140073 results about "Algorithm" patented technology

Predictive modeling of consumer financial behavior, including determination of likely responses to particular marketing efforts, is provided by application of consumer transaction data to predictive models associated with merchant segments. The merchant segments are derived from the consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors represent specific merchants, and are aligned in a vector space as a function of the degree to which the merchants co-occur more or less frequently than expected. Consumer vectors are developed within the vector space, to represent interests of particular consumers by virtue of relative vector positions of consumer and merchant vectors. Various techniques, including clustering, supervised segmentation, and nearest-neighbor analysis, are applied separately or in combination to generate improved predictions of consumer behavior.

A slot machine enables a player to select one or more symbols as wild symbols ("Select-A-Wild"). In one embodiment, the game displays three spin buttons. Each spin button is permanently associated with a symbol. All of the associated symbols have matching symbols on the reel strips. Each of the associated symbols have the same probability of appearing on the reel strips. The player selects a wild symbol by pressing a spin button. The symbol associated with the pressed spin button acts as a wild symbol for that spin. After each spin, the player receives awards for all winning combinations.

A system and method for detecting malware by modeling the behavior of malware and comparing a suspect executable with the model. The system and method extracts feature elements from malware-infected applications, groups the feature elements into feature sets, and develops rules describing a malicious probability relationship between the feature elements. Using malware-free and malware-infected applications as training data, the system and method heuristically trains the rules and creates a probability model for identifying malware. To detect malware, the system and method scans the suspect executable for feature sets and applies the results to the probability model to determine the probability that the suspect executable is malware-infected.

Techniques for factoring one or more source graphs into a composite graph containing nodes representing analogous elements of the source graphs and a variability graph containing nodes representing differences in the source graphs. The composite graph is made by taking analogous input trees from the source graphs and traversing the trees from top to bottom looking for nodes in each tree at each level that are analogous to the nodes at that level in the other input trees. The sets of analogous nodes are found by first automatically correlating the nodes in the level currently being examined. Correlation may, for example, be based on similar values of a property of the nodes being correlated. Representations of the sets of correlated nodes are then displayed to a user, who indicates which sets of correlated nodes are in fact analogous. The user may also indicate that the nodes in a set of correlated nodes are not analogous or that nodes that were found by the automatic correlation not to be autonomous are in fact. The analogous nodes are allocated to a corresponding node at a corresponding level in the composite graph; the other nodes are allocated to a set of anomalous nodes. One application for the techniques is managing graphs which are models of catalogs of items.

The present invention relates to a method and apparatus for explicit filtering in ambiguous text entry. The invention provides embodiments including various explicit text entry methodologies, such as 2-key and long pressing. The invention also provides means for matching words in a database using build around methodology, stem locking methodology, word completion methodology, and n-gram searches.

Hashes are short summaries or signatures of data files which can be used to identify the file. Hashing multimedia content (audio, video, images) is difficult because the hash of original content and processed (e.g. compressed) content may differ significantly. The disclosed method generates robust hashes for multimedia content, for example, audio clips. The audio clip is divided (12) into successive (preferably overlapping) frames. For each frame, the frequency spectrum is divided (15) into bands. A robust property of each band (e.g. energy) is computed (16) and represented (17) by a respective hash bit. An audio clip is thus represented by a concatenation of binary hash words, one for each frame. To identify a possibly compressed audio signal, a block of hash words derived therefrom is matched by a computer (20) with a large database (21). Such matching strategies are also disclosed. In an advantageous embodiment, the extraction process also provides information (19) as to which of the hash bits are the least reliable. Flipping these bits considerably improves the speed and performance of the matching process.

We describe a method of reinforcement learning for a subject system having multiple states and actions to move from one state to the next. Training data is generated by operating on the system with a succession of actions and used to train a second neural network. Target values for training the second neural network are derived from a first neural network which is generated by copying weights of the second neural network at intervals.

A system and a method for creating a focus-exposure model of a lithography process are disclosed. The system and the method utilize calibration data along multiple dimensions of parameter variations, in particular within an exposure-defocus process window space. The system and the method provide a unified set of model parameter values that result in better accuracy and robustness of simulations at nominal process conditions, as well as the ability to predict lithographic performance at any point continuously throughout a complete process window area without a need for recalibration at different settings. With a smaller number of measurements required than the prior-art multiple-model calibration, the focus-exposure model provides more predictive and more robust model parameter values that can be used at any location in the process window.

A separation surface set storage part stores information defining a plurality of separation surfaces which separate a feature space into at least one known class region respectively corresponding to at least one known class and an unknown class region. Each of the at least one known class region is separated from outside region by more than one of the plurality of separation surfaces which do not intersect to each other. A data classification apparatus determine a classification of a classification target data whose inner product in the feature space is calculable by calculating to which region of the at least one known class region and the unknown class region determined by the information stored in the separation surface set storage part the classification target data belongs. A method and apparatus for data classification which can simultaneously perform identification and outlying value classification with high reliability in a same procedure are provided.

Sound, robust methods identify the most suitable, parsimonious set of tests to use with respect to prioritized, sequential anomaly detection in a collected batch of sample data. While the focus is on detecting anomalies in network traffic flows and classifying network traffic flows into application types, the methods are also applicable to other anomaly detection and classification application settings, including detecting email spam, (e.g. credit card) fraud detection, detecting imposters, unusual event detection (for example, in images and video), host-based computer intrusion detection, detection of equipment or complex system failures, as well as of anomalous measurements in scientific experiments.

Activities, actions and events during user performance of physical activity may be detected using various algorithms and templates. Templates may include an arrangement of one or more states that may identify particular event types and timing between events. Templates may be specific to a particular type of activity (e.g., types of sports, drills, events, etc.), user, terrain, time of day and the like.

The present invention provides systems and methods for dynamic detection and prevention of electronic fraud and network intrusion using an integrated set of intelligent technologies. The intelligent technologies include neural networks, multi-agents, data mining, case-based reasoning, rule-based reasoning, fuzzy logic, constraint programming, and genetic algorithms. The systems and methods of the present invention involve a fraud detection and prevention model that successfully detects and prevents electronic fraud and network intrusion in real-time. The model is not sensitive to known or unknown different types of fraud or network intrusion attacks, and can be used to detect and prevent fraud and network intrusion across multiple networks and industries.

A text-to-pronunciation system (11) includes a large training set of word pronunciations (19) and an extractor for extracting language specific information from the training set to produce pronunciations for words not in its training set. A learner (13) forms pronunciation guesses for words in the training set and for finding a transformation rule that improves the guesses. A rule applier (15) applies the transformation rule found to guesses. The learner (13) repeats the finding of another rule and the rule applier (15) applies the new rule to find the rules that improves the guesses the most.

Methods, systems, devices and computer program code (software) products operable within a computer graphics system or other computer system enable quasi-Monte Carlo (QMC) light transport simulation by efficient ray tracing.

This invention provides a novel means for creating a histogram for use in minimizing response time and resource consumption when optimizing a query in a database, and other like structures, the histogram being created by placing ordered elements into specific range until the next element to be considered for inclusion in the range is a predetermined distance from the (generalized) mean value associated with the elements within the range, whereupon that next element is placed in the following range. Similarly, the following ranges are closed when the next element to be considered for inclusion in the range is greater than a predetermined distance from the (generalized) mean value associated with the elements in that range, whereupon that next element is placed in the following range. For each range, the location and size of the range is recorded with, for example, the mean value, the slope or other attribute characterizing one or more elements in the range. The invention has also applications in pattern recognition, message routing, and in actuarial sciences.

A process control system is configured via manipulation of objects that model system components, e.g., sensors, blocks, control processors, historians, workstations, etc. Individual objects include parameters that characterize the underlying components and/or the behavior of the objects themselves. These parameters are derived from the “parents,” from which the objects are created. Derived characteristics need not be defined explicitly but, rather, are defined implicitly or by reference. These derived characteristics may be overridden for an individual object and, thereby, its progeny. Although objects have class-like characteristics (i.e., insofar as they are definitional in nature), they can be created at configuration time, without the need for recompilation.

There is disclosed a technique for detecting risky domains. The technique comprises collecting information in connection with a domain. The technique also comprises generating a profile comprising at least one metric associated with the domain based on the collected information. The technique further comprises determining the riskiness in connection with the domain based on the generated profile.

A system and method for distinguishing human input events from malware-generated events includes one or more central processing units (CPUs), one or more input devices and memory. The memory includes program code that when executed by the CPU causes the CPU to obtain a first set of input events from a user utilizing the input device. The first input events are used to obtain or derive a feature indicative of the user, such as a multi-dimensional feature vector as provided by a support vector machine. Second input events are then obtained, and the second input events are classified against the feature to determine if either the user or malware initiated the second input events.