule-based heuristics identifying spurious alert patterns, or combinations of these techniques.

There is provided a system and methods to determine an improved solution to a Mixed Integer Problem (MIP) using a quantum-assisted MIP solver. The methods are performed by a digital processor in communication with a quantum processor. Methods include: selecting at least one feasible solution determined by an MIP solver, determining a first sub-problem of the MIP based on the at least one feasible solution; casting the first sub-problem as Binary Quadratic Models (BQMs); solving the BQMs using the quantum processor to generate sample solutions; determining a second sub-problem based on at least the sample solutions, and obtaining a current solution to the MIP by evaluating the second sub-problem; and updating an incumbent solution if the current solution improves over the current incumbent solution. The quantum-assisted MIP solver uses hybrid crossover and mutation heuristics to improve the convergence time and accuracy of solutions obtained using Branch-and-Cut solvers.

The invention relates to the technical field of SAR (Synthetic Aperture Radar) remote sensing imaging, in particular to a scattering heuristic SAR tomography method for structured three-dimensional reconstruction, which comprises the following steps of: S1, acquiring and preprocessing multi-baseline SAR data; s2, inverting scattering parameters based on an attribute scattering center model and orthogonal matching pursuit; s3, segmenting a building area according to coherence and morphological indication; s4, combining direction perception expansion with region updating, determining a continuous scattering region, and giving reference height prior; and S5, constructing a regional adaptive dictionary, sparsely solving the elevation and geocoding to generate a structured point cloud. According to the method, physical prior and regional statistics are cooperated, geometric continuity and anti-noise robustness are improved, pseudo peaks and calculation redundancy are reduced, and upgrading from sparse point cloud to a structured model is realized.

The invention discloses a question answering method and a related device, and relates to the technical field of large language models.According to the question answering method, a large language model which is obtained through pre-training and has the capacity of discovering the association between a question to be answered and similar questions and utilizing the similar questions for answering serves as an answering model, and after the question to be answered is obtained, the question to be answered is answered through the association between the question to be answered and the similar questions; the method comprises the steps of determining similar questions corresponding to questions and analysis of each similar question, calling a trained answering model to answer to-be-answered questions based on the similar questions and the analysis of the similar questions for each similar question, and obtaining answers output by the answering model; and finally, determining a final answer of the question based on each answer output by the answer model. According to the scheme, the model can give answers by referring to the answering process of the similar questions or obtain'heuristic 'from the answering thought of the similar questions to solve the difficult problem that the questions cannot be answered during direct answering, so that the diversity of the answers generated by the model is improved, and the question answering accuracy is improved.

Described herein are techniques for automatically detecting root cause failures in a computing environment. A data center may experience a critical system failure that renders the data center inoperable. An administrator may utilize a root cause detector to analyze alerts generated from the data center to automatically detect the root cause of the critical system failure. Once detected, the administrator may investigate the device with the root cause failure to repair the data center. In some examples, the root cause detector may compare alerts received from the data center with root cause failures in a failure repository that it is familiar with to determine whether the present sequence of alerts is similar to alert patterns it has seen before. The root cause detector may be implemented with a classifier model, a large language model, a rule-based heuristics identifying spurious alert patterns, or combinations of these techniques.

The invention relates to the technical field of new energy power prediction, and discloses a new energy power prediction method and system. According to the method, through multi-source data preprocessing and correlation screening, key influence factors are focused to construct an initial prediction model; a dynamic mixed meta-heuristic optimization strategy is adopted, global efficient exploration is carried out on the upper layer model, local fine optimization is carried out on the lower layer model, collaborative optimization is carried out on hyper-parameters of the initial prediction model in stages, and finally a target prediction model is formed and used for real-time power prediction. According to the method, through the synergistic effect of the upper-layer model and the lower-layer model of dynamic mixed meta-heuristic optimization, the advantages of different algorithms are combined, and it is ensured that the hyper-parameter optimization process is more likely to converge to a globally optimal solution. The technical problems that an existing single optimization algorithm often has limitation and is prone to falling into local optimum or low in convergence speed are solved.

In some cases, images and depth maps can define bins with objects in random configurations. It is recognized herein that current approaches to training deep neural networks to perform grasp computations lack capabilities and efficiencies, such that the resulting grasp computations and grasps can be imprecise or cumbersome, among other shortcomings. Synthetic depth images can be labeled with grasp annotations that are generated based on heuristic-based analyses, so as to define annotated synthetic datasets. The annotated synthetic datasets can be used to train neural networks to determine the best grasp locations for different objects arranged in a variety of positions with respect to each other.

A process, system and medium for detecting anomalous authentication requests to a protected resource during an authentication journey, in order to regulate step-up authentication are described. The process includes obtaining request features from the authentication request that triggered the authentication journey. The process includes processing, by an ensemble of Machine Learning (ML) models and a set of rule-based heuristics, a set of features based on the request features, the set of features associated with a userID. The process includes deriving risk sub-scores for each ML model and each heuristic. The process includes deriving a risk score based on the risk sub-scores. The process includes determining that the risk score exceeds an explanation-triggering threshold. The process includes providing, to a node in the authentication journey, the risk score with an explanation of the risk score. The system and medium are configured to execute the process, and configured to provide the explanation.

Aspects of this technical solution can generate, according to one or more first environment metrics, a three-dimensional (3D) model including a first surface corresponding to one or more physical ways through a physical environment, the one or more first environment metrics indicative of boundaries of the one or more physical ways, generate, according to one or more second environment metrics, one or more geometric two-dimensional (2D) objects on the first surface, the second environment metrics indicative of the one or more physical ways, identify, according to one or more viewpoint metrics indicative of cameras of a physical object configured to move along the one or more physical ways, one or more viewpoints oriented to capture corresponding portions of the 3D model, and render, from the one or more corresponding portions of the 3D model, one or more 2D images each corresponding to respective ones of the viewpoints.

Systems and techniques for generation and curation of a professional headshot from a set of image data. The systems and techniques images from the set of image data based on characteristics of the representation of the individual within the image. The systems and techniques further include determining a bounding box to define a headshot, the bounding box determined based on guidelines established by heuristics and / or machine learning algorithms trained using data labeled based on heuristics.

The invention discloses a two-stage reinforcement learning SAT solving method for structure perception and a related device, and belongs to the field of chip design. Chip signal polarity and logic consistency information is introduced in a variable initialization stage, an initial feature vector for structure perception is constructed, and the limitation that traditional graph modeling can only represent a local logic relation is broken through; a two-stage reinforcement learning framework composed of a main path and a sampling path is designed, the sampling path is used for exploring a chip logic solution space, the main path is used for stably utilizing an optimal verification strategy, and the problems of unstable training convergence, single strategy and the like are effectively relieved. Based on a reward-guided feature updating mechanism, dynamic evolution of chip signal features and enhanced feedback are explicitly bound, the interpretability of a solver is improved, and the difference between neural reasoning and chip logic symbol reasoning is reduced. A signal assignment decision is executed based on GNN multi-round message passing results, and a verification track is continuously optimized, so that efficient chip design verification capability can be obtained without depending on an artificial heuristic mode.

The invention discloses a heuristic-based iterative legalization method and system for an analog circuit layout, and the method comprises the steps: dividing layout groups according to a special structure and an array module in a layout based on the initial layout of the layout containing overlapped devices or modules; constructing a constraint graph for describing the relative position relationship between the layout groups, and solving symmetric constraint conflicts in the layout so as to ensure that all symmetric constraints are met; performing simulation processing on a non-overlapping constraint, a minimum area constraint and a minimum spacing constraint through a code simulation solver to generate an initial legal layout; and iteratively adjusting the constraint graph based on a heuristic method, identifying a suspicious group and adjusting the constraint edge of the suspicious group, and re-verifying and repairing the symmetric constraint after each adjustment until the layout area converges. According to the method, the symmetric constraint is synchronously verified and repaired in each iterative optimization, so that the layout area is minimized on the premise of ensuring the layout legality, and the solving efficiency is greatly improved at the same time.

The present application relates to the technical field of GPU computing optimization, and in particular to a sparse attention computing method, device and medium for GPU, wherein the method realizes the high efficiency of long context reasoning through the geometric perception sparse attention framework of ball hashing, and combines a large-scale parallel hashing optimization algorithm and a load adaptive computing kernel. Compared with the existing sparse attention methods based on heuristics or gradient learning, the present application realizes higher retrieval recall rate, lower preprocessing overhead and efficient hardware adaptation to irregular sparse patterns.

Methods, systems, and storage media for providing a heuristic multilanguage tokenizer in a web browser environment are disclosed. Exemplary implementations can receive a query; generate a query set comprising at least one query token; generate a result set from a searchable phrase, wherein the result set comprises at least one phrase token; generating at least one supplemental set. Determining that any of the sets comprise at least one of: a first character-type, a second character-type, or an emoji. In response to determining the first character-type: determining a word boundary and extracting a diacritic. In response to determining that the query, result set and the supplemental set comprise the second-type or the emoji, adding a space around each second character-type or emoji in the set; and segmenting the set into a plurality of tokens.

Aspects of the disclosure relate to arranging a pick up and drop off locations between a driverless vehicle and a passenger. As an example, a method of doing so may include receiving a request for a vehicle from a client computing device, wherein the request identifies a first location. Pre-stored map information and the first location are used to identify a recommended point according to a set of heuristics. Each heuristic of the set of heuristics has a ranking such that the recommended point corresponds to a location that satisfies at least one of the heuristics having a first rank and such that no other location satisfies any other heuristic of the set of heuristics having a higher rank than the first rank. The pre-stored map information identifying a plurality of pre-determined locations for the vehicle to stop, and the recommended point is one of the plurality of pre-determined locations. The recommended point is then provided the client computing device for display on a display of the client computing device with a map.

Systems and methods for implementation of a multi-agent reinforcement learning based decision system for a materials handling system, including initializing a simulation environment comprising decision points for dispatching materials and attributes of the materials handling system, the simulation environment configured to request a decision for materials dispatch at the decision points to the multi-agent reinforcement learning based decision system; initializing the reinforcement learning agents representative of the decision points for the multi-agent reinforcement learning based decision system; initializing domain expert heuristics for the decision points for the multi-agent reinforcement learning based decision system; and iteratively training the reinforcement learning based decision system with the initialized domain expert heuristics on the simulation environment.

The invention belongs to the technical field of robot path planning, and particularly relates to a sampling optimal path planning method fusing adaptive heuristic and symmetric bidirectional search, which comprises the following steps: based on an improved EIT * algorithm, realizing symmetric bidirectional search by constructing a quadruple structure of a forward tree, a forward inertia tree, a reverse tree and a reverse inertia tree; in the random geometric graph approximate construction stage, an elliptical information set is adopted to limit a sampling area, and bidirectional edges are defined by connecting radius r or k neighbors; a middle encounter strategy is adopted, so that forward inert search and reverse inert search meet in the middle and share heuristic information, and the search efficiency is improved; the forward search and the reverse search respectively utilize heuristic information provided by inert search of the opposite side to carry out path exploration, the total cost is calculated after meeting and is continuously optimized, and finally an optimal path is obtained through pruning; according to the method, the initial solution obtaining speed is effectively increased, the path quality is improved, and a higher planning success rate is shown in a complex environment.

The application relates to the technical field of binary program analysis and rewriting, and discloses an immediate number symbolization judgment method and system of a binary rewriting program, which comprises a preprocessing step, a basic evidence analysis step, an intermediate evidence analysis step, a high-level evidence analysis step and a fusion decision step. The preprocessing layer carries out structural analysis and semantic modeling on a binary file, extracts candidate immediate numbers and meta information, the basic evidence layer carries out preliminary weighting based on explicit rules, the intermediate evidence layer carries out quantitative evaluation through heuristics and statistical patterns, the high-level evidence layer carries out high-precision analysis on the immediate numbers with semantic ambiguity, the fusion decision layer summarizes the evidences at all levels, forms a comprehensive tendency value through weighted calculation, classifies the immediate numbers, and generates a final symbolization strategy. The application significantly improves the accuracy and interpretability of symbolization judgment, balances the analysis precision and processing efficiency, and is suitable for binary files of different compilers, platforms and file stripping states.

Quantitative techniques to measure and reduce the entropy in customer centric marketing activities in large enterprises using geometric methods and heuristics. Quantitative real time representations of marketing efforts, their effects and relative returns as line segments of a triangle with their lengths denoting the digitally encoded weights of the relations of the corresponding constituents pairwise. Hierarchically entropy deduction using mathematical formulae to deduce entropy from a nine-point circle formed of the triangle constructed to empirically assess marketing activities to customer journeys. Identification means to determine effectiveness and sensitivity to such marketing efforts by particularized customer types and clusters with automated recommendations in the form of prescription of lower activity levels of a segment or total suspension of marketing activity levels to reduce the overall entropy in marketing activity.

Computerized methodologies are disclosed that are directed to detecting anomalies within a time-series data set. A first aspect of the anomaly detection process includes analyzing the regularity of the data points of the time-series data set and determining whether a data aggregation process is to be performed based on the regularity of the data points, which results in a time-series data set having data points occurring at regular intervals. A seasonality pattern may be determined for the time-series data set, where a silhouette score is computed to measure the quality of the fit of the seasonality pattern to the time-series data. The silhouette score may be compared to a threshold and based on the comparison, the seasonality pattern or a set of heuristics may be utilized in an anomaly detection process. When the seasonality pattern is utilized, the seasonality pattern may be utilized to generate thresholds indicating anomalous behavior.

Implementations generally relate to text clustering with heuristic and multi-metric control. In some implementations, a method includes receiving an electronic source document containing text. The method further includes dividing the text into text units, encoding the text units, and transforming the text units into numerical values. The method further includes generating a graph of the text units based on the numeric values, where the graph includes nodes corresponding to the text units and edges corresponding to pairs of the text units. The method further includes ordering the text units into text clusters based on the graph of the text units. The method further includes generating an electronic target document that presents the text clusters based on one or more preference heuristics.

The present application relates to a kind of heuristic Q-learning-based multi-robot full coverage path planning method, including constructing global gridding map and dividing mutually non-overlapping connected sub-region by DARP algorithm;Each robot independent sub-environment is constructed and Q-learning agent is initialized;Agent uses the enhanced state definition of "absolute coordinate+local environment topological feature", and the optimal action is selected by heuristic strategy of unvisited priority and backtrack distance guidance;The Q value table is updated based on the comprehensive function of coverage reward, dynamic repeated access penalty and completion reward;Path is generated after iteration training to full coverage or reaching preset number.The present application reduces 23.8% of total moving steps under the premise of ensuring 100% coverage, reduces path repetition rate from 26.35% to 3.36%, reduces 44.1% of turning number, significantly improves work efficiency, and can be widely applied to collaborative work scenarios such as cleaning robot cluster and agricultural automatic harvester group.