| Entries |
| Document | Title | Date |
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| 20080201283 | APPARATUSES, METHODS AND SYSTEMS FOR ANTICIPATORY INFORMATION QUERYING AND SERVING ON MOBILE DEVICES BASED ON PROFILES - The disclosure details the implementation of apparatuses, methods, and systems for anticipatory information querying and serving on mobile devices based on profiles. Information and/or advertisement providers may use a code triggered information server to serve context, demographic, and behavior targeted information to users via mobile devices. Users register interest in the provision of information by scanning or observing codes or information. The scans, together with geographic, temporal, and user-specific information, are obtained by the server that receives, processes, and records the message. Based on these messages and a user profile—which may include continuously updated user-specific behavior information, situational and ambient information, an accumulated history of scanned code messages, and integration with outside database information—the server selects information to serve to the users' mobile devices from an information base. In one embodiment, information may also be served to users based solely on the user profiles, and without any initiating code scan. This may be based on predicted space-time trajectories derived from the accumulated history of scanned codes. For example, a user who frequently scans codes related to fast food, has a stated interest in sweets, and it projected to pass a particular fast food restaurant at a particular time may be served an advertisement for a dessert product at that restaurant on his/her cell phone shortly before he/she is projected to pass it. | 08-21-2008 |
| 20080208782 | Imbibition gas well stimulation via neural network design - A method for stimulation of gas hydrocarbon production via imbibition by utilization of surfactants. The method includes use of fuzzy logic and neural network architecture constructs to determine surfactant use. | 08-28-2008 |
| 20080208783 | Spatio-Temporal Learning Algorithms In Hierarchical Temporal Networks - A spatio-temporal learning node is a type of HTM node which learns both spatial and temporal groups of sensed input patterns over time. Spatio-temporal learning nodes comprise spatial poolers which are used to determine spatial groups in a set of sensed input patterns. The spatio-temporal learning nodes further comprise temporal poolers which are used to determine groups of sensed input patterns that temporally co-occur. A spatio-temporal learning network is a hierarchical network including a plurality of spatio-temporal learning nodes. | 08-28-2008 |
| 20080222067 | PREDICTION METHOD OF NEAR FIELD PHOTOLITHOGRAPHY LINE FABRICATION USING BY THE COMBINATION OF TAGUCHI METHOD AND NEURAL NETWORK - A method of building a set of experimental prediction model that requires fewer experimental frequency, shorter prediction time and higher prediction accuracy by using the advantages of combining the experimental data of Taguchi method and neural network learning is disclosed. The error between the experimentally measured result of photolithography and the simulated result of the theoretical model of near field photolithography is set as an objective function of an inverse method for back calculating fiber probe aperture size, which is adopted in the following Taguchi experiment. The analytical result of Taguchi neural network model of the present invention proves that the Taguchi neural network model can provide more accurate prediction result than the conventional Taguchi network model, and at the same time, improve the demerit of requiring massive training examples of the conventional neural network. | 09-11-2008 |
| 20080228679 | SYMBOLIC DEPTH-FIRST SEARCHES USING CONTROL FLOW INFORMATION FOR IMPROVED REACHABILITY ANALYSIS - Methods are provided for performing depth-first searches of concrete models of systems using control flow information of the system for improved reachability analysis. The concrete model's control structure and dependencies are extracted and an over-approximated (conservative) abstract control model is created. The abstract control model simulates the concrete model during model checking. Model checking the abstract control model produces execution traces based on the control paths of the concrete model. These execution traces may be used to guide a state space search on the concrete model during invariant checking to determine satisability of one or more selected invariants of the system. | 09-18-2008 |
| 20080228680 | Neural-Network Based Surrogate Model Construction Methods and Applications Thereof - Various neural-network based surrogate model construction methods are disclosed herein, along with various applications of such models. Designed for use when only a sparse amount of data is available (a “sparse data condition”), some embodiments of the disclosed systems and methods: create a pool of neural networks trained on a first portion of a sparse data set; generate for each of various multi-objective functions a set of neural network ensembles that minimize the multi-objective function; select a local ensemble from each set of ensembles based on data not included in said first portion of said sparse data set; and combine a subset of the local ensembles to form a global ensemble. This approach enables usage of larger candidate pools, multi-stage validation, and a comprehensive performance measure that provides more robust predictions in the voids of parameter space. | 09-18-2008 |
| 20080228681 | System and Method for Content Development - This invention relates to a system and methods for developing content. In general, in one aspect, a method for developing content includes electronically distributing a specification for content to a distributed community of content developers, receiving submissions from each of a subset of the community of content developers in response to the distributed specification, holding a first vote in which a group of voters rank a first number of submissions and identify the order in which they predict the submissions will be ranked by others, selecting the highest scoring submissions in the first vote, holding a second vote to evaluate the submissions that receive the highest score in the first vote; and selecting a winner based on the second vote. | 09-18-2008 |
| 20080243739 | Remote Hit Predictor - In one embodiment, a first node comprises at least one memory request source and a node controller coupled to the memory request source. The node controller comprises a remote hit predictor configured to predict a second node to have a coherent copy of a block addressed by a memory request generated by the memory request source, and the node controller is configured to issued a speculative probe to the second node responsive to the prediction and to the memory request. | 10-02-2008 |
| 20080275830 | Annotating audio-visual data - A method of annotating audio-visual data is disclosed. The method includes detecting a plurality of facial expressions in an audience based on a stimulus, determining an emotional response to the stimulus based on the facial expressions and generating at least one annotation of the stimulus based on the determined emotional response. | 11-06-2008 |
| 20080301074 | SYSTEMS, METHODS, AND SOFTWARE FOR HYPERLINKING NAMES - Hyperlinking or associating documents to other documents based on the names of people in the documents has become more desirable. Although there is an automated system for installing such hyperlinks into judicial opinions, the system is not generally applicable to other types of names and documents, nor well suited to determine hyperlinks for names that might refer to two or more similarly named persons. Accordingly, the inventor devised systems, methods, and software that facilitate hyperlinking names in documents, regardless of type. One exemplary system includes a descriptor module and a linking module. The descriptor module develops descriptive patterns for selecting co-occurent document information that is useful in recognizing associations between names and professional classes. The linking module tags names in an input document, extracts co-occurent information using the descriptive patterns, and uses a Bayesian inference network that processes a (non-inverse-document-frequency) name-rarity score for each name along with the name and selected co-occurent document information to determine appropriate hyperlinks to other documents, such as entries in professional directories. | 12-04-2008 |
| 20080306893 | Methods and systems for predicting occurrence of an event - Embodiments of the present invention are directed to methods and systems for training a neural network having weighted connections for classification of data, as well as embodiments corresponding to the use of such a neural network for the classification of data, including, for example, prediction of an event (e.g., disease). The method may include inputting input training data into the neural network, processing, by the neural network, the input training data to produce an output, determining an error between the output and a desired output corresponding to the input training data, rating the performance neural network using an objective function, wherein the objective function comprises a function C substantially in accordance with an approximation of the concordance index and adapting the weighted connections of the neural network based upon results of the objective function. | 12-11-2008 |
| 20080313115 | Behavioral Profiling Using a Behavioral WEB Graph and Use of the Behavioral WEB Graph in Prediction - A method for predicting demographic phenomena has steps for (a) determining behavioral characteristics of a specific population group related by one or more of interest or behavior; (b) creating one or more browsing software agents incorporating behavioral characteristics from step (a) and enabled to browse a network graph; (c) executing the software agents against the network graph, and noting resulting network phenomena; (d) monitoring the network graph in absence of execution of the software agents; and (e) in the event of reappearance of phenomena from step (c), concluding that real persons of the specific population group are active in producing the network phenomena. | 12-18-2008 |
| 20090006294 | IDENTIFICATION OF EVENTS OF SEARCH QUERIES - Techniques for analyzing and modeling the frequency of queries are provided by a query analysis system. A query analysis system analyzes frequencies of a query over time to determine whether the query is time-dependent or time-independent. The query analysis system forecasts the frequency of time-dependent queries based on their periodicities. The query analysis system forecasts the frequency of time-independent queries based on causal relationships with other queries. To forecast the frequency of time-independent queries, the query analysis system analyzes the frequency of a query over time to identify significant increases in the frequency, which are referred to as “query events” or “events.” The query analysis system forecasts frequencies of time-independent queries based on queries with events that tend to causally precede events of the query to be forecasted. | 01-01-2009 |
| 20090006295 | METHOD AND APPARATUS FOR IMPLEMENTING DIGITAL VIDEO MODELING TO GENERATE AN EXPECTED BEHAVIOR MODEL - A computer implemented method, apparatus, and computer usable program product for generating an expected behavior model. The process parses event data derived from video data to identify behavioral patterns, wherein the event data comprises metadata describing events occurring in a selected environment. The process analyzes the behavioral patterns to identify a set of expected behavioral patterns occurring in the environment, and generates the expected behavioral model using the expected behavioral patterns. | 01-01-2009 |
| 20090024547 | MULTI-INTELLIGENT SYSTEM FOR TOXICOGENOMIC APPLICATIONS (MISTA) | 01-22-2009 |
| 20090024548 | Compatibility Scoring of Users in a Social Network - The compatibility score of individuals in a social network is computed based on the compatibility of interests expressed by these individuals. The compatibility score between any two interests is calculated as the log of the estimated probability that a member of the social network will express both interests as his or her interests divided by the product of: (i) the estimated probability that a member of the social network will express the first of the two interests as his or her interest and (ii) the estimated probability that a member of the social network will express the second of the two interests as his or her interest. The compatibility score between two individuals is calculated as the sum of the compatibility scores between each interest appearing in a set of interests expressed by the first of the two individuals and each interest appearing in a set of interests expressed by the second of the two individuals. | 01-22-2009 |
| 20090030861 | Probabilistic Prediction Based Artificial Intelligence Planning System - A probabilistic prediction based artificial intelligence planning system comprises at least one processing unit capable of executing a set of instructions for a probabilistic prediction and modeling system; an input means for providing an input in communication with the processing unit; an output means for providing an output in communication with the processing unit; and an evaluation function for providing a score. The score is sent to the input means. A best output function provides a best output value to the processor based on probabilistic prediction values communicated from the probabilistic prediction and modeling system. Inputs and outputs are treated exactly the same within the probabilistic prediction and modeling system. Hypothetical outputs are used to test possible states within the probabilistic prediction and modeling system and evaluated by the best output function. An undo function can reverse the effect of applying a hypothetical output. | 01-29-2009 |
| 20090063378 | APPARATUS AND METHODS FOR PREDICTING A SEMICONDUCTOR PARAMETER ACROSS AN AREA OF A WAFER - Apparatus and methods are provided for predicting a plurality of unknown parameter values (e.g. overlay error or critical dimension) using a plurality of known parameter values. In one embodiment, the method involves training a neural network to predict the plurality of parameter values. In other embodiments, the prediction process does not depend on an optical property of a photolithography tool. Such predictions may be used to determine wafer lot disposition. | 03-05-2009 |
| 20090076990 | METHOD AND SYSTEM FOR AUTOMATICALLY CONTROLLING IN-PROCESS SOFTWARE DISTRIBUTIONS - A method and system for controlling an in-process software distribution to computing devices. A time of a disturbance in an environment of a computing device is predicted based on a change in a pressure exerted on the computing device. A checkpoint in a time interval of a distribution of a set of software upgrade increments to the computing device is automatically determined. The checkpoint is prior to the time of the disturbance. The determination of the checkpoint utilizes length(s) of increment(s) of the set of increments. Prior to the checkpoint, the computing device receives the increment(s). The computing device requests an interruption of the distribution beginning at the checkpoint. In one embodiment, the computing device is a buoy-like energy capture and generation device. | 03-19-2009 |
| 20090076991 | METHOD AND APPARATUS FOR VISUALIZATION OF DATA AVAILABILITY AND RISK - The advanced data availability tool uses predictive analysis to fill gaps in a data set and then displays actual data, predicted data, and confidence intervals for the actual data and the predicted data. The advanced data availability tool has a data collection tool, a data table created by the data collection tool, a data table analyzer, a predicted data table, and an enhanced data display. | 03-19-2009 |
| 20090076992 | COMPUTER IMPLEMENTED METHOD FOR AUTOMATICALLY EVALUATING AND RANKING SERVICE LEVEL AGREEMENT VIOLATIONS - The invention relates to ranking Service Level Agreement violations. A method for ranking said Service Level Agreements comprising determining a set of attributes for Service Level Agreements subject to violation, and predicting importance of Service Level Agreement violations using a model which performs ordinal regression based on said attributes of Service Level Agreements. | 03-19-2009 |
| 20090083203 | METHOD FOR CONSTRUCTING DATABASE TO DEDUCE DISEASE AND PROVIDING U-HEALTH SERVICE - A method for constructing a database to deduce a disease and constructing a Bayesian network for U-Health application, the method including the steps of analyzing U-Health information, which includes a disease, a symptom, and a treatment, and constructing a plurality of U-Health ontologies required for service provision, setting a meta-model defining cause-and-effect relationships between the constructed U-Health ontologies and selecting at least two specific U-Health ontologies from among the plurality of U-Health ontologies, setting the selected U-Health ontologies as nodes, and generating a Bayesian network by applying the meta-model to the set nodes. | 03-26-2009 |
| 20090094179 | Classifying Environment of a Data Processing Apparatus - Data processing apparatus is disclosed comprising: a sensor module configured to sense a first profile comprised of one or more attributes of an environment of said data processing apparatus; and a classification module configured to assign a prediction factor to each of said one or more attributes of said first profile and to store each said attribute and assigned prediction factor as a stored profile. | 04-09-2009 |
| 20090112781 | PREDICTING AND USING SEARCH ENGINE SWITCHING BEHAVIOR - Aspects of the subject matter described herein relate to predicting and using search engine switching behavior. In aspects, switching components receive a representation of user interactions with at least one browser. The switching components derive information from the representation that is useful in predicting whether a user will switch search engines. The derived information and information about a user's current interaction with a browser is then used by a switch predictor to predict whether the user will switch search engines. This prediction may be used in a variety of ways examples of which are given herein. | 04-30-2009 |
| 20090132450 | SYSTEMS AND METHODS FOR MULTIVARIATE INFLUENCE ANALYSIS OF HETEROGENOUS MIXTURES OF CATEGORICAL AND CONTINUOUS DATA - Systems, methods, and computer readable storage medium with executable instructions for detecting outliers and hidden relationships in heterogeneous data sets are provided. Features of the invention pertain to design and operation of various predictive models that identify multivariate outliers and influential observations by recognizing systematic local relationships within heterogeneous data sets or subpopulations of heterogeneous data sets. Multivariate outliers and influential observations are identified by utilizing general distance metrics which are specific to and defined for any number of individual observations within heterogeneous data sets. Aspects of the invention may be applied to sets of data that are large and complex (e.g. loan portfolios, health insurance company data, homeland security profiles, etc.) or sets of data having a more-limited scope (e.g. medical or drug research, etc.). | 05-21-2009 |
| 20090132451 | Prediction by Single Neurons and Networks - An artificial neuron integrates current and prior information, each of which predicts the state of a part of the world. The neuron's output corresponds to the discrepancy between the two predictions, or prediction error. Inputs contributing prior information are selected in order to minimize the error, which can occur through an anti-Hebbian-type plasticity rule. Current information sources are selected to maximize errors, which can occur through a Hebbian-type rule. This insures that the neuron receives new information from its external world that is not redundant with the prior information that the neuron already possesses. By learning on its own to make predictions, a neuron or network of these neurons acquires information necessary to generate intelligent and advantageous outputs. | 05-21-2009 |
| 20090138420 | Methods And System For Modeling Network Traffic - A method and system are provided for modeling network traffic in which an artificial neural network architecture is utilized in order to intelligently and adaptively model the capacity of a network. Initially, the network traffic is decomposed into a plurality of categories, such as individual users, application usage or common usage groups. Inputs to the artificial neural network are then defined such that a respective combination of inputs permits prediction of bandwidth capacity needs for that input condition. Outputs of the artificial neural network are representative of the network traffic associated with the respective inputs. For example, a plurality of bandwidth profiles associated with respective categories may be defined. An artificial neural network may then be constructed and trained with those bandwidth profiles and then utilized to relate predict future bandwidth needs for the network. | 05-28-2009 |
| 20090150314 | SYSTEM AND METHOD FOR REAL-TIME RECOGNITION OF DRIVING PATTERNS - System and method for real-time, automatic, recognition of large time-scale driving patterns employs a statistical pattern recognition framework, implemented by means of feed-forward neural network utilizing models developed for recognizing, for example, four classes of driving environments, namely highway, main road, suburban traffic and city traffic, from vehicle performance data. A vehicle control application effects changes in vehicle performance aspects based on the recognized driving environment. | 06-11-2009 |
| 20090157579 | SYSTEM AND METHOD OF FORECASTING PRINT JOB RELATED DEMAND - A print demand forecasting system is provided for use with a print production system in which print demand data is collected for each print job processed during a selected time interval. The print demand data is processed with a computer implemented service manager to obtain a first demand series with two or more demand components and a second demand series with one demand component. Each one of the two or more demand components is less than a selected variability level and the one demand component is greater than the selected variability level. The computer implemented service manager is adapted to (1) generate a first demand related forecast with a combination of the two or more demand components, and (2) use a neural network to generate a second demand related forecast with the one demand component. | 06-18-2009 |
| 20090164397 | Human Level Artificial Intelligence Machine - A method and system for creating exponential human artificial intelligence in robots, as well as enabling a human robot to control a time machine to predict the future accurately and realistically. The invention provides a robot with the ability to accomplish tasks quickly and accurately without using any time. This permits a robot to cure cancer, fight a war, write software, read a book, learn to drive a car, draw a picture or solve a complex math problem in less than one second. | 06-25-2009 |
| 20090177602 | SYSTEMS AND METHODS FOR DETECTING UNSAFE CONDITIONS - Systems and methods are disclosed to detect unsafe system states by capturing and analyzing data from a plurality of sensors detecting parameters of the system; and applying temporal difference (TD) learning to learn a function to approximate an expected future reward given current and historical sensor readings. | 07-09-2009 |
| 20090187520 | Demographics from behavior - A system and method for modeling online users' behavior for predicting demographic information is disclosed. The system allows advertisement providers to target anonymous users based on a user's browsing history, search queries, as broken down into behavioral targeting categories, as well as other features, such as behavioral targeting segments. | 07-23-2009 |
| 20090187521 | PREDICTIVE MONITORING FOR EVENTS AT COMPUTER NETWORK RESOURCES - Computer resources in a computer network can be predictively monitored where those resources are conventionally monitored using a monitoring rule. For predictive monitoring, the current values of the parameters of the monitoring rule are tracked at regular intervals. The current values are used in an “inverted” or predictive form of the conventional monitoring rule to derive a predictive value that is indicative of the imminence of a defined event. The monitoring system may be instructed to report a predictive value that exceeds a predetermined percentage of the final value at which the resource event will be deemed to have occurred. The earlier report increases the chances the network manager will have sufficient time to take appropriate preemptive action to prevent actual occurrence of the event. | 07-23-2009 |
| 20090192957 | Computer-Implemented Data Storage Systems And Methods For Use With Predictive Model Systems - Systems and methods for performing fraud detection. As an example, a system and method can be configured to contain a raw data repository for storing raw data related to financial transactions. A data store contains rules to indicate how many generations or to indicate a time period within which data items are to be stored in the raw data repository. Data items stored in the raw data repository are then accessed by a predictive model in order to perform fraud detection. | 07-30-2009 |
| 20090210369 | SYSTEMS AND METHODS OF PREDICTING RESOURCE USEFULNESS USING UNIVERSAL RESOURCE LOCATORS - A method, system and apparatus are provided to train a usefulness prediction model to generate a usefulness prediction in connection with a given universal resource locator (URL), the training of the usefulness prediction model being based on a training set of URLs and a count of negative URLs and a count of positive URLs identified by the training set, and for each feature extacted from the URLs in the training set, a count of the positive URLs in the training set that include the feature and a count of the negative URLs in the training set that include the feature. One or more features of the given URL are extracted, and the extracted features are used together with the usefulness prediction model to generate a usefulness prediction for the given URL. | 08-20-2009 |
| 20090210370 | METHOD FOR DETERMINING THE PERMITTED WORKING RANGE OF A NEURAL NETWORK - A method for checking whether an input data record is in the permitted working range of a neural network in which a definition of the complex envelope which is formed by the training input records of the neural network, and of its surroundings as the permitted working range of a neural network and checking whether the input data record is in the convex envelope. | 08-20-2009 |
| 20090210371 | DATA ADAPTIVE PREDICTION FUNCTION BASED ON CANDIDATE PREDICTION FUNCTIONS - In one embodiment, a method for predicting an outcome is provided. The method comprises: determining a known data set of data, the known data set of data including an input variable and an output variable; determining a plurality of candidate prediction functions, each prediction function adapted to determine a candidate predicted outcome for the output variable using a different algorithm; determining a combination of the plurality of candidate prediction functions based on the known data set; determining a second set of data, the second set of data including data for the input variable; and determining, based on the input variable, a predicted outcome for the output variable using a data adaptive prediction function, wherein the data adaptive prediction function uses the combination of candidate predicted outcomes from the plurality of candidate prediction functions determined using the data from the input variable to determine the predicted outcome. | 08-20-2009 |
| 20090248600 | ESTIMATING TRANSACTION RISK USING SUB-MODELS CHARACTERIZING CROSS-INTERACTION AMONG CATEGORICAL AND NON-CATEGORICAL VARIABLES - In one aspect, input data for a predictive model characterizing a level of risk for a data transaction is received that includes values for categorical variables and one or more of binary variables and continuous variables the predictive model. Thereafter, one or more of the categorical variables is associated with one of a plurality of keys. Each key having corresponding coefficients for at least a subset of the binary variables and the continuous variables and the coefficients being dependent on a value for the key. A composite value based on values for each of at least a subset of the binary variables and the continuous variables as calculated using the corresponding coefficients for each key can then be generated. Scoring of the data transaction using the binary variables, the continuous variables, and the composite variables can then be initiated by the predictive model. Related apparatus, systems, techniques and articles are also described. | 10-01-2009 |
| 20090271343 | Automated entity identification for efficient profiling in an event probability prediction system - A computer-implemented method and system for automated entity identification for efficient profiling in an event probability prediction system. A first subset of entities belonging to one or more entity classes is defined. At least one historical profile is constructed for each entity in the subset of entities based on a set of possible outcomes of transaction behavior of each entity in the first subset of entities. Based on the historical profiles, a second subset of entities having transaction behavior associated with a transaction is selected, the transaction behavior being predictive of at least one targeted outcome from the set of possible outcomes. The first subset of entities is redefined with the second subset of entities. | 10-29-2009 |
| 20090319456 | MACHINE-BASED LEARNING FOR AUTOMATICALLY CATEGORIZING DATA ON PER-USER BASIS - Architecture that employs machine-based learning to automatically categorize data on a per-user basis. Auto-tagging reduces the burden on infoworkers by creating a machine learning model to learn from user tagging behavior or preferences. Once this information is obtained, a trained model for this specific user is used to assign tags to incoming data, such as emails. The architecture finds particular applicability to compliance and message retention policies that otherwise would mandate extra work for the infoworker. The architecture learns the tagging behavior of a user and uses this learned behavior to automatically tag data based on the user's prior tagging habits. A regression algorithm is employed to process the training data according to an n-dimensional framework for prediction and application of the tag(s) to the incoming messages. | 12-24-2009 |
| 20100049681 | BASE OIL PROPERTIES EXPERT SYSTEM - A method for predicting properties of lubricant base oil blends, comprising the steps of generating an NMR spectrum, HPLC-UV spectrum, and FIMS spectrum of a sample of a blend of at least two lubricant base oils and determining at least one composite structural molecular parameter of the sample from said spectrums. SIMDIST and HPO analyses of the sample are then generated in order to determine a composite boiling point distribution and molecular weight of the sample from such analysis. A composite structural molecular parameter is applied, and the composite boiling point distribution and the composite molecular weight to a trained neural network is trained to correlate with the composite structural molecular parameter composite boiling point distribution and the composite molecular weight so as to predict composite properties of the sample. The properties comprise Kinematic Viscosity at 40 C, Kinematic Viscosity at 100 C, Viscosity Index, Cloud Point, and Oxidation Performance. | 02-25-2010 |
| 20100082511 | JOINT RANKING MODEL FOR MULTILINGUAL WEB SEARCH - Described is a technology in which a classifier is built to rank documents of different languages found in a query based at least in part on similarity to other documents and the relevance of those other documents to the query. A joint ranking model, e.g., based upon a Boltzmann machine, is used to represent the content similarity among documents, and to help determine joint relevance probability for a set of documents. The relevant documents of one language are thus leveraged to improve the relevance estimation for documents of different languages. In one aspect, a hidden layer of units (neurons) represents clusters (corresponding to relevant topics) among the retrieved documents, with an output layer representing the relevant documents and their features, and edges representing a relationship between clusters and documents. | 04-01-2010 |
| 20100094789 | METHOD AND A SYSTEM FOR ESTIMATING THE IMPACT AREA OF A MILITARY LOAD LAUNCHED FROM AN AIRCRAFT - A system and a method are for the estimation of the impact area of a smart load that can be launched from an aircraft as a function of data or signals indicative of the aircraft flight conditions upon release of the load and of predetermined impact conditions on the target. The estimation of a polygonal impact area defined by the coordinates of a central point and of a predetermined number of vertices is by corresponding neural networks. | 04-15-2010 |
| 20100153324 | PROVIDING RECOMMENDATIONS USING INFORMATION DETERMINED FOR DOMAINS OF INTEREST - Techniques are described for determining and using information related to domains of interest, such as by automatically analyzing documents and other information related to a domain in order to automatically determine relationships between particular terms within the domain. Such automatically determined information may then be used to assist users in obtaining information from the domain that is of interest (e.g., documents with contents that are relevant to user-specified terms and/or to other terms that are determined to be sufficiently related to the user-specified terms). For example, recommendations may be automatically generated for a user by using information about specified preferences or other interests of the user with respect to one or more terms and identifying other particular terms that are sufficiently probable to be of interest to that user, such as based on a generated probabilistic representation of relationships between particular terms for the domain. | 06-17-2010 |
| 20100169253 | ARTIFICIAL NEURAL NETWORK FOR BALANCING WORKLOAD BY MIGRATING COMPUTING TASKS ACROSS HOSTS - Methods and apparatuses for balancing computing workload via migrating computing tasks are disclosed. An artificial neural network (ANN) is trained based on the workload distribution over time for a host. The ANN predicts the workload for the host, and an indication may be sent to migrate at least one computing task away from the host. The indication is sent when the method is operating in a proactive mode and when the predicted workload is outside of a desired operating range. Some embodiments monitor the workload; and automatically switch the method to the proactive mode, when a difference between the monitored workload and the predicted workload is small. Other embodiments monitor the workload; and automatically switch the method to a reactive mode, when the monitored workload is outside of a failsafe operating range for the particular host. | 07-01-2010 |
| 20100179935 | SPIKING DYNAMICAL NEURAL NETWORK FOR PARALLEL PREDICTION OF MULTIPLE TEMPORAL EVENTS - A system and method for determining events in a system or process, such as predicting fault events. The method includes providing data from the process, pre-processing data and converting the data to one or more temporal spike trains having spike amplitudes and a spike train length. The spike trains are provided to a dynamical neural network operating as a liquid state machine that includes a plurality of neurons that analyze the spike trains. The dynamical neural network is trained by known data to identify events in the spike train, where the dynamical neural network then analyzes new data to identify events. Signals from the dynamical neural network are then provided to a readout network that decodes the states and predicts the future events. | 07-15-2010 |
| 20100198765 | PREDICTION BY SINGLE NEURONS - Associative plasticity rules are described to control the strength of inputs to an artificial neuron. Inputs to a neuron consist of both synaptic inputs and non-synaptic, voltage-regulated inputs. The neuron's output is voltage. Hebbian and anti-Hebbian-type plasticity rules are implemented to select amongst a spectrum of voltage-regulated inputs, differing in their voltage-dependence and kinetic properties. An anti-Hebbian-type rule selects inputs that predict and counteract deviations in membrane voltage, thereby generating an output that corresponds to a prediction error. A Hebbian-type rule selects inputs that predict and amplify deviations in membrane voltage, thereby contributing to pattern generation. In further embodiments, Hebbian and anti-Hebbian-type plasticity rules are also applied to synaptic inputs. In other embodiments, reward information is incorporated into Hebbian-type plasticity rules. It is envisioned that by following these plasticity rules, single neurons as well as networks may predict and maximize future reward. | 08-05-2010 |
| 20100205130 | ADAPTIVE TRANSIENT MULTI-NODE HEAT SOAK MODIFIER - A method of adjusting parameters for one or more heat soak models for a system in a transient performance model, comprising: simulating the system in a transient prediction module; generating a first prediction of the system from the transient prediction module; generating a transfer function in response to the first prediction and a set of field data utilizing a neural network module; applying the transfer function from the neural network module to the transient prediction module; adjusting parameters of a first heat soak model for the system in the transient prediction module in response to the application of the transfer function to match with the set of field data; and generating a calculated modifier in response to the adjustment of parameters of the first heat soak model, the calculated modifier is a calculated difference between the first prediction and a second prediction as defined by the transfer function. | 08-12-2010 |
| 20100217732 | Unbiased Active Learning - Techniques described herein create an accurate active-learning model that takes into account a sample selection bias of elements, such as images, selected for labeling by a user. These techniques select a first set of elements for labeling. Once a user labels these elements, the techniques calculate a sample selection bias of the selected elements and train a model that takes into account the sample selection bias. The techniques then select a second set of elements based, in part, on a sample selection bias of the elements. Again, once a user labels the second set of elements the techniques train the model while taking into account the calculated sample selection bias. Once the trained model satisfies a predefined stop condition, the techniques use the trained model to predict labels for the remaining unlabeled elements. | 08-26-2010 |
| 20100241600 | Method, Apparatus and Computer Program Product for an Instruction Predictor for a Virtual Machine - An apparatus for providing an instruction predictor for a virtual machine may include a processor and a memory storing executable instructions that in response to execution by the processor cause the apparatus to train a neural network to predict a future instruction corresponding to a current instruction based on past instructions provided to the neural network, and provide the future instruction predicted to a virtual machine to enable the virtual machine to manage operation of the virtual machine based on the future instruction. A corresponding method and computer program product are also provided. | 09-23-2010 |
| 20100332432 | OPERATING A COMMUNICATIONS NETWORK - In operating a communications network, a set of derived events occurs in dependence on at least one primary event in the communications network. A learning method for determining the set of derived events by analyzing the at least one primary event and by predicting the set of derived events based on relations concerning network entities and/or events in the communications network is implemented to support the management of the communications network in a more efficient way. | 12-30-2010 |
| 20100332433 | Predictive Model for Density and Melt Index of Polymer Leaving Loop Reactor - The present invention discloses a method for predicting the melt index and density of the polymer in terms of the operating conditions in the reactor and vice-versa, to select the operating conditions necessary to obtain the desired product specifications. | 12-30-2010 |
| 20110016070 | Method for Predicting Future Environmental Conditions - An average environmental condition for a specified target date and time is determined by indexing a database of time series data to retrieve the environment condition for each day and time where an orbital position of the earth with respect to the sun is nearest to the orbital position of the earth on the target date and time. The average environmental condition is then determined from the retrieved environmental conditions. | 01-20-2011 |
| 20110035347 | SYSTEMS AND METHODS FOR IDENTIFYING PROVIDER NONCUSTOMERS AS LIKELY ACQUISITION TARGETS - The present invention is directed towards systems and methods for predicting one or more desired properties of external nodes or properties of their relations with internal nodes, based on a selected group of nodes about which it is known whether the nodes have the desired properties, or it is known whether they have a desired relation property with an internal node. The method comprises storing in one or more data structures a first data set regarding external nodes and a second data set regarding nodes with known properties in a selected group, each data set having one or more data items representing one or more events relating to or attributes of each node in the data set, the second data set including one or more types of data items not included in the first data set. The method then models the second data set to identify from the second data one or more modeled events or attributes of internal nodes in the selected group that are statistically likely to identify the nodes or their relations, that have the desired properties and predicts which of the external nodes are statistically likely to have the one or more desired properties, or desired relation property with internal node, based on the identified plurality of modeled events or attributes and the events or attributes in the first data set. | 02-10-2011 |
| 20110066579 | Neural network system for time series data prediction - A neural network system for predicting time series data. The system receives analyzed data obtained by multiresolution analysis of the time series data. The input processing layer of the system includes a series of neurons corresponding to the different levels of analysis, each neuron receiving the analyzed data for it own level. The output of each of these neurons is supplied as an additional input to the neuron for the next lower level of analysis. A predicted value is derived from the output of the neuron at the lowest level. The passing of results from one level to another improves prediction accuracy and simplifies the structure of any further processing layers. | 03-17-2011 |
| 20110071970 | AUTOMATED CONTROL OF A POWER NETWORK USING METADATA AND AUTOMATED CREATION OF PREDICTIVE PROCESS MODELS - Automated control of a power network is provided by: providing multiple intelligent power controllers (IPCs) associated with multiple components of the power network, each IPC being associated with a different component; obtaining at least one raw data stream representative of at least one operational aspect of at least one component of the multiple components; and automatically associating, by at least one intelligent power controller associated with at least one component, metadata with the at least one raw data stream to produce at least one self-identifying data stream. The associated metadata describes one or more characteristics of the at least one raw data stream, and the at least one self-identifying data stream facilitates automated creation of predictive process models to assist in automated control of the power network by an IPC manager of the power network. | 03-24-2011 |
| 20110087627 | USING NEURAL NETWORK CONFIDENCE TO IMPROVE PREDICTION ACCURACY - Systems and methods may be provided for generating a prediction using neural networks. The system and methods may include training a plurality of neural networks with training data, calculating an output value for each of the plurality of neural networks based at least in part on input evaluation points, applying a weight to each output value based at least in part on a confidence value for each of the plurality of neural networks; and generating an output result. | 04-14-2011 |
| 20110137842 | METHOD FOR CONSTRUCTING A TREE OF LINEAR CLASSIFIERS TO PREDICT A QUANTITATIVE VARIABLE - A method for using predictive modeling of a physical process in order to determine and implement a solution to the physical process. The method includes analyzing the physical process to determine the relevant physical relationships, observations, data, and outcome probabilities associated with the physical relationships of the physical process, and storing representations of the relevant physical relationships, observations, data and outcome probabilities in a memory of a computer. The method also includes recursively analyzing the stored representations by the computer, and generating at least one tree structure that models the physical process, the tree structure including at least one root node, a plurality of decision nodes, and a plurality of end nodes. The method further includes partitioning observations at each decision node into probable outcomes using target partitioning, generating a plurality of other decision nodes based on the target partitioning, determining a solution to the physical process based on criterion variables, by the computer using the tree structure of the physical process to arrive at an end node, and implementing a physical solution to the physical process. | 06-09-2011 |
| 20110145179 | FRAMEWORK FOR THE ORGANIZATION OF NEURAL ASSEMBLIES - A framework for organization of neural assemblies. Stable neural circuits are formed by generating comprehensions. A packet of neurons projects to a target neuron after stimulation. A target neuron in STDP state is recruited if it fires within a STDP window. Recruitment leads to temporary stabilization of the synapses. The stimulation periods followed by decay periods lead to an exploration of cut-sets. Comprehension results in successful predictions and prediction-mining leads to flow. Flow is defined as the production rate of signaling particles needed to maintain communication between nodes. The comprehension circuit competes for prediction via local inhibition. Flow can be utilized for signal activation and deactivation of post-synaptic and pre-synaptic plasticity. Flow stabilizes the comprehension circuit. | 06-16-2011 |
| 20110153534 | Expression Profiles to Predict Relapse of Prostate Cancer - The present invention provides a method for preparing a reference model for cancer relapse prediction that provides higher resolution grading than Gleason score alone. The method encompasses obtaining from different individuals a plurality of prostate carcinoma tissue samples of known clinical outcome representing different Gleason scores; selecting a set of signature genes having an expression pattern that correlates positively or negatively in a statistically significant manner with the Gleason scores; independently deriving a prediction score that correlates gene expression of each individual signature gene with Gleason score for each signature gene in said plurality of prostate carcinoma tissue samples; deriving a prostate cancer gene expression (GEX) score that correlates gene expression of said set of signature genes with the Gleason score based on the combination of independently derived prediction scores in the plurality of prostate cancer tissue samples; and correlating said GEX score with the clinical outcome for each prostate carcinoma tissue sample. A set of signature genes is provided that encompasses all or a sub-combination of GI_2094528, KIP2, NRG1, NBL1, Prostein, CCNE2, CDC6, FBP1, HOXC6, MKI67, MYBL2, PTTG1, RAMP, UBE2C, Wnt5A, MEMD, AZGP1, CCK, MLCK, PPAP2B, and PROK1. Also provided a methods for predicting the probability of relapse of cancer in an individual and methods for deriving a prostate cancer gene expression (GEX) score for a prostate carcinoma tissue sample obtained from an individual. | 06-23-2011 |
| 20110161267 | SYSTEMS AND METHODS FOR TRAINING NEURAL NETWORKS BASED ON CONCURRENT USE OF CURRENT AND RECORDED DATA - Various embodiments of the invention are neural network adaptive control systems and methods configured to concurrently consider both recorded and current data, so that persistent excitation is not required. A neural network adaptive control system of the present invention can specifically select and record data that has as many linearly independent elements as the dimension of the basis of the uncertainty. Using this recorded data along with current data, the neural network adaptive control system can guarantee global exponential parameter convergence in adaptive parameter estimation problems. Other embodiments of the neural network adaptive control system are also disclosed. | 06-30-2011 |
| 20110184898 | Weight-Prediction System and Method Thereof - The present invention provides a weight-prediction method comprising the following steps: providing the first-period data and the second-period data of basic users and proving users, respectively; using an Artificial Neural Network method to analyze the first-period data and second-period data of basic users to calculate a basic parameter; analyzing the first period data of proving users by using a Fuzzy Inference system based on the basic parameter and then calculate a predictive data; comparing the predictive data with the second-period data of proving users and determining if the predictive data is in an acceptable range. If the predictive data is certainty in the acceptable range, the basic parameter is defined as a predictive parameter. | 07-28-2011 |
| 20110196818 | INFRASTRUCTURE AND ARCHITECTURE FOR DEVELOPMENT AND EXECUTION OF PREDICTIVE MODELS - A system that enables development and execution of predictive models comprises a centralized data management system, a data extraction tool a model validation tool and a model execution tool. In embodiments, a data management system includes a data management server that can be accessed via a web browser that stores data. An extraction tool includes a data filter adapted to filter data based on, for example, a population criteria, a sample size, and a date range criteria. A model validation tool validates the model. A model execution tool allows a user to score the model. | 08-11-2011 |
| 20110208681 | SYSTEM AND METHOD FOR CORRELATING PAST ACTIVITIES, DETERMINING HIDDEN RELATIONSHIPS AND PREDICTING FUTURE ACTIVITIES - A system and method of data correlation and analysis of past activities and prediction of future activity that includes establishing a database comprising structured metadata on at least one computer and a distributed system of networked computers having a plurality of access levels, configuring a database front-end to parse incoming data, the database front-end defining a plurality of entities and events, each having one or more attributes, and correlation IDs, which are user defined attribute relationships that correlate events and entities, collecting data, parsing the incoming data in the database front-end into structured metadata, the distributed analysis engine correlating the incoming structured metadata with structured metadata in the database and determining micro-patterns in the structured metadata, generating a graphical representation of a composite network of the determined micro-patterns as super-objects, and predicting future activity from the one or more super-objects representing the determined micro-patterns. | 08-25-2011 |
| 20110246404 | Method for Allocating Trip Sharing - A method and system for allocating users as trip accompanies provides for: creating a first trip prediction algorithm, collecting input parameters, predicting by the first trip prediction algorithm using the collected input parameters as input, at least one first trip, executing a matching method, the matching method comparing attributes of the predicted at least one first trip with attributes of at least one second trip, and allocating the first and the second user to each other as trip accompanies in dependence of the matching score of the first and second potential trip data object. | 10-06-2011 |
| 20110251986 | COMBINED-MODEL DATA COMPRESSION - Data compression technology (“the technology”) is disclosed that can employ two or more prediction models contemporaneously. The technology receives data from one or more sources; shifts or re-sample one of more corresponding signals; creates a prediction model of uncompressed samples using at least two different individual or composite models; selects a subset of the models for prediction of samples; determines an order in which signals will be compressed; formulates a combined predictions model using the selected subset of models; predicts a future value for the data using the combined compression model; defines a function that has as parameters at least the predicted future values for the data and actual values; selects a compression method for the values of the function; and compresses the data using at least the predicted value of the function. | 10-13-2011 |
| 20110276527 | BALANCE POINT DETERMINATION - Systems and methods for predicting energy usage of an asset are provided. Among several implementations of methods implemented by a computer, one embodiment of a computer-implemented method includes selecting one of a plurality of base temperatures that allows a linear equation to estimate energy consumption by an asset as a function of an average daily demand on the asset to attain a desired temperature. The computer-implemented method also includes inserting the selected base temperature in a non-linear equation for modeling the asset's energy consumption. | 11-10-2011 |
| 20110282817 | ORGANIZATION-SEGMENT-BASED RISK ANALYSIS MODEL - Apparatus and methods for reducing infrastructure failure rates. The apparatus and methods may compile and store data related to the organizational segments associated with the approval and implementation of an infrastructure change. Variables may be derived from the data using a range of methods, and multiple variable values may be consolidated. A model may be developed based on the values and relationships of the derived variables. The model may be applied to assess the risk involved in a prospective infrastructure change. | 11-17-2011 |
| 20110282818 | SYSTEM AND METHOD OF PREDICTING GAS SATURATION OF A FORMATION USING NEURAL NETWORKS - Predicting gas saturation of a formation using neural networks. At least some of the illustrative embodiments include obtaining a gamma count rate decay curve one each for a plurality of gamma detectors of a nuclear logging tool (the gamma count rate decay curves recorded at a particular borehole depth), applying at least a portion of each gamma count rate decay curve to input nodes of a neural network, predicting a value indicative of gas saturation of a formation (the predicting by the neural network in the absence of a formation porosity value supplied to the neural network), and producing a plot of the value indicative of gas saturation of the formation as a function of borehole depth. | 11-17-2011 |
| 20110302118 | FEATURE SET EMBEDDING FOR INCOMPLETE DATA - Methods and systems for classifying incomplete data are disclosed. In accordance with one method, pairs of features and values are generated based upon feature measurements on the incomplete data. In addition, a transformation function is applied on the pairs of features and values to generate a set of vectors by mapping each of the pairs to a corresponding vector in an embedding space. Further, a hardware processor applies a prediction function to the set of vectors to generate at least one confidence assessment for at least one class that indicates whether the incomplete data is of the at least one class. The method further includes outputting the at least one confidence assessment. | 12-08-2011 |
| 20110320392 | METHODS FOR PREDICTING CANCER RESPONSE TO EGFR INHIBITORS - The presently-disclosed subject matter relates to biomarker profiling of samples obtained from carcinoma subjects who are candidates for treatment with a therapeutic EGFR inhibitor. More specifically, the presently-disclosed subject matter provides methods of biomarker profiling which allow one skilled in the art to predict whether a patient is likely to respond well to treatment with an EGFR inhibitor. | 12-29-2011 |
| 20120023051 | SIGNAL CODING WITH ADAPTIVE NEURAL NETWORK - The invention relates to sparse parallel signal coding using a neural network which parameters are adaptively determined in dependence on a pre-determined signal shaping characteristic. A signal is provides to a neural network encoder implementing a locally competitive algorithm for sparsely representing the signal. A plurality of interconnected nodes receive projections of the input signal, and each node generates an output once an internal potential thereof exceeds a node-dependent threshold value. The node-dependent threshold value for each of the nodes is set based upon the pre-determined shaping characteristic. In one embodiment, the invention enables to incorporate perceptual auditory masking in the sparse parallel coding of audio signals. | 01-26-2012 |
| 20120036098 | ANALYZING ACTIVITIES OF A HOSTILE FORCE - Historical data is processed to identify possible future hostile activities in high threat environments. Pieces of the historical data are collected in computer-readable memory as memory entities, where the memory entities are categorized according to types of attacks and locations of attacks. The memory entities contain attributes taken from the pieces of historical data. A computer system is used to analyze the memory entities with an Associative Memory, wherein correlations of the attributes of the different memory entities are identified. Patterns are discovered from the correlations. The patterns are made available so future hostile activities can be identified. | 02-09-2012 |
| 20120047099 | NON-INTRUSIVE EVENT-DRIVEN PREDICTION - A method, system, and computer usable program product for non-intrusive event-driven prediction of a metric in a data processing environment are provided in the illustrative embodiments. At least one set of events is observed in the data processing environment, the set of events being generated by several processes executing in the data processing environment. A subset of the set of events are tracked for an observation period, the tracking resulting in bookkeeping information about the subset of events. A pattern of events is detected in the bookkeeping information. The pattern is formed as a tuple representing a process in the several processes, the metric corresponding to the process. A prediction model is selected for the tuple. The prediction model is supplied with the tuple and executed to generate a predicted value of the metric. | 02-23-2012 |
| 20120066163 | TIME TO EVENT DATA ANALYSIS METHOD AND SYSTEM - A time to event data analysis method and system. The present invention relates to the analysis of data to identify relationships between the input data and one or more conditions. One method of analysing such data is by the use of neural networks which are non-linear statistical data modelling tools, the structure of which may be changed based on information that is passed through the network during a training phase. A known problem that affects neural networks is the issue of overtraining which arises in overcomplex or overspecified systems when the capacity of the network significantly exceeds the needed parameters. The present invention provides a method of analysing data, such as bioinformatics or pathology data, using a neural network with a constrained architecture and providing a continuous output that can be used in various contexts and systems including prediction of time to an event, such as a specified clinical event. | 03-15-2012 |
| 20120101968 | SERVER CONSOLIDATION SYSTEM - A computer program product for a network management device, including: a computer readable storage medium to store a computer readable program, wherein the computer readable program, when executed on a computer, causes the computer to perform operations for server management in a computer network. The operations include: receiving resource usage data generated at a network communication device coupled between a server and a network management device, wherein the resource usage data describes resource usage of the server; and classifying the server into a cluster of servers based on the resource usage data from the network communication device and a cluster characterization for the cluster. The cluster includes a plurality of servers with similar resource usage data, and the cluster is one of a plurality of clusters managed by the network management device. | 04-26-2012 |
| 20120123983 | AUTOMATIC SYSTEMS AND METHODOLOGIES FOR EARTHQUAKE PREDICTION AND WARNING - A system for predicting earthquakes including first sensing functionality for sensing at least one earthquake prediction parameter at least a first point in time prior to an expected earthquake event, second sensing functionality for sensing at least one earthquake prediction parameter at least a second point in time prior to the expected earthquake event, the second point in time being different from the first point in time, and prediction functionality operative in response to outputs from the first sensing functionality and from the second sensing functionality to provide a prediction of an expected earthquake event. | 05-17-2012 |
| 20120123984 | OPTIMAL PERSISTENCE OF A BUSINESS PROCESS - Embodiments of the invention provide for automatically selecting optimal fetch settings for business processes as a function of database query load and relational context by determining whether data loaded for data retrieval points is dependent upon a query result from another query process and automatically selecting an eager fetch setting if dependent upon a query result from another query process, or a lazy fetch setting if not. Usage of the data retrieval points is monitored with respect to defined units of work to define retrieval patterns and automatically update the fetch settings, including by revising selected eager fetch settings to lazy fetch settings if a datasize of a defined retrieval pattern is larger than a permissible memory resource threshold. | 05-17-2012 |
| 20120130930 | METHOD FOR DETECTION OF TUNNEL EXCAVATION BY BRILLOUIN OPTICAL TIME DOMAIN REFLECTOMETRY - A non transitory computer readable medium and a method of detecting excavation of an underground tunnel, the method includes: propagating a light pulse through an underground optic fiber; generating detection signals responsive to Brillion scattered light resulting from the propagating of the light pulse through the underground optic fiber; wherein the detection signals represent tension values at multiple locations along the underground optic fiber; and processing the detection signals to detect excavation of the underground tunnel. | 05-24-2012 |
| 20120143806 | Electronic Communications Triage - Triaging electronic communications in a computing system environment can mitigate issues related to large volumes of incoming electronic communications. This can include an analysis of user-specific electronic communication data and associated behaviors to predict which communications a user is likely to deem important or unimportant. Client-side application features are exposed based on the evaluation of communication importance to enable the user to process arbitrarily large volumes of incoming communications. | 06-07-2012 |
| 20120150779 | Automated Entity Identification for Efficient Profiling in an Event Probability Prediction System - A computer-implemented method and system for automated entity identification for efficient profiling in an event probability prediction system. A first subset of entities belonging to one or more entity classes is defined. At least one historical profile is constructed for each entity in the subset of entities based on a set of possible outcomes of transaction behavior of each entity in the first subset of entities. Based on the historical profiles, a second subset of entities having transaction behavior associated with a transaction is selected, the transaction behavior being predictive of at least one targeted outcome from the set of possible outcomes. The first subset of entities is redefined with the second subset of entities. | 06-14-2012 |
| 20120158630 | INFORMATION PROPAGATION PROBABILITY FOR A SOCIAL NETWORK - One or more techniques and/or systems are disclosed for predicting propagation of a message on a social network. A predictive model is trained to determine a probability of propagation of information on the social network using both positive and negative information propagation feedback, which may be collected while monitoring the social network over a desired period of time for information propagation. A particular message can be input to the predictive model, which can determine a probability of propagation of the message on the social network, such as how many connections may receive at least a portion of the message and/or a likelihood of at least a portion of the message reaching respective connections in the social network. | 06-21-2012 |
| 20120158631 | ANALYZING INPUTS TO AN ARTIFICIAL NEURAL NETWORK - Systems, methods, and associated software are described for receiving first inputs and first outputs, providing the first inputs and first outputs to an artificial neural network (ANN) for training, creating a boundary such that the first inputs fall within the boundary or on a boundary line defining the boundary, wherein additional inputs are considered to be valid if they fall within the boundary and are considered to be invalid if they fall outside the boundary, receiving second inputs, separating the valid second inputs from the invalid second inputs, determining a percentage of the second inputs that are invalid, and when the percentage exceeds a predetermined threshold, retraining the ANN and redefining the boundary such that the second inputs fall within the boundary. | 06-21-2012 |
| 20120233104 | SYSTEM AND METHOD FOR PROVIDING ADAPTIVE MANUFACTURING DIAGNOSES IN A CIRCUIT BOARD ENVIRONMENT - An example method is provided and includes collecting inputs for a circuit board under test; evaluating historical repair records using a neuron network; providing repair actions for the circuit board based on the historical repair records; and providing an output reflecting a particular component of the circuit board to be replaced or to be repaired, where the output is associated with a developed probability of successfully fixing an issue that was identified by the test. In more specific implementations, the inputs include fault syndromes and log files associated with the circuit board under test. Additionally, at least one of the inputs of the neuron network is a syndrome vector extracted from a failure log. In yet other instances, particular outputs having higher probabilities are selected as the repair actions. The neuron network can be weighted using diagnosis knowledge weights. | 09-13-2012 |
| 20120239603 | METHOD AND SYSTEM FOR CONTROLLING ENVIRONMENTAL CONDITIONS OF DIFFERENT ENTITIES - The invention relates to a controlling system for adjusting environmental conditions of at least one entity, wherein the entity has desired environmental conditions for at least two different states. The system comprises equipments controlled by controlling means for changing and/or maintaining the environmental condition of the entities. The controlling means is adapted to provide controlling parameters to equipments for adjusting the environmental condition of said entity so that at least one parameter used for controlling the environmental condition of said entity depends on at least one measured environmental condition parameter of another entity being different from the entity, which environmental condition is adjusted by said equipment. | 09-20-2012 |
| 20120265720 | INFRASTRUCTURE AND ARCHITECTURE FOR DEVELOPMENT AND EXECUTION OF PREDICTIVE MODELS - A system that enables development and execution of predictive models comprises a centralized data management system, a data extraction tool a model validation tool and a model execution tool. In embodiments, a data management system includes a data management server that can be accessed via a web browser that stores data. An extraction tool includes a data filter adapted to filter data based on, for example, a population criteria, a sample size, and a date range criteria. A model validation tool validates the model. A model execution tool allows a user to score the model. | 10-18-2012 |
| 20120278265 | EFFORT ESTIMATION USING TEXT ANALYSIS - A system, method and program product for estimating effort of implementing a system based on a use case specification document. A system is provided that includes: a volumetrics processor that quantifies a structure of the document and evaluates a format of the document; a domain processor that identifies a domain of the system associated with the document; a complexity processor that defines a set of complexity variables associated with the document based on the structure of the document, a format of the document and a domain of the document; and a neural network that estimates an effort based on the set of complexity variables. | 11-01-2012 |
| 20120303564 | SYSTEMS AND METHODS FOR PREDICTING CHARACTERISTICS OF AN ARTIFICIAL HEART USING AN ARTIFICIAL NEURAL NETWORK - A system configured to predict characteristics of an artificial heart is described. The system includes a processor and memory in electronic communication with the processor, and an artificial neural network configured to receive an input vector of a predetermined length to train the artificial neural network, produce an output vector based on the input vector, and compare the output vector with a target vector of the predetermined length. When the output vector does not match the target vector within a predetermined error rate, the network is configured to adjust at least one weight, and when the output vector matches the target vector within the predetermined error rate, the network is configured to execute the input vector to produce an estimate at least one characteristic of the artificial heart. | 11-29-2012 |
| 20120317060 | SYSTEMS, DEVICES, AND METHODS FOR PARAMETER OPTIMIZATION - A computerized method for optimizing parameters is described. A system can initialize a group of parameters to respective values within a set of allowable models and bound a partition function across a number of variable pairs to generate a plurality of bounds. The system can also determine new values for the group of parameters that minimize a sum of the plurality of bounds. The system can set the group of parameters to the new values and optimize the parameters by iteratively performing the bounding, determining and setting. The system can stop optimizing when a termination condition is reached. | 12-13-2012 |
| 20130036077 | NEURAL NET FOR USE IN DRILLING SIMULATION - A method of optimizing a drilling tool assembly including inputting well data into an optimization system, the optimization system having an experience data set and an artificial neural network. The method further including comparing the well data to the experience data set and developing an initial drilling tool assembly based on the comparing the well data to the experience data, wherein the drilling tool assembly is developed using the artificial neural network. Additionally, the method including simulating the initial drilling tool assembly in the optimization system and creating result data in the optimization system based on the simulating. | 02-07-2013 |
| 20130080362 | CUSTOMER JOURNEY PREDICTION AND RESOLUTION - Customer journey prediction and resolution is accomplished via a predictive model in which each user is mapped onto all available user journey information corresponding to a specific business. The predictive model is analyzed to understand the characteristics, preferences, and lowest effort resolution for the user related to the services that are subscribed to by the user. The predictive model is analyzed to predict the service or collection of services for each user. Embodiments interact with, provide and receive information from, and react to and/or deliver action to the customer across channels and across services. All customer and system behavior, data, and action is tracked and coordinated and leveraged for continuous feedback and performance improvement. | 03-28-2013 |
| 20130103627 | METHOD FOR PREDICTING THE PROPERTIES OF CRUDE OILS BY THE APPLICATION OF NEURAL NETWORKS - A method for predicting the properties of crude oils by the application of neural networks articulated in phases and characterized by determining the T | 04-25-2013 |
| 20130117208 | Predictive Service for Third Party Application Developers - Disclosed is an apparatus, method and computer program device which send a prediction request to a prediction service to construct a prediction result, receives a prediction result and an estimation of accuracy of the prediction result from the prediction service and configures an application to adapt to at least one of user preferences, behavior and habits based upon the prediction result and estimation of accuracy of the prediction result. The prediction service can include an over-the-air server which is coupled to a prediction server or a dedicated API residing on one or more user devices and configured to access a prediction server. | 05-09-2013 |
| 20130204816 | STEAM TURBINE PERFORMANCE TESTING - A steam turbine performance testing system, including at least one computer hardware device, including a neural network created using a dynamic steam turbine thermodynamic model and preliminary data collected from a steam turbine; a network tester for testing the neural network with testing data; a current performance calculator for calculating a current performance of the steam turbine from operation data of the steam turbine; and a projected performance calculator for calculating a projected performance of the steam turbine from the current performance. | 08-08-2013 |
| 20130204817 | Systems and Methods For Forecasting Using Process Constraints - A characteristic forecasting system is disclosed. The characteristic forecasting system may have a memory and a processor. The memory may store instructions, that, when executed, enable the processor to generate at least one chromosome using a genetic algorithm, the chromosome including data values for variables of one or more equations used to generate forecast data for a target item. The processor may also be enabled to calculate a chromosome value for the chromosome based on a goal function associated with the genetic algorithm and determine at least one process parameter value for the chromosome at a time interval of the forecast data. The processor may also compare the process parameter value to a process constraint value representing a process limitation associated with the target item and modify the chromosome value for the chromosome responsive to a determination that the process parameter value does not satisfy the process constraint value. | 08-08-2013 |
| 20130254145 | INTEGRATED CIRCUIT CONVERGING INTERCONNECT MODE CONTROL - An integrated circuit includes one or more transaction data sources and one or more transaction data destinations connected via interconnect circuitry comprising a plurality of interconnect nodes. Within the interconnect nodes there are one or more converging interconnect nodes. A converging interconnect node includes prediction data generation circuitry for reading characteristics of a current item of transaction data from the converging interconnect node and generating associated prediction data for a future item of transaction data which will be returned to the converging interconnect node at a predetermined time in the future. This prediction data is stored within prediction data storage circuitry and is read by prediction data evaluation circuitry to control processing of a future item of transaction data corresponding to that prediction data when it is returned to the converging interconnect node. The interconnect circuitry may have a branching network topology or recirculating ring based topology. | 09-26-2013 |
| 20130262357 | CLINICAL PREDICTIVE AND MONITORING SYSTEM AND METHOD - A clinical predictive and monitoring system comprising a data store operable to receive and store data associated with a plurality of patients selected from medical and health data; and a number of social, behavioral, lifestyle, and economic data; at least one predictive model to identify at least one high-risk patient associated with at least one medical condition; a risk logic module operable to apply the at least one predictive model to the patient data to determine at least one risk score associated the at least one medical condition and identify at least one high-risk patient; a data presentation module operable to present notification and information to an intervention coordination team about the identified at least one high-risk patient; and an artificial intelligence tuning module adapted to automatically adjust parameters in the predictive model in response to trends in the patient data. | 10-03-2013 |
| 20130275352 | Identifying and Forecasting Shifts in the Mood of Social Media Users - Quantitatively identifying and forecasting shifts in a mood of social media users is described. An example method includes categorizing the textual messages generated from the social media users over a selected period of time into a plurality of word categories, with each word category containing a set of words associated with the mood of social media users. A score indicating an intensity of the mood of the social media users is calculated for each word category, wherein a value of the score and its corresponding time point define a data point for the word category. Subsequently, breakpoints in the mood of social media users are determined so that the breakpoints minimize a sum of square errors representing a measurement of a consistency of all data points from inferred values of the scores of the data points derived using the breakpoints over the selected period of time. Further, space of all possible breakpoints for the word categories are searched to identify a defined number and locations of the breakpoints. Finally the breakpoints over the selected period of time are interpreted to identify the shifts in the mood of social media users and trends between breakpoints. | 10-17-2013 |
| 20130290231 | PATIENT CONDITION DETECTION AND MORTALITY - When prediction onset of a medical condition for a patient, multiple sources of knowledge ( | 10-31-2013 |
| 20130297540 | SYSTEMS, METHODS AND COMPUTER-READABLE MEDIA FOR GENERATING JUDICIAL PREDICTION INFORMATION - Systems, methods and computer-readable storage media for generating judicial prediction information are described. A judicial information prediction system may be configured to receive an analysis request comprising judicial request elements and to access at least one judicial information source associated with the analysis request. The judicial request elements may include items of interest associated with the prediction of a legal action. For example, the judicial request elements may include a court, a judge, an area of the law, and a legal action, such as a motion to dismiss. The judicial information prediction system may operate to analyze the at least one judicial information source based on the judicial request elements to generate judicial prediction information. For instance, the judicial prediction information may indicate the likelihood of success of a legal event based on the circumstances specified in the analysis request. | 11-07-2013 |
| 20130318018 | NEURAL NETWORK-BASED TURBINE MONITORING SYSTEM - A neural network-based system for monitoring a turbine compressor. In various embodiments, the neural network-based system includes: at least one computing device configured to monitor a turbine compressor by performing actions including: comparing a monitoring output from a first artificial neural network (ANN) about the turbine compressor to a monitoring output from a second, distinct ANN about the turbine compressor; and predicting a probability of a malfunction in the turbine compressor based upon the comparison of the monitoring outputs from the first ANN and the second, distinct ANN. | 11-28-2013 |
| 20130339279 | OPTIMAL PERSISTENCE OF A BUSINESS PROCESS - Aspects of the invention provide for automatically selecting optimal fetch settings for business processes as a function of database query load and relational context by determining whether data loaded for data retrieval points is dependent upon a query result from another query process and automatically selecting an eager fetch setting if dependent upon a query result from another query process, or a lazy fetch setting if not. Usage of the data retrieval points is monitored with respect to defined units of work to define retrieval patterns and automatically update the fetch settings, including by revising selected eager fetch settings to lazy fetch settings if a data size of a defined retrieval pattern is larger than a permissible memory resource threshold. | 12-19-2013 |
| 20130346351 | ASSESSING ACCURACY OF TRAINED PREDICTIVE MODELS - A system includes a computer(s) coupled to a data storage device(s) that stores a training data repository and a predictive model repository. The training data repository includes retained data samples from initial training data and from previously received data sets. The predictive model repository includes at least one updateable trained predictive model that was trained with the initial training data and retrained with the previously received data sets. A new data set is received. A richness score is assigned to each of the data samples in the set and to the retained data samples that indicates how information rich a data sample is for determining accuracy of the trained predictive model. A set of test data is selected based on ranking by richness score the retained data samples and the new data set. The trained predictive model is accuracy tested using the test data and an accuracy score determined. | 12-26-2013 |
| 20140019391 | WEB ANALYTICS NEURAL NETWORK MODELING PREDICTION - A system and method are disclosed for optimizing website effectiveness. Original input data associated with a plurality of website effectiveness variables is processed using a website effectiveness model to generate a first website effectiveness value, which in turn is processed to generate a dependent variable. Input data corresponding to an individual website effectiveness variable is then processed to generate changed input data, which in turn is processed by the website effectiveness model with the original input data and the dependent variable to generate a second website effectiveness value. The first and second website effectiveness values are then processed to determine the effect of the changed data on the first website effectiveness value. | 01-16-2014 |
| 20140052678 | HIERARCHICAL BASED SEQUENCING MACHINE LEARNING MODEL - A hierarchical based sequencing (HBS) machine learning model. In one example embodiment, a method of employing an HBS machine learning model to predict multiple interdependent output components of an MOD output decision may include determining an order for multiple interdependent output components of an MOD output decision. The method may also include sequentially training a classifier for each component in the selected order to predict the component based on an input and based on any previous predicted component(s). | 02-20-2014 |
| 20140136458 | HOSPITAL UNIT DEMAND FORECASTING TOOL - An embodiment in accordance with the present invention provides a method of forecasting a demand for a particular hospital unit. The method can be executed by programming the steps into a computer readable medium. One step includes logging a total number of beds in the particular hospital unit and available nursing slots to determine a capacity for the particular hospital unit. The method also includes analyzing data for patients scheduled to stay in the particular hospital unit data to predict stochastic arrivals in order to estimate a total inflow. A length of stay of a patient in the particular hospital unit is predicted using a survival analysis based on physician orders to estimate a total outflow. Additionally, the method includes executing an algorithm designed to use the capacity, total inflow, and total outflow to determine the demand for the particular hospital unit. | 05-15-2014 |
| 20140143192 | PREDICTION DEVICE, PREDICTION METHOD, AND COMPUTER READABLE MEDIUM - For each item, a factorial effect value is derived that represents the SN ratio of the prediction object to each data including the data of the item relative to the SN ratio of the prediction object to each data excluding the data of the item. The strength of the SN ratio of the comprehensive estimated value to the data of a plurality of items selected in descending order of the derived factorial effect value is calculated for each value of the number of items. On the basis of the calculated SN ratio of the comprehensive estimated value, the number of items is determined. In descending order of the derived factorial effect value, items in the determined number of items are selected. On the basis of the data of the selected items, a change of the prediction object is predicted by using a method such as a T-method. | 05-22-2014 |
| 20140180986 | SYSTEM AND METHOD FOR ADDRESSING OVERFITTING IN A NEURAL NETWORK - A system for training a neural network. A switch is linked to feature detectors in at least some of the layers of the neural network. For each training case, the switch randomly selectively disables each of the feature detectors in accordance with a preconfigured probability. The weights from each training case are then normalized for applying the neural network to test data. | 06-26-2014 |
| 20140195468 | METHOD FOR MATCHING SPARKLE APPEARANCE OF COATINGS - This disclosure is directed to a process for producing one or more predicted target sparkle values of a target coating composition. An artificial neural network can be used in the process. The process disclosed herein can be used for color and appearance matching in the coating industry including vehicle original equipment manufacturing (OEM) coatings and refinish coatings. A system for producing one or more predicted target sparkle values of a target coating composition is also disclosed. | 07-10-2014 |
| 20140244558 | METHOD FOR MATCHING SPARKLE APPEARANCE OF COATINGS - This disclosure is directed to a process for producing one or more predicted target sparkle values of a target coating composition. An artificial neural network can be used in the process. The process disclosed herein can be used for color and appearance matching in the coating industry including vehicle original equipment manufacturing (OEM) coatings and refinish coatings. A system for producing one or more predicted target sparkle values of a target coating composition is also disclosed. | 08-28-2014 |
| 20140258197 | SYSTEM AND METHOD FOR CONTEXTUAL ANALYSIS - Computerized contextual analysis systems and methods suitable for monitoring situations, regions, and groups characterized by volatility and uncertainty are provided. Via use of exemplary systems and methods, decision makers, for example politicians, warfighters, and analysts can gain insight into the cultures, attitudes, events, and relationships that may impact their missions. | 09-11-2014 |
| 20140258198 | SYSTEM AND METHOD FOR REVEALING CORRELATIONS BETWEEN DATA STREAMS - The disclosed techniques can provide users with a tool having an integrated, user-friendly interface and having automated mechanisms which can reveal correlations between data streams to the users in a clear and easily understandable way, thereby enabling the users to easily digest the vast amount of information contained in activities within one or more network, to understand the correlations among the activities, to stay informed and responsive to current or new trends, and even to predict future trends. Among other benefits, the disclosed techniques are especially useful in the context of discovering impacts of social networking activities on other types of commercial activities. | 09-11-2014 |
| 20140304204 | Neural Network System and Uses Thereof - A multifunctional neural network system for prediction which includes memory components to store previous values of data within a network. The memory components provide the system with the ability to learn relationships/patterns existent in the data over time. | 10-09-2014 |
| 20140310219 | SYSTEMS AND METHODS FOR STORAGE MODELING AND COSTING - The present invention provides systems and methods for data storage. A hierarchical storage management architecture is presented to facilitate data management. The disclosed system provides methods for evaluating the state of stored data relative to enterprise needs by using weighted parameters that may be user defined. Also disclosed are systems and methods evaluating costing and risk management associated with stored data. | 10-16-2014 |
| 20140351187 | Method and System for Validating Energy Measurement in a High Pressure Gas Distribution Network - A method and system for validating energy measurement in a high pressure gas distribution network. The method comprises the steps of calculating a validation energy value using an artificial neural network (ANN) engine based on measured parameters associated with a gas flow in the gas distribution network; measuring an actual energy value of the gas flow; and comparing the validation energy value and the actual energy value, wherein the actual energy value is validated if the validation energy value and the actual energy value are substantially equal. | 11-27-2014 |
| 20140351188 | PREFETCH SYSTEM AND METHOD - A system and method for prefetching data. Address logs are separated into streams and a model associated with each stream. Each stream address is forecasted according to its respective model and pages corresponding to one or more forecasted stream addresses are retrieved from memory based on their respective models. | 11-27-2014 |
| 20140351189 | PREDICTION OF USER RESPONSE ACTIONS TO RECEIVED DATA - A system is provided for automatically predicting actions a user is likely to take in response to receiving data. The system may be configured to monitor and observe a user's interactions with incoming data and to identify patterns of actions the user may take in response to the incoming data. The system may enable a trainer component and a classifier component to determine the probability a user may take a particular action and to make predictions of likely user actions based on the observations of the user and the identified pattern of the user's actions. The system may also be configured to continuously observe the user's actions to fine-tune and adjust the identified patterns of user actions and to update the probabilities of likely user actions in order increase the accuracy of the predicted user action in response to incoming data. | 11-27-2014 |
| 20140358833 | DETERMINING AN ANOMALOUS STATE OF A SYSTEM AT A FUTURE POINT IN TIME - A prediction technique to predict an anomalous state of a processing environment at a future point in time. One or more values of one or more metrics of the processing system are obtained. For the one or more metrics, one or more predicted values are determined for one or more points in time in the future. Based the predicted values, one or more change values for one or more points in time are determined, and based on the one or more change values, a determination is made as to whether an anomalous state exists within the processing system. | 12-04-2014 |
| 20140365415 | SYSTEM AND METHOD FOR PREDICTING A CRITERIA OF INTEREST - A method for predicting a criteria of interest based upon tag location data, derived tag location data, statistical data or the like corresponding to an object. The method may include receiving an input that defines one or more objects and one or more criteria of interest. Further, the method may include accessing tag location data, derived tag location data or the like for the object. The method may also include generating a prediction for a criteria of interest for at least one of the one or more criteria of interest for at least one of the one or more objects, wherein the a prediction for a criteria of interest is generated based on at least one of tag location data, derived tag location data or the like for the one or more objects related to the one or more criteria of interest. An associated apparatus, computer program product and system are also provided. | 12-11-2014 |
| 20140372353 | INFORMATION PROCESSING SYSTEM AND DATA UPDATE CONTROL METHOD - An arithmetic operation unit receives a request to update a first data group during restoration using first history information and generates second history information indicating a history of updates. The arithmetic operation unit predicts a time taken until completion of restoration using the second history information on the basis of an amount of the second history information. The arithmetic operation unit compares the predicted time with a threshold and limits at least part of updates of the first data group during the restoration using the second history information on the basis of the comparison result. | 12-18-2014 |
| 20150026108 | Managing Computer Server Capacity - Systems and methods are disclosed for using machine learning (e.g., neural networks and/or combinatorial learning) to solve the non-linear problem of predicting the provisioning of a server farm (e.g., cloud resources). The machine learning may be performed using commercially available products, such as the SNNS product from The University of Stuttgard of Germany. The system, which includes a neural network for machine learning, is provided with an identification of inputs and outputs to track, and the system provides correlations between those. Rather than static rules, the machine learning provides dynamic provisioning recommendations with corresponding confidence scores. Based on the data collected/measured by the neural network, the provisioning recommendations will change as well as the confidence scores. | 01-22-2015 |
| 20150026109 | METHOD AND SYSTEM FOR PREDICTING POWER CONSUMPTION - In order to predict power consumption, previous measurements, which indicate the actual amount of power consumed in the past, and errors between previous estimates and the previous measurements, are used as first input data, and power consumption estimates for each prediction technique are simultaneously calculated by using the first input data in at least two prediction techniques. Next, the power consumption estimates calculated by each prediction technique and errors between the power consumption estimates and an actual measurement are used as second input data, and the final power consumption is predicted by making an additional power consumption prediction based on the second input data. | 01-22-2015 |
| 20150032677 | METHOD, DEVICE AND DATABASE FOR RECONSTRUCTING INTENDED ACTIVITIES FROM NEURAL SIGNALS - The invention relates to a method for reconstructing intended activities from a first representation of neural signals which is indicative of an intended activity. For second representations, a degree of agreement between the first representation and each second representation from a plurality of predetermined second representations that are indicative of intended activities is determined on the basis of a predetermined agreement criterion, and a second representation of the neural signals is selected from the plurality of second representations on the basis of the degree of agreement. The selected second representation is the reconstructed intended activity. The invention further relates to a database for use in a method according to the invention and a signal processing device for reconstructing intended activities. | 01-29-2015 |
| 20150032678 | OPTIMAL PERSISTENCE OF A BUSINESS PROCESS - Aspects of the invention provide for automatically selecting optimal fetch settings for business processes as a function of database query load and relational context by monitoring usage of a data retrieval point with respect to a defined unit of work. A multilayer feed-forward neural network is used to predict, as a function of training sets composed of historical data generated by the monitored usage of the data retrieval point, a future value of a data size of results from an eager fetch setting for the data retrieval point. The eager fetch is automatically revised to a lazy fetch setting in response to determining that the future data size value of the eager fetch setting results is larger than a permissible memory resource threshold. | 01-29-2015 |
| 20150039544 | RESOURCE PRODUCTION FORECASTING - A method can include providing a trained neural network; providing a set of production values where the set includes, for example, a cumulative production value for an interval, an average production value for the interval, a first production value for the interval and a last production value for the interval; and predicting at least one production value for a subsequent interval based at least in part on the trained neural network and the provided set of production values. Various other apparatuses, systems, methods, etc., are also disclosed. | 02-05-2015 |
| 20150088795 | Minimizing Global Error in an Artificial Neural Network - Computer systems, machine-implemented methods, and stored instructions are provided for minimizing an approximate global error in an artificial neural network that is configured to predict model outputs based at least in part on one or more model inputs. A model manager stores the artificial neural network model. The model manager may then minimize an approximate global error in the artificial neural network model at least in part by causing evaluation of a mixed integer linear program that determines weights between artificial neurons in the artificial neural network model. The mixed integer linear program accounts for piecewise linear activation functions for artificial neurons in the artificial neural network model. The mixed integer linear program comprises a functional expression of a difference between actual data and modeled data, and a set of one or more constraints that reference variables in the functional expression. | 03-26-2015 |
| 20150100528 | PREDICTIVE ANALYTIC SYSTEMS AND METHODS - The methods, apparatus, and systems described herein facilitate decision-making by providing predictions of outcomes and behaviors. The methods include receiving a communication between an agent and a prospect, analyzing density of keywords in a text version of the communication to determine the type of communication and amount of value time, determining if the communication is a first meaningful contact based on the type of communication and amount of value time, and predicting a likelihood of a prospect's action based on the determination. | 04-09-2015 |
| 20150106312 | METHOD AND SYSTEM FOR PROVIDING DASH OPTIMIZATION FOR MOBILE DEVICES - An approach is provided for location-based TCP throughput predictions and carrier-assisted video rate adaptation, including predicting a future location of a user device based on one or more location parameters associated with the device, a user of the device, or a combination thereof, predicting a transmission control protocol (TCP) throughput for at least one segment of a multimedia file based on the future location of the device, and transmitting the at least one segment based on the predicted TCP throughput. | 04-16-2015 |
| 20150106313 | PREDICTIVE MODELING OF HIGH-BYPASS TURBOFAN ENGINE DETERIORATION - A method, medium, and system to receive actual operational flight data for an engine of a particular type and configuration; train a neural network to generate an indicator of the health of the engine based on multiple different inputs to the neural network at a time the flight data was acquired; determine a deterioration factor for the engine, based at least in part, on an operational climate for the engine; and provide a record of the determined deterioration factor. | 04-16-2015 |
| 20150112907 | Systems and Methods for Real-Time Forecasting and Predicting of Electrical Peaks and Managing the Energy, Health, Reliability, and Performance of Electrical Power Systems Based on an Artificial Adaptive Neural Network - A system for utilizing a neural network to make real-time predictions about the health, reliability, and performance of a monitored system are disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a virtual system modeling engine, an analytics engine, an adaptive prediction engine. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The adaptive prediction engine can be configured to forecast an aspect of the monitored system using a neural network algorithm. The adaptive prediction engine is further configured to process the real-time data output and automatically optimize the neural network algorithm by minimizing a measure of error between the real-time data output and an estimated data output predicted by the neural network algorithm. | 04-23-2015 |
| 20150347898 | Predicting Well Markers from Artificial Neural-Network-Predicted Lithostratigraphic Facies - This disclosure generally describes methods and systems, including computer-implemented methods, computer-program products, and computer systems, for predicting well markers. One computer-implemented method includes separating neural-network (NN)-predicted facies output associated with a plurality of wells into two sets, a first set of NN-predicted facies output of training wells and a second set of NN-predicted facies output of target wells, calculating, for each training well of the plurality of wells, a sameness score between zones of NN-predicted facies output and human-identified lithostratigraphic units (finer zones), calculating a mean sameness score for the finer zones for all training wells, identifying finer zones with a mean sameness score greater than a threshold value as dominant facies zones, and iterating over each target well to calculate a top and depth position of each dominant facies zone determined based upon the NN-predicted facies output of the target well. | 12-03-2015 |
| 20150356402 | QUICK ANALYSIS OF RESIDUAL STRESS AND DISTORTION IN CAST ALUMINUM COMPONENTS - A computer-implemented system and method of rapidly predicting at least one of residual stress and distortion of a quenched aluminum casting. Input data corresponding to at least one of topological features, geometrical features and quenching process parameters associated with the casting is operated upon by the computer that is configured as a neural network to determine output data corresponding to at least one of the residual stress and distortion based on the input data. The neural network is trained to determine the validity of at least one of the input data and output data and to retrain the network when an error threshold is exceeded. Thereby, residual stresses and distortion in the quenched aluminum castings can be predicted using the embodiments in a tiny fraction of the time required by conventional finite-element based approaches. | 12-10-2015 |
| 20150371134 | PREDICTING CIRCUIT RELIABILITY AND YIELD USING NEURAL NETWORKS - A system and method for predicting a product characteristic are provided. The system includes a data acquisition module configured to acquire raw data associated with to-be predicted prediction information, a data conversion module configured to convert the raw data into computable normalized data, and a result prediction module configured to calculate a prediction result based on the normalized data and compare the prediction result with a predetermined standard value. The result prediction module includes a neural network prediction model configured to calculate the prediction result based on the normalized data. The prediction information may include reliability and/or yield to prevent major reliability or yield problems from occurring during manufacturing of semiconductor devices. | 12-24-2015 |
| 20160017132 | Method And System For Predicting Biocomposite Formulations And Processing Considerations Based On Product To Be Formed From Biocomposite Material - A system and method for predicting the formulation and processing method and processing parameters for the formation of a biocomposite material is provided. The system and method utilizes the desired properties for the biocomposite material and utilizes these properties m a prediction system to determine the particular formulation, processing method and processing parameters for the formation of a biocomposite material having the desired characteristics. This information is output from the prediction system to a biocomposite material manufacturing system in order to form the biocomposite material and an end product formed therefrom that has the desired characteristics input into the prediction system. | 01-21-2016 |
| 20160048757 | Systems and Methods for Real-Time Forecasting and Predicting of Electrical Peaks and Managing the Energy, Health, Reliability, and Performance of Electrical Power Systems Based on an Artificial Adaptive Neural Network - A system for utilizing a neural network to make real-time predictions about the health, reliability, and performance of a monitored system are disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a virtual system modeling engine, an analytics engine, an adaptive prediction engine. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The adaptive prediction engine can be configured to forecast an aspect of the monitored system using a neural network algorithm. The adaptive prediction engine is further configured to process the real-time data output and automatically optimize the neural network algorithm by minimizing a measure of error between the real-time data output and an estimated data output predicted by the neural network algorithm. | 02-18-2016 |
| 20160105327 | AUTOMATED UPGRADING METHOD FOR CAPACITY OF IT SYSTEM RESOURCES - Embodiments provide a method for performing an automatic execution of a Box and Jenkins method for forecasting the behavior of said dataset. The method may include pre-processing the dataset including providing one or more missing values to the dataset, removing level discontinuities and outliers, and removing one or more last samples from the dataset, obtaining a trend of the pre-processed dataset including identifying and filtering the trend out of the dataset based on a coefficient of determination methodology, detecting seasonality to obtain a resulting stationary series including computing an auto correlation function of the dataset, repeating the detecting step on an aggregate series of a previous dataset, and removing detected seasonality based on a seasonal differencing process, and modeling the resulting stationary series under an autoregressive-moving-average (ARMA) model. | 04-14-2016 |
| 20160117441 | MATHEMATICAL PROCESSES FOR DETERMINATION OF PEPTIDASE CLEAVAGE - The present invention provides a bioinformatic methodology for prediction of peptidase cleavage sites based on principal component analysis and based on training sets obtained by experimental protein cleavage. This invention is not limited to training sets derived from CSL approaches, nor to any other experimental determination of cleavage site. Undoubtedly there will be new approaches to developed for experimental measurement of cleavage sites and these too may be the source of training sources for the present invention. | 04-28-2016 |
| 20160140437 | METHOD TO PREDICT THE EFFLUENT AMMONIA-NITROGEN CONCENTRATION BASED ON A RECURRENT SELF-ORGANIZING NEURAL NETWORK - An intelligent method is designed for predicting the effluent ammonia-nitrogen concentration in the urban wastewater treatment process (WWTP). The technology of this invention is part of advanced manufacturing technology, belongs to both the field of control engineering and environment engineering. In order to improve the predicting efficiency, a recurrent self-organizing neural network, which can adjust the structure and parameters concurrently to train the parameters, is developed to design this intelligent method. This intelligent method can predict the effluent ammonia-nitrogen concentration with acceptable accuracy and solve the problem that the effluent ammonia-nitrogen concentration is difficult to be measured online. Moreover, the online information of effluent ammonia-nitrogen concentration, predicted by this intelligent method, can enhance the quality monitoring level and alleviate the current situation of wastewater to strengthen the whole management of WWTP. | 05-19-2016 |
| 20160180214 | SHARP DISCREPANCY LEARNING | 06-23-2016 |
| 20170236052 | ARRANGEMENT AND METHOD FOR PREDICTING ROAD FRICTION WITHIN A ROAD NETWORK | 08-17-2017 |
| 20180025273 | OPTIMIZING NEURAL NETWORKS FOR GENERATING ANALYTICAL OR PREDICTIVE OUTPUTS | 01-25-2018 |
| 20180025284 | Methods and systems for identifying patterns in data using delimited feature-regions | 01-25-2018 |
| 20190147356 | GENERATING A PREDICTIVE BEHAVIOR MODEL FOR PREDICTING USER BEHAVIOR USING UNSUPERVISED FEATURE LEARNING AND A RECURRENT NEURAL NETWORK | 05-16-2019 |
| 20220138557 | Deep Hybrid Graph-Based Forecasting Systems - In implementations of deep hybrid graph-based forecasting systems, a computing device implements a forecast system to receive time-series data describing historic computing metric values for a plurality of processing devices. The forecast system determines dependency relationships between processing devices of the plurality of processing devices based on time-series data of the processing devices. Time-series data of each processing device is represented as a node of a graph and the nodes are connected based on the dependency relationships. The forecast system generates an indication of a future computing metric value for a particular processing device by processing a first set of the time-series data using a relational global model and processing a second set of the time-series data using a relational local model. The first and second sets of the time-series data are determined based on a structure of the graph. | 05-05-2022 |
| 20220138568 | MODEL-BASED REINFORCEMENT LEARNING FOR BEHAVIOR PREDICTION - In various examples, reinforcement learning is used to train at least one machine learning model (MLM) to control a vehicle by leveraging a deep neural network (DNN) trained on real-world data by using imitation learning to predict movements of one or more actors to define a world model. The DNN may be trained from real-world data to predict attributes of actors, such as locations and/or movements, from input attributes. The predictions may define states of the environment in a simulator, and one or more attributes of one or more actors input into the DNN may be modified or controlled by the simulator to simulate conditions that may otherwise be unfeasible. The MLM(s) may leverage predictions made by the DNN to predict one or more actions for the vehicle. | 05-05-2022 |
