Inventors list

Assignees list

Classification tree browser

Top 100 Inventors

Top 100 Assignees

Learning task

Subclass of:

706 - Data processing: artificial intelligence

706015000 - NEURAL NETWORK

Patent class list (only not empty are listed)

Deeper subclasses:

Class / Patent application numberDescriptionNumber of patent applications / Date published
706020000 Classification or recognition 115
706021000 Prediction 94
706019000 Constraint optimization problem solving 23
706017000 Approximation 14
706018000 Association 13
706023000 Control 12
706022000 Signal processing (e.g., filter) 4
20130036078DEVICE AND METHOD RESPONSIVE TO INFLUENCES OF MIND - V An anomalous effect detector (02-07-2013
20090164398SIGNAL PROCESSING APPARATUS, SIGNAL PROCESSING METHOD, SIGNAL PROCESSING PROGRAM AND LEARNING APPARATUS - Disclosed herein is a signal processing apparatus for carrying out signal processing to convert input data into output data with a quality higher than the quality of the input data, the data processing apparatus including: a first data extraction section; a nonlinear feature quantity computation section; a processing-coefficient generation section; a second data extraction section; and a data prediction section.06-25-2009
20090259609METHOD AND SYSTEM FOR PROVIDING A LINEAR SIGNAL FROM A MAGNETORESISTIVE POSITION SENSOR - A method and system for providing a linear signal from a non-contact magnetoresistive position sensors utilizing a multilayer perception neural network. The neural network multiplies a number of non-linear inputs from the magnetoresistive position sensor by a number of first layer interconnection weights, which are summed by a number of first layer summing nodes and processed by a number of nonlinear activation function. The processed data can then be multiplied by a number of second layer interconnection weights and summed by an output layer-summing node. The output from the output layer-summing node can further be processed by an output activation function in order to produce a linear output signal.10-15-2009
20100057653DEVICE AND METHOD RESPONSIVE TO INFLUENCES OF MIND - V An anomalous effect detector (03-04-2010
20130085972METHOD FOR ACQUIRING PROCESS PARAMETERS FOR A FILM WITH A TARGET TRANSMITTANCE - In a method for acquiring process parameters for a film, a computer divides parameter sets into a training data group and a test data group. Then, the computer inputs the training data group to a neural network (NN) so as to obtain relationship among parameter sets of the training data group and transmittances, and uses the test data group to estimate accuracy of the NN. Further, the computer modifies the NN until an error value of estimated parameters, which are acquired by the NN according to the obtained relationship, is smaller than a predetermined value, and uses the NN to acquire practical parameters corresponding to a target transmittance when the error value is smaller than the predetermined value.04-04-2013
20090157577METHOD AND APPARATUS FOR OPTIMIZING MODELS FOR EXTRACTING DOSE AND FOCUS FROM CRITICAL DIMENSION - A method includes defining a reference model of a system having a plurality of terms for modeling data associated with the system. A reference fit error metric is generated for the reference model. A set of evaluation models each having one term different than the reference model is generated. An evaluation fit error metric for each of the evaluation models is generated. The reference model is replaced with a selected evaluation model responsive to the selected evaluation model having an evaluation fit error metric less than the reference fit error metric. The model evaluation is repeated until no evaluation model has an evaluation fit error metric less than the reference fit error metric. The reference model is trained using the data associated with the system, and the trained reference model is employed to determine at least one characteristic of the system.06-18-2009
20120166374ARCHITECTURE, SYSTEM AND METHOD FOR ARTIFICIAL NEURAL NETWORK IMPLEMENTATION - Systems and methods for a scalable artificial neural network, wherein the architecture includes: an input layer; at least one hidden layer; an output layer; and a parallelization subsystem configured to provide a variable degree of parallelization to the artificial neural network by providing scalability to neurons and layers. In a particular case, the systems and methods may include a back-propagation subsystem that is configured to scalably adjust weights in the artificial neural network in accordance with the variable degree of parallelization. Systems and methods are also provided for selecting an appropriate degree of parallelization based on factors such as hardware resources and performance requirements.06-28-2012
20130166484SYSTEMS, METHODS, AND APPARATUS FOR 3-D SURFACE MAPPING, COMPLIANCE MAPPING, AND SPATIAL REGISTRATION WITH AN ARRAY OF CANTILEVERED TACTILE HAIR OR WHISKER SENSORS - Systems, methods, and apparatus are provided using signals from a set of tactile sensors mounted on a surface to determine a surface topography. An example method includes receiving a set of moment and force input data from one or more identified topographies. The example method includes using a neural network to receive input from a training data set based on the first set of moment and force input data from the one or more identified topographies. Network weights to be used by the neural network to produce the training data set are modified via an evolutionary algorithm that tests vectors of candidate network weights. The example method includes receiving a moment and force input from a test object surface and reconstructing the surface topology based on the neural network outputs.06-27-2013
20090164395MODELING, DETECTING, AND PREDICTING USER BEHAVIOR WITH HIDDEN MARKOV MODELS - Mechanisms model, detect, and predict user behavior as a user navigates the Web. In one embodiment, mechanisms model user behavior using predictive models, such as discrete Markov processes, where the user's behavior transitions between a finite number of states. The user's behavior state may not be directly observable (e.g., a user does not proactively indicate what behavior state he is in). Thus, the behavior state of a user is usually only indirectly observable. Mechanisms use predictive models, such as hidden Markov models, to predict the transitions in the user's behavior states.06-25-2009
20080243734Method for computer-assisted processing of measured values detected in a sensor network - There is described a method for computer-assisted processing of measured values detected in a sensor network, with the sensor network comprising a plurality of sensor nodes, which each feature one or more sensors for detection of the measured values, with the measured values of a number of adjacent sensor nodes being known in a sensor node. A multi-area neural network will be mapped onto a corresponding sensor network by the inventive method, which creates the opportunity, with the aid of the information from adjacent sensors, even with incorrect or failed measurements of a sensor node, of guaranteeing detection of a global situation at the location of the sensor node. A sensor network operated with such a method is in such cases more robust against the failure of a few sensors, since a corresponding measured value can be estimated in a suitable way, so that the measurement not available can be replaced by the estimated measured value. The individual sensors of the sensor nodes can thus be of a simpler construction with the same level of robustness of the sensor network, since failures of sensors have less effect on the functional integrity of the sensor network.10-02-2008
20080275828Method and system for independently observing and modifying the activity of an actor processor - A system, method, and computer program product for observing and modifying activity in an actor processor are presented. An observer module is provided for observing a physical property of an actor processor. The observer module comprises a property-observing sensor for detecting and sampling a physical property of the actor processor and for generating an observation signal based on the physical property. The observer module further comprises an observer processor coupled with the property-observing sensor for receiving the observation signal, the observer processor operative to generate an observer output signal based on the observation signal. The observer module permits the observer processor to monitor the actor processor in a manner that isolates an instruction set of the observer processor from direct manipulation by means of an instruction set of the actor processor. Observer processors may be used in a recursive manner to provide a completely-coupled observation module.11-06-2008
20080243733RATING MEDIA ITEM RECOMMENDATIONS USING RECOMMENDATION PATHS AND/OR MEDIA ITEM USAGE - A media item recommendation rating system and method. A recommendation rating for media items is established and dynamically updated in response to media items being recommended to other users. A recommendation server or other device receives a report of a media item recommendation and updates a recommendation rating in response. The recommendation rating may also be updated based on how often a recommended media item is used or played. Thus, a media item's recommendation rating is affected by events relating to its recommendation, as opposed simple play-based ratings that are updated on any play action regardless of whether related to a recommendation or not. Simple play-based ratings do not distinguish between ordinary usages or plays and those resulting from recommendations. Recommendation of a media item to another user may be a better indicator of the user's likeability or popularity of a given media item, since a recommendation is an endorsement by another.10-02-2008
20090157576MEASURING AND LIMITING DISTRACTIONS ON COMPUTER DISPLAYS - Techniques are described herein for determining a distractibility measure for an item to be displayed on a display. The distractibility measure for an item is determined based on the individual distractibility measures for one or more of: the static distraction of the item, the onset response of the item, the optic-flow motion of the item, and the change in velocity of objects in the item. Each individual distractibility measure can be further multiplied by a weighting factor which affects the composition of the distractibility measure for the item. The distractibility measure for the item can be further based on the size of the item, how far away the item is from a primary content on the display, and the distractibility measure of the primary content on the display. The distractibility measure for the item can be compared to a maximum level of distractibility for automatically determining whether the item should be displayed on the display. Finally, the techniques described herein can be combined with other techniques which detect specific types of visual content in an item.06-18-2009
20090164396NETWORK ANALYZER - This invention relates to using artificial intelligence for analyzing real-life collected data from an operation system, modeling the collected data to identify characteristics of events, analyzing the models to conclude an optimal solution for maximizing the performance of the operation system.06-25-2009
20090177601STATUS-AWARE PERSONAL INFORMATION MANAGEMENT - Described is a technology by which personal information that comes into a computer system is intelligently managed according to current state data including user presence and/or user attention data. Incoming information is processed against the state data to determine whether corresponding data is to be output, and if so, what output modality or modalities to use. For example, if a user is present and busy, a notification may be blocked or deferred to avoid disturbing the user. Cost analysis may be used to determine the cost of outputting the data. In addition to user state data, the importance of the information, other state data, the cost of converting data to another format for output (e.g., text-to-speech), and/or user preference data, may factor into the decision. The output data may be modified (e.g., audio made louder) based on a current output environment as determined via the state data.07-09-2009
20080270333SYSTEM AND METHOD FOR DETERMINING SEMANTICALLY RELATED TERMS USING AN ACTIVE LEARNING FRAMEWORK - Systems and methods for determining semantically related terms using an active learning framework such as Transductive Experimental Design are disclosed. Generally, to enhance a keyword suggestion tool, an active learning module trains a model to predict whether a term is relevant to a user. The model is then used to present the user with terms that have been determined to be relevant based on the model so that an online advertisement service provider may more efficiently provide a user with terms that are semantically related to a seed set.10-30-2008
20090254502FEEDBACK SYSTEMS AND METHODS FOR RECOGNIZING PATTERNS - Pattern classification systems and methods are disclosed. The pattern classification systems and methods employ one or more classification networks that can parse multiple patterns simultaneously while providing a continuous feedback about its progress. Pre-synaptic inhibition is employed to inhibit feedback connections to permit more flexible processing. Various additional improvements result in highly robust pattern recognition systems and methods that are suitable for use in research, development, and production.10-08-2009
20090259606DIVERSIFIED, SELF-ORGANIZING MAP SYSTEM AND METHOD - A diversified, self-organizing map (SOM) system and method creates a number of special-purpose SOMs by filtering and training from a SOM Database which contains user preference data entries that include a wide range of fields or attributes of user preferences. Each special-purpose SOM is trained with a filtered subset of user preference data for fields and attributes related to its special purpose. Two or more special-purpose SOMs are harnessed inter-cooperatively together to provide recommendations of preferred items in response to queries. Multiple SOMs can be maintained at different websites and harnessed together through a global SOM interface. The system can function more efficiently than a single large SOM using a monolithic database with single-type data entries of large dimensionality.10-15-2009
20080306892MULTIPHASE FLOW METER FOR ELECTRICAL SUBMERSIBLE PUMPS USING ARTIFICIAL NEURAL NETWORKS - A multiphase flow meter used in conjunction with an electrical submersible pump system in a well bore includes sensors to determine and transmit well bore pressure measurements, including tubing and down hole pressure measurements. The multiphase flow meter also includes at least one artificial neural network device to be used for outputting flow characteristics of the well bore. The artificial neural network device is trained to output tubing and downhole flow characteristics responsive to multiphase-flow pressure gradient calculations and pump and reservoir models, combined with standard down-hole pressure, tubing surface pressure readings, and the frequency applied to the electrical submersible pump motor.12-11-2008
20110029470SYSTEMS, METHODS, AND APPARATUS FOR RECONSTRUCTION OF 3-D OBJECT MORPHOLOGY, POSITION, ORIENTATION AND TEXTURE USING AN ARRAY OF TACTILE SENSORS - Systems, methods, and apparatus are provided using signals from a set of tactile sensors mounted on a surface to determine the three-dimensional morphology (e.g., size, shape, orientation, and/or position) and texture of objects of arbitrary shape. Analytical, numerical, and/or neural network approaches can be used to interpret the sensory data.02-03-2011
20110040713MEDICAL SYSTEM, APPARATUS AND METHOD - There is provided a method of generating a pulmonary index value of a patient, which includes receiving two or more measured patient parameters, wherein at least one of the measured parameters originates from a pulmonary sensor; and computing the pulmonary index value based on the two or more measured patient parameters.02-17-2011
20090119236NEURAL NETWORKS WITH LEARNING AND EXPRESSION CAPABILITY - A neural network comprising a plurality of neurons in which any one of the plurality of neurons is able to associate with itself or another neuron in the plurality of neurons via active connections to a further neuron in the plurality of neurons.05-07-2009
20080222064Processes and Systems for Automated Collective Intelligence - The present invention relates to the field of collective intelligence. More specifically, to the collaborative acquisition of knowledge and the relationships among said knowledge and the application of acquired knowledge and relationships to solving problems. The present invention presents an interface to a community of users that will create nodes and relationships in an artificial neural network and then weight each node and relationship through votes from one or more users.09-11-2008
20100185573Method and Apparatus for Diagnosing an Allergy of the Upper Respiratory Tract Using a Neural Network - The invention relates to a method and means for performing a diagnosis of a medical condition and, in particular, an allergy associated with the upper respiratory tract, using an artificial neural network.07-22-2010
20100017351NEURAL NETWORK BASED HERMITE INTERPOLATOR FOR SCATTEROMETRY PARAMETER ESTIMATION - Generation of a meta-model for scatterometry analysis of a sample diffracting structure having unknown parameters. A training set comprising both a spectral signal evaluation and a derivative of the signal with respect to at least one parameter across a parameter space is rigorously computed. A neural network is trained with the training set to provide reference spectral information for a comparison to sample spectral information recorded from the sample diffracting structure. A neural network may be trained with derivative information using an algebraic method wherein a network bias vector is centered over both a primary sampling matrix and an auxiliary sampling matrix. The result of the algebraic method may be used for initializing neural network coefficients for training by optimization of the neural network weights, minimizing a difference between the actual signal and the modeled signal based on a objective function containing both function evaluations and derivatives.01-21-2010
20080262989MULTIPLEX DATA COLLECTION AND ANALYSIS IN BIOANALYTE DETECTION - Method and device to collect multiplex data simultaneously in analyte detection and analyze the data by experimentally trained software (machine-learning) is disclosed. Various ways (magnetic particles and microcoils) are disclosed to collect multiple reporter (tag) signals. Multiplex detection can increase the biomolecule analysis efficiency by using small sample size and saving assay reagents and time. Machine learning and data analysis schemes are also disclosed. Multiple affinity binding partners, each labeled by a unique reporter, are contacted with a sample and a single spectrum is taken to detect multiple reporter signals. The spectrum is deconvoluted by experimentally trained software to identify multiple analytes.10-23-2008
20100049680METHOD FOR PROJECTING WAFER PRODUCT OVERLAY ERROR AND WAFER PRODUCT CRITICAL DIMENSION - A method for projecting wafer product overlay error of the present invention is disclosed, the steps of the method comprises:(a) sample equipment overlay error data, equipment condition data, and actual wafer product overlay error data; (b) establish a neural network, the equipment overlay error data and the equipment condition data are inputs of the neural network, the generated output of the neural network is projected wafer product overlay error data, and the actual wafer product overlay error data is the target output of the neural network; and (c) set a mean square error target, train the neural network continuously until the mean square error of the neural network is no longer bigger than the mean square error target. Additionally a method for projecting wafer product critical dimension is also presented in the present invention.02-25-2010
20120233103SYSTEM FOR APPLICATION PERSONALIZATION FOR A MOBILE DEVICE - A system for controlling applications of a wireless mobile device includes a server for receiving data related to an adaptive user profile and for controlling operations of applications within the wireless mobile device. An adaptive neural/fuzzy logic control application implemented within the network server generates the adaptive user profile responsive to the received data. The adaptive user profile controls operations of the applications within the wireless mobile device and changes in real time responsive to the received data.09-13-2012
20120330869Mental Model Elicitation Device (MMED) Methods and Apparatus - A mental-model elicitation process and apparatus, called the Mental-Model Elicitation Device (MMED) is described. The MMED is used to give rise to more effective end-user mental-modeling activities that require executive function and working memory functionality. The method and apparatus is visual analysis based, allowing visual and other sensory representations to be given to thoughts, attitudes, and interpretations of a user about a given visualization of a mental-model, or aggregations of such visualizations and their respective blending. Other configurations of the apparatus and steps of the process may be created without departing from the spirit of the invention as disclosed.12-27-2012
20080235169Protective, Compact Cover for Topographic Maps and Other Large-Format Documents - This folding, compact document cover is an apparatus that practically and conveniently protects a specially folded U.S. Geological Survey (USGS) topographic map or virtually any other large-format document with its French-folded binding and easy to handle protective element that is capable of allowing the reader to flip between quadrants without the hassle of continued folding and refolding or rolling and unrolling.09-25-2008
20130103626METHOD AND APPARATUS FOR NEURAL LEARNING OF NATURAL MULTI-SPIKE TRAINS IN SPIKING NEURAL NETWORKS - Certain aspects of the present disclosure support a technique for neural learning of natural multi-spike trains in spiking neural networks. A synaptic weight can be adapted depending on a resource associated with the synapse, which can be depleted by weight change and can recover over time. In one aspect of the present disclosure, the weight adaptation may depend on a time since the last significant weight change.04-25-2013
20130159229MULTI-MODAL NEURAL NETWORK FOR UNIVERSAL, ONLINE LEARNING - In one embodiment, the present invention provides a neural network comprising multiple modalities. Each modality comprises multiple neurons. The neural network further comprises an interconnection lattice for cross-associating signaling between the neurons in different modalities. The interconnection lattice includes a plurality of perception neuron populations along a number of bottom-up signaling pathways, and a plurality of action neuron populations along a number of top-down signaling pathways. Each perception neuron along a bottom-up signaling pathway has a corresponding action neuron along a reciprocal top-down signaling pathway. An input neuron population configured to receive sensory input drives perception neurons along a number of bottom-up signaling pathways. A first set of perception neurons along bottom-up signaling pathways drive a first set of action neurons along top-down signaling pathways. Action neurons along a number of top-down signaling pathways drive an output neuron population configured to generate motor output.06-20-2013
20130204814METHODS AND APPARATUS FOR SPIKING NEURAL COMPUTATION - Certain aspects of the present disclosure provide methods and apparatus for spiking neural computation of general linear systems. One example aspect is a neuron model that codes information in the relative timing between spikes. However, synaptic weights are unnecessary. In other words, a connection may either exist (significant synapse) or not (insignificant or non-existent synapse). Certain aspects of the present disclosure use binary-valued inputs and outputs and do not require post-synaptic filtering. However, certain aspects may involve modeling of connection delays (e.g., dendritic delays). A single neuron model may be used to compute any general linear transformation x=AX+BU to any arbitrary precision. This neuron model may also be capable of learning, such as learning input delays (e.g., corresponding to scaling values) to achieve a target output delay (or output value). Learning may also be used to determine a logical relation of causal inputs.08-08-2013
20130204815METHOD FOR THE COMPUTER-AIDED LEARNING OF A RECURRENT NEURAL NETWORK FOR MODELING A DYNAMIC SYSTEM - A method for the computer-aided learning of a recurrent neural network for modeling a dynamic system which is characterized at respective times by an observable vector with one or more observables as entries is provided. The neural network includes both a causal network with a flow of information that is directed forwards in time and a retro-causal network with a flow of information which is directed backwards in time. The states of the dynamic system are characterized by first state vectors in the causal network and by second state vectors in the retro-causal network, wherein the state vectors each contain observables for the dynamic system and also hidden states of the dynamic system. Both networks are linked to one another by a combination of the observables from the relevant first and second state vectors and are learned on the basis of training date including known observables vectors.08-08-2013
20130212051NONDESTRUCTIVE METHOD TO PREDICT ISOSTATIC STRENGTH IN CERAMIC SUBSTRATES - A method of examining a cellular structure includes the steps of providing an inspecting device, a neural network and a target cellular structure that includes a plurality of target cells extending therethrough and further includes a target face exposing an arrangement of the target cells; inspecting the arrangement of cells on the face of the target cellular structure using the inspecting device; representing the arrangement of cells with numerically defined target cell parameters; inputting the target cell parameters into the neural network; and generating an output from the neural network based on the target cell parameters, the output being indicative of a strength of the target cellular structure.08-15-2013

Patent applications in class Learning task

Patent applications in all subclasses Learning task