486 results about "Decision rule" patented technology

Machine translation decoding is accomplished by receiving as input a text segment in a source language to be translated into a target language, generating an initial translation as a current target language translation, applying one or more modification operators to the current target language translation to generate one or more modified target language translations, determining whether one or more of the modified target language translations represents an improved translation in comparison with the current target language translation, setting a modified target language translation as the current target language translation, and repeating these steps until occurrence of a termination condition. Automatically generating a tree (e.g., either a syntactic tree or a discourse tree) can be accomplished by receiving as input a tree corresponding to a source language text segment, and applying one or more decision rules to the received input to generate a tree corresponding to a target language text segment.

A system for developing and implementing empirically derived algorithms to generate decision rules to determine participant noncompliance and fraud with research protocols in clinical trials allows for the identification of complex patterns of variables that detect or predict participant noncompliance and fraud with research protocol, including performance and enrollment goals, in the clinical trial. The data may be used to overall predict the performance of any participant in a clinical trial, allowing selection of participants that tend to produce useful, high-quality results. The present invention can also be used to monitor participant compliance with the research protocol and goals to determine preferred actions to be performed. Optionally, the invention may provide a spectrum of noncompliance, from minor noncompliance needing only corrective feedback, to significant noncompliance requiring participant removal from the clinical trial or from future clinical trials. The algorithms and decision rules can also be domain-specific, such as detecting non-compliance or fraud among subjects in a cardiovascular drug trial, or demographically specific, such as taking into account gender, age or location, which provides for algorithms and decision rules to be optimized for the specific sample of participants being studied.

A method and data processing system assesses the security vulnerability of a network. A system object model database is created and supports the information data requirements of disparate network vulnerability analysis programs. Only the required data from the system object model database representing the network is imported to the programs, which then analyze the network to produce data results from each program. These data results are stored in a common system model database and within the data fact base. Goal oriented fuzzy logic decision rules are applied to determine the vulnerability posture of the network.

The present invention is a method and system for automatically recognizing which products a shopper intends to find or purchase based on the shopper's trajectory in a retail aisle. First, the system detects and tracks the person to generate the trajectory of the shopper. Then some of the dynamic features are extracted from the shopper trajectory. The shopper trajectory features of a given trajectory are typically the positions, the motion orientations, and speeds at each point of the trajectory. A shopper behavior model is designed based on some of the primitive actions of shoppers. The last step of the method is to analyze a given shopper trajectory to estimate the shopper's intention. The step either utilizes decision rules based on the extracted shopper trajectory features, or utilizes a trained Hidden Markov Model, to estimate the progression of the primitive actions from the trajectory. The decoded progression of the shopper behavior states is then interpreted to finally determine the shopper's intention.

Character recognition includes detecting a union of characters, preprocessing the union of characters, comparing the preprocessed union of characters with one or more template symbols, and applying a decision rule to either reject a template symbol or decide that the template symbol is included in the union of characters. Such preprocessing involves representing the union of characters as one or more curves, and parameterizing the curve(s); and, regarding various classes of transformation, forming one or more shapes for the curve(s). The comparing operation involves forming one or more geometric proximity measures, and determining for every shape the values of those measures between the shape and correspondingly determined shapes for the template symbols. Applying a decision rule involves selecting one or more template symbols in consideration of the values.

A method and information processor are provided which allow a user of a user terminal to receive a list of credit financing opportunities based on the user's financing objectives in which credit data is received from the user terminal. The credit data includes the user's objective and a credit profile. Credit financing opportunities are determined based on the credit data and one or more predetermined decision rules. An output is generated and provided to the user terminal which includes a comparison of at least part of the credit profile with the determined credit financing opportunities.

An automated decision engine is utilized to screen incoming alarms using a knowledge-base of decision rules. The decision rules are updated with the assistance of a data mining engine that analyzes historical data. “Normal” alarm events, sequences, or patterns generated by sensors under conditions not associated with unusual occurrences (such as intrusion attacks) are characterized and these characterizations are used to contrast normal conditions from abnormal conditions. By identifying frequent occurrences and characterizing them as “normal” it is possible to easily identify anomalies which would indicate a probable improper occurrence. This provides very accurate screening capability based on actual event data.

The present invention relates to application processing and decisioning systems and processes. One embodiment of the invention includes a method for automating decisioning for a credit request associated with an applicant. The method includes providing a user computer interface adapted to receive information associated with an applicant, and further adapted to display and receive information associated with at least one decision rule. The method also includes receiving information associated with an applicant through the user computer interface; receiving information associated with the applicant from at least one data source; and receiving a selection of information associated with a plurality of decision rules through the user computer interface. Furthermore the method includes receiving a selection of rule flow information associated with the plurality of decision rules through the user computer interface; generating a plurality of decision rules based at least in part on the information associated with the applicant, based at least in part on the information associated with the applicant from at least one data source, and based at least in part on the selection of information associated with a plurality of decision rules, wherein an outcome associated with the at least one decision rule can be obtained; and based in part on at least the rule flow information, displaying at least a portion of the plurality of decision rules through the user computer interface.

The present invention provides an application virtualization framework that allows dynamic allocation and de-allocation of compute resources, such as memory. Runtime applications are pooled across multiple application servers and compute resources are allocated and de-allocated in such a way that resource utilization is optimized. In addition, objects are either stored in local memory or in a non-local memory pool depending on certain criteria observed at runtime. Decision logic uses decision rules to determine whether an object is to be stored locally or in a memory pool. Resource management logic monitors the memory pool to determine which locations are available for storing objects and how much memory is available overall in the memory pool for storing objects.

A computer-implemented method for collating relevant information to assist a user in a decision-making process is provided. The method includes receiving, from a plurality of data sources, a plurality of pieces of data including contextual features, and populating at least one contextual model with the plurality of pieces of data. The at least one contextual model groups pieces of data based at least in part on the contextual features. The method also includes calculating one or more contextual relationships based on the at least one contextual model, and determining one or more pieces of data of the at least one contextual model to provide to a user based on the one or more contextual relationships and one or more decision making rules.

The invention discloses a decision making method and system based on big data. The method integrates a series of decision making influence factors commonly used for all industries in advance and has the setting function of setting all the influence factors so that users in all walks of life can customize acquisition rules, decision making rules and the like required by decision making through simple setting, and for instance, the users can set industry types and data keywords to formulate the acquisition rules. Meanwhile, a universal classification model is built in advance, data to be classified can be classified by the model on the basis of auxiliary information set by the users according to service requirements of the users, so that data required by the users can be further mined conveniently, and target data are obtained. For instance, acquired medical data can be classified in terms of diseases by the model according to the auxiliary information of the diseases set by the users, and finally decisions are made according to the decision making rules set by the users. It is clear that the decision making method and system based on big data overcome the defect that an existing decision making method is not universal.

An equalizer for use in a communication receiver includes an infinite impulse response (IIR) feedback filter operated in acquisition and tracking feedback modes on a sample by sample basis to form a hybrid Decision Feedback Equalizer (DFE) architecture. In acquisition mode, soft decision samples from the filtered received signal are input to the IIR filter. In the tracking mode, hard decision samples from a slicer are input to the IIR filter. Acquisition and tracking operating modes are selected in accordance with a set of decision rules on a sample by sample basis based on the quality of the current hard decision. If the current hard decision is low quality, then the soft decision sample (acquisition mode) is used. If the current hard decision is high quality, then the hard decision sample (tracking mode) is used. In such manner, the DFE is operated in a hybrid mode, i.e., using both soft and hard decisions on a sample by sample basis. Furthermore, the decision rules that are responsive to signal reliability to select acquisition and tracking modes are adaptive.

A system for developing and implementing empirically derived algorithms to generate decision rules to predict invalidity of subject reported data and fraud with research protocols in surveys allows for the identification of complex patterns of variables that detect or predict subject invalidity of subject reported data and fraud with the research protocol in the survey. The present invention may also be used to monitor invalidity of subject reported data within a research protocol to determine preferred actions to be performed. Optionally, the invention may provide a spectrum of invalidity, from minor invalidity needing only corrective feedback, to significant invalidity requiring subject removal from the survey. The algorithms and decision rules can also be domain-specific, such as detecting invalidity or fraud among subjects in a workplace satisfaction survey, or demographically specific, such as taking into account gender or age. The algorithms and decision rules may be optimized for the specific sample of subjects being studied.

The present invention relates to a road traffic sign identification method with multiple-camera integration based on the DS evidence theory, belonging to the technical field of image processing. According to the method, five types of road traffic indication signs which are going straight, turning left, turning right, going straight and turning left, and going straight and turning right are mainly identified, and the method is divided into two parts which are training and testing. In a training stage, the direction gradient histogram feature of a training sample is extracted, thus a sample characteristic and a category label are introduced into a support vector machine to carry out classification training. In a testing stage, an interested region is obtained through image pre-processing, the direction gradient histogram feature of the interested region is extracted and is sent into a classifier to carry out classification, according to the credibility of the sign to be identified obtained by the classifier belonging to each category, and combined with a DS evidence theory data integration method and a maximum credibility decision rule, a final sign identification result is determined. According to the invention, a multiple-camera data integration method based on the DS evidence theory is employed, the information of multiple cameras are integrated to obtain a final identification result, and the road traffic signs can be stably and efficiently identified.

The presently disclosed method and system includes a network of computer devices, sensors, and actuators operating in concert with application software to actively detect, identify, and localize threats and generate real-time countermeasures designed to delay and/or mitigate damage that may be caused by the threats. Application software, in the form of automated reasoning and logic control, initiates preparatory and countermeasure sequences automatically, which may be used by users or automatically executed by the system to at least delay an attack to a physical asset/area by adversaries with use of non-lethal actuators. Learned scenarios are generated and continuously adapted via feedback loops and decision rules to provide preparatory and countermeasure sequences that maximize results with minimal expenditure of assets.

The invention discloses an injection point extracting method in SQL (Structured Query Language) injection vulnerability detection, which is used for solving the technical problem of poor accuracy of the traditional method in the SQL injection vulnerability detection under a Web environment. According to the technical scheme, the injection point extracting method comprises the steps of: firstly, preprocessing a downloaded webpage, dividing the webpage into a simple webpage and a complex webpage, extracting a data injection point of the simple webpage, extracting a data injection point of the complex webpage, extracting a test case through constructing the test case, analyzing service response, and establishing a decision rule of an SQL injection vulnerability. According to the invention, starting from obtaining the data injection points of a Web application system, through constructing the targeted test case, the used test case is used for carrying out character string, numerical value, annotation and delay testing according to the types and the parameter compositions of the data injection points, thus the test of the injection vulnerability caused by simple filtration of URL (Uniform Resource Locator) parameters and table submitted data is effectively dealt; and through analysis response, the established decision rule of the SQL injection vulnerability is improved in testing accuracy.

The method discloses a method for sorting signals of a radar radiation source by using coverage in a complex dense environment and relates to a radar radiation source signal sorting method by using coverage, aiming at solving the problem of poor sorting ability of the prior art in the complex dense environment as well as in a serious overlapping condition of characteristic parameters. The method comprises the following steps of: choosing the characteristic parameters of a radar signal capable of being standardized as input characteristic vectors, initializing a radar signal sample parameter space; carrying out supervisory learning on the radar signal with parameter prior information in the radar signal sample parameter space by using a coverage algorithm; separating the radar signals of different kinds by using different fields by using the coverage algorithm; expressing the membership degree of the characteristic parameters of each radar signal to be sorted and identified in relative with each field by using a cloud model, and obtaining the membership value of the membership degree of the radar signal to be sorted and identified in relative with each field; and sorting the signal to be detected by using decision rules. The method for sorting signals of the radar radiation source by using coverage in the complex dense environment is suitable for sorting radar radiation source signals.

A method and system for detecting abnormal events in a given environment comprises a model construction step comprising: a) a step of unsupervised initialization of Q groups; b) a step of definition of a model of normality consisting of 1-class SVM classifiers; c) a step of optimum distribution of the audio signals in the Q different groups; d) repetition of the steps b and c until a stop criterion C₁, is checked and a model M is obtained; and a step of use of the model(s) M obtained from the construction step comprising the analysis of an unknown audio signal S_T assigning a score to a 1-class SVM classifier, and a comparison of all the scores fq obtained using decision rules in order to determine the presence or absence of an anomaly in the audio signal analyzed.

The invention provides an emotion recognition system and method based on a wearable bracelet. The system comprises a physiological signal acquisition module, a physiological signal preprocessing module, a physiological signal feature extraction module, an emotion classification module and a comprehensive evaluation module; the physiological signal acquisition module is used for acquiring three kinds of physiological data of electrocardiograph, heart rate and skin electricity of a wearer; the physiological signal preprocessing module is used for carrying out data segmentation and denoising on the three kinds of physiological data and then transmitting the physiological data to the physiological signal feature extraction module; the physiological signal feature extraction module is used forcarrying out feature extraction on the three kinds of physiological data; the emotion classification module is used for carrying out emotion recognition on the three kinds of physiological data and outputting three emotion states; the comprehensive evaluation module adopts a weight-based voting decision rule, voting decision is carried out on the three emotional states, a current emotional state label of the wearable bracelet wearer is determined comprehensively, and a recognition result is obtained. According to the system, the cognitive and management capability of the wearer on the emotionof the wearer is improved, and a healthier psychological state can be possessed.