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20020116572

10077061

1. A method of refreshing a dynamic memory intended for storing variables involved in operations performed by a processor, characterized in that it comprises: a step of planning in the course of which an order and a timing of said operations are established; a step of estimating a retention time specific to each variable; a step of forecasting at least one instant at which each variable must be refreshed, an instant at which a given variable must be refreshed being defined as a function of the retention time of said given variable and the timing of the operations in which said given variable is involved; a step of placement in the course of which said variables are placed in said memory; a step of refreshing in the course of which said variables are refreshed at the instants defined during the forecasting step.

20160179929

15067454

1. A method for classifying a uniform resource locator (URL), the method comprising: configuring a bloom filter to identify a pre-determined set of URL categories; programming the bloom filter with a set of URLs, each URL in the set of URLs belonging to a respectively corresponding URL category of the pre-determined URL categories; extracting a second URL in-line with a data transmission from a network packet stream; determining a set of hash strides for the second URL; querying the bloom filter with each hash stride of the set of hash strides, the querying being performed in a data plane; identifying a sub-set of categories from the pre-determined set of URL categories, the sub-set of categories being URL categories that correspond to the second URL; determining a first URL category based, at least in part, on a programmable mapping table that defines the sub-set of URL categories as the first URL category; and reporting the first URL category as a classification of the second URL in human-readable form.

7794982

11721737

1. A method for searching for a genetic polymorphism, which genetic polymorphism is capable of identifying a gene the expression level of which differs between alleles of said gene, comprising the steps of: (a) selectively amplifying cDNAs derived from about 3 kb or more intranuclear RNAs among cDNAs derived from RNAs of a biological sample, using random primers and phi29 DNA polymerase; (b) detecting genetic polymorphisms in the amplified cDNAs; (c) comparing expression levels of RNAs from the respective alleles, using the amplified cDNAs, at each of the detected polymorphisms; and (d) selecting among said detected polymorphisms to identify at least one polymorphism that is characterized by significantly different expression levels between alleles thereof.

9861299

13209316

1. A health risk assessment device comprising: a mouthpiece; a flow path in fluid communication with the mouthpiece; a sensor disposed in the flow path and configured to generate a signal representative of a dynamically changing air flow in the flow path provided by a user through the mouthpiece; a user interface configured to allow the user to provide an age of the user; and a processor coupled to the sensor and configured to: digitally filter the signal representative of the dynamically changing air flow to generate flow data for the dynamically changing air flow; implement a test to evaluate the flow data, the test determining time elapsed to reach peak flow; evaluate the flow data to validate that the dynamically changing air flow is qualified for spirometric parametric determination based on the test; integrate the flow data to determine a volume for the dynamically changing air flow; and determine spirometric data for the air flow based on the volume; wherein the processor is further coupled to the user interface and configured to determine an output specifying a lung age gap based on the spirometric data and the age of the user.

5390283

07965474

1. A method for optimizing configuration of a computer-controlled part pick-and-place machine for placing parts on a PCB, said machine comprising a support for the PCB, a plurality of gripping devices, a plurality of numbered feeders for holding parts for placing on the PCB, and means for activating the gripping devices to pick up selected parts from selected feeders and place them on selected positions on the PCB in accordance with one of a plurality of charges, each charge representing a specific set of parts which are picked and placed as a group and each group movement constituting one charge and a list of charges necessary to place on the parts on the PCB constituting a charge map capable of controlling operation of the machine, the method comprising the steps of: (a) creating an initial population of chromosome strings each representing a set of parameters that control how a charge map is generated for controlling operation of the machine in order to place a given set of parts at given part locations on a given PCB, (b) providing a charge map generator, responsive to a given chromosome string, for generating the configuration and for computing a placement time for placing the given set of parts-on the given PCB, with the machine in the configuration, (c) using a genetic algorithm to generate from the chromosome strings new chromosome strings, (d) evaluating the new chromosome strings generated in step (c) by supplying same to the charge map generator, (e) iterating steps (c) and (d) substituting those new chromosome strings for the chromosome strings if the new chromosome strings result in a lesser placement time than the chromosome strings, until a specified number of chromosome strings have been generated and evaluated or the chromosome population has been brought to convergence, and (f) outputting a best chromosome string found through iteration as representing a desired machine configuration.

9449279

14852255

1. A tangible machine-readable medium comprising instructions which, when executed, cause a machine to at least: process usage data to identify first and second user-invoked applications which were accessed sequentially on a wireless device in a time period, the first and second applications being accessed sequentially without an intermediary application being accessed after the first application is accessed and prior to the second application being accessed; build, using an aggregator, a behavior model based on the identified applications, the behavior model to describe user behavior associated with the wireless device; execute, using a predictor, the behavior model to predict usage of an application on the wireless device; based on the prediction, monitor usage of the wireless device to determine an accuracy of the prediction; and update the behavior model based on the accuracy of the prediction, at least one of the aggregator or the predictor includes a logic circuit.

20080244332

11694864

1. One or more computer-readable media containing executable code for performing the following steps: recording execution of a plurality of instructions; detecting one or more race conditions in the plurality of instructions; filtering the one or more race conditions; and generating a race condition report based on the filtering.

8630831

12920914

1. A method of simulating compressible multi-phase fluid flow to characterize a subterranean structure containing a fracture corridor, comprising: representing the subterranean structure using a model, wherein the model includes a first grid and a second, finer grid that resolves the fracture corridor to define a connected collection of cells and wherein about the connected collection of cells, cells of the first grid are mathematically coupled to cells of the second grid; and performing, by a processor, a streamline simulation using the model wherein a multiscale pressure solver solves for pressure and fluxes using multipoint flux approximations for cell interface fluxes of a flux field for tracing streamlines that characterize the subterranean structure and the fracture corridor and wherein the multiscale pressure solver accounts for mobility and compressibility of the compressible multi-phase fluid.

7962530

12150093

1. A method of retrieving information related to a known melody from a database, the steps comprising: a) inputting at least a fragment of a known melody to a computer interface; b) deriving relative pitch information and relative rhythm information for said inputted melodic fragment; c) encoding said derived relative pitch information and said relative rhythm information to create an encoded representation of said melodic fragment, the encoded representation being in the form of a KP code; and d) comparing said encoded representation to a database of pre-compiled, encoded musical information representative of at least one melody.

7961956

12584316

1. A computer-implemented method for classifying a pattern into one of two classes, a class-of-interest or a class-other, comprising: receiving a set of labeled patterns from a class-of-interest, a set of unlabeled patterns from an input data set, and an estimate of the a priori probability of said class-of-interest in said input data set, said input-data-set being at least one of an image, video or speech data set; estimating a within-class scatter matrix; estimating a between-class scatter matrix; computing the eigenvalues and eigenvectors for an adaptive Fisher's criterion; selecting an eigenvector with the largest eigenvalue as the adaptive Fisher's discriminant projection vector; projecting each labeled pattern in said set of labeled patterns from said class-of-interest and each unlabeled pattern in said set of unlabeled patterns from said input data set onto said adaptive Fisher's discriminant projection vector to provide a set of labeled scalar measurements for said class-of-interest patterns and a set of unlabeled scalar measurements for the input data set patterns; executing a training stage using said a priori probability of said class-of-interest in said input data set, said set of labeled scalar measurements for said class-of-interest patterns, and said set of unlabeled scalar measurements for said input data set patterns, to estimate parameters for a function providing a least squares approximation of a class-of-interest posterior probability function; classifying said pattern into one of two classes, a class-of-interest or a class-other, in accordance with a conditional test defined by an adaptive Bayes decision rule; and wherein said pattern is classified using an optimal Fisher's linear decision boundary as either said class-of-interest or as said class-other by projecting said pattern onto said adaptive Fisher's discriminant projection vector and classifying said pattern as either said class-of-interest or as said class-other using said adaptive Bayes decision rule, without prior knowledge of said class-other.

9201980

14083483

1. A method for reconstruction comprising: providing a directed input graph generated from a set of n-grams and statistics for the n-grams, edges of the graph being joined through nodes of the graph, each edge having an associated label and a multiplicity of at least one, each of the n-grams in the set being represented by a respective one of the labels, whereby a Eulerian cycle through the graph traverses each edge the respective multiplicity of times; with a processor, iteratively applying a plurality of reduction rules to generate a refined graph, the reduction rules comprising: a first reduction rule configured for identifying a division point node of the input graph, or of a refined graph generated therefrom, which divides the respective graph into at least two connected components and wherein there is a unique incoming edge to the division point node in the first connected component and a unique outgoing edge from the division point node in the second connected component, the first reduction rule configured for generating a new edge with a label which is derived from the labels of the unique incoming edge and the unique outgoing edge; and a second reduction rule configured for identifying a node of the input graph or of a refined graph generated therefrom which includes a first incoming edge and a first outgoing edge for which the multiplicity of one of the first incoming edge and the first outgoing edge is greater than a degree of the node minus the multiplicity of the other of the first incoming edge and the first outgoing edge, where the degree of the node is a sum of incoming edges of the node multiplied by their multiplicities, the second reduction rule configured for merging the first incoming edge and the first outgoing edge to create a new edge with a label which is derived from the first incoming edge and the first outgoing edge; and after the applying of the plurality of reduction rules, outputting information based on the labels of the refined graph.

9805713

14681652

1. A method performed by one or more computers of an automated speech recognition system, the method comprising: receiving data indicating a candidate transcription for an utterance and a particular context for the utterance; accessing a language model of the automated speech recognition system, the language model including a respective score for each of a plurality of features, each feature corresponding to a word or phrase occurring in an associated context that includes one or more preceding words, wherein the automated speech recognition system is configured to obtain a score for a feature such that: (i) if the language model includes a score for the feature, the one or more computers retrieve the score for the feature that is included in the language model, and (ii) if the language model does not include a score for the feature, the one or more computers obtain a score for the feature that is based on scores for other features associated with a same context as the feature; determining that the language model does not include a score for a feature corresponding to the candidate transcription in the particular context; in response to determining that the language model does not include a score for a feature corresponding to the candidate transcription in the particular context, operating the automated speech recognition system to obtain a score corresponding to the candidate transcription in the particular context, wherein the score is determined based on one or more scores included in the language model for one or more of the plurality of features that are associated with the particular context; determining, using the language model and the determined score, a probability score indicating a likelihood of occurrence of the candidate transcription in the particular context; selecting, based on the probability score, a transcription for the utterance from among a plurality of candidate transcriptions; and providing the selected transcription to a client device as output of the automated speech recognition system.

20040169883

10787330

1. A system for processing a digital image, comprising: a data storage area comprising a plurality of digital images; an image handler configured to obtain at least a portion of a digital image from the data storage area; an image processing algorithm comprising instructions for processing a digital image; and an execution manager configured to execute the image processing algorithm instructions on the digital image obtained by the image handler.

20080300881

12143599

1. A statistical language model generating device comprising: readout means for reading out a grammar to be used for recognizing speech to be input in an application program in a grammar method; and statistical language model generating means for generating a statistical language model to be used for recognizing speech input in said application program by dictation on the basis of said grammar read out by said readout means.

20040034848

10639674

1. A method comprising: in a processing system, receiving a rule set as a single package; generating a dependency graph for the rule set; and generating a sequence of processing logic for optimal processing of inputted facts.

20120246494

13483252

1. A method for generating at least one identifier for uniquely identifying an electronic device, wherein the electronic device contains a number of static storage elements, and wherein the method comprises: cycling power provided to the static storage elements multiple times; reading values of the static storage elements after the power cycling to collect a set of power-up states; repeating the cycling and reading for multiple environmental temperatures of the device to collect the sets of power-up states for the multiple environmental temperatures; determining expected values of the power-up states for the static storage elements from the collected sets of power-up states; computing probabilities that the expected values of the power-up states will be assumed by the static storage elements; and computing the at least one identifier from the collected sets of power-up states and the computed probabilities.

8326586

12570799

1. A method for designing a glass antenna, the method for automatically designing a glass antenna by combining an EM simulation tool with an optimization algorithm, a preparation step of controlling an equivalence coding condition of a glass and a location of an antenna power feeding unit so that a simulation of a glass antenna can be possible through an EM (engineering model) simulator, changing a vehicle structure with a mesh number appropriate for applying an optimization algorithm, and determining a proper initial prototype according to a kind of vehicle and glass size and shape; a performance optimization step of optimizing a glass antenna performance by operating the EM simulator with the optimization algorithm after the preparation step is completed; and a mass production optimization step of redesigning an optimized glass antenna as a final glass antenna shape applicable to a mass production when the optimized glass antenna is obtained after the performance optimization step is completed, wherein the performance optimization step comprises: encoding and decoding the glass antenna shape of the initial prototype by utilizing the EM simulator; filtering a design in which the glass antenna shape or a condition is not suitable; determining cost values which are indexes indicating the performance of the glass antenna; determining a Pareto-Cost value after the simulation of one generation is completed; determining a convergence of the Pareto cost value; and creating a new generation and circulating to the decoding step in case the Pareto-Cost value does not converge, while obtaining the optimized glass antenna in case the Pareto cost value converges.

20130013392

13176668

1. A computer-implemented method for high performance advertisement serving by exploiting processor thread assignments in a processor having multiple threads, the method comprising: receiving, in memory, at least two digital advertisements, an instance of the digital advertisement corresponding to a plurality of decision trees, evaluation of a decision tree resulting in a tree score; determining, from the processor, a number of cores within the processor; iteratively assigning, to a next one of the number of cores, a next decision tree to calculate the tree score; and iteratively accumulating a plurality of the tree scores to form a tree score subtotal.

20150221131

14420426

1. A method for processing a 3D model, comprising the steps of: accessing the 3D model; determining a set of mesh edges connecting a first vertex of the 3D model and a second vertex of the 3D model; determining a respective geometric variation for each one of a plurality of edges, the plurality of edges belonging to the set of mesh edges; determining structural information in a neighborhood around the first vertex of the 3D model responsive to the respective geometric variations; and processing the first vertex in response to the structural information.

20090327811

12493160

1. A system for representation of a real world problem situation said system comprising: a. a computer software process acquiring or capable of accepting a set of input data comprising: seed facts, said set of input data representing real world objects pertaining to a real-world problem situation; b. the computer software process generating new data consisting of additional not-previously-known facts about said real-world problem situation, said additional not-previously known facts comprising acquired facts and reasoned facts; c. the computer software process utilizing: a fact structured representation method representing a first group of facts about a problem situation; a rule structured representation method for representing a first group of rules about a class of problem situations; d. said computer software process representing a plurality of causal features of said problem situation such that a reasoning process results; e. said reasoning process further characterized as performing some elements of deep reasoning.

7729932

10685839

1. A computer readable storage medium encoded with a project assessment program for assessing a project comprising a plurality of processes when executed by a computer computing forecasts of said project based on a set of information, comprising: selecting at least one scheme by a user input from the group of schemes comprising of an estimation of frequency distribution, an estimation of mutual-correlation of estimated value pattern, an estimation of standard deviation, an estimation of time-series information, and an estimation of fit to a predetermined pattern; based on the at least one selected scheme, retrieving: process planning information of each process included in the project; up-to-date actual process information of said each process of the project, forecast model information of said each process of the project defined as probability distribution variations of a plurality of parameters of said processes, wherein the probability distribution variations are quantitative values of the project, computing estimated values of variations in at least two parameters of said processes using the process planning information, the up-to-date actual process information and the forecast model information, wherein the estimated values of variations are different from the probability distribution variations, wherein the estimated values of variations are computed using the probability distribution variations, the probability distribution variations are determined by using standard normalized random numbers computed by a Molo algorithm, assessing the project by determining one of either said estimated values of parameters of at least one process of the processes included in the project, or a comparison of said estimated values of variations to a predetermined criterion; wherein in the scheme of the estimation of mutual-correlation of estimated value pattern, a first process is selected from the processes of the project, estimated values of variations calculated for the first process are put into a sequence, one of estimated values of the variations is set as a first reference estimated value, and other estimated values of the variations of the first process are normalized based on the first reference estimated value so as to obtain a sequence X, a second process is selected from the processes of the project, estimated values of the variations calculated for the second process are put into a sequence, one of the estimated values of the variations is set as a second reference estimated value, and other estimated values of the variations of the second process are normalized based on the first reference estimated value so as to obtain a sequence Y, a mutual-correlation between the sequence X and the sequence Y is computed to assess how high the mutual-correlation therebetween is, thereby to find the estimated values of the variations by equation: (Mutual-Correlation)= E (( X−E ( X )( Y−E ( Y ))/(√ V ( X )√ V ( Y )) wherein, ensemble averages of the sequences X and Y are defined as E(X) and E(Y), and square variances of the sequences X and Y are defined as V(X) and V(Y), respectively.

7849025

12017048

1. A method comprising the following computer-executable acts: receiving a computer-implemented relational model, wherein the computer-implemented relational model includes atoms and relationships between atoms; and modifying the relational model by selectively removing at least one of atoms or relationships from the relational model.

9558033

14711249

1. A method of providing data processing flows, said method comprising: obtaining, by a processor, an information processing flow; and generalizing, using the processor, the information processing flow to provide, based on the information processing flow, a pattern to be used to define a plurality of flows to be used to create one or more platform specific flows to be executed, the generalizing comprising: identifying one or more different points of variability from the information processing flow, at least one point of variability of the one or more different points of variability being variability of a component of the information processing flow; and encapsulating one or more fragments of the information processing flow into one or more components of the pattern, the one or more components of the pattern usable in defining the plurality of flows from the pattern, wherein the pattern created from generalizing the information processing flow identifies the one or more different points of variability, wherein a component of the one or more components of the pattern incorporates the component of the information processing flow and the at least one point of variability of the component of the information processing flow, the incorporated at least one point of variability of the component of the information processing flow being selectable to create at least one platform specific flow of the one or more platform specific flows, and wherein the one or more different points of variability provide one or more derivations of the information processing flow to be used to define the plurality of flows.

8005690

11835593

1. A system for determining compliance with a regimen, comprising: a client device associated with each individual of a group, the client device configured for acquiring a first set of data from each individual of the group and a second set of data from each individual of the group, said client device including a display, an input device for receiving information, an output device configured to alert an individual that information is requested from the individual and a data port, the second set of data being acquired after a time period has elapsed after acquisition of the first set of data; a medical measurement device coupled to said data port of said client device, the medical measurement device configured for obtaining measurement data from each individual of the group, the medical measurement device including at least one of a blood glucose meter, blood pressure monitor and medication dispensing device, the client device configured for receiving the measurement data and for integrating said measurement data within said first set of data and said second set of data; and a server device, the server device operably connected to the client device associated with each individual of a group via a communication pathway, the server device configured for analyzing the first set of data and the second set of data to assess a change for the group by comparing the second set of data to the first set of data after performing a regimen, said regimen including a prescribed diet, exercise and medication, wherein an award is distributed to the group based upon the change.

20120259988

13082449

1. A method of monitoring a first communications connection and a second communications connection in anticipation of performing a change-over, the method comprising: identifying a data communications requirement of a first network device currently communicating via the first communications connection with a second network device; transmitting a data test message over at least one of the existing first communications connection and the second communications connection; receiving a response to the data test message; and determining a change-over from the first communications connection to the second communications connection would provide a more optimal data connection based on the identified data communications requirements of the first network device.

20120303625

13353188

1. A method of managing heterogeneous data, comprising: generating a master catalog of properties preparing an object model catalog containing a plurality of object models, each object model including at least one property listed in the master catalog defining a data set definition including a plurality of data objects, each data object an instantiation of a respective object model from the object model catalog collecting data in accordance with the data set definition, collecting data performed, at least in part, by an automatic data collection system.

20170154237

14956166

1. A method of extracting a sky portion, comprising: receiving a plurality of images, the plurality of images time-sequenced and the plurality of images each comprising a plurality of corresponding data elements; identifying a first type of data elements among the plurality of corresponding data elements of each image among the plurality of images based on color values; removing a corresponding data element from the first type of data elements in response to a determination that the corresponding data element is identified as a first type of data element in less than a first threshold number of images among the plurality of images; and generating a representative comprising at least a set of neighbouring first type of data elements indicative of at least a sky component.

20060083295

10969637

1. A communication method comprising: characterizing a communications channel; determining a data rate that maximizes channel throughput; and configuring a transmitter to send a transmit signal with said data rate.

7530038

11530038

1. A computer-based method for designing the layout of an electronic circuit with several components, which are represented as planar geometrical shapes, the method comprising the steps: defining and storing at least one geometrical relation between at least two components; and defining and storing at least one component as parent component and at least one component, which is geometrically related to said parent component, as child component; and modifying the position or the planar geometrical shape of any component; and calculating new positions for components to maintain the geometrical relations between the modified component and selected other components for which a geometrical relation has been defined; and maintaining automatically said geometrical relation between parent component and child component only when said parent component is modified but not when the child component is modified.

9336059

14322266

1. A computer-implemented method for forecasting an available capacity for processing a workload in a networked computing environment, comprising: receiving and storing, in a computer data structure, capacity data corresponding to a set of peer systems in the networked computing environment; categorizing the workload into a category based on an application type of the workload; accessing historical data related to workload handling for each of the set of peer systems; forecasting the available capacity of the set of peer systems to process the workload based on the capacity data, the category, and the historical data; determining a consistency factor for each of the set of peer systems to prioritize the set of peer systems for processing the workload based on the historical data; and prioritizing the set of peer systems for processing the workload based on the capacity data, the category, and the consistency factor.

20020118759

09947781

1. A video coding method applied to a sequence of frames and based on a three-dimensional (3D) decomposition with motion estimation and compensation on couples of frames, said decomposition being a wavelet transform that leads from the original set of picture elements (pixels) of the frames to transform coefficients constituting a hierarchical pyramid, and a spatio-temporal orientation tree—in which the roots are formed with the pixels of the approximation subband resulting from the 3D wavelet transform and the offspring of each of these pixels is formed with the pixels of the higher sub-bands corresponding to the image volume defined by these root pixels—defining the spatio-temporal relationship inside said hierarchical pyramid, said method being characterized in that, for obtaining an encoded bitstream scalable in SNR (signal-to-noise ratio), spatial and temporal resolutions, it comprises the steps of: (A) organizing the transform coefficients of said tree in a structure of 3D macroblocks, separated by resolution flags respectively associated to the beginning of each macroblock, and blocks, the size of each block fitting the lowest approximation sub-band which contains all the transform coefficients at the coarsest resolution, and all the blocks within each 3D macroblock being themselves organized in successive two-dimensional (2D) macroblocks belonging to a specific spatial decomposition level and grouped for all the frames of a specific temporal decomposition level; (B) scanning the coefficients of each 3D macroblock in a predetermined order defined, inside each block, by the spatial orientation of said block and, inside a 3D macroblock, by an association of blocks having the same location in all the frames of a temporal decomposition level; (C) encoding said scanned coefficients bitplane by bitplane.

9916539

14408957

1. A computer implemented method for generating a probabilistic model usable to identify instances of a target feature in geophysical data sets stored on a memory device, the computer implemented method including: (a) using a computer processing unit to generate a probabilistic model from a training library, the model for use in identifying instances of the target feature in the geophysical data sets, the training library including one or more target examples, each target example including a signature being indicative of the target feature, and one or more non-target examples, each non-target example including a signature being indicative of a non-target feature; (b) applying, using the computer processing unit, the probabilistic model to one or more of the geophysical data sets to generate a plurality of results, each result associated with a processed geophysical data set and indicating a level of certainty as to whether that geophysical data set includes the target feature; (c) processing, using the computer processing unit, the plurality of results according to an acceptability criteria in order to identify a plurality of candidate results, the candidate results being results associated with data sets having potential significance to the performance of the probabilistic model; (d) receiving a selection of one or more of the candidate results and for each selected candidate result displaying on a display the result and its associated geophysical data set to assist a user in making an assessment as to whether or not the probabilistic model is an acceptable model for the processing of the geophysical data sets; (e) receiving from a user an assessment as to whether or not the probabilistic model is an acceptable model; and (f) if the assessment received indicates the probabilistic model is an acceptable model for processing the geophysical data, outputting the probabilistic model and/or the training library; and wherein if the assessment received at step (e) indicates the probabilistic model is not an acceptable model for the processing of the geophysical data, the method further includes: (g) receiving a selection of at least one example to be added to the training library, each example including a signature of either a target or non-target feature and being included in a data set associated with a candidate result, and modifying the training library by adding the at least one example; and/or presenting the training library examples to the user, receiving a selection of one or more examples for removal from the training library, and modifying the training library by removing the example or examples selected for removal; and (h) repeating steps (a) to (f) in respect of the modified training library.

8311747

13014835

1. A method for planning a treatment of a patient, the method comprising: displaying, using at least one computer system, a three-dimensional model of at least a portion of at least one coronary artery of the patient; displaying, using the at least one computer system, a first fractional flow reserve for the at least one coronary artery, wherein the first fractional flow reserve is calculated by the at least one computer system; modifying, using the at least one computer system, the three-dimensional model based on information regarding an image of a stent positioned on the three-dimensional model; and displaying, using the at least one computer system, a second fractional flow reserve for the at least one coronary artery, wherein the second fractional flow reserve is calculated by the at least one computer system based on the modified three-dimensional model.

20120301105

13427610

1. A computer program product embodied in a non-transitory computer-readable medium, the computer program product comprising an algorithm adapted to effectuate a method for analyzing visual events comprising: selecting a plurality of visual events in a visual recording, wherein a visual event is a local visual feature occurring over a plurality of video frames; for one or more occurrences of the plurality of visual events, representing an occurrence of a visual event as a point process, to create a plurality of point processes; constructing a non-parametric representation of the plurality of point processes; and assessing statistical relationships between pairs of point processes from the plurality of point processes.

20120283886

13508719

1. A method for maximising thermal efficiency of a power plant, the method comprising: obtaining the current state of the plant from available measured data; obtaining a set of Variables representing a current state of the power plant; applying a set of constraints to the Variables; generating a revised set of Variables representing a revised state of the power plant, the generation based at least partly on: Euler's equation ρ ∂ v → ∂ t = ∂ ( ρ v → ) ∂ t + ρ ( v → · ∇ ) v → = - ∇ P - ∑ F → k · j →. ; and the conservation of mass equation ∂ ( A ρ k ) ∂ t + ∇ · [ A ( j → k + ρ k v → ) ] = ∂ ∂ t [ A × M k ∑ j = 1 r v kj ( ρ n ~ j ) ] ; and a mathematical description of a reversible continuum; and testing the revised set of Variables within a mathematical model for convergence.

10019494

14811090

1. A method for presenting data mapping alternatives for creating a visual representation of a set of data, comprising: receiving the set of data for analysis; modifying the set of data to form a modified set of data; receiving a selection of a chart type; analyzing the modified set of data and properties associated with the modified set of data for the chart type in response to the selection of the chart type; based on the analysis, determining data mapping alternatives for the modified set of data for the chart type, wherein determining the data mapping alternatives for the modified set of data for the chart type comprises: determining different data orientations for the modified set of data for the chart type, determining different series mappings for the different data orientations for the chart type, and determining different axis mappings for the different series mappings to form a different series-axis mapping for each data mapping alternative; ranking the data mapping alternatives based on the different data orientations, the different series-axis mappings and filtering rules; and presenting, in a user interface, the data mapping alternatives for the chart type as applied to the modified set of data for selection based at least on the ranking.

9195800

14254599

1. A computer-implemented method of identifying a personalized cardiovascular device based on patient-specific geometrical information, the method comprising: acquiring a geometric model of at least a portion of a patient's vascular system; obtaining one or more geometric quantities of one or more blood vessels of the patient's vascular system; determining the presence of a pathology characteristic at a location of the patient's vascular system represented in the geometric model based on the one or more obtained geometric quantities; defining one or more geometric design variables of a personalized cardiovascular device to treat the pathology characteristic; generating an objective function defined by the one or more geometric design variables and hemodynamic or solid mechanics performance characteristics of the personalized cardiovascular device; modifying the geometric model by iteratively updating one or more of the geometric design variables of the personalized cardiovascular device and modeled deployment of the iteratively updated personalized cardiovascular device in the geometric model; and optimizing the objective function to identify a plurality of values of the one or more geometric design variables that result in desired hemodynamic or solid mechanics performance characteristics of the personalized cardiovascular device based on the iteratively updated geometric design variables.

10073444

14919720

1. A computer-implemented method for scheduling a treelike hybrid K-cluster tool with a plurality of branches, sub-trees (ST) and extended sub-trees (EST) to generate a one-wafer cyclic schedule, the treelike hybrid K-cluster tool having K single-cluster tools denoted as C 1 , C 2 ,. .. , C K , with C 1 being a head tool of the treelike hybrid K-cluster tool, the single-cluster tool C k , k∈ K , having a robot R k for wafer handling, Θ being the cycle time for all the branches as iteratively calculated with a maximum fundamental period Π being a given initial cycle time, where the one-wafer cyclic schedule of the treelike hybrid K-cluster tool cannot be found with the given initial cycle time, the method comprising: determining fundamental periods (FP) Π 1 , Π 2 ,. .. , Π K for the single-cluster tools C 1 , C 2 ,. .. , C K and applying a maximum fundamental period Π=max{Π 1 , Π 2 ,. .. , Π K } as the given initial cycle time Θ; determining a one-wafer cyclic schedule for each of the ST (ST i ) by increasing the cycle time Θ by Δ, wherein the ST comprises one or more branches; wherein the determining of one-wafer cyclic schedule for each of the ST comprises: determining the k such that C k is an upstream adjacent tool of C j ; determining a one-wafer cyclic schedule for each of the EST (EST j ) based on a generating algorithm by increasing the cycle time Θ by Δ if C k is not a fork tool, wherein the EST comprises one or more ST and the generating algorithm further comprises: (S1.1) determining, responsive for finding that k∉F and A i(n[i]) ≠0, a time increment Δm according to: Δ m =A m(n[m]) /Σ p∈S m B[p ] if m>i and Δ m = Δ i = { Φ i ⁡ ( S , S ) - A i ⁡ ( n ⁡ [ i ] ) for ⁢ ⁢ Condition ⁢ ⁢ 1 , Φ i ⁡ ( S , S ) 1 + ∑ p ∈ S i ⁢ B ⁡ [ p ] for ⁢ ⁢ Condition ⁢ ⁢ 2 , Φ i ⁡ ( D , S ) 1 + ∑ p ∈ S i ⁢ B ⁡ [ p ] for ⁢ ⁢ Condition ⁢ ⁢ 3 , ⁢ ⁢ if ⁢ ⁢ m = i , (S1.2) computing, responsive for finding that Δ=min{Δ p |p∈S i }=Δ i , (S1.2.1) in EST i or B i , for p∉S i : responsive for finding that p∉L, ω p((b[p] _ 1)−1) =A p((b[p] _ 1)−1) +Δ; responsive for finding that p∈L, ω p0 =A p0 +Δ; (S1.2.2) in EST i or B i , responsive for finding that p∈S i and p∉F: further responsive for finding that p∈L, ω pj =A pj +Y for j∈D[p], or for j∈ n[p] {n[p]}; further responsive for finding that p∈L, ω p((b[p] _ 1)−1 =A p((b[p] _ 1)−1 +Σ q∈S p {p} B[q]×A i(n[i]) /(Σ p∈S i B[p])+Δ, or ω p0 =A p0 +Δ; and ω p(n[p]) =A p(n[p]) −Σ q∈S p B[q]×Y; (S1.2.3) in EST i , responsive for finding that p∈S i and p∈F: ω pj = A pj + Y , j ∈ D ⁡ [ p ] ; ω p ⁡ ( ( b ⁡ [ p ] ⁢ ⁢ _ ⁢ ⁢ 1 ) - 1 ) = A p ⁡ ( ( b ⁡ [ p ] ⁢ ⁢ _ ⁢ ⁢ 1 ) - 1 ) + ∑ q ∈ S p ⁢ ⁢ _ ⁢ ⁢ 1 ⁢ B ⁡ [ q ] × Y + Δ ; ω p ⁡ ( ( b ⁡ [ p ] ⁢ ⁢ _ ⁢ ⁢ d ) - 1 ) = A p ⁡ ( ( b ⁡ [ p ] ⁢ ⁢ _ ⁢ ⁢ d ) - 1 ) + ( ∑ q ∈ S p ⁢ ⁢ _ ⁢ ⁢ d ⁢ B ⁡ [ q ] + 1 ) × Y , ⁢ d ∈ { 2 , 3 , … ⁢ , f ⁡ [ p ] } ; and ω p ⁡ ( n ⁡ [ p ] ) = A p ⁡ ( n ⁡ [ p ] ) - ∑ q ∈ S p ⁢ B ⁡ [ q ] × Y ; (S1.3) responsive for finding that Δ=min{Δ p |p∈S i }=Δ f ≠Δ i , performing the Steps (S1.2.1), (S1.2.2) and (S1.2.3) with Y=Δ f , followed by repeating the Steps (S1.1), (S1.2) and (S1.3); and the generating algorithm is implemented by a wafer handling robot in a wafer processing device in a semiconductor manufacturing industry for determining the one-wafer cyclic schedule for each of the EST, an optimized schedule is obtained with an optimal cycle time Θ for the treelike hybrid K-cluster tool; where: Condition 1 is that 0 < A i ⁡ ( n ⁡ [ i ] ) ≤ ∑ p ∈ S i ⁢ B ⁡ [ p ] × Φ i ⁡ ( S , S ) 1 + ∑ p ∈ S i ⁢ B ⁡ [ p ] and an S-S case is considered, Condition 2 is that A ( i + 1 ) ⁢ ( n ⁡ [ i + 1 ] ) ≥ ∑ p ∈ S i ⁢ B ⁡ [ p ] × Φ i ⁡ ( S , S ) 1 + ∑ p ∈ S i ⁢ B ⁡ [ p ] and the S-S case is considered, Condition 3 is that a D-S case is considered; Y=Φ i (S,S)−A (i(n[i]) when the Condition 1 is satisfied; Y = Φ i ⁡ ( S , S ) 1 + ∑ p ∈ S i ⁢ B ⁡ [ p ] when the Condition 2 is satisfied; Y = Φ i ⁡ ( D , S ) 1 + ∑ p ∈ S i ⁢ B ⁡ [ p ] when the Condition 3 is satisfied; L denotes an index set of leaf tools in the treelike hybrid K-cluster tool; F denotes an index set of fork tools in the treelike hybrid K-cluster tool; ST j denotes a ST with C j being a fork tool and a root node of the ST, and EST i is an EST of ST j with none of C i , C i+1 ,. .. , C j−2 , C j−1 being a fork tool; B i , starting from C i and ends at C m , m∈L, denotes a linear multi-cluster tool in the treelike K-cluster tool; ω ij is a robot waiting time of R i at Step j as obtained after the cycle time is increased by Δ, j∈Ω n[i] , n[i] being an index of a last processing step of C i ; A kj is a robot waiting time at Step j for R k as determined for the cycle time Θ; S i is a set of single-cluster tools connected to C i and selected from the K single-cluster tools; B[k]=n[k]−1; b[p]_d is an index denoting a d-th outgoing module of C p ; D[p] be a set of processing steps in C p ; f[i], 1≤f[i]≤n[i], denotes the number of outgoing modules in C i , i∉L; Φ i+1 (S,S)=4λ i+1 +3μ i+1 +A (i+1)(n[i+1]) −Θ+4λ i +3μ i when C i and C i+1 are S-S, and Φ i+1 (D, S)=4λ i+1 +3μ i+1 +A (i+1)(n[i+1]) −Θ+λ i , when C i and C i+1 are D-S; λ i is a time taken by R i to load or unload a wafer in C i ; μ i is a time taken by R i to move between process modules of C i for transiting from one processing step to another; L ={1, 2,. .. , L} for a positive integer L; and Ω L =N L ∪{0}.

9612273

14556058

1. A system for inspecting a surface with cloud based processing, comprising: an inspection module to generate surface data by inspecting a surface; a client connected to said inspection module having a processor configured to: compress the size of said surface data to generate compressed surface data; packetize said compressed surface data to generate packetized surface data; a bidirectional communication pathway configured to: i. transfer said packetized surface data from said client to a cloud, wherein said cloud comprises multiple interconnected computing nodes that are remotely located from said client; ii. transfer surface properties and surface analytics from said cloud to said client; and a processor located in said cloud configured to: reconstruct said packetized surface data to generate said compressed surface data; decompress the size of said compressed surface data to generate said surface data; i. compute said surface properties using said surface data; ii. compute said surface analytics from said surface properties and a prior information set, wherein said prior information set is a collection of surface properties, previously stored in said cloud, obtained by inspecting at least two surfaces, whereby said surface properties and said surface analytics are reliably used to improve the yield of an article produced with said surface.

20110137876

12767354

1. A method comprising: encoding knowledge about a topic domain into a data modeling technique; generating a set of candidate conditional functional dependencies based on a data set of said topic domain; and applying said set of candidate conditional functional dependencies and said data modeling technique encoded with said topic domain knowledge to said data set to obtain a plurality of data quality rules for said data set.

20030206171

10428818

1. An apparatus for creating a three-dimensional caricature based on a user's input facial image, comprising: a memory unit for storing ASM(Active Shape Model)s required to create the three-dimensional caricature, and a three-dimensional polygon face basic model; a pre-processing unit for detecting positions of eyes from the input facial image and normalizing the size of the facial image; a facial feature initial position-detecting unit for detecting each initial position for facial features from the normalized facial image; an ASM loading unit for loading the ASMs stored in the memory unit in the normalized facial image so as to correspond to the detected initial position; an ASM adapting unit for adapting the loaded ASMs so as to extract feature points for the facial features; and a caricature-creating unit for creating a three-dimensional polygon face caricature by loading the three-dimensional polygon face basic model from the memory unit and then modifying the loaded three-dimensional polygon face basic model according to coordinate values of the feature points extracted through the ASM adapting unit.

8633686

11807196

1. A method of determining an anhysteretic B-H characteristic of a material utilizing a primary coil and a secondary coil wound around a test sample of the material, comprising: holding the test sample in a test fixture with the primary coil and the secondary coil wound around the test sample; introducing a range of electric current through the primary coil and electronically recording the primary coil current and resulting secondary coil voltage over time; determining a measured magnetizing flux linkage versus primary coil current characteristic values for the sample from the recorded primary coil current and secondary coil voltage; establishing an expression for predicted magnetizing flux linkage incorporating spatial variation in the magnetic field intensity within the sample and based in part on anhysteretic magnetic properties of the material; and utilizing said expression to obtain values of characterizing parameters that cause a curve to fit within said measured magnetizing flux linkage versus primary coil current characteristic values, wherein said anhysteretic B-H characteristic is defined by said characterizing parameter values.

20110196661

12770268

1. A system for estimating the volume, mass or weight of an individual animal comprising: a representation of an individual animal, generated from an image-capturing machine, stored on a computer readable medium; a virtual spatial model of a reference object; and a computer particularly programmed with the capability of reshaping the virtual spatial model, the representation of the individual animal, or both, into an approximate fit with each other; wherein the computer is particularly programmed to estimate the mass, weight, or volume of the individual animal as a function of an amount that the virtual spatial model, the representation of the individual animal, or both, were reshaped.

20140355843

14364280

1. A three-dimensional face recognition method based on intermediate frequency information in a geometric image, the method comprising: step 1: making a pre-treatment to a test model and a pre-treatment of a library model, wherein the making of either pre-treatment involves: step 1.1 face cutting: locate a nose tip point from a plurality of points according to a shape index feature and geometric constraints of a facial point cloud, define a sphere with the nose tip point as a center of the sphere and a radius of 90 mm, discard outside points of the plurality of points that are outside of the sphere, wherein the facial point cloud comprising the points that are within the sphere; step 1.2 facial surface smoothing: carry out posture correction for the facial point cloud by principal component analysis (PCA), to obtain 3 orthogonal axial directions; take the nose tip point as an origin, choose a first eigenvector corresponding to a maximum eigenvalue as Y-axis, choose a second eigenvector corresponding to a minimum eigenvalue as Z-axis, establish a right-handed spatial three-dimensional coordinate system, wherein each point in the facial point cloud can be denoted by coordinates x, y, and z uniquely in the right-handed spatial three-dimensional coordinate system; and carry out triangularization for the facial point cloud in the right-handed spatial three-dimensional coordinate system, to obtain spatial triangular meshes; smooth the facial triangular meshes with a mesh-based smoothing algorithm for 10 iterations for noise removal, to obtain surface smoothed three-dimensional facial meshes; step 1.3 cutting of upper face discard the points below a y=−10 plane among the three-dimensional facial meshes, and keep an upper part of the three-dimensional face that is less affected by a facial expression; and step 1.4 diluting of facial point cloud: take samples from the facial point cloud evenly by a spatial distance at 1 mm interval, to obtain a diluted point cloud; carry out triangular meshing for the diluted point cloud, calculate and save side lengths γ l1 , γ l2 , γ l3 (l=1,2,. .. , η) of each spatial triangular patch among the three-dimensional facial meshes, where, η is a number of the spatial triangular patches among the three-dimensional facial meshes, and an average side length among all triangular patches is γ ; if any spatial triangular patch has a side in length greater than 4 γ , discard the spatial triangular patch but keep vertexes of the spatial triangular patch; step 2 mapping and extending of intermediate frequency information in a geometric image: step 2.1 mapping the coordinate information of facial point clouds of the test model and the library model to a plane respectively, to form geometric images of the test model and the library model respectively, wherein the method for obtaining the geometric images is as follows: step 2.1.1 mesh parameterization: map boundary points among the pre-treatment of the three-dimensional facial meshes to four sides of a square with a size of 512×512 pixels, and map non-boundary points among the three-dimensional facial meshes except the boundary points to an area within the square by mesh parameterization, to obtain planar meshes φ; take any vertex of the square as origin, take the directions in which two sides intersect at the origin as positive directions, and establish a counter-clockwise coordinate system MON, wherein any point in the plane can be denoted uniquely by coordinates m and n; on the four sides of the square, take b points evenly in a counter-clockwise direction, starting from the origin, wherein the coordinates of the samples are (m t 0 , n t 0 ) (t=1,2,. .. b), b is a number of boundary points among the three-dimensional facial meshes; and denote the vertexes of the three-dimensional facial meshes as f q (q=1,2,. .. , τ), where, τ is a number of vertexes, coordinates of corresponding points mapped from the vertexes to the area within the square are (m q , n q ), where, m q and n q are solutions of the following linear equation set: { Lm q = Ln q = 0 , ∀ f q ∉ B m q = m q 0 , n q = n q 0 , ∀ f q ∈ B , where, L is a Laplacian matrix of the three-dimensional facial meshes, and B is the boundary points among the three-dimensional facial meshes; and step 2.1.2 generating a geometric image: append the three-dimensional coordinates of the vertexes f q =(x q ,y q ,z q ) among the three-dimensional facial meshes to the corresponding points (m q ,n q ) as attributes of the points (m q ,n q ); determine the attributes of the points within the area within the square by linear interpolation, to obtain a two-dimensional image with three-dimensional coordinate attributes, which is referred to as a geometric image G; and step 2.2 Filter the geometric images G of the test model and the library model respectively, to obtain intermediate frequency information of the test model and the library model, wherein a method of filtering geometric image is as follows: step 2.2.1 carrying out multi-scale Haar wavelet filtering for a geometric image G; step 2.2.1.1 carrying out row transformation and column transformation for the geometric image G with a Haar transformation matrix sequentially, to obtain a low frequency coefficient set and horizontal, vertical, and diagonal high frequency coefficient sets; denote the low frequency coefficient set as LL 1 , and denote the horizontal, vertical, and diagonal high frequency coefficient sets as HL 1 , LH 1 , and HH 1 respectively; and step 2.2.1.2 carrying out Haar wavelet filtering for the low frequency coefficient set LL 1 again as that described in step 2.2.1.1, and output secondarily filtered low frequency coefficient set and horizontal, vertical, and diagonal high frequency coefficient sets, which are denoted as LL 2 , HL 2 , LH 2 , and HH 2 respectively; repeat the filtering for 5 cycles, with the low frequency coefficient set obtained in a previous cycle as an input in each filtering cycle, and output new low frequency coefficient set and horizontal, vertical, and diagonal high frequency coefficient sets; and step 2.2.2 extracting intermediate frequency information from a geometric image: extracting and saving a horizontal high frequency coefficient set HL 5 , vertical high frequency coefficient set LH 5 , and diagonal high frequency coefficient set HH 5 that are outputted in a final filtering cycle, take HL 5 , LH 5 , and HH 5 as attributes of the pixels, and construct three images in size of 16×16 pixels, which are referred to as horizontal intermediate frequency information image, vertical intermediate frequency information image, and diagonal intermediate frequency information image respectively; step 3: calculating a degree of similarity between the test model and the library model with wavelet domain structure similarity algorithm respectively, wherein a method of calculation is as follows: step 3.1 calculating an HL degree of similarity S HL between the horizontal intermediate frequency information image of the test model and the horizontal intermediate frequency information image of the library model, a LH degree of similarity S LH between the vertical intermediate frequency information image of the test model and the vertical intermediate frequency information image of the library model, and an HH degree of similarity S HH between the diagonal intermediate frequency information image of the test model and the diagonal intermediate frequency information image of the library model, sum up S HL , S LH , and S HH , and take the sum as the degree of similarity between the test model and the library model, wherein; the S HL , S LH , and S HH are obtained with the horizontal intermediate frequency information image, vertical intermediate frequency information image, and diagonal intermediate frequency information image to be matched and are calculated with wavelet domain structure similarity algorithm; the wavelet domain structure similarity algorithm is as follows: step 3.1.1 sorting the x of all pixels in the horizontal intermediate frequency information image, vertical intermediate frequency information image, and diagonal intermediate frequency information image in an order of corresponding pixel respectively, according to the three attributes (x, y, z) of each pixel in the horizontal intermediate frequency information image, vertical intermediate frequency information image, and diagonal intermediate frequency information image, and construct an x channel for the horizontal intermediate frequency information image, vertical intermediate frequency information image, and diagonal intermediate frequency information image respectively; construct and obtain y channels and z channels, similarly, for the horizontal intermediate frequency information image, vertical intermediate frequency information image, and diagonal intermediate frequency information image, and denote the channels as: C χ = [ c 1 , 1 c 1 , 2 … c 1 , 16 c 2 , 1 c 2 , 2 … c 2 , 16 ⋮ ⋮ ⋱ ⋮ c 16 , 1 c 16 , 2 … c 16 , 16 ] , where, χ is x, y, or z, C χ represents x channel, y channel, or z channel, c 1,1 is an element in row 1 and column 1 in C χ , c 1,2 is an element in row 1 and column 2 in C χ ,. .. , c 2,1 is an element in row 2 and column 1 in C χ ,. .. , and C 16,16 is an element in row 16 and column 16 in C χ ; the horizontal intermediate frequency information image, vertical intermediate frequency information image, or diagonal intermediate frequency information image is referred to as an intermediate frequency information image; calculate a degree of similarity s x of x channel, a degree of similarity s y of y channel, and a degree of similarity s z of z channel between two intermediate frequency information images to be matched, sum s x , s y , and s z , and take the sum as the degree of similarity S HL , S LH , or S HH between the two intermediate frequency information images to be matched, wherein the s x , s y , and s z are obtained with the following method: the x, y, or z channel of the intermediate frequency information image of the test model is represented by C p χ = [ c 1 , 1 p c 1 , 2 p … c 1 , 16 p c 2 , 1 p c 2 , 2 p … c 2 , 16 p ⋮ ⋮ ⋱ ⋮ c 16 , 1 p c 16 , 2 p … c 16 , 16 p ] , and a same channel of the corresponding intermediate frequency information image of the library model is represented by C g χ = [ c 1 , 1 g c 1 , 2 g … c 1 , 16 g c 2 , 1 g c 2 , 2 g … c 2 , 16 g ⋮ ⋮ ⋱ ⋮ c 16 , 1 g c 16 , 2 g … c 16 , 16 g ] , where, p indicates C p χ comes from the test model, g indicates C g χ comes from the library model; a number of rows and a number of columns of elements in C p χ and C g χ are denoted as α and β respectively, a 3×3 pixel neighboring region in C p χ is represented by C p χ ( α , β ) = [ c α - 1 , β - 1 p c α - 1 , β p c α - 1 , β + 1 p c α , β + 1 p c α , β p c α , β + 1 p c α + 1 , β + 1 p c α + 1 , β p c α + 1 , β + 1 p ] , the element c α,β p is a central element in the 3×3 pixel neighboring region in C p χ , a 3×3 pixel neighboring region in C g χ is represented by C g χ ( α , β ) = [ c α - 1 , β - 1 g c α - 1 , β g c α - 1 , β + 1 g c α , β + 1 g c α , β g c α , β + 1 g c α + 1 , β + 1 g c α + 1 , β g c α + 1 , β + 1 g ] , , the element c α,β g is a central element in the 3×3 pixel neighboring region in C g χ , and the structure similarity {tilde over (s)} χ (α,β) between c α,β p and c α,β g is: s ~ χ ( α , β ) = ( 2 ∑ e 1 = α - 1 α + 1 ∑ e 2 = β - 1 β + 1  c e 1 , e 2 p   c e 1 , e 2 g  + 0.1 ) ( 2  ∑ e 1 = α - 1 α + 1 ∑ e 2 = β - 1 β + 1 c e 1 , e 2 p ( c e 1 , e 2 g ) *  + 0.1 ) ( ∑ e 1 = α - 1 α + 1 ∑ e 2 = β - 1 β + 1  c e 1 , e 2 p  2 + ∑ e 1 = α - 1 α + 1 ∑ e 2 = β - 1 β + 1  c e 1 , e 2 p  2 + 0.1 ) ( 2 ∑ e 1 = α - 1 α + 1 ∑ e 2 = β - 1 β + 1  c e 1 , e 2 p ( c e 1 , e 2 g ) *  + 0.1 ) where, e1 and e2 represent a row suffix and a column suffix of the elements in C p χ (α,β) and C g χ (α,β) and (c e1,e2 g )* is a conjugate value of c c1,c2 g ; Let α=2,3,. .. , 15, β=2,3,. .. ,15, take the average of {tilde over (s)} χ (α,β) as the structure similarity between C p χ and C g χ : s χ = 1 196 ∑ α = 2 15 ∑ β = 2 15 s ~ χ ( α , β ) ; and step 4: identity recognizing of three-dimensional face: repeat steps 1 to 3, to obtain the degree of similarity between the test model and each library model, compare the degrees of similarity between the test model and the library models, and judge that the library model with a maximum degree of similarity is a same identity as the test model.

20130130217

13301641

1. An e-learning lesson delivery platform comprising: (a) a digital processing device that is optionally connected to a computer network, wherein said processing device comprises an operating system configured to perform executable instructions; and (b) a computer program, provided to said digital processing device, including executable instructions that create a lesson delivery server, wherein said server comprises: i. a plurality of learning activities, wherein said activities are organized according to an instructional plan designed to accomplish one or more educational objectives in at least one area of skill, interest, or expertise, wherein said plan identifies one or more learning activities for use in a guided environment and identifies one or more learning activities for assignment as independent work; ii. a software module for displaying and providing access to said one or more learning activities in a guided environment, wherein said module is adapted to support a mentor's implementation of said instructional plan; iii. a software module for assigning said one or more learning activities as independent work to one or more learners, wherein said module is only accessible by a mentor; and iv. a software module for displaying and providing access to learning activities assigned as independent work, wherein said module is accessible by a mentor or a learner.

20140032657

13557200

1. A computer implemented method for collecting behavioral data from a user comprising: collecting information regarding a user's consumption of digital content using a computer device; determining at least one topic associated with the content being consumed by the user; presenting to the user on the computer device an initial survey associated with the at least one topic associated with the content being consumed, the initial survey including an inquiry regarding whether the user planned an action with regard to the at least one topic; and presenting to the user on the computer device, after the initial survey and in response to the user affirming the planned action with regard to the at least one topic, a follow on survey comprising at least one inquiry regarding whether the user performed the planned action.

8730242

12800526

1. A method of performing time slice-based visual prediction, comprising: presenting for display a user-adjustable moveable element; applying smoothing to data values in a data set to generate smoothed data values, wherein moving the moveable element causes a smoothing time interval over which the smoothing is applied to change in length; calculating, by at least one processor, a weighted moving aggregate of the smoothed data values over previous time slices to predict data values; generating, by the at least one processor, a visual accuracy indicator to indicate a quality of prediction of the predicted data values at different times, wherein the visual accuracy indicator includes accuracy indications representing respective accuracies of the predicted data values, wherein moving the moveable element causes visual changing of the accuracy indications; presenting, in a visualization for display by a display device, data values from the data set and the predicted data values, wherein the data values from the data set and the predicted data values are represented as corresponding cells; and presenting the visual accuracy indicator including the accuracy indications for display by the display device.

7615353

12498183

1. A method of identifying a human tumor as likely to be responsive or non-responsive to treatment with tivozanib, comprising: (a) measuring, in a sample from the human tumor, the relative expression level of each gene in a predictive gene set (PGS), wherein the PGS consists of the following genes: AIF1, APBB1IP, ARHGAP30, C3AR1, CCR1, CD37, CD53, CD86, CLEC7A, CSF1R, CSF2RB, CTSS, CYBB, DOCK2, EVI2A, EVI2B, FPR3, GMFG, GPR65, HCK, HCLS1, HLA-DMA, IL10RA, ITGB2, LAIR1, LCP1, LCP2, LILRB1, LILRB2, LST1, LY86, MNDA, MS4A6A, MYO1F, NCF4, SLA, SLAMF8, TLR1, TYROBP, PLEK, CYTH4, and PTPRC; and (b) calculating a PGS score according to the algorithm PGS. score = 1 42 * ∑ i = 1 42 ⁢ ⁢ Ei wherein E 1 , E 2 ,. .. E 42 are the expression values of the 42 genes in the PGS, and wherein a PGS score below a defined threshold indicates that the tumor is likely to be responsive to tivozanib, and a PGS score above the defined threshold indicates that the tumor is likely to be resistant to tivozanib.

20080263104

12146782

1. A method on an information processing system for modifying at least one data warehouse schema based on detected changes in an associated observation model, the method comprising: determining if at least one new observation model has been created; determining if at least one existing observation model is associated with the new observation model; in response to the existing observation model being associated with the new observation model, identifying at least one changed attribute by comparing the new observation model and the existing observation model; updating a set of files associated with the existing observation model to reflect the changed attribute between the new observation model and the existing observation model.

20050100217

10702663

1. A method of associating input handwritten characters with specific characters from a group of known characters, comprising: classifying input handwritten characters as print or cursive; analyzing print-classified handwritten characters using a first set of character recognition steps; and analyzing cursive-classified handwritten characters using a second set of character recognition steps, the second set of character recognition steps including at least one recognition algorithm not implemented in the first set.

8606743

13239882

1. A method to calculate time weight in an RDF graph, comprising: providing one or more triples of the RDF graph to an inference engine module, the one or more triples comprising a time information; providing an epoch time to the inference engine; calculating an elapsed time from the epoch time to the time value; and inversely weighting the time information by the elapsed time to provide a calculated time weight.

20040166506

10632393

1. A method of performing a primer extension reaction, comprising: obtaining an amplicon having a sequence generated from a target nucleic acid and a sequence generated from a first strand amplification primer, by amplifying a target nucleic acid having a variant nucleotide flanked by an invariant nucleotide, wherein a first strand amplification primer is employed that comprises a 5′ tag substantially incapable of hybridizing to the target nucleic acid under amplification conditions, and wherein the 5′ tag contains the variant nucleotide of the target nucleic acid, and employing a second strand amplification primer; employing the amplicon in a primer extension reaction wherein the identity of the variant nucleotide in the sequence generated from the target nucleic acid is determined by hybridizing a first identification primer immediately adjacent to the variant nucleotide in the sequence generated from the target nucleic acid; hybridizing a second identification primer immediately adjacent to the variant nucleotide in the sequence generated from the amplification primers; extending the first and the second identification primers in the presence of one or more nucleotides and a polymerizing agent; determining the identity of the variant nucleotide generated from the target nucleic acid; and comparing extension product of the first identification primer and extension product of the second identification primer, thereby performing the primer extension reaction.

8924328

13537288

1. A method of generating a configuration advisory tool constructed and arranged to provide optimized configurations for data storage systems located at remote sites on a network in response to configuration queries from the data storage systems, the method comprising: receiving current storage system data from a particular data storage system located at a particular remote site on the network; storing the current storage system data in a database that stores previous storage system data that had been received from previous data storage systems located at the remote sites on the network prior to receiving the current storage system data; and generating, on a host computer, a predictive model configured to output particular values of configuration management parameters to the remote site on the network in response to the host computer receiving values of input parameters that are indicative of a configuration query, the predicative predictive model including model parameters based on the current storage system data and the previous storage system data, the particular values of the configuration parameters being indicative of an optimal configuration of the data storage system located at the remote site on the network.

20080037846

11463845

1. A method comprising using a point spread function based rule to classify regions in a dataset.

7979252

11766547

1. A computer-implemented system that facilitates model enhancement, comprising: at least one processor configured to provide a modeling component that builds and runs a model based on data associated with a user, the model indicating interruptability of the user; and a sampling component that determines a time at which to obtain additional data associated with the user for building the model, the data being obtained by probing the user, and the time being determined based on: failure analysis of the model; and a state of the user.

20050021317

10613366

1. A method to select features for maximum entropy modeling, the method comprising: determining gains for candidate features during an initialization stage and for only top-ranked features during each feature selection stage; ranking the candidate features in an ordered list based on the determined gains; selecting a top-ranked feature in the ordered list with a highest gain; and adjusting a model using the selected using the top-ranked feature.

20080294941

11630700

1. A method of generating a test case for an application or system modelled using a Stochastic Automata Network model Including a plurality of automata, including the steps of: a) setting an initial global state as the current global state ( 100 ), wherein a global state comprises a set of local states each corresponding to one of the automata; b) creating a record of the initial global state ( 101 ); c) selecting an event ( 103 ) from a set of events that can be applied to the current global state; d) creating a record of the selected event ( 104 ); e) identifying those of the automata affected by the selected event ( 105 ) and updating the current global state by updating the states of the affected automata ( 106 ); f) creating a record of the current global state ( 107 ); and repeating steps c) to f) until a termination condition is satisfied.

9814196

15065304

1. A plant, a plant part, or a seed of soybean variety SJ1312462, wherein a representative sample of seed of said soybean variety SJ1312462 has been deposited under ATCC Accession Number PTA-122773.

20090228472

12044267

1. A computer-implemented method comprising: selecting a first search object from a set of search objects; and performing a pairwise comparison between the first search object and each other search object from the set of search objects to generate a rank distribution for the first search object, wherein the pairwise comparison is based on a score distribution for each of the set of search objects, wherein the rank distribution comprises a set of probabilities, each probability corresponding to the probability that the first search object has a particular rank.

7512519

11563048

1. A method of determining accuracy of predicted system behavior comprising: creating a plurality of noise adjusted analytical models by adding noise to each of a plurality of analytical performance models, wherein each noise adjusted analytical model is an analytical performance, queuing model specifying behavior of a queuing system and is associated with a set of predefined analytical model parameters, wherein the queuing system represented by each of the noise adjusted analytical models comprises a plurality of nodes with each node being represented as a queuing server; inference processing the noise adjusted analytical models to derive a set of inferred analytical model parameters for each noise adjusted analytical model that, if provided to a network solver, produces the noise adjusted analytical model from which the inferred analytical model parameters were inferred, wherein each set of inferred analytical model parameters depends upon a current noise adjusted analytical model and each prior noise adjusted analytical model; and for each set of inferred analytical model parameters, determining a measure of error between the set of inferred analytical model parameters and the set of predefined analytical model parameters associated with the noise adjusted analytical model from which the set of inferred analytical model parameters was derived.

20080243665

12132499

1. A retirement planning method for computing a possible future value of a portfolio of an investor, comprising the steps of: employing a data processing system for: (a) receiving user inputs comprising an initial value of the portfolio and a current age of the investor; (b) randomly drawing a number between 0 to 1 ; (c) determining a mortality rate of the investor in accordance with a mortality table based on the current age of the investor; (d) comparing the randomly drawn number with said determined mortality rate using a preselected logical relation such that: ( 1 ) if the randomly drawn number satisfies said preselected logical relation with said determined mortality rate of the investor, define the current age as the age of death of the investor; ( 2 ) if the randomly drawn number does not satisfy said preselected logical. relation with said determined mortality rate, advance the current age of the investor to a next age group indicated in the mortality table and repeat steps (b) through (d); (e) computing a future value of the portfolio using the age of death defined in step (d)( 1 ), a predetermined rate of return, and the initial value of the portfolio; and (f) outputting the computed future value of the portfolio.

7689227

11366958

1. A method for performing a two-level, weight-based hash comprising: receiving a list of frequency band classes, each of said frequency band classes having a weight assigned; hashing a mobile station to a frequency band class based on the weight assigned to each of said frequency band classes; receiving a list of frequency band subclasses for the frequency band class to which the mobile station is hashed, each frequency contained within the subclasses having a weight assigned; hashing the mobile station to a subclass channel in the band class based on the weight assigned to each of said frequencies contained within the subclasses; and using the channel for subsequent communications.

9613074

14334069

1. A computer-implemented method executed by one or more processors, the method comprising: identifying a source dataset from a source database; extracting a schema defining the source database; analyzing data within the source dataset to generate a value model, the value model describing features of data in each column of each table of the source dataset; analyzing data within the source database to determine data dependency; and generating a data specification file combining the extracted schema, the value model, and the data dependencies.

20020123690

10098069

1. An infrared (IR) sensing assembly which measures temperature of an object, the sensing assembly comprising: a substrate having a first surface and a second surface; a first sensing element connected to the first surface, wherein the first sensing element receives IR radiation from an IR source; a second sensing element connected to the second surface, wherein the sensing element being substantially shielded from IR radiation; and at least one thermal insulation layer surrounding at least a portion of the substrate, first sensing element, and second sensing element.

9315806

14411628

1. A genetic construct comprising: a DNA element; a first compatible end element and a second compatible end element flanking the DNA element, wherein the first and second compatible end elements are capable of annealing to each other; a barcode element; a third compatible end element and a fourth compatible end element flanking the barcode element, wherein the third and fourth compatible end elements are capable of annealing to each other but are not capable of annealing to the first or second compatible end elements; and a separation site located between the fourth compatible end element and the first compatible end element, wherein the DNA element, first compatible end element and second compatible end element are on one side of the separation site, and the barcode element, third compatible end element and fourth compatible end element are on the other side of the separation site.

20090094216

12061742

1. A method for transforming a concept-based query into SQL query statements, comprising the steps of: transforming an inputted concept-based query into logic rules; checking validity of said logic rules; optimizing logic rules that are valid; and translating optimized logic rules into SQL query statements.

20100010949

12498760

1. A learning device comprising: a plurality of learning modules, each of which performs update learning to update a plurality of model parameters of a pattern learning model that learns a pattern using input data; model parameter sharing means for causing two or more learning modules from among the plurality of learning modules to share the model parameters; and classification means for classifying the plurality of learning modules on the basis of the plurality of model parameters of each of the learning modules after the update learning.

20070088730

11252099

1. A cross hierarchy performance management system comprising: a database comprising: performance management data aggregated and organized into a first hierarchy, a second hierarchy, and a cross hierarchy between the first hierarchy and the second hierarchy; a performance management target set by the performance management data in the cross hierarchy; an aggregated metric associated with the cross hierarchy; a memory comprising a performance management reporting program operable to: obtain a portfolio view selection from an operator; retrieve a portion of the performance management data including the performance management target based on the portfolio view selection; render the portion of the performance management data and aggregated metrics as a portfolio view; and; a processor coupled to the database and the memory which executes the performance management reporting program.

9396271

14106860

1. A method to convert subjective metadata into statistical opinion data, comprising: a computer application launched by a user reacting to a web resource, said web resource comprising a uniform resource identifier or a local device file of said user which is assigned a uniform resource identifier by said application, said application comprising a survey comprising: a plurality of opinion choices, a graphical slider comprising a slide knob permitted to move along a slide track, functions permitting classification of said web resource and entry of comments and tags by said user, and a function to save inputs to said survey; creating subjective metadata regarding a web resource, comprising: said survey displays said plurality of opinion choices singularly or collectively, wherein an opinion choice consists of two words, each the antonym of the other and displayed on opposing ends of said slide track, wherein each said antonym names a specific human value, belief, opinion, feeling, emotion, or judgment, wherein one said antonym is negatively valued and its pairing antonym is positively valued, wherein user movement of said slide knob towards a said antonym indicates a said user subjective opinion in regards to a web resource and said subjective opinion is subjective metadata linked to said web resource, wherein a plurality of said subjective opinions may be indicated by said user during said survey, wherein additional said subjective metadata is created by said comments and tags entered by said user, and wherein said application requires said user to indicate at least one said subjective opinion prior to permitting said function to save inputs; objectification of said subjective metadata, comprising: said application assigns a maximum value of negative 10.00 to said negatively valued antonyms and a maximum value of positive 10.00 to said positively valued antonyms, said graphical slider further comprises a text box within which an opinion score is generated by said movement of said slide knob, wherein said movement is calibrated to display said opinion score ranging from negative 10.00 to positive 10.00 in increments of negative 0.01 if said movement is towards a negatively valued antonym or in increments of positive 0.01 if said movement is towards a positively valued antonym, wherein said application assigns said opinion score to a said subjective opinion when said user creates said opinion score for a subsequent said opinion choice or activates the said function to save inputs, and wherein an assigned opinion score objectifies said subjective metadata by combining said subjective opinion with a mathematical value represented by said opinion score; creating and outputting statistical opinion data, comprising: said user activating said function to save inputs creates an opinion survey, said opinion survey comprises database entry and storage of at least one said opinion score, uniform resource identifier data regarding a web resource, data captured by said functions permitting classification of said web resource and entry of comments and tags, and relational data elements, wherein said relational data elements comprise available and inferable network, analytic, geographic, and demographic data linking said opinion survey to said web resource and said user, wherein said database comprises the sum of all data from all said opinion surveys, wherein said database is cross referenced and searchable by query, wherein outputs comprising a subset of said web resources or said users containing said opinion survey data matches to said query are forward lookup statistical opinion data, and wherein outputs comprising cumulative said opinion score rankings of said users or said web resources by respective said antonyms are reverse lookup statistical opinion data.

20150339587

14283377

1. An adaptive fuzzy rule controlling system for a software defined storage (SDS) system to control performance parameters in a storage node, comprising: a traffic monitoring module, for acquiring observed values of performance parameters in the storage node; an adaptive neural fuzzy inference module, for learning a dynamic relationship between configurations of a plurality of storage devices in the storage node and the performance parameters during a period of time, and outputting fuzzy rules which is built according to the dynamic relationship; a traffic forecasting module, for providing forecasted values of the performance parameters in a particular point in time in the future; and a fuzzy rule control module, for arranging the configuration of the storage devices in the storage node in the particular point in time in the future according to the fuzzy rules and the forecasted values so that a specified value of one specified performance parameter can be achieved in the particular point in time in the future, wherein the storage node is operated by SDS software.

9940433

15458549

1. A method comprising: obtaining a DNA sample from an individual; determining the genotype of the individual based on the DNA sample; accessing, from a non-transitory computer readable storage medium of a computing device, a plurality of reference panels each associated with one of a plurality of different communities, each community comprising a plurality of nodes, each node corresponding to a reference genotype of a different reference individual; determining, with a computer processor associated with the computing device, amounts of DNA overlap between the genotype and each of the reference panels; for each reference panel, inputting the amount of DNA overlap between the genotype and the reference panel into a model specific for a community associated with the reference panel to generate a score for the community with respect to the genotype, the model comprising a first set of features defined from amounts of DNA overlap between reference individuals in the reference panel of that community and a second set of features defined as the number of connections between the individual and individuals in one of the reference panels within specified ranges of amounts of estimated IBD; and generating, with the computer processor, a report summarizing the communities for which the individual's score exceeds a threshold.

8014580

11931030

1. A method for determining a pixon map for pixon smoothing of an object based on a data set, the method comprising: receiving the data set; receiving an input object associated with the data set; determining, in a series of steps, statistical objects for a set of pixon kernel functions, wherein each step includes selecting a pixon kernel function from the set of pixon kernel functions, smoothing the input object on the basis of the selected pixon kernel function, thereby creating a smoothed object, and determining the statistical object for the selected pixon kernel function on the basis of the smoothed object, the data set, and a Mighell-like statistical weight; determining contributions of the pixon kernel functions to the pixon map based on the statistical objects; and assigning values to the pixon map corresponding to the contributions of the pixon kernel functions.

20160072457

14794412

1. A radio frequency system configured to facilitate wireless communication between electronic devices, wherein the radio frequency system comprises: a power amplifier configured to amplify an analog electrical signal received from a transceiver to generate an amplified analog electrical signal using a power amplifier supply voltage, wherein the amplified analog electrical signal is configured to be wirelessly transmitted from the radio frequency system at a desired output power; and an adjustable power supply circuitry configured to output the power amplifier supply voltage, wherein the adjustable power supply circuitry is configured to: output the power amplifier supply voltage with a first magnitude based at least in part on a first detrough function when the desired output power is a first output power; and output the power amplifier supply voltage with a second magnitude based at least in part on a second detrough function when the desired output power is a second output power, wherein the first magnitude and the second magnitude are different, the first detrough function and the second detrough function are different, and the first output power and the second output power are different.

20090245633

12308235

1. A method comprising restoring iteratively at least one color component in an input image with a deblur parameter, increasing said deblur parameter at each iteration, stopping the iteration when an overshooting in the final image exceeds a predetermined value, defining the number of iterations and determining quality of an image is determined according to the number of iterations.

8781804

12902928

1. A method of estimating a load carrying capacity of a bridge, the load carrying capacity estimation method comprising: estimating, by a processor, a mode coefficient of the bridge using an acceleration signal supplied from an accelerometer that is installed in the bridge; updating, by the processor, an analysis model of the bridge using the estimated mode coefficient; applying, by the processor, a dead load to the updated analysis model, to estimate a first stress that is generated in the bridge using the dead load applied to the updated analysis model; applying, by the processor, a design live load to the updated analysis model, to estimate a second stress that is generated in the bridge using the design live load applied to the updated analysis model; estimating, by the processor, a rating factor of the bridge by a permission stress method using the first stress and the second stress; estimating, by the processor, the load carrying capacity of the bridge using the design live load and the rating factor of the bridge; doubly integrating, by the processor, the acceleration signal when a vehicle or vehicles pass the bridge to estimate displacement of the bridge during passing of the vehicle or vehicles; applying, by the processor, the dynamic displacement to a low-pass filter, to estimate static displacement of the bridge; and obtaining, by the processor, an impact coefficient from the dynamic and static displacement of the bridge in which the impact coefficient is a ratio of a difference between the dynamic displacement and the static displacement with respect to the static displacement, wherein the load carrying capacity of the bridge is estimated using the design live load, the rating factor, and the impact coefficient of the bridge.

20160154778

14558146

1. A method for modeling tabular data with mixed column and pivot table layout, the method comprising: receiving tabular data containing a set of columns and rows, from a data source, wherein columns in the set of columns contain a category label and metric data; determining, for one or more columns in the set of columns, a corresponding metric type; identifying grouped and ungrouped columns in the set of columns, wherein grouped columns comprise one or more pivot type columns, wherein a pivot type column corresponds to a column sharing a metric data type with an adjacent column, and wherein ungrouped columns comprise one or more columnar columns, wherein a columnar column corresponds to a column having unique metric types compared to adjacent columns; and generating a table comprising a first sub-table and a second sub-table, wherein the first sub-table comprises the tabular data of the ungrouped columns and the second sub-table comprises the tabular data of the grouped columns.

8645321

11443364

1. A system, comprising: a first database implemented in a computer-readable storage medium and comprising a data entity and a first data model that models the data entity, wherein the first database is communicatively coupled to a network; an agent comprising a data rule concerning asynchronous updates to the data entity; a plurality of heterogeneous second databases implemented in a computer-readable storage medium and comprising the data entity having respective second data models modeling the data entity, wherein the second databases are communicatively coupled to the network; and a messaging module comprising the agent and configured to send a synchronization message across the network to at least one of the second databases according to the data rule and the first data model, the data rule providing mapping between the data entity as modeled by the first data model and the data entity as modeled by at least one of the respective second data models.

20110295559

13105329

1. A system for maintaining intelligent assets, comprising: a processor; and memory connected to the processor, wherein the memory is encoded with instructions and wherein the instructions when executed comprise: instructions for a work performance acquisition module to obtain work performance data of the intelligent assets from a device operation production control system; instructions for a status monitoring module to obtain operation status data of the intelligent assets; instructions for a loss calculation module to calculate a loss degree of the intelligent assets according to the obtained work performance and operation status data; and instructions for a maintenance determining module to determine whether the intelligent assets need maintenance according to the loss degree of the intelligent assets.

7542971

10768675

1. A method for collaborative note taking based on a speech of a speaker and providing a summary to a user in an audience of the speaker, the method comprising: receiving a first set of information from the speech; performing speech recognition on the first set of information and determining selected portions of the speech; determining portions of context information corresponding to a domain information from a presentation information source temporally associated with the selected portions of the speech; determining at least one language model based on the selected portions of the speech and the temporally associated portions of context information from the presentation information source, wherein the at least one language model is dynamically determined; applying the language model to the first set of information to extract salient tokens from the first set of information; verifying relevance of the salient tokens based on the presentation information source to obtain verified tokens; generating the summary including the extracted salient tokens, wherein generating the summary includes assembling the verified tokens; displaying the summary to the user; and receiving collaborative user feedback information relating to the summary and adjusting the language model according to the collaborative user feedback, wherein the method is implemented by a computer.

7526628

11538312

1. A computer-implemented method for optimizing cache efficiency, comprising the steps of: receiving a set of configuration parameters, the configuration parameters comprising optimization parameters; locating a set of functions to be optimized in an application; generating one or more ordering permutations of the functions for placement in a cache memory; rejecting permutations of the one or more ordering permutations that do not satisfy the optimization parameters; and selecting a permutation from the one or more ordering permutations, wherein the selected permutation satisfies the optimization parameters to a greater degree than the remaining one or more ordering permutations, wherein the optimization parameters comprise a parameter indicating the maximum acceptable limit for hits per line in the cache and a parameter indicating the maximum acceptable limit for the number of cache lines which are at the maximum acceptable limit for hits per line in the cache.

7657874

10924544

1. A generic type parameter constraint system embodied on a computer readable storage medium comprising: a processor; a memory communicatively coupled to the processor, the memory having stored therein computer-executable instructions to implement the system, including: a generic type parameter component that receives at least one generic type declaration that instantiates a generic type, wherein the at least one generic type declaration includes: a type identifier that uniquely identifies the generic type; a type parameter that is a placeholder for one or more instantiation types that are assigned at instantiation; one or more pattern types that define a set of one or more constraints on the one or more instantiation types of the type parameter; and one or more keywords that define a plurality of different types of comparisons to utilize to determine whether the one or more constraints have been satisfied, wherein the plurality of different comparison types comprises using multiple instantiation types, multiple pattern types and a plurality of different relationships between the instantiation types and pattern types; and a comparison component that determines conformity of instantiation of the generic type by the generic type declaration based upon examining whether assignment of the one or more instantiation types to the type parameter satisfies the one or more constraints defined by the one or more pattern types based on the plurality of different comparison types defined by the one or more keywords, wherein the comparison component generates an error upon the one or more instantiation types not satisfying the one or more constraints defined by the one or more pattern types based on the plurality of different comparison types defined by the one or more keywords, wherein the generic type and the pattern type are defined as types in a type system.

20180101173

15289882

1. A drone, comprising: a depth sensor configured to provide information for determining a distance between the drone and a moving base; and a processor configured to control a computer vision tracking algorithm based on the distance and to control drone movement based on the computer vision tracking algorithm.

20060235818

11106009

1. A method comprising: receiving a recursive query, wherein the recursive query comprises a recursive predicate associated with a recursive column in a table; and estimating a number of rows that the recursive query will retrieve, wherein the estimating further comprises recursively probing an index associated with the table.

20070033228

11499056

1. A method for dynamically ranking links to items of audio content returned to a user in response to the execution of a query by a search engine, the method comprising: receiving a query to identify links to one or more items of audio content; parsing the query into one or more logical units; determining an annotation for each of the one or more logical units; selecting a ranking heuristic according to the annotation for each of the one or more logical units; and ranking a result set according to the selected ranking heuristic.

10002301

15708485

1. A method for Arabic handwriting recognition, the method comprising: acquiring, an input image representative of a handwritten Arabic text from a user; partitioning, using processing circuitry of a server, the input image into a plurality of regions; determining, using the processing circuitry, a bag of features representation for each region of the plurality of regions; modeling, using the processing circuitry, each region independently by multi stream discrete Hidden Markov Model (HMM); and identifying, using processing circuitry, a recognized text based on the HMM models.

8805875

12573587

1. A machine-implemented method of information retrieval, comprising: receiving, at a processing device, object-oriented data from multiple data sources; receiving, at the processing device, a query from a query application that formulates the query and supplies the query to an information retrieval system, wherein the query application is not part of the information retrieval system, the query comprising an ordered set of clause definitions each comprising a clause pipeline and a time constraint, wherein the clause pipeline comprises an ordered set of clause specifications that comprises: an expansion operation and/or a filter operation, wherein a first clause specification in the clause pipeline operates on an initial set of objects of the object-oriented data, and each subsequent clause specification in the clause pipeline operates on one or more objects that is produced from a respective previous clause specification that is executed directly before the subsequent clause specification within the ordered set of clause specifications, parsing, at the processing device, the query into a graph of data nodes; processing, at the processing device, the data nodes in the graph on the object-oriented data to generate a current object set, wherein each data node is processed using a data model generated by a model builder component, the model builder component obtaining data for the data model from a given data source of the multiple data sources; and returning, at the processing device, the current object set to the query application in response to the query.

8498842

12770158

1. A method for automatic path determination in a CAD system, comprising: receiving a set of intersecting curves in a hardware CAD system; receiving by the CAD system a selection of a source curve and a destination curve from the set of intersecting curves; receiving a selection intent rule by the CAD system; calculating a first chain of curves that includes the source curve, by the CAD system, through the set of intersecting curves according to the selection intent rule; and storing a selected path in the CAD system, the selected path being a chain of curves between the source curve and the destination curve with a minimum number of deviations from the selection intent rule; wherein the stored selected path is the shortest path between the source curve and the destination curve of multiple paths having the same minimum number of deviations from the selection intent rule.

5553610

08385244

1. A method for acoustic-resonance, near-IR spectroscopy comprising the steps of: simultaneously applying to a subject under study near-IR radiation and an acoustic wave; collecting acoustical and near-IR spectra emitted from the subject under study as a result of the applied near-IR radiation and acoustic wave; and analyzing the collected spectra.

20110170762

12684170

1. A method comprising: capturing image data from a manufactured material using an image capture device; applying a first one of a plurality of normalization functions to the image data to produce a first normalized value for each pixel of the image; applying a second one of plurality of normalization functions to the image data to produce a second normalized value for each of the pixels; computing a final normalized value for each pixel as a sum of a portion of the first normalized value and a portion of the second normalized value for each of the pixels; processing the final normalized values to identify regions on the manufactured material containing anomalies that represent potential defects in the manufactured material; analyzing the anomalies to determine a defect distribution profile for the manufactured material; and adjusting the portion of the first normalized value and the portion of the second normalized value used when computing the final normalized value for each of the pixels so that the defect distribution profile is substantially uniform.

8768745

12183154

1. A print demand forecasting system for use with a print production system in which multiple print jobs are processed over a selected time interval, comprising: a data collection tool, said data collection tool collecting print demand data for each print job processed during the selected time interval; mass memory for storing the collected print demand data; a computer implemented service manager for processing the stored print demand data to obtain a first time series component and a second time series component, said computer implemented service manager corresponding the first time series component with a first forecast model and the second time series component with a second forecast model; and a selector process, operating with said computer implemented service manager to select one of the first forecast model and the second forecast model by: solving a plurality of first logistic regression equations associated with the first forecast model to determine a first estimation of one or more probability values for a next observation, wherein the plurality of first logistic regression equations vary as a function of the first time series component, solving a plurality of second logistic regression equations associated with the second forecast model to determine a second estimation of one or more probability values for a next observation, wherein the plurality of second logistic regression equations vary as a function of the second time series component, wherein one or more of the first estimation and the second estimation have an error of 2.4% or less, and selecting one of the first forecast model and the second forecast model whose estimation is associated with a highest probability value, wherein the selected one of the first forecast model and the second forecast model is used to obtain forecast data forecasting print production demand for a selected time outside of the selected time interval, wherein the selector process is configured to: smooth the print demand data to obtain smoothed values, compute a histogram with the smoothed values, obtain at least one threshold from the histogram, and use the at least one threshold to segment the print demand data.

9470743

14196219

1. A computer-implemented method comprising: collecting sample test information from a plurality of test-only structures of a first wafer, prior to completion of said first wafer; gathering finished test data from all die of said first wafer, after completion of said first wafer; constructing a yield prediction model based on said sample test information and on said finished test data; and predicting, using said model, a percentage of die of said first wafer that will meet a particular specification.

20150347731

14292266

1. A method for generating a human likeness score based on a Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA), the method comprising: selecting a visual advertising asset; generating the CAPTCHA comprising a graphical interface, the visual advertising asset and a graphical object depicted within the graphical interface; from a user, receiving an input within the graphical interface, the input defining selection of the graphical object within the graphical interface; extracting a motion pattern from the input; comparing the motion pattern to a motion model based on input patterns of previous users; and calculating the human likeness score of the user based on the comparison of the motion pattern to the motion model, the human likeness score lying within a continuum of discrete human likeness scores.

20090148010

11719634

1. A method for computer-assisted detection (CAD) of regions or volumes of interest (“regions”) within medical image data that includes CAD processing to detect and delineate candidate regions, and post-CAD machine learning in a training phase to maximize specificity and reduce the number of false positives reported after processing non-training data, which method includes the steps of: training a classifier on a set of medical image training data selected to include a number of regions known to be true and false for a ground truth, identifying and segmenting the regions using said CAD processing, extracting features to create a pool of features to qualify the regions, including at least one of a 3D histogram-based feature, and a 3D gradient-based feature, applying a genetic algorithmic processor to the pool of features to determine a minimal sub-set of features for use by a support vector machine (SVM) to identify candidate regions within non-training data with improved specificity; detecting, within non-training data, candidate regions segmenting the candidate regions within the non-training data; extracting a set of features relating to each segmented candidate region; and mapping candidate regions by the SVM using the sets of features.

20090210195

12031613

1. A method, comprising: selecting via one or more computing platforms a cohort of users associated with a value for an engagement metric in an intersection of a first probability distribution for a first group of users and of a second probability distribution for a second group of users; and comparing via the one or more computing platforms behavior of the first and second groups based at least in part on the selected cohort of users.

6112128

09327647

1. A system for predicting the outcome of college football games comprising, in combination: means for entering a season game number for two competing college football teams; means for entering a number of returning starting players for each of the two competing college football teams; means for predicting a winner of a college football game based on the number of returning starting players for each of the two competing college football teams.

20070271278

11457064

1. A computerized method of representing a dataset, comprising: obtaining a dataset, the dataset defining an attribute space; decomposing the attribute space into a plurality of attribute subspaces; generating a parent taxonomy of the obtained dataset with respect to one of the plurality of attribute subspaces, the parent taxonomy organizing the obtained dataset into a plurality of data subsets; generating a child taxonomy with respect to another one of the plurality of attribute subspaces, the child taxonomy organizing each of the plurality of data subsets within the parent taxonomy into at least one data subset; iteratively repeating generating the child taxonomy until a predetermined termination condition is satisfied, wherein the child taxonomy of a preceding iteration is the parent taxonomy of the current iteration; and assigning category labels to the data subsets.

9418339

14948970

1. A system for adjusting a set of predicted future data points for a time series data set, comprising: a processor and; a non-transitory computer readable storage medium containing instructions that, when executed with the processor, cause the processor to perform operations including: receiving the time series data set, wherein the time series data set includes a plurality of data points that correspond to a plurality of discrete values; generating a set of counts for the time series data set by analyzing the time series data, wherein a count corresponds to a number of instances of a particular discrete value in the time series data set; automatically selecting an optimal discrete probability distribution for the set of counts from a set of candidate discrete probability distributions based on a selection criterion; generating a set of parameters corresponding to the optimal discrete probability distribution; selecting a statistical model for the time series data set, wherein selecting the statistical model includes using a set of statistical models and the selection criterion; generating the set of predicted future data points for the time series data set, wherein generating the set of predicted future data points includes using the selected statistical model; adjusting the set of predicted future data points for the time series data set, wherein adjusting the set of predicted future data points includes using the set of parameters corresponding to the optimal discrete probability distribution; and using the adjusted set of predicted future data points to provide a predicted future data point based on received user input associated with the time series data set.

9009635

13887248

1. A processor-implemented method comprising: using a processor: inputting at least one user-supplied assertion, a stimulus, and a test bench; for each of a plurality of different verification tools, simulating the test bench and capturing simulation results; evaluating result differences between the verification tools with an assertion status difference engine that dynamically determines assertion equivalences; and identifying and outputting differences indicating an assertion inconsistency.

8068674

11849503

1. A method of confirming the identity of an item including an identifier, the method comprising: acquiring an image of the item including the identifier; extracting a first set of geometric point features from the image of the item; reading identification code data from the identifier included with the item; retrieving from a database a second set of geometric point features based on the identification code data, the database comprising sets of geometric point features associated with a plurality of known items; comparing via a processor the first set of geometric point features from the image with the second set of geometric point features retrieved based on the identification code data; and determining whether the item in the image corresponds to the identification code data read from the identifier included with the item based on the comparing of the first set of geometric point features with the second set of geometric point features.

8280687

11584038

1. A method of testing an electronic circuit comprising: receiving a signature from a time compactor associated with the electronic circuit; determining a list of initial candidate fault locations using one or more error functions associated with the time compactor, the candidate fault locations corresponding to logic instances in the electronic circuit, the error functions being indicative of scan cells in the electronic circuit that at least partially contribute to the value of one or more failing compactor bits in the signature, the one or more error functions having been determined at least in part by simulating an unloading of error values from the scan cells in the electronic circuit into the time compactor; and storing the list of initial candidate fault locations on one or more computer readable media.