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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/11/10/2009&amp;uspat=on</link>
<description>USPTO Class 706 Data processing: artificial intelligence</description>
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<lastBuildDate>Thu, 12 Nov 2009 03:32:06 EST</lastBuildDate>

<item>
<title><![CDATA[Systems and methods for storing a pattern occurring in situation representations for context representation in an application]]></title>
<link>http://www.freepatentsonline.com/7617165.html</link>
<description><![CDATA[Systems and methods are provided for storing a pattern occurring in situation representations for context representation in an application. A system stores a pattern occurring in situation representations for context representation in an application. The system includes a first storage medium for storing a plurality of situations in which the user is or has been involved, as a respective plurality of situation representations having at least one property determined by the respective situation. The system also includes a pattern identifier for identifying a pattern including a common property shared by at least two of the plurality of situation representations in the plurality of stored situation representations. The system further includes a second storage medium for storing a representation of the pattern. The first storage medium is arranged to remove the at least two of the plurality of situation representations from the first storage medium in response to the identification of the pattern.]]></description>
<pubDate>Tue, 10 Nov 2009 08:00:00 EST</pubDate>
</item>

<item>
<title><![CDATA[Kernels and kernel methods for spectral data]]></title>
<link>http://www.freepatentsonline.com/7617163.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>Tue, 10 Nov 2009 08:00:00 EST</pubDate>
</item>

<item>
<title><![CDATA[Neural network for aeroelastic analysis]]></title>
<link>http://www.freepatentsonline.com/7617166.html</link>
<description><![CDATA[A system and method of performing aeroelastic analysis using a neural network. Input parameters, such as mass and location, contributing to aeroelastic characterization are determined and constrained. A model of a structure to be analyzed can be constructed. The model can include a number of locations where the input parameters can be varied. The aeroelastic characteristic of the structure can be analyzed using a finite element model to determine a number of output characteristics, each of which can correspond to at least one of a plurality of input samples. A neural network can be generated for determining the aeroelastic characteristic based on input parameters. The input sample/output characteristic pairs can be used to train the neural network. The weights and bias values from the trained neural network can be used to generate a non-linear transfer function that generates the aeroelastic characteristic in response to input parameters.]]></description>
<pubDate>Tue, 10 Nov 2009 08:00:00 EST</pubDate>
</item>

<item>
<title><![CDATA[System, method and computer program product for a collaborative decision platform]]></title>
<link>http://www.freepatentsonline.com/7617169.html</link>
<description><![CDATA[A decision making system, method and computer program product are provided. Initially, a plurality of attributes is defined. Thereafter, first information regarding the attributes is received from a receiving business. Second information is then received regarding proposed products or services in terms of the attributes. Such second information is received from a supplying business. In use, a decision process is executed based on the first information and the second information.]]></description>
<pubDate>Tue, 10 Nov 2009 08:00:00 EST</pubDate>
</item>

<item>
<title><![CDATA[Generated anomaly pattern for HTTP flood protection]]></title>
<link>http://www.freepatentsonline.com/7617170.html</link>
<description><![CDATA[A system and method to detect and mitigate denial of service and distributed denial of service HTTP “page” flood attacks. Detection of attack/anomaly is made according to multiple traffic parameters including rate-based and rate-invariant parameters in both traffic directions. Prevention is done according to HTTP traffic parameters that are analyzed once a traffic anomaly is detected. This protection includes a differential adaptive mechanism that tunes the sensitivity of the anomaly detection engine. The decision engine is based on a combination between fuzzy logic inference systems and statistical thresholds. A “trap buffer” characterizes the attack to allow an accurate mitigation according to the source IP(s) and the HTTP request URL's that are used as part of the attack. Mitigation is controlled through a feedback mechanism that tunes the level of rate limit factors that are needed in order to mitigate the attack effectively while letting legitimate traffic to pass.]]></description>
<pubDate>Tue, 10 Nov 2009 08:00:00 EST</pubDate>
</item>

<item>
<title><![CDATA[Efficiency of training for ranking systems based on pairwise training with aggregated gradients]]></title>
<link>http://www.freepatentsonline.com/7617164.html</link>
<description><![CDATA[The subject disclosure pertains to systems and methods for facilitating training of machine learning systems utilizing pairwise training. The number of computations required during pairwise training is reduced by grouping the computations. First, a score is generated for each retrieved data item. During processing of the data item pairs, the scores of the data items in the pair are retrieved and used to generate a gradient for each data item. Once all of the pairs have been processed, the gradients for each data item are aggregated and the aggregated gradients are used to update the machine learning system.]]></description>
<pubDate>Tue, 10 Nov 2009 08:00:00 EST</pubDate>
</item>

<item>
<title><![CDATA[Apparatus and method for controlling portable device]]></title>
<link>http://www.freepatentsonline.com/7617168.html</link>
<description><![CDATA[An apparatus and method for controlling a portable device. An apparatus for controlling a portable device includes: a touch sensor which comprises at least one touch cell which is chosen by a touch of a user; a first calculation unit which calculates a first probability value while taking dependency relationships among the chosen at least one touch cell into consideration; a second calculation unit which calculates a second probability value without taking the dependency relationships among the chosen at least one touch cell into consideration; a determination unit which determines whether a current touch cell combination of the chosen at least one touch cell is a registered touch cell combination or an unregistered touch cell combination with reference to the first probability value and the second probability value; and an output unit which outputs a function execution signal according to the results of the determining.]]></description>
<pubDate>Tue, 10 Nov 2009 08:00:00 EST</pubDate>
</item>

<item>
<title><![CDATA[Document clustering based on entity association rules]]></title>
<link>http://www.freepatentsonline.com/7617182.html</link>
<description><![CDATA[For each document in a document set, entities are identified and a set of association rules, based on appearance of the entities in the paragraphs of the documents in the set, are derived. Documents are clustered based on the association rules. As documents are added to the clusters, additional association rules specific to the clusters can optionally be derived as well.]]></description>
<pubDate>Tue, 10 Nov 2009 08:00:00 EST</pubDate>
</item>

<item>
<title><![CDATA[Process for the iterative construction of an explanatory model]]></title>
<link>http://www.freepatentsonline.com/7617171.html</link>
<description><![CDATA[A process for iterative construction of an explanatory model including at least one rule calculated from a plurality of experiments, each of which rules is associated with at least one indicator of the quality of a corresponding rule including determining a rule, of a set of new experiments in an application space, conducting the new experiments to obtain the corresponding results, and calculating and optionally updating indicators of quality of the rule as a function of the experiments and of corresponding results.]]></description>
<pubDate>Tue, 10 Nov 2009 08:00:00 EST</pubDate>
</item>

<item>
<title><![CDATA[Machine vision system for enterprise management]]></title>
<link>http://www.freepatentsonline.com/7617167.html</link>
<description><![CDATA[A system for use in managing activity of interest within an enterprise is provided. The system comprises a computer configured to (i) receive sensor data that is related to key activity to the enterprise, such key activity comprising a type of object and the object's activity at a predetermined location associated with the enterprise, the sensor providing information from which an object's type and activity at the predetermined location can be derived, (ii) process the sensor data to produce output that is related to key activity to the enterprise, and (ii) store the information extracted from the processed data in a suitable manner for knowledge extraction and future analysis. According to a preferred embodiment, the object is human, machine or vehicular, and the computer is further configured to correlate sensor data to key activity to the enterprise and the output includes feedback data based on the correlation.]]></description>
<pubDate>Tue, 10 Nov 2009 08:00:00 EST</pubDate>
</item>

<item>
<title><![CDATA[Using percentile data in business analysis of time series data]]></title>
<link>http://www.freepatentsonline.com/7617172.html</link>
<description><![CDATA[A real time data processing system, method and program product for processing a stream of data events. A system is provided that includes: a running profile processing system for updating a running profile each time a new data event value is inputted, wherein the running profile includes percentile data; and an analysis system for analyzing the running profile, wherein the analysis system performs a composite analysis that utilizes: (a) results obtained from comparing a new data event value to the percentile data, and (b) stored results obtained from previous compare operations.]]></description>
<pubDate>Tue, 10 Nov 2009 08:00:00 EST</pubDate>
</item>

<item>
<title><![CDATA[Method and system for improving security of postage indicia utilizing resolution and pixel size]]></title>
<link>http://www.freepatentsonline.com/7617173.html</link>
<description><![CDATA[The present invention includes methods for printing and verifying postage indicia. At least a portion of the indicia is printed with a resolution characteristic that may be changed from indicium to indicium. Each indicium includes data that indicates the resolution used to print the indicium or indicium portion.]]></description>
<pubDate>Tue, 10 Nov 2009 08:00:00 EST</pubDate>
</item>

<item>
<title><![CDATA[Method and system for automatic service composition]]></title>
<link>http://www.freepatentsonline.com/7617174.html</link>
<description><![CDATA[A method and system for automatic service composition is disclosed, wherein a service registry stores a plurality of service specifications received from at least one service provider in a service repository, and receives service requests from service requestors. A composition engine of the service registry can automatically generate a series of actions from service specifications stored in the service repository according to the service request, and then passes those actions to the service requestor for being processed. In order to facilitate the preceding procedure of automatic composition, the composition engine pre-processes service requests and service specifications, and declares suitable new objects. The composition engine also post-processes the planning resolution generated by the planning module for translating the format of the planning resolution into a processing language, and finally passes the resolution to a processing engine of the service requestor for being processed.]]></description>
<pubDate>Tue, 10 Nov 2009 08:00:00 EST</pubDate>
</item>

<item>
<title><![CDATA[Characterizing context-sensitive search results as non-spam]]></title>
<link>http://www.freepatentsonline.com/7617199.html</link>
<description><![CDATA[Methods and apparatus assessing, ranking, organizing, and presenting search results associated with a user's current work context are disclosed. The system disclosed assesses, ranks, organizes and presents search results against a user's current work context by comparing statistical and heuristic models of the search results to a statistical and heuristic model of the user's current work context. In this manner, search results are assessed, ranked, organized, and/or presented with the benefit of attributes of the user's current work context that are predictive of relevance, such as words in a user's document (e.g., web page or word processing document) that may not have been included in the search query. In addition, search results from multiple search engines are combined into an organization scheme that best reflects the user's current task. As a result, lists of search results from different search engines can be more usefully presented to the user.]]></description>
<pubDate>Tue, 10 Nov 2009 08:00:00 EST</pubDate>
</item>

<item>
<title><![CDATA[Displaying context-sensitive ranked search results]]></title>
<link>http://www.freepatentsonline.com/7617200.html</link>
<description><![CDATA[Methods and apparatus assessing, ranking, organizing, and presenting search results associated with a user's current work context are disclosed. The system disclosed assesses, ranks, organizes and presents search results against a user's current work context by comparing statistical and heuristic models of the search results to a statistical and heuristic model of the user's current work context. In this manner, search results are assessed, ranked, organized, and/or presented with the benefit of attributes of the user's current work context that are predictive of relevance, such as words in a user's document (e.g., web page or word processing document) that may not have been included in the search query. In addition, search results from multiple search engines are combined into an organization scheme that best reflects the user's current task. As a result, lists of search results from different search engines can be more usefully presented to the user.]]></description>
<pubDate>Tue, 10 Nov 2009 08:00:00 EST</pubDate>
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