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		    <title>PatentStorm -&gt; Patents -&gt; Data processing: artificial intelligence</title>
		    <link>http://www.patentstorm.us/rss/class/patents/rss-706.xml</link>
		    <description>Recent patents filings in USPTO Class 706 Data processing: artificial intelligence.</description>
		    <pubDate>Tue, 21 May 2013 16:09:20</pubDate>
		    <managingEditor>patents@patentstorm.us</managingEditor>
		    <language>en</language><atom10:link xmlns:atom10="http://www.w3.org/2005/Atom" rel="self" type="application/rss+xml" href="http://feeds.feedburner.com/Patentstorm-Patents-DataProcessingArtificialIntelligence" /><feedburner:info uri="patentstorm-patents-dataprocessingartificialintelligence" /><atom10:link xmlns:atom10="http://www.w3.org/2005/Atom" rel="hub" href="http://pubsubhubbub.appspot.com/" /><item>
			         <title><![CDATA[System and method for multimedia information retrieval]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/MXu1aBSPyXE/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8447767&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-21&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; ; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A system and method for information retrieval are disclosed. The method includes querying a multimedia collection with a first component of a multimedia query (e.g., a text-based part of the query) to generate first comparison measures between the first component of the query and respective objects in the collection for a first media type (e.g., text). The multimedia collection is queried with a second component of the multimedia query (e.g., an image-based part of the query) to generate ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/MXu1aBSPyXE" height="1" width="1"/&gt;</description>
			         
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			         <title><![CDATA[Local results processor for use in a pattern matching accelerator]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/S3wBPiXY7fA/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8447749&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-21&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; ; ; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A pattern matching accelerator (PMA) for assisting software threads to find the presence and location of strings in an input data stream that match a given pattern. The patterns are defined using regular expressions that are compiled into a data structure comprised of rules subsequently processed by the PMA. The patterns to be searched in the input stream are defined by the user as a set of regular expressions. The patterns to be searched are grouped in pattern context sets. The sets of ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/S3wBPiXY7fA" height="1" width="1"/&gt;</description>
			         
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			         <title><![CDATA[Adaptive case-based reasoning system using dynamic method for knowledge acquisition]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/m27ZDCGLg_M/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8447720&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-21&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventor:&lt;/strong&gt; &amp;nbsp;&lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A method includes receiving a user-specified context comprising one or more natural language contextual antecedents. Then, for each contextual antecedent, a modified contextual antecedent is created by converting each contextual antecedent to a sequence of integers using a word base. Each modified contextual antecedent is compared to each of a plurality of cases stored in a case base, where each case includes one or more case antecedents and one or more case consequents. The case ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/m27ZDCGLg_M" height="1" width="1"/&gt;</description>
			         
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			         <title><![CDATA[Compilation of causal rules into continuations]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/KkolDMVwiZw/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8447719&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-21&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventor:&lt;/strong&gt; &amp;nbsp;&lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A method of compiling causal rules into continuations for use in root cause analysis of a system comprising a plurality of inter-related elements, comprising defining observable events occurring on system elements; defining at least one of a cause and a result of each of the events; defining causal rules, each rule describing a causal relationship between an event and one of its cause and its result; and compiling the causal relationships as continuations in a continuation passing style ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/KkolDMVwiZw" height="1" width="1"/&gt;</description>
			         
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			         <title><![CDATA[Method and apparatus for filtering streaming data]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/9sbEVPzqXuI/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8447718&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-21&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; ; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A method of content filtering of data containers of multiple content types is based on generating a set of encoded filters and a set of encoded rules for each content type. Each encoded filter is expressed as a respective operator, from among user-defined operators, and two operands including a content descriptor and a descriptor criterion. A filter has a binary state and a rule may be based on a single filter or a number of selected filters. An apparatus implementing the method has a user ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/9sbEVPzqXuI" height="1" width="1"/&gt;</description>
			         
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			         <title><![CDATA[Policy and charging rules node expired message handling]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/3xU2UNEX3YM/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8447717&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-21&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;Various exemplary embodiments relate to a method and related network node and machine-readable storage medium including a policy and charging rules node (PCRN) receiving a plurality of related service requests from different devices. The PCRN may proceed to generate a policy and charging control (PCC) rule based on at least one service request and other information stored in the PCRN if a mate service request does not arrive during the duration of a waiting timer. If the mate service ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/3xU2UNEX3YM" height="1" width="1"/&gt;</description>
			         
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			         <title><![CDATA[Dynamic constraint satisfaction problem solver with inferred problem association removal]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/O-7T9oLqUyY/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8447716&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-21&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; ; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A constraint solver for solving a constraint satisfaction problem network that comprises a plurality of nodes and a plurality of constraints. The solver receives a request to remove an inferred problem association and determines one or more user decisions that triggered the inferred problem ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/O-7T9oLqUyY" height="1" width="1"/&gt;</description>
			         
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			         <title><![CDATA[Apparatus and associated methods in relation to carbon nanotube networks]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/E763bqQBQI8/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8447715&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-21&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; ; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;In one or more embodiments described herein, there is provided an apparatus comprising a substrate, and a plurality of carbon nanotubes (semiconducting nano-elements) disposed and fixed with said substrate. The nanotubes are disposed and fixed on said substrate such that they define a carbon nanotube network substantially at the percolation threshold of the network. As the network is at the percolation threshold, this provides for one or more signal paths extending from an input region to ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/E763bqQBQI8" height="1" width="1"/&gt;</description>
			         
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			         <title><![CDATA[System for electronic learning synapse with spike-timing dependent plasticity using phase change memory]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/HxV3VSJW7UA/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8447714&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-21&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; ; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A system, method and computer program product for producing spike-dependent plasticity in an artificial synapse is disclosed. According to one embodiment, a method for producing spike-dependent plasticity in an artificial neuron comprises generating a pre-synaptic spiking event in a first neuron when a total integrated input to the first neuron exceeds a first predetermined threshold. A post-synaptic spiking event is generated in a second neuron when a total integrated input to the second ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/HxV3VSJW7UA" height="1" width="1"/&gt;</description>
			         
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			         <title><![CDATA[Automated legal evaluation using a neural network over a communications network]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/nIrpEwcR9EQ/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8447713&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-21&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventor:&lt;/strong&gt; &amp;nbsp;&lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A method for legal knowledge modeling and automated legal evaluation, such as for online, questionnaire-based legal analysis, is provided. Information, such as facts and characteristics of a legal situation, as it relates to a legal conclusion, are modeled in an artificial neural network. The artificial neural network may comprise a plurality of nodes, wherein each node is associated with one or more weights and a function that calculates a legal conclusion based on one or more input values ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/nIrpEwcR9EQ" height="1" width="1"/&gt;</description>
			         
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			         <title><![CDATA[Invariant object recognition]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/rYXVHJ4HUk4/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8447712&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-21&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventor:&lt;/strong&gt; &amp;nbsp;&lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A system and method of identifying the computing architecture used by the mammalian visual system and to implement it in simulations and software algorithms, and in hardware components, is ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/rYXVHJ4HUk4" height="1" width="1"/&gt;</description>
			         
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			         <title><![CDATA[Architecture of a hierarchical temporal memory based system]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/H_pKo4jGZgQ/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8447711&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-21&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; ; ; ; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A hierarchical temporal memory (HTM) based system may be provided as a software platform. The software platform includes: a runtime engine arranged to run an HTM network; a first interface accessible by a set of tools to configure, design, modify, train, debug, and/or deploy the HTM network; and a second interface accessible to extend a functionality of the runtime ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/H_pKo4jGZgQ" height="1" width="1"/&gt;</description>
			         
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			         <title><![CDATA[Method and system for reducing links in a Bayesian network]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/FB3lYBA5KoI/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8447710&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-21&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventor:&lt;/strong&gt; &amp;nbsp;&lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A method and system for reducing a number of links in a Bayesian network. A first modified Bayesian network based on a primary Bayesian network that has a plurality of links is generated, wherein the first modified Bayesian network does not include a first subset of the plurality of links. A second modified Bayesian network based on the primary Bayesian network is generated, wherein the second modified Bayesian network does not include a second subset of the plurality of links. The first ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/FB3lYBA5KoI" height="1" width="1"/&gt;</description>
			         
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			         <title><![CDATA[Information terminal and control method for storing image pickup data of a sales floor, and totaling and displaying sales data]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/bHqnq5XZudI/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8447709&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-21&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventor:&lt;/strong&gt; &amp;nbsp;&lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;According to one embodiment, an information terminal includes a storing unit configured to store image pickup data picked up in time series by an image pickup apparatus that picks up images of a sales floor and sales data obtained by recording sales of a commodity displayed in the sales floor and date and time of registration of the sales; a list display unit configured to list-display videos of a predetermined sales floor in time series at every predetermined time interval on the basis of ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/bHqnq5XZudI" height="1" width="1"/&gt;</description>
			         
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			         <title><![CDATA[Method and apparatus that carries out self-organizing of internal states of a state transition prediction model, and obtains a maximum likelihood sequence]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/iYc6Qkx5UK8/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8447708&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-21&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventor:&lt;/strong&gt; &amp;nbsp;&lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;An information processing device includes a model learning unit that carries out learning for self-organization of internal states of a state transition prediction model which is a learning model having internal states, a transition model of the internal states, and an observation model where observed values are generated from the internal states, by using first time series data, wherein the model learning unit learns the observation model of the state transition prediction model after the ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/iYc6Qkx5UK8" height="1" width="1"/&gt;</description>
			         
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			         <title><![CDATA[Automated control of a power network using metadata and automated creation of predictive process models]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/JGkRU739rHg/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8447707&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-21&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; ; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;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 ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/JGkRU739rHg" height="1" width="1"/&gt;</description>
			         
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			         <title><![CDATA[Method for computer-aided control and/or regulation using two neural networks wherein the second neural network models a quality function and can be used to control a gas turbine]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/GahMBk34t9A/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8447706&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-21&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A method for a computer-aided control of a technical system is provided. The method involves use of a cooperative learning method and artificial neural networks. In this context, feed-forward networks are linked to one another such that the architecture as a whole meets an optimality criterion. The network approximates the rewards observed to the expected rewards as an appraiser. In this way, exclusively observations which have actually been made are used in optimum fashion to determine a ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/GahMBk34t9A" height="1" width="1"/&gt;</description>
			         
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			         <title><![CDATA[Pattern generation method, pattern generation apparatus, and program]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/FQY3FFgMECQ/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8447705&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-21&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventor:&lt;/strong&gt; &amp;nbsp;&lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;Disclosed is an apparatus that generates automatically a characteristic pattern in time series data by clustering a plurality of time series subsequences generated from the time series data. The apparatus includes a time series subsequence generation unit that generates a plurality of time series subsequences from the time series data, a phase alignment unit that aligns a phase of the generated time series subsequence, a clustering unit that performs clustering of a plurality of the time ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/FQY3FFgMECQ" height="1" width="1"/&gt;</description>
			         
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<item>
			         <title><![CDATA[Recognizing gestures from forearm EMG signals]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/a_Gi32J5NFY/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8447704&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-21&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; ; ; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A machine learning model is trained by instructing a user to perform proscribed gestures, sampling signals from EMG sensors arranged arbitrarily on the user's forearm with respect to locations of muscles in the forearm, extracting feature samples from the sampled signals, labeling the feature samples according to the corresponding gestures instructed to be performed, and training the machine learning model with the labeled feature samples. Subsequently, gestures may be recognized using the ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/a_Gi32J5NFY" height="1" width="1"/&gt;</description>
			         
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			      <feedburner:origLink>http://www.patentstorm.us/patents/8447704/description.html</feedburner:origLink></item>
<item>
			         <title><![CDATA[Method and system for providing contents based on past queries]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/lrxv-7lX__Y/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8442987&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-14&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; ; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;An example of a method includes identifying a formulated query and at least one past query associated with an input query. The method also includes determining a first similarity score between the input query and the formulated query. Further, the method includes updating the first similarity score based on a second similarity score between the input query and the at least one past query, and based on a third similarity score between the formulated query and the at least one past query. ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/lrxv-7lX__Y" height="1" width="1"/&gt;</description>
			         
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<item>
			         <title><![CDATA[System and method for compiling geospatial data for on-line collaboration]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/eojTvRPrD9A/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8442963&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-14&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A system and method for compiling geospatial data for on-line collaboration is provided. A database of categories is maintained. Each category includes one or more waymarks and is associated with at least one variable having one or more attributes, which each specify a type of data storable under that variable. A search is performed and one of the categories is selected. A new waymark is formed under the selected category. First, geospatial data defining a location is received from a user. ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/eojTvRPrD9A" height="1" width="1"/&gt;</description>
			         
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<item>
			         <title><![CDATA[Recovering in deduplication systems]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/wpaND3HQdsk/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8442952&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-14&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; ; ; ; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A method is used in recovering in deduplication systems. Metadata of a data object is evaluated for determining deduplication status for the data object. Based on the deduplication status, the data object is ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/wpaND3HQdsk" height="1" width="1"/&gt;</description>
			         
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<item>
			         <title><![CDATA[Web-based visualization mash-ups for industrial automation]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/H7UMHz_jmcM/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8442933&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-14&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; ; ; ; ; ; ; ; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A visualization system that generates visual mash-ups for industrial automation includes a mash-up component that combines output from a subset of disparate sources into a common interface. The disparate sources include at least one of equipment, computers, or devices within an industrial automation environment. A visualization component generates and displays a mash-up visualization that includes information associated with the common ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/H7UMHz_jmcM" height="1" width="1"/&gt;</description>
			         
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			      <feedburner:origLink>http://www.patentstorm.us/patents/8442933/description.html</feedburner:origLink></item>
<item>
			         <title><![CDATA[System and method for making decisions]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/MbnjxSvtTYU/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8442932&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-14&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventor:&lt;/strong&gt; &amp;nbsp;&lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;Systems and methods are disclosed that assist in making decisions, valuing a choice or action, promoting creative solutions, and reducing risks. Among other situations, the systems and methods consider situations where the user or users are evaluating a possible single choice or several alternative choices. The criteria to evaluate the decisions are input into a grid, and the user or users rate the one or more alternatives against the different criteria. Given several alternative choices or ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/MbnjxSvtTYU" height="1" width="1"/&gt;</description>
			         
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			      <feedburner:origLink>http://www.patentstorm.us/patents/8442932/description.html</feedburner:origLink></item>
<item>
			         <title><![CDATA[Graph-based data search]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/PBkp9mCdJXY/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8442931&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-14&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;Computer based systems and methods for searching data transiting a network using a graph-based search model. A set of rules that describe strings or patterns of data to be identified in the data set, is expressed as a graph. As blocks of the data set are obtained for processing, the state of the graph is updated based upon the value of the received data block. The transition to the next state depends upon both the current state and the received data block. As blocks of data are received and ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/PBkp9mCdJXY" height="1" width="1"/&gt;</description>
			         
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<item>
			         <title><![CDATA[Untangled Euler diagrams]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/ml-BWc5k_ZE/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8442930&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-14&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventor:&lt;/strong&gt; &amp;nbsp;&lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A modified Euler diagram may use multiple convex shapes to display sets or members of sets. In one embodiment, a compact Euler diagram may represent members once, with the sets split into separate regions that may form a containment hierarchy over the members. The split set regions may be drawn with simple convex shapes and joined with connecting lines, which may be concave shapes. In another embodiment, each set may be illustrated with a single convex shape and the members of more than one ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/ml-BWc5k_ZE" height="1" width="1"/&gt;</description>
			         
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<item>
			         <title><![CDATA[Predicting item-item affinities based on item features by regression]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/oWaDOo_GP0Y/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8442929&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-14&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; ; ; ; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;Two items are determined to be similar to each not only based on previous actual user behavior, but also based on the observed relatedness of the characteristics of those two items. A first characteristic and a second characteristic are determined to have some affinity for each other if a high proportion of users who select items having the first characteristics also select items that have the second characteristic, and vice-versa. Two items having characteristics with high affinity for ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/oWaDOo_GP0Y" height="1" width="1"/&gt;</description>
			         
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			      <feedburner:origLink>http://www.patentstorm.us/patents/8442929/description.html</feedburner:origLink></item>
<item>
			         <title><![CDATA[Method and apparatus for employing rules to filter streaming data]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/L_Dz9UQukiA/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8442928&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-14&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; ; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;Content filtering of data containers is based on defining content types, receiving identifiers of a set of rules applicable to each content type, and determining dependence of at least one rule on other rules. The identifiers are sorted into rule strata where rules within each rule stratum are independent of each other and rules within each rule stratum beyond a first stratum depend on at least one rule of at least one preceding rule stratum. Upon receiving a data container of multiple ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/L_Dz9UQukiA" height="1" width="1"/&gt;</description>
			         
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			         <title><![CDATA[Dynamically configurable, multi-ported co-processor for convolutional neural networks]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/R27mrnkrVzs/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8442927&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-14&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; ; ; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A coprocessor and method for processing convolutional neural networks includes a configurable input switch coupled to an input. A plurality of convolver elements are enabled in accordance with the input switch. An output switch is configured to receive outputs from the set of convolver elements to provide data to output branches. A controller is configured to provide control signals to the input switch and the output switch such that the set of convolver elements are rendered active and a ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/R27mrnkrVzs" height="1" width="1"/&gt;</description>
			         
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<item>
			         <title><![CDATA[Information filtering system, information filtering method and information filtering program]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/KRWoMCcQOSE/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8442926&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-14&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventor:&lt;/strong&gt; &amp;nbsp;&lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A string matching unit &lt;b&gt;110&lt;/b&gt; specifies a category of an input document &lt;b&gt;801&lt;/b&gt; by string matching of the input document &lt;b&gt;801&lt;/b&gt; and a classifying keyword shown by matching condition information &lt;b&gt;109&lt;/b&gt;. Learning data &lt;b&gt;209&lt;/b&gt; shows statistic information of each category. A classifying unit &lt;b&gt;220&lt;/b&gt; specifies the category of the input document &lt;b&gt;801&lt;/b&gt; based on a correspondence ratio of the input document &lt;b&gt;801&lt;/b&gt; and the statistic information shown by the learning data ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/KRWoMCcQOSE" height="1" width="1"/&gt;</description>
			         
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			      <feedburner:origLink>http://www.patentstorm.us/patents/8442926/description.html</feedburner:origLink></item>
<item>
			         <title><![CDATA[Music recommendation method and apparatus]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/nd9pS4Osm2E/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8442925&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-14&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventor:&lt;/strong&gt; &amp;nbsp;&lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A music recommendation method may include obtaining the music belongingness function of music, which is the set of granularity of music in different dimensions, wherein the dimension is the classification of music and the granularity is the classification of the dimension; obtaining the user belongingness function of a user, which is the set of granularity indicating likes of user in different dimensions; calculating a granularity correlation function by using the music belongingness ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/nd9pS4Osm2E" height="1" width="1"/&gt;</description>
			         
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			         <title><![CDATA[Systems and methods for detecting the presence of a biological status using clustering]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/NjSJ3gPA3Ss/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8442924&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-14&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; ; ; ; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A method for determining the presence of a biological entity. The method may include entering into a digital computer, at least a plurality of first input values associated with a first genetic element (e.g., mecA), a plurality of second input values associated with a second genetic element (femA), and a plurality of third input values associated with a third genetic element (e.g., orfX) associated with a plurality of samples. Each sample includes a first input value in the plurality of ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/NjSJ3gPA3Ss" height="1" width="1"/&gt;</description>
			         
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<item>
			         <title><![CDATA[Temporal document trainer and method]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/iJ0zWrHmy98/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8442923&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-14&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventor:&lt;/strong&gt; &amp;nbsp;&lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;An electronic document sorter is trained to classify documents based on their temporal qualities. The invention can be used in environments such as automated news aggregators, search engines and other electronic systems which compile information having temporal ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/iJ0zWrHmy98" height="1" width="1"/&gt;</description>
			         
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			      <feedburner:origLink>http://www.patentstorm.us/patents/8442923/description.html</feedburner:origLink></item>
<item>
			         <title><![CDATA[Location ranking using social graph information]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/X7c23S9_DIs/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8438156&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-07&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;In one embodiment, a user of a social networking system requests to check in a place near the user's current location. The social networking system generates a list of places near the user's current location, ranks the places in the list of places near the user's current location by a distance between each place and the user's current location, as well as activity of the user and the user's social contacts for each place, and returns the ranked list to the ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/X7c23S9_DIs" height="1" width="1"/&gt;</description>
			         
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			      <feedburner:origLink>http://www.patentstorm.us/patents/8438156/description.html</feedburner:origLink></item>
<item>
			         <title><![CDATA[System and method for presenting events]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/dJvymFzeQns/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8438150&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-07&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventor:&lt;/strong&gt; &amp;nbsp;&lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A system and method are disclosed for presenting attainable events in the form of an event widget on the client computer browser. This system and method provides for embedding an event widget onto a third party, or affiliate, website so that when the website is viewed by a computer end user, the widget displays a tailored list of events. The tailored list of events include events that may be filtered and ranked based on webmaster settings and refined by system users. The events may be data ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/dJvymFzeQns" height="1" width="1"/&gt;</description>
			         
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			         <title><![CDATA[Methods, systems, and computer program products for determining availability of presentable content via a subscription service]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/0M3qmBbaXfY/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8438145&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-07&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;Methods, systems, and computer program products for determining availability of presentable content via a subscription service are disclosed. According to one aspect, a method includes receiving an indication of first presentable content associated with a first subscription service. Metadata associated with the selected first presentable content is received. Based on the received metadata, it is determined whether second presentable content corresponding to the first presentable content is ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/0M3qmBbaXfY" height="1" width="1"/&gt;</description>
			         
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			         <title><![CDATA[Suggesting and refining user input based on original user input]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/htHFMCEHkbo/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8438142&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-07&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; ; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;Systems and methods to generate modified/refined user inputs based on the original user input, such as a search query, are disclosed. The method may be implemented for Roman-based and/or non-Roman based language such as Chinese. The method may generally include receiving an original user input and identifying core terms therein, determining potential alternative inputs by replacing core term(s) in the original input with another term according to a similarity matrix and/or substituting a ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/htHFMCEHkbo" height="1" width="1"/&gt;</description>
			         
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			      <feedburner:origLink>http://www.patentstorm.us/patents/8438142/description.html</feedburner:origLink></item>
<item>
			         <title><![CDATA[Probabilistic implementation of system health prognosis]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/n_0TCzxChzU/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8438129&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-07&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;Described is a system for health prognosis of a component based on the use of a graphical probabilistic model. The model comprises a layer of at least one component node representing the health of a component, a layer of at least one health observation node representing a health observation, and a layer of at least one usage node representing a usage observation. Additionally, the system is configured to collect component failure data, which is used in generating normalized failure ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/n_0TCzxChzU" height="1" width="1"/&gt;</description>
			         
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			      <feedburner:origLink>http://www.patentstorm.us/patents/8438129/description.html</feedburner:origLink></item>
<item>
			         <title><![CDATA[Empirical modeling of confusion matrices]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/UwhWJAxAT8Q/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8438128&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-07&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventor:&lt;/strong&gt; &amp;nbsp;&lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A method and system of estimating the performance of a classifier system based on a reported confusion matrix includes, in one embodiment, parameters fit to observed confusion matrices, such that the expected performance of decision detection versus the probability of not-in-library reports can be estimated based on the forced decision confusion matrix. The approach also lends itself to a general methodology for modeling classes of confusers in a statistical manner, which can be extended to ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/UwhWJAxAT8Q" height="1" width="1"/&gt;</description>
			         
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			      <feedburner:origLink>http://www.patentstorm.us/patents/8438128/description.html</feedburner:origLink></item>
<item>
			         <title><![CDATA[Behaviour pattern analysis system, mobile terminal, behaviour pattern analysis method, and program]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/6e3Ot5bgofU/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8438127&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-07&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventor:&lt;/strong&gt; &amp;nbsp;&lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;Provided is a mobile terminal including a movement sensor that detects a movement of a user and outputs movement information, acquires information on a building existing at a current location or information on buildings existing in a vicinity of the current location, analyses the movement information output from the movement sensor, and detects a first behavior pattern corresponding to the movement information from multiple first behavior patterns obtained by classifying behaviors performed ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/6e3Ot5bgofU" height="1" width="1"/&gt;</description>
			         
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<item>
			         <title><![CDATA[Targeted maximum likelihood estimation]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/nyrc0iGRknA/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8438126&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-07&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventor:&lt;/strong&gt; &amp;nbsp;&lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A method for obtaining an estimator for a distribution pertaining to a dataset is provided. In an illustrative embodiment, the method includes obtaining a dataset; determining a question pertaining to the data; determining an initial estimator descriptive of a distribution of the data; and selectively modifying the initial estimator based on the question, yielding a targeted estimator in response thereto. In a more specific embodiment, selectively modifying the initial estimator includes ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/nyrc0iGRknA" height="1" width="1"/&gt;</description>
			         
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<item>
			         <title><![CDATA[System for assembling behavior models of technology components]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/7BlzzkYk1Jw/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8438125&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-07&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A system is described for assembling a behavior model of a technology component. The system may include a memory, and a processor. The memory may store a behavior model of a technology component. The processor may to monitor the technology component over a period of time to determine a plurality of parameter values associated with each state of a plurality of states of the technology component. The processor may process the plurality of parameter values associated with each state to ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/7BlzzkYk1Jw" height="1" width="1"/&gt;</description>
			         
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<item>
			         <title><![CDATA[System and method of a knowledge management and networking environment]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/icezVledMHU/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8438124&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-07&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; ; ; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;Systems and methods of a knowledge management networking are disclosed here. In one aspect, embodiments of the present disclosure include a method, which may be implemented on a system, of hosting a web-space having a plurality of objects, the plurality of objects to include one or more of, representations of a set of users, a set of web-items, and a set of nets; wherein a net of the set of nets is a subset of the web-space comprising a sub-plurality of the plurality of objects. One ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/icezVledMHU" height="1" width="1"/&gt;</description>
			         
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<item>
			         <title><![CDATA[Method and apparatus for configuring a communication channel]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/HkPwMsJcfKw/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8438123&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-07&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventor:&lt;/strong&gt; &amp;nbsp;&lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A method of configuring a communication channel prior to the transmission of an input signal along the communication channel, the communication channel comprising a plurality of sub-channels, the method comprising determining the strength of the input signal and in accordance with the determined signal strength, selecting a set of the plurality of sub-channels and transmitting said input signal along the set of sub-channels in parallel, wherein each of the sub-channels has a predetermined ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/HkPwMsJcfKw" height="1" width="1"/&gt;</description>
			         
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<item>
			         <title><![CDATA[Predictive analytic modeling platform]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/8FKfgjNo6Q0/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8438122&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-07&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training a predictive model. In one aspect, a method includes receiving over a network predictive modeling training data from a client computing system. The training data and multiple training functions obtained from a repository of training functions are used to train multiple predictive models. A score is generated for each of the trained predictive models, where each score ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/8FKfgjNo6Q0" height="1" width="1"/&gt;</description>
			         
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<item>
			         <title><![CDATA[Method and system for data analysis and synthesis]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/HKXEVNoBOmM/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8438121&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-07&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A system (&lt;b&gt;100&lt;/b&gt;) for analyzing and synthesizing a plurality of sources of sample data (&lt;b&gt;310, 320&lt;/b&gt;) by automated learning and regression. The system includes data storage (&lt;b&gt;110&lt;/b&gt;) with a stored multi-task covariance function, and an evaluation processor (&lt;b&gt;102&lt;/b&gt;) in communication with the data storage (&lt;b&gt;110&lt;/b&gt;). The evaluation processor (&lt;b&gt;102&lt;/b&gt;) performs regression using the stored sample data and multi-task covariance function and synthesizes prediction data for use ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/HKXEVNoBOmM" height="1" width="1"/&gt;</description>
			         
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			      <feedburner:origLink>http://www.patentstorm.us/patents/8438121/description.html</feedburner:origLink></item>
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			         <title><![CDATA[Machine learning hyperparameter estimation]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/8THMWBRWH-s/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8438120&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-05-07&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventor:&lt;/strong&gt; &amp;nbsp;&lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A method of determining hyperparameters (HP) of a classifier (&lt;b&gt;1&lt;/b&gt;) in a machine learning system (&lt;b&gt;10&lt;/b&gt;) iteratively produces an estimate of a target hyperparameter vector. The method comprises the steps of selecting from the random sample the hyperparameter vector producing the best result in the present and any previous iterations, and updating the estimate of the target hyperparameter vector by using said selected hyperparameter vector. The random sample may be restricted by ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/8THMWBRWH-s" height="1" width="1"/&gt;</description>
			         
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<item>
			         <title><![CDATA[System and method for supporting data warehouse metadata extension using an extender]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/_TUHsWPXQCs/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8433673&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-04-30&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; ; ; ; ; ; ; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;An extender associated with a data warehouse can support metadata extension for the data warehouse. The extender can create an ETL extension plan for extending the data warehouse. The ETL extension plan includes one or more ETL plan elements that indicate extensions on ETL metadata objects. The one or more ETL plan elements within the ETL extension plan can be ordered, and ETL metadata extension can be performed based on the ETL extension ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/_TUHsWPXQCs" height="1" width="1"/&gt;</description>
			         
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<item>
			         <title><![CDATA[Method and apparatus for vehicle component health prognosis by integrating aging model, usage information and health signatures]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/5domQaklIGU/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8433672&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-04-30&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; ; ; ; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;A system and method for determining the health of a component includes retrieving measured health signatures from the component, retrieving component usage variables, estimating component health signatures using an aging model, determining an aging derivative using the aging model and calculating an aging error based on the estimated component health signatures, the aging derivative and the measured health ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/5domQaklIGU" height="1" width="1"/&gt;</description>
			         
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<item>
			         <title><![CDATA[Determining a meaning of a knowledge item using document based information]]></title>
			         <link>http://feedproxy.google.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~3/vqXSY0hr68I/description.html</link>
			         <description>&lt;ul&gt;&lt;li&gt;&lt;strong&gt;Patent Number:&lt;/strong&gt; &amp;nbsp;8433671&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Publication Date:&lt;/strong&gt; &amp;nbsp;2013-04-30&lt;/li&gt;&lt;li&gt;&lt;strong&gt;Inventors:&lt;/strong&gt; &amp;nbsp;; &lt;/li&gt;&lt;/ul&gt;
&lt;p&gt;Systems and methods that determine a meaning of a knowledge item using related information are described. In one aspect, a knowledge item is received, related information associated with the knowledge item is received, at least one related meaning based on the related information is determined, and a knowledge item meaning for the knowledge item based at least in part on the related meaning is determined. Several algorithms and types of related information useful in carrying out such ...&lt;br /&gt;&lt;img src="http://feeds.feedburner.com/~r/Patentstorm-Patents-DataProcessingArtificialIntelligence/~4/vqXSY0hr68I" height="1" width="1"/&gt;</description>
			         
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