Abstract: The design and process scheduling optimization method is a technique that circumvents problems encountered in previous optimization techniques. The method performs comparison against multiple possible outcomes rather than a single-valued benchmark. Pattern Recognition (PR) techniques are utilized to classify the work planned at specified cut-off dates during the planning stage. Classification is used to monitor and evaluate the progress during the construction stage. The PR technique generalizes a virtual benchmark to represent the whole project based on multiple possible outcomes generated at a given cut-off date. The generalization feature offers a potential tool to overcome the problem of variation in the quality of data collected. Patterns are constructed to encode work of the project at different cut-off dates. The present invention utilizes a robust pattern recognition method applied to Critical Path Method (CPM) procedures to monitor and evaluate progress of construction projects.

Abstract: A string matching device extracts a string by shifting a start position from an input data stream, compares the extracted string with a sub-string included in a target string, and when a prefix of a target string is detected from the string extracted from the input data stream, it selects one of the strings output in the next stage based on the start position of the corresponding string and uses it to detect the sub-string. Also, the device can consecutively detect at least one target string from the input data stream by using a state transition process.

Abstract: The invention provides a computer-implemented chunking process (named “OP”) to assign and physically associate unique spatial representations (“chunk codes”) in one representational (coding) space (“chunk coding space”) with unique temporal sequences of spatial codes (“items”) in another coding space (“item coding space”). OP differs from all prior chunking processes in that, for any particular item sequence, it activates a unique chunk code for that sequence during presentation of that sequence's first item, even if other sequences from the input domain may begin with the same item.

Abstract: It is possible to acquire existing techniques in a neural network model currently studied and developed so as to generalize them as an element technique, and provide modeling of a basic unit of bottom-up approach using the neural network by adding new values to the existing techniques. A network learning device builds up a network of basic units in a network section, acquires an input from a sensor input section for evaluating it, changes a coupling weight coefficient by using a correlation operation so that the evaluation value satisfies a predetermined evaluation value, and inserts a new neural network according to need.

Abstract: A system and method for processing information in unstructured or structured form, comprising a computer running in a distributed network with one or more data agents. Associations of natural language artifacts may be learned from natural language artifacts in unstructured data sources, and semantic and syntactic relationships may be learned in structured data sources, using grouping based on a criteria of shared features that are dynamically determined without the use of a priori classifications, by employing conditional probability constraints.

Abstract: The invention provides techniques for building multiple predictive models of individuals' affinities for attributes of objects and/or services. The accuracies of multiple predictive models are measured and the models are combined based on the measurements, resulting in a more accurate predictive model of individual-specific affinities for attributes of the objects and/or services.

Abstract: A method for functional analysis of neurophysiological data by decomposing neurophysiological data and EEG signal to form a plurality of signal features. The signal features may then optionally be analyzed to determined one or more patterns.

Abstract: An apparatus for detecting volatile compounds derived from explosive materials with very high sensitivity. The apparatus is composed of field effect transistors of non-oxidized silicon nanowires modified with specific functional groups including, in particular, amine, imine and/or carboxyl moieties. Further a system is provided comprising the apparatus in conjunction with learning and pattern recognition algorithms and methods of use thereof for detecting and quantifying specific explosive compounds.

Abstract: In general, the invention is directed to a technique for selection of parameter configurations for a neurostimulator using neural networks. The technique may be employed by a programming device to allow a clinician to select parameter configurations, and then program an implantable neurostimulator to deliver therapy using the selected parameter configurations. The parameter configurations may include one or more of a variety of parameters, such as electrode configurations defining electrode combinations and polarities for an electrode set implanted in a patient. The electrode set may be carried by one or more implanted leads that are electrically coupled to the neurostimulator. In operation, the programming device executes a parameter configuration search algorithm to guide the clinician in the selection of parameter configurations. The search algorithm relies on a neural network that identifies potential optimum parameter configurations.

Abstract: This invention is in the field of machine learning and neural associative memory. In particular the invention discloses a neural associative memory structure for storing and maintaining associations between memory address patterns and memory content patterns using a neural network, as well as methods for storing and retrieving such associations. Bayesian learning is applied to achieve non-linear learning.

Abstract: A simulated consciousness method (10) for an improved human/computer interface. A computer system (12) is programmed to have a Digital Life Form (32) possessing a plurality of attributes (65). A plurality of actions (64) taken relative to objects (60) in the environment (30) contribute to simulated feelings (76) which ultimately control the viability of the Digital Life Form (32). When there are not sufficient energy packets (66) to sustain the Digital Life Form (32) then simulated death 52 results. Therefore, only actions (64) which contribute to the viability of the Digital Life Form (32) are repeated in the long-run. Some of those actions (64) include perception of reality, concept formation, and natural language processing.

Abstract: A method and system are provided for characterizing a reservoir of interest by comparing measured downhole fluid analysis measurement data with predicted downhole fluid analysis measurement data for the corresponding depth within the reservoir. The downhole fluid analysis measurement data may comprise the results of compositional analysis, gas-oil ratio measurements, and spectrophotometry measurements. The compositional analyses may be delumped to characterize the compositional components of the downhole fluid and equations of state may be used to predict compositional gradients and fluid properties with depth. The method and system enable the user to characterize a reservoir as to its states of compartmentalization and equilibrium.

Abstract: A system and method is disclosed which integrates a machine learning solution into a large scale, distributed transaction processing system using a supporting architecture comprising a combination of computer hardware and software. Methods of using a system comprising such supporting architecture provide application designers access to the functionality included in a machine learning solution, but might also provide additional functionality not supported by the machine learning solution itself.

Abstract: A conversational user interface (CUI) is implemented in a computer by mimicking the organization and retrieval of linguistic memories of human conversation. To mimic the organization of linguistic memories in a human brain, the artificial memories are stored as sequences of patterns, are stored in invariant form, are organized hierarchically, and are recalled auto-associatively. To mimic the recall of linguistic memories in a human brain, the same algorithm performs the recall of the various memories. Each artificial memory is a pairing of the invariant representation and an associated responsive message. When a received utterance is determined to match the invariant representation of a memory, the memory is evoked and the associated responsive message is presented to the person.

Abstract: The invention is directed to a method for training at least one learning-capable system comprising the steps of providing a predetermined training data set corresponding to a predetermined number of subjects comprising a predetermined input data set and a predetermined outcome data set, augmenting the input data set and/or the outcome data set, and training each learning-capable system using the augmented input data set and/or the augmented outcome data set.

Abstract: A method and system are provided for classifying data items such as a document based upon identification of element instances within the data item. A training set of classes is provided where each class is associated with one or more features indicative of accurate identification of an element instance within the data item. Upon the identification of the data item with the training set, a confidence factor is computed that the selected element instance is accurately identified. When a selected element instance has a low confidence factor, the associated features for the predicted class are changed by an annotator/expert so that the changed class definition of the new associated feature provides a higher confidence factor of accurate identification of element instances within the data item.

Abstract: A method and computer program product to capture expert knowledge and data using probabilistic models. A custom layered structure and nodes reduce the complexity of the model, allowing for representation of the model using tables. An editor is used for entry and verification of expert knowledge and data into tables and a probabilistic model is generated from the tables.

Abstract: Determining the relevance of a web node is disclosed. A seed score value of a first type is assigned to a seed set of nodes. A score value of a second type is derived for the web node based on a mapping of a reachability relationship between one or more seed nodes and the web node. A score value of the first type is derived for the web node based on a mapping of a reachability relationship between the web node and one or more evaluation nodes having derived weight values of the second type. Content analysis is performed.

Abstract: A method for predicting a future occurrence of an event involves obtaining a history of prior occurrences of the event. A plurality of variables is created that are associated with the event. Weights are assigned to each variable. An artificial neural network is accessed and trained with the history of past occurrences of the event by comparing an output of the artificial neural network to the past occurrence of the event. The weights are adjusted until the output corresponds to the past occurrence of the event.

Abstract: Systems and methods are disclosed for routing callers to agents in a contact center utilizing a multi-layer processing approach to matching a caller to an agent. A first layer of processing may include two or more different computer models or methods for scoring or determining caller-agent pairs in a routing center. The output of the first layer may be received by a second layer of processing for balancing or weighting the outputs and selecting a final caller-agent match. The two or more methods may include conventional queue based routing, performance based routing, pattern matching algorithms, affinity matching, and the like. The output or scores of the two or more methods may be processed be the second layer of processing to select a caller-agent pair and cause the caller to be routed to a particular agent.

Abstract: The invention is based on a machine-tool monitoring device. It is proposed that the machine-tool monitoring device have a signal unit (24) provided for ultra-broadband operation.

Abstract: A method of implicitly forming a context in a dialogue with a computer includes forming megacategories linking one or more attributes to one of a plurality of response templates. The method also includes determining a megacategory by comparing attributes having true variables from a previous input to attributes having true variables in the present input and selecting an appropriate response template corresponding to the megacategory.

Abstract: In a parallel pulse signal processing apparatus including a plurality of pulse output arithmetic elements (2), a plurality of connection elements (3) which parallelly connect predetermined arithmetic elements, and a gate circuit (5) which selectively passes pulse signals from the plurality of connection elements, the arithmetic element inputs a plurality of time series pulse signals, executes predetermined modulation processing on the basis of the plurality of time series pulse signals which are input, and outputs a pulse signal on the basis of a result of modulation processing, wherein the gate circuit selectively passes, of the signals from the plurality of connection elements, a finite number of pulse signals corresponding to predetermined upper output levels.

Abstract: Designs for cognitive memory systems storing input data, images, or patterns, and retrieving it without knowledge of where stored when cognitive memory is prompted by query pattern that is related to sought stored pattern. Retrieval system of cognitive memory uses autoassociative neural networks and techniques for pre-processing query pattern to establish relationship between query pattern and sought stored pattern, to locate sought pattern, and to retrieve it and ancillary data.

Abstract: An emulated intelligence system includes an input for receiving information in the form of a query and a parsing system for parsing the query into grammatical elements. A database of individual concepts is included, each concept defining relationships between the concept and other concepts in the database. A conceptualizing system defines a list of related concepts associated with each of the parsed elements of the query and the embodied relationships associated therewith and a volition system then determines if additional concepts are associated with an action that may be associated with pre-stored criteria. An action system is then provided for defining an action to be taken based upon the relationships defined by the conceptualizing system.

Abstract: The present invention provides a unique system and method that can employ machine learning techniques to automatically fill one or more fields across a diverse array of web forms. In particular, one or more instrumented tools can collect input or entries of form fields. Machine learning can be used to learn what data corresponds to which fields or types of fields. The input can be sent to a central repository where other databases can be aggregated as well. This input can be provided to a machine learning system to learn how to predict the desired outputs. Alternatively or in addition, learning can be performed in part by observing entries and then adapting the autofill component accordingly. Furthermore, a number of features of database fields as well as constraints can be employed to facilitate assignments of database entries to form values—particularly when the web form has never been seen before by the autofill system.

Abstract: Dynamically filtering and classifying messages, as good messages, bulk periodicals, or spam. A regular expression recognizer, and pre-trained neural networks. The neural networks distinguish “likely good” from “likely spam,” and also operate at a more discriminating level to distinguish among the three categories above. A dynamic whitelist and blacklist; sending addresses are collected when the number of their messages indicates the sender is good or a spammer. A dynamically selected set of regular expressions input to the neural networks.

Abstract: According to an aspect of the present invention, there is provided with a behavior determining apparatus including: an elemental-behavior extracting unit configured to extract, from trajectory data in which positions of a traveling body in an observation area are held at predetermined time intervals, L (L is an integer equal to or larger than 1) elemental behavior quantities among a stopping time, a stop frequency, a walking time, a running time, a direction change frequency, a sum of angle change quantities at the times when the traveling body changes a traveling direction, a sum of absolute values of angle change quantities at the times when the traveling body changes a traveling direction, a gravitational center set vector, and a pre-stop average speed; and a behavior determining unit configured to determine a behavior of the traveling body from the L elemental behavior quantities.

Abstract: A first total number of nodes in a first node set directly linked to a first node can be computed. A second total number of nodes in a second node set directly linked to a second node can be computed. A shared total number of nodes in a union of the first node set and the second node set can be computed. A mutual information metric can then be computed from the first total, the second total, and the shared total. A decision as to whether a new connection should be added between the first node and the second node, which were not previously directly connected, can be determined from the value of mutual information metric.

Abstract: A method and apparatus is provided for annotation-based behavior extensions. A primary contributor is identified based on a first attribute. Behaviors from a secondary contributor are linked to the primary contributor once a second attribute is examined. Finally, one or more files are generated using the linked behaviors from the primary and secondary contributors.

Abstract: A computer model of a physical structure (or object) can be generated using context-based hypothesis testing. For a set of point data, a user selects a context specifying a geometric category corresponding to the structure shape. The user specifies at least one seed point from the set that lies on a surface of the structure of interest. Using the context and point data, the system loads points in a region near the seed point(s), and determines the dimensions and orientation of an initial surface component in the context that corresponds to those points. If the selected component is supported by the points, that component can be added to a computer model of the surface. The system can repeatedly find points near a possible extension of the surface model, using the context and current surface component(s) to generate hypotheses for extending the surface model to these points.

Abstract: Methods, systems and computer-readable media for finding similarities between visual objects by evaluating user interactions with a collection of visual objects are provided. Using a neural network, human interactions with a collection of visual objects are evaluated to ascertain relationships or connections between visual objects. The relationship between visual objects indicates that the visual objects are similar. Once relationships between visual objects are identified, a user may select one or more visual objects and receive suggested visual objects that are similar to the one or more visual objects selected by the user.

Abstract: For each document in a document set, entities are identified and a set of association rules, based on appearance of the entities in the paragraphs of the documents in the set, are derived. Documents are clustered based on the association rules. As documents are added to the clusters, additional association rules specific to the clusters can optionally be derived as well.

Abstract: Enhancements to Bayesian prediction models for network location traffic provide increased accuracy in web traffic predictions. The enhancements include implementing user advertising target queries to determine preferred edges of a Bayesian model, employing hierarchical data structures to cleanse training data for a Bayesian model, and/or augmenting existing data with new training data to enhance a previously constructed Bayesian model. Preferred edge enhancements for the Bayesian model utilize target attribute derived preferred edges and/or explicitly specified preferred edges. Training data is cleansed utilizing tag hierarchies that can employ parent-child relationships, ancestor relationships, and/or network location specific parameters. New training data can also be employed to adjust probabilities in a previously constructed Bayesian model. The new training data can be weighted differently than data represented by the previously constructed Bayesian model.

Abstract: A location of a missing object is predicted based on past sightings of objects including the missing object, and a new sighting of the objects except for the missing object. For a respective given object in the objects, the past sightings are memorized based on respective distances of respective remaining objects from the respective given object. Distance-based memorization may take place using an agent or associative memory for a respective given object. Then, for a respective given object, except for the missing object, a distance of the missing object from the respective given object is predicted, based on the past sightings that have been memorized and the new sighting, to obtain candidate locations for the missing object. The candidate locations are then disambiguated, to predict the location of the missing object.

Abstract: A technique includes receiving a first dataset that is indicative of seismic data acquired in a seismic survey of a field of wells and receiving a second dataset that is indicative of wellbore data acquired in a wellbore survey conducted in at least one of the wells. The technique includes determining a mechanical earth model for the field based at least in part on the seismic data and the wellbore data.

Abstract: Systems and methods are disclosed for forming reconfigurable neural networks with interconnected FPGAs each having a packet router. Neural network nodes are formed within the FPGAs and connections between nodes within an FPGA and connections to nodes external to the FPGA are made using packet routers that are configured within each FPGA. The FPGAs can be connected to each other using high-speed interconnects, such as high-speed serial digital interconnects. The FPGA arrays with packet routing allow for dynamic and reconfigurable neural networks to be formed thereby greatly improving the performance and intelligence of the neural network.

Abstract: It is possible to acquire existing techniques in a neural network model currently studied and developed so as to generalize them as an element technique, and provide modeling of a basic unit of bottom-up approach using the neural network by adding new values to the existing techniques. A network learning device builds up a network of basic units in a network section, acquires an input from a sensor input section for evaluating it, changes a coupling weight coefficient by using a correlation operation so that the evaluation value satisfies a predetermined evaluation value, and inserts a new neural network according to need.

Abstract: A simulated consciousness method (10) for an improved human/computer interface. A computer system (12) is programmed to have a Digital Life Form (32) possessing a plurality of attributes (65). A plurality of actions (64) taken relative to objects (60) in the environment (30) contribute to simulated feelings (76) which ultimately control the viability of the Digital Life Form (32). When there are not sufficient energy packets (66) to sustain the Digital Life Form (32) then simulated death 52 results. Therefore, only actions (64) which contribute to the viability of the Digital Life Form (32) are repeated in the long-run. Some of those actions (64) include perception of reality, concept formation, and natural language processing.

Abstract: A computer-implemented method is provided for ranking features within a large dataset containing a large number of features according to each feature's ability to separate data into classes. For each feature, a support vector machine separates the dataset into two classes and determines the margins between extremal points in the two classes. The margins for all of the features are compared and the features are ranked based upon the size of the margin, with the highest ranked features corresponding to the largest margins. A subset of features for classifying the dataset is selected from a group of the highest ranked features. In one embodiment, the method is used to identify the best genes for disease prediction and diagnosis using gene expression data from micro-arrays.

Abstract: Systems and techniques for classification of incoming text messages into categories. An initial set of categories is created and populated with a set of initial messages in each category. Incoming messages are assigned to a category based on text analysis of the incoming message and analysis of dissimilarities and differences between the message and messages already in the category. A set of unclassified messages is also maintained for incoming messages that do not fit an established category. Periodically, unclassified messages are reassigned to one of the established categories or to new categories created based on analysis of the unclassified messages.

Abstract: A data processing system processes data arrays that collectively describe cyclic behavior of at least one variable in several entities in a physical process. Each cycle comprises several time slots. An input routine (2-4) receives multiple data arrays, each data array containing multiple data items, each of which describes a variable of an entity in one time slot. A magnitude-determination routine (2-6) determines a specific magnitude parameter, such as average, volume or peak, for each of the several entities. A scaling routine (2-8) scales the data arrays between entities such that the specific magnitude parameters are suppressed and only their shape is maintained. A training routine (2-10) trains a clustering system with a first plurality of the scaled data arrays, to determine a set of cluster centers. After training, a clustering routine (2-12) applies a second plurality of the scaled data arrays to the trained clustering system.

Abstract: A method for analyzing documents is disclosed. The method compares concepts consisting of groups of terms for similarity within a corpus of document, clusters documents that contain certain concept term sets together. It may also rank the documents within each cluster according to the frequency of term co-occurrence within the concepts.

Abstract: A mobile communication apparatus including means for enabling the mobile communication apparatus for character input, comparing inputted characters with words in a list, prioritizing and listing matches of the comparison in a priority list depending on current context. Further, displaying the priority list, enabling selection from the displayed priority list and enabling updating of the list depending on current context. A corresponding application, apparatus, system, user interface and computer program is also disclosed.

Abstract: A mobile communication apparatus including means for enabling the mobile communication apparatus for character input, comparing inputted characters with words in a list, prioritizing and listing matches of the comparison in a priority list depending on current context. Further, displaying the priority list, enabling selection from the displayed priority list and enabling updating of the list depending on current context. A corresponding application, apparatus, system, user interface and computer program is also disclosed.

Abstract: A mobile communication apparatus including means for enabling the mobile communication apparatus for character input, comparing inputted characters with words in a list, prioritizing and listing matches of the comparison in a priority list depending on current context. Further, displaying the priority list, enabling selection from the displayed priority list and enabling updating of the list depending on current context. A corresponding application, apparatus, system, and computer program is also disclosed.

Abstract: An autonomous classification device which enables the creation of autonomous classifiers that are easy to deploy, adapt and optimize in the environment in which they are used. The classifier is autonomous in that it can perform three functions that define autonomic systems: automatically configure itself in an environment, optimize its performance using the environment and mechanisms for performance, and continually adapting to improve performance and heal itself in a changing environment.

Abstract: Designs for cognitive memory systems storing input data, images, or patterns, and retrieving it without knowledge of where stored when cognitive memory is prompted by query pattern that is related to sought stored pattern. Retrieval system of cognitive memory uses autoassociative neural networks and techniques for pre-processing query pattern to establish relationship between query pattern and sought stored pattern, to locate sought pattern, and to retrieve it and ancillary data.

Abstract: A method of determining an appropriate response to an input includes linking a plurality of attributes to a plurality of response templates using a plurality of Boolean expressions. Each attribute is associated with a set of patterns. Each pattern within the set of patterns is equivalent. The method also includes determining an appropriate response template from the plurality of response templates based on the input.

Abstract: An improved Artificial Neural Network (ANN) is disclosed that comprises a conventional ANN, a database block, and a compare and update circuit. The conventional ANN is formed by a plurality of neurons, each neuron having a prototype memory dedicated to store a prototype and a distance evaluator to evaluate the distance between the input pattern presented to the ANN and the prototype stored therein. The database block has: all the prototypes arranged in slices, each slice being capable to store up to a maximum number of prototypes; the input patterns or queries to be presented to the ANN; and the distances resulting of the evaluation performed during the recognition/classification phase. The compare and update circuit compares the distance with the distance previously found for the same input pattern updates or not the distance previously stored.