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<title>freepatentsonline.com: Data processing: artificial intelligence</title>
<link>http://www.freepatentsonline.com/result.html?query_txt=ccl/706%20and%20isd/04/24/2008&amp;usapp=on</link>
<description>USPTO Class 706 Data processing: artificial intelligence</description>
<language>en-us</language>
<lastBuildDate>Wed Apr 30 17:03:31 EDT 2008</lastBuildDate>

<item>
<title><![CDATA[NOVELTY DETECTION SYSTEMS, METHODS AND COMPUTER PROGRAM PRODUCTS FOR REAL-TIME DIAGNOSTICS/PROGNOSTICS IN COMPLEX PHYSICAL SYSTEMS]]></title>
<link>http://www.freepatentsonline.com/20080097945.html</link>
<description><![CDATA[Sensors are configured to repeatedly monitor variables of a physical system during its operation. A novelty detection system is responsive to the sensors and is configured to repeatedly observe into an associative memory, states of associations among the variables that are repeatedly monitored, during a learning phase. The novelty detection system is further configured to thereafter observe at least one state of associations among the variables that are sensed relative to the states of associations that are in the associative memory, to identify a novel state of associations among the variables. The novelty detection system may determine whether the novel state is indicative of normal operation or of a potential abnormal operation. Multiple layers of learning for real-time diagnostics/prognostics also may be provided.]]></description>
<pubDate>April 24, 2008</pubDate>
</item>

<item>
<title><![CDATA[Apparatus and Method for Estimating User Interest Degree of a Program]]></title>
<link>http://www.freepatentsonline.com/20080097949.html</link>
<description><![CDATA[The present invention provides a method for estimating a user's interest degree in a playing program, the method includes the steps of: monitoring the user's behavior on the program including at least two segments, and each of them correspond to different time weight; determining the length and the time weight of the segment played corresponding to the user's behavior; and acquiring the user's interest degree in the program according to the length and the time weight of segment played.]]></description>
<pubDate>April 24, 2008</pubDate>
</item>

<item>
<title><![CDATA[Scalable Knowledge Extraction]]></title>
<link>http://www.freepatentsonline.com/20080097951.html</link>
<description><![CDATA[The present invention provides a method for extracting relationships between words in textual data. Initially, a classifier is trained to identify text data having a specific format, such as situation-response or cause-effect, using a training corpus. The classifier receives input identifying components of the text data having the specified format and then extracts features from the text data having the specified format, such as the part of speech for words in the text data, the semantic role of words within the text data and sentence structure. These extracted features are then applied to text data to identify components of the text data which have the specified format. Rules are then extracted from the text data having the specified format.]]></description>
<pubDate>April 24, 2008</pubDate>
</item>

<item>
<title><![CDATA[KERNELS AND KERNEL METHODS FOR SPECTRAL DATA]]></title>
<link>http://www.freepatentsonline.com/20080097940.html</link>
<description><![CDATA[Support vector machines are used to classify data contained within a structured dataset such as a plurality of signals generated by a spectral analyzer. The signals are pre-processed to ensure alignment of peaks across the spectra. Similarity measures are constructed to provide a basis for comparison of pairs of samples of the signal. A support vector machine is trained to discriminate between different classes of the samples. to identify the most predictive features within the spectra. In a preferred embodiment feature selection is performed to reduce the number of features that must be considered.]]></description>
<pubDate>April 24, 2008</pubDate>
</item>

<item>
<title><![CDATA[REAL-TIME PREDICTIVE COMPUTER PROGRAM, MODEL, AND METHOD]]></title>
<link>http://www.freepatentsonline.com/20080097943.html</link>
<description><![CDATA[A method for predicting a future occurrence of an event involves obtaining a history of prior occurrences of the event. A plurality of variables is created that are associated with the event. Weights are assigned to each variable. An artificial neural network is accessed and trained with the history of past occurrences of the event by comparing an output of the artificial neural network to the past occurrence of the event. The weights are adjusted until the output corresponds to the past occurrence of the event.]]></description>
<pubDate>April 24, 2008</pubDate>
</item>

<item>
<title><![CDATA[Statistical Message Classifier]]></title>
<link>http://www.freepatentsonline.com/20080097946.html</link>
<description><![CDATA[A system and method are disclosed for improving a statistical message classifier. A message may be tested with a machine classifier, wherein the machine classifier is capable of making a classification on the message. In the event the message is classifiable by the machine classifier, the statistical message classifier is updated according to the reliable classification made by the machine classifier. The message may also be tested with a first classifier. In the event that the message is not classifiable by the first classifier, it is tested with a second classifier, wherein the second classifier is capable of making a second classification. In the event that the message is classifiable by the second classifier, the statistical message classifier is updated according to the second classification.]]></description>
<pubDate>April 24, 2008</pubDate>
</item>

<item>
<title><![CDATA[Real Time Context Learning by Software Agents]]></title>
<link>http://www.freepatentsonline.com/20080097948.html</link>
<description><![CDATA[Providing dynamic learning for software agents in a simulation. Software agents with learners are capable of learning from examples. When a non-player character queries the learner, it can provide a next action similar to the player character. The game designer provides program code, from which compile-time steps determine a set of raw features. The code might identify a function (like computing distances). At compile-time steps, determining these raw features in response to a scripting language, so the designer can specify which code should be referenced. A set of derived features, responsive to the raw features, might be relatively simple, more complex, or determined in response to a learner. The set of such raw and derived features form a context for a learner. Learners might be responsive to (more basic) learners, to results of state machines, to calculated derived features, or to raw features. The learner includes a machine learning technique.]]></description>
<pubDate>April 24, 2008</pubDate>
</item>

<item>
<title><![CDATA[DATA MINING PLATFORM FOR BIOINFORMATICS AND OTHER KNOWLEDGE DISCOVERY]]></title>
<link>http://www.freepatentsonline.com/20080097939.html</link>
<description><![CDATA[The data mining platform comprises a plurality of system modules, each formed from a plurality of components. Each module has an input data component, a data analysis engine for processing the input data, an output data component for outputting the results of the data analysis, and a web server to access and monitor the other modules within the unit and to provide communication to other units. Each module processes a different type of data, for example, a first module processes microarray (gene expression) data while a second module processes biomedical literature on the Internet for information supporting relationships between genes and diseases and gene functionality. In the preferred embodiment, the data analysis engine is a kernel-based learning machine, and in particular, one or more support vector machines (SVMs). The data analysis engine includes a pre-processing function for feature selection, for reducing the amount of data to be processed by selecting the optimum number of attributes, or “features”, relevant to the information to be discovered.]]></description>
<pubDate>April 24, 2008</pubDate>
</item>

<item>
<title><![CDATA[DATA MINING PLATFORM FOR BIOINFORMATICS AND OTHER KNOWLEDGE DISCOVERY]]></title>
<link>http://www.freepatentsonline.com/20080097938.html</link>
<description><![CDATA[The data mining platform comprises a plurality of system modules, each formed from a plurality of components. Each module has an input data component, a data analysis engine for processing the input data, an output data component for outputting the results of the data analysis, and a web server to access and monitor the other modules within the unit and to provide communication to other units. Each module processes a different type of data, for example, a first module processes microarray (gene expression) data while a second module processes biomedical literature on the Internet for information supporting relationships between genes and diseases and gene functionality. In the preferred embodiment, the data analysis engine is a kernel-based learning machine, and in particular, one or more support vector machines (SVMs). The data analysis engine includes a pre-processing function for feature selection, for reducing the amount of data to be processed by selecting the optimum number of attributes, or “features”, relevant to the information to be discovered.]]></description>
<pubDate>April 24, 2008</pubDate>
</item>

<item>
<title><![CDATA[BEHAVIOR PREDICTION APPARATUS AND METHOD]]></title>
<link>http://www.freepatentsonline.com/20080097950.html</link>
<description><![CDATA[A behavior estimation apparatus inputs a prediction period, an annual consumption expenditure, extracts, from behavioral statistical data including an occurrence probability of each of behaviors of human during each time period, the occurrence probability of each behavior within the prediction period as a prior probability distribution, obtains a consumption time ratio between replaceable behaviors, and calculates estimated occurrence probabilities of the behaviors which minimize a Kullback-Leibler divergence with respect to the prior probability distribution and satisfy a condition that a ratio between estimated occurrence probabilities of the replaceable behaviors within the prediction period equals a consumption time ratio between the replaceable behaviors, a condition that a sum of expenditures of the behaviors per unit time equals an expenditure per unit time calculated from the annual consumption expenditure, and a condition that a sum of the occurrence probabilities and a sum of the estimated occurrence probabilities are “1”.]]></description>
<pubDate>April 24, 2008</pubDate>
</item>

<item>
<title><![CDATA[System and Method for Automated Suspicious Object Boundary Determination]]></title>
<link>http://www.freepatentsonline.com/20080097942.html</link>
<description><![CDATA[A system and method is provided for automated suspicious object boundary determination using a machine learning system ( 300 ) and genetic algorithms. The machine learning system ( 300 ) is trained ( 204 ) and tested ( 205 ) using sets of pre-categorized examples. Genetic algorithms assign initial parameter values ( 201 ), evaluate the system's performance (206) during testing and assign a performance rating ( 207 ), whereupon if the rating is acceptable, the current machine learning system's settings are assigned as default parameters ( 209 ) for future suspicious object segmentation. However, if the performance rating is unacceptable, the genetic algorithms adjust the settings ( 210 ) and retrain the system using the newly adjusted settings.]]></description>
<pubDate>April 24, 2008</pubDate>
</item>

<item>
<title><![CDATA[METHODS AND SYSTEMS FOR TRANSDUCTIVE DATA CLASSIFICATION]]></title>
<link>http://www.freepatentsonline.com/20080097936.html</link>
<description><![CDATA[A system, method, data processing apparatus, and article of manufacture are provided for classifying data. Labeled data points are received, each of the labeled data points having at least one label indicating whether the data point is a training example for data points for being included in a designated category or a training example for data points being excluded from a designated category; receiving unlabeled data points; receiving at least one predetermined cost factor of the labeled data points and unlabeled data points; training a transductive classifier using MED through iterative calculation using the at least one cost factor and the labeled data points and the unlabeled data points as training examples; applying the trained classifier to classify at least one of the unlabeled data points, the labeled data points, and input data points; and outputting a classification of the classified data points, or derivative thereof.]]></description>
<pubDate>April 24, 2008</pubDate>
</item>

<item>
<title><![CDATA[System and Method for Automatically Creating, Installing and Configuring Extensions of Functionalities in the System Nodes of a Distributed Network]]></title>
<link>http://www.freepatentsonline.com/20080097947.html</link>
<description><![CDATA[The invention relates to a system and to a method for automatically creating, installing and configuring extensions of functionalities in the system nodes of a distributed network, in particular in a distributed automatic system, provided with at least one system diagnosis tool which analyses the current state of the system of the distributed network and combines in system status data which is guided to at least one knowledge-based planning tool. The knowledge-based planning tool creates installation data for the novel system extensions which are based on control and data from the system status data and a planning data base and guides said installation data, respectively, to at least one installation and configuration tool, which is provided in the system nodes. The installation and configuration tool, of the respective system node automatically selects, from the installation data, the extension of the functionalities, which are to be installed, in the system nodes of the distributed network, installs and configures the latter, and enables, after the configuration of the installed software packets, the functionalities of the distributed network to be re-established.]]></description>
<pubDate>April 24, 2008</pubDate>
</item>

<item>
<title><![CDATA[REAL-TIME PREDICTIVE COMPUTER PROGRAM, MODEL, AND METHOD]]></title>
<link>http://www.freepatentsonline.com/20080097944.html</link>
<description><![CDATA[A method for predicting a future occurrence of an event involves obtaining a history of prior occurrences of the event. A plurality of variables is created that are associated with the event. Weights are assigned to each variable. An artificial neural network is accessed and trained with the history of past occurrences of the event by comparing an output of the artificial neural network to the past occurrence of the event. The weights are adjusted until the output corresponds to the past occurrence of the event.]]></description>
<pubDate>April 24, 2008</pubDate>
</item>

<item>
<title><![CDATA[Distributed method for integrating data mining and text categorization techniques]]></title>
<link>http://www.freepatentsonline.com/20080097937.html</link>
<description><![CDATA[A method for prediction analysis using text categorization is provided. The method includes the steps of: grouping a plurality of text documents into a plurality of classes; selecting a top m most discriminatory terms for each class of documents using statistical based measures; determining for each document the presence or absence of each of the discriminatory terms; learning rule-based models of each class of documents using a rule learning algorithm; determining, for at least a portion of the plurality of documents, if a given learned rule has been satisfied by each respective document; creating a database of the rules associated with documents satisfying the rules; and performing distributed data mining to form a predictive result based on at least a portion of the plurality of documents.]]></description>
<pubDate>April 24, 2008</pubDate>
</item>

<item>
<title><![CDATA[Learning algorithm for ranking on graph data]]></title>
<link>http://www.freepatentsonline.com/20080097941.html</link>
<description><![CDATA[Described are techniques for ranking a data set of objects. A graph representing the data set is provided. Examples of ranking preferences are provided for a portion of objects in the data set. Each of the examples indicates a ranking of a first object of the portion with respect to a second object of the portion. In accordance with the examples, a function, f, is determined that ranks the objects of the data set. A ranking of the objects of the data set is determined using the function f.]]></description>
<pubDate>April 24, 2008</pubDate>
</item>

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