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: A method and an apparatus operate like a human neural network to analyze and store input information and form patterns according to the input and stored information. The apparatus has a preprocessing unit (3), an activity computation unit (5), a mutual repression unit (6), and a composition unit (7). The apparatus receives an input pattern, calculates the similarity and activity levels of each stored pattern with respect to the input pattern, and repeats a predetermined number of times the activity calculation of each stored pattern according to the calculated activity level (A(i)), a negative repression coefficient, and the activity levels of the other stored patterns. The apparatus applies final activity levels to cell values of the stored patterns, totals the cell values through the stored patterns, and generates a new pattern according to the totaled cell values.

Abstract: The subject system provides reduced-dimension mapping of pattern data. Mapping is applied through conventional single-hidden-layer feed-forward neural network with non-linear neurons. According to one aspect of the present invention, the system functions to equalize and orthogonalize lower dimensional output signals by reducing the covariance matrix of the output signals to the form of a diagonal matrix or constant times the identity matrix. The present invention allows for visualization of large bodies of complex multidimensional data in a relatively “topologically correct” low-dimension approximation, to reduce randomness associated with other methods of similar purposes, and to keep the mapping computationally efficient at the same time.

Abstract: A system and method for organizing a plurality of sets color values into a plurality of color groups, such as paint, pigments, or dye formulations, is provided. The inputs to the system are the color values of a proposed paint, dye or colorant formulation and color measurement angles. The system includes an input device for entering a plurality of sets of color values and an artificial intelligence cluster model coupled to the input device. The cluster model produces an output signal indicative of the one color group to which a set of color values belongs. The artificial intelligence model may be embodied in a neural network. More specifically, the cluster model may be a self-organizing map neural network.

Abstract: Computerized aesthetic judgment of images is disclosed. In one embodiment, a computer-implemented method inputs a training set of images, where each image has a corresponding set of aesthetic scores. The method trains a classifier based on the training set, and outputs the classifier. An image can then be input into the classifier, such that an aesthetic score for the image is generated by the classifier and output. Furthermore, recommendations can be generated to improve the aesthetic score for the image, which are also output.

Abstract: An associative artificial neuron and method of forming output signals of an associative artificial neuron includes receiving a number of auxiliary input signals; forming from the auxiliary input signals a sum weighted by coefficients and applying a non-linear function to the weighted sum to generate a non-linear signal. The neuron and method further include receiving a main input signal and forming, based on the main signal and the non-linear signal, the function S OR V, which is used to generate a main output signal, and at lest one of three logical functions S AND V, NOT S AND V, and S AND NOT V. The at least one logical function is used to generate an additional output signal for the associative artificial neuron.

Abstract: A system for consolidating and sorting event data include a computing platform communicatively coupled to a computer readable medium and a network. The computer readable medium may store an application that includes at least one node mapped into a tree. The at least one node may have a data element reference including a pointer to a data element that includes event data received via the network. In addition, the node may have a row indicator node count, a least child reference, a greatest child reference, a lesser sibling reference, a greatest sibling reference, a parent reference, and a status manager reference.

Abstract: A dynamic memory processor for time variant pattern recognition and an input data dimensionality reduction is provided having a multi-layer harmonic neural network and a classifier network. The multi-layer harmonic neural network receives a fused feature vector of the pattern to be recognized from a neural sensor and generates output vectors which aid in discrimination between similar patterns. The fused feature vector and each output vector are separately provided to corresponding positional king of the mountain (PKOM) circuits within the classifier network. Each PKOM circuit generates a positional output vector with only one element having a value corresponding to one, the element corresponding to the element of its input vector having the highest contribution. The positional output vectors are mapped into a multidimensional memory space and read by a recognition vector array which generates a plurality of recognition vectors.

Abstract: A method and system for collaborative decision making. The method and system include receiving in a computer system via a network alternative choices, criteria, weights for the criteria and assessments of the alternative choices. Assessments and determination of weights include combinations of pairwise comparison with direct entry or multiple choice. A relative analysis of the alternative choices is provided. A shift constant may be determined. A sensitivity analysis may be performed. Direct entry may comprise determination of grades employing a value function. Assessments of criteria may be combined to form analysis of respective criteria not directly assessed by the set of individuals.

Abstract: Determination as to whether a variable is numeric or non-numeric. In one embodiment, a variable is input having a plurality of values, where each value has a count. The variable is determined to be numeric or non-numeric by assessing closeness of counts for adjacent values of the variable. Whether the variable is numeric or non-numeric is then output.

Abstract: Learning using a neural network is improved for a classification problem by recollecting input patterns from the learned data without storing the original input data patterns. The neural network includes input elements in an input layer, middle elements in a middle layer and output elements in an output layer. The elements between two layers are related with each other by a corresponding weight. An output function of the middle and output layers includes a radial basis function (RBF). The recollected input patterns are generated based upon two parameters including a first vector indicating a central position o the RBF and a second vector indicating a range and a direction of the RBF. The recollected input patterns are used to improve additional learning of a new set of input patterns.

Abstract: A system and method for organizing a plurality of sets color values into a plurality of color groups, such as paint, pigments, or dye formulations, is provided. The inputs to the system are the color values of a proposed paint, dye or colorant formulation and color measurement angles. The system includes an input device for entering a plurality of sets of color values and an artificial intelligence cluster model coupled to the input device. The cluster model produces an output signal indicative of the one color group to which a set of color values belongs. The artificial intelligence model may be embodied in a neural network. More specifically, the cluster model may be a self-organizing map neural network.

Abstract: An apparatus and method for sensor signal prediction and for improving sensor signal response time, is disclosed. An adaptive filter or an artificial neural network is utilized to provide predictive sensor signal output and is further used to reduce sensor response time delay.

Abstract: There is provided a basic association unit for creating an information processing apparatus capable of performing information processing like information processing that actually occurs in central nerve systems of animals including human beings. The association unit is an unit for repeating input and output signals having m input terminals and n output terminals. When a first input signal which is a rectangular wave signal in the form of a pulse is simultaneously input to input terminals in a quantity less than m, an output signal having the same contents as the first input signal is output from particular output terminals which are associated with the input terminals in advance.

Abstract: A method and apparatus for determining tag location is disclosed. Tag reference data may be stored, e.g., in the form of a lookup table, as a trained neural network, and so on, and used to determine the location of tags. Readings used to determine tag location and/or preliminary tag locations may be filtered to produce reliable tag location indications. Packages of user configurable parameters can be provided and used for the filtering of the preliminary tag locations. Confidence levels may also be generated for determined tag locations and used, for example, to indicate how well an asset location system can distinguish between different tag locations.

Abstract: A neural processing module is disclosed which combines a weighted synapse array that performs “primitive arithmetic” (products and sums) in parallel with a weight change architecture and a data input architecture that collectively maximize the use of the weighted synapse array by providing it with signal permutations as frequently as possible. The neural processing module is used independently, or in combination with other modules in a planar or stacked arrangement.

Abstract: A system and method for generating itemset associations in a memory storage system comprising many transactions, with each transaction including one or more items capable of forming the itemset associations. The method involves generating a lexicographic tree structure having nodes representing itemset associations meeting a minimum support criteria. In a recursive manner, for each lexicographic least itemset (node) P of the lexicographic tree structure, candidate extensions of the node P are first determined. Then, the support of each of the candidate extensions is counted to determine frequent extension itemsets of that node P, while those itemsets not meeting a predetermined support criteria are eliminated. Child nodes corresponding to the frequent extensions and meeting the predetermined support criteria are created. For each frequent child of node P, all itemset associations for all descendants of node P are generated first. Thus, the lexicographic tree structure is generated in a depth first manner.

Abstract: An adaptive agent including an artificial neural network having a plurality of input nodes for receiving input signals and a plurality of output nodes generating responses. A situation value unit receives a plurality of the responses and generating a situation value signal. A change sensor coupled to receive the situation value signal generates an output signal representing a change of the situation value signal from a prior time to a current time. A connection coupling the change sensor output to one of the input nodes of the artificial neural network.

Abstract: The invention includes a method, an article of manufacture and a system related to providing medical information and other information helpful in providing medical services.

Abstract: The improved neural network of the present invention results from the combination of a dedicated logic block with a conventional neural network based upon a mapping of the input space usually employed to classify an input data by computing the distance between said input data and prototypes memorized therein. The improved neural network is able to classify an input data, for instance, represented by a vector A even when some of its components are noisy or unknown during either the learning or the recognition phase. To that end, influence fields of various and different shapes are created for each neuron of the conventional neural network. The logic block transforms at least some of the n components (A1, . . . , An) of the input vector A into the m components (V1, . . . , Vm) of a network input vector V according to a linear or non-linear transform function F. In turn, vector V is applied as the input data to said conventional neural network.

Abstract: A system and method for increasing the speed of operation of a theorem prover relating to program verification using adaptive pattern matching technique is disclosed. Source code in a specific programming language is converted to one or more formulae, each representing a specific reformulation of the source code that facilitates program verification. Each formula derived from the source code is converted into an E-graph which is a particular type of a directed acyclic graph having leaf nodes and interior nodes. Some of the nodes of an E-graph may be related to other nodes through equivalence relationships. Equivalence relationships between a group of nodes is stored in a data structure called an equivalence class. A collection of rules defining the grammar of the programming language is stored in an axiom database. Rules and conjectures can dynamically be added to the axiom database. Each rule or conjecture to be tested is converted into a pattern.

Abstract: A device and a method for creation of emotions are provided for an interface of information, such as an artificial agent and a personified agent, intervened between a human being (i.e., user) and an electronic apparatus. For instance, an emotion creating device is configured by a neural network, a behavior determination engine and a feature determination engine. The neural network inputs user information, representing conditions of the user, and apparatus information, representing conditions of the apparatus, so as to produce emotional states. Herein, a present set of emotional states are produced in consideration of a previous set of emotional states. The emotional states represent prescribed emotions such as pleasure, anger, sadness and surprise. The behavior determination engine refers to a behavior determination database using the user information and the emotional states of the neural network so as to determine a behavior of the interface.

Abstract: A classification neural network for piecewise linearly separating an input space to classify input patterns is described. The multilayered neural network comprises an input node, a plurality of difference nodes in a first layer, a minimum node, a plurality of perceptron nodes in a second layer and an output node. In operation, the input node broadcasts the input pattern to all of the difference nodes. The difference nodes, along with the minimum node, identify in which vornoi cell of the piecewise linear separation the input pattern lies. The difference node defining the vornoi cell localizes input pattern to a local coordinate space and sends it to a corresponding perceptron, which produces a class designator for the input pattern.

Abstract: The subject system provides reduced-dimension mapping of pattern data. Mapping is applied through conventional single-hidden-layer feed-forward neural network with non-linear neurons. According to one aspect of the present invention, the system functions to equalize and orthogonalize lower dimensional output signals by reducing the covariance matrix of the output signals to the form of a diagonal matrix or constant times the identity matrix. The present invention allows for visualization of large bodies of complex multidimensional data in a relatively "topologically correct" low-dimension approximation, to reduce randomness associated with other methods of similar purposes, and to keep the mapping computationally efficient at the same time.

Abstract: An associative storage includes: a plurality of storage elements having at least one internal signal outputting element for outputting an internal outputting signal toward another storage element and at least one internal signal inputting element for receiving an internal inputting signal outputted form another storage element, a learning signal inputting element for each respective storage element for inputting a learning signal thereto, a recalling signal inputting element for each respective storage element for inputting a recalling signal thereto, an internal state switching element for each respective storage element for turning an internal state of the respective storage element to an ON state when either the learning signal or the recalling signal is inputted thereto and/or the sum of the internal inputting signals exceeds a threshold value while the internal state is kept in an OFF state, wherein the internal state switching element outputs the internal signal towards another storage element when the