Abstract: A pattern matching technique for high throughput network processing includes a simple yet powerful special purpose architecture and a set of novel string matching algorithms that can work in unison. The novel set of algorithms allow for bit-level partitioning of rules such that may be more easily implemented in hardware or software. The result is a device that maintains tight worst case bounds on performance, can be updated with new rules without interrupting operation, compiles in seconds instead of hours, and is ten times more efficient than the existing best known solutions in this area.

Abstract: Systems and methods of classifying domain names are disclosed. Character-based n-grams are derived from a domain name in order to classify such domain name in one or more categories. In one aspect, a geometrical approach is used. Domain name character-based n-grams are mapped to vector points in a multidimensional space. The relationship between a domain name vector point and vector points of other domain names is used as an indicator of the classification of the domain name vector point. In another aspect, a statistical approach is used. Relative frequencies of one or more character-based n-grams in various classifications are used as indicators. Each character-based n-gram can be associated with a respective probability that indicates a likelihood that the character-based n-gram is found in a domain name of a given classification. Such a probability can serve as an estimator of a classification of a new domain name having such character-based n-gram.

Abstract: The present invention discloses a system, method, apparatus, and computer usable product code adapting a media stream based upon biometric feedback. The system can include one or more biometric sensor, a media handling component, an importance inference engine, and a media processing component. The biometric sensor can be able to receive biometric feedback from at least one user. The media handling component can be a media creation component (e.g., video recorder, camera, etc.) and/or a media playback component (e.g., video player, music player, etc.). The importance inference engine can be configured to analyze biometric feedback received from the biometric sensor to determine an emotional interest level that a provider of the biometric feedback has in the media of the media handling component. The media processing component can modify the media of the media handling component based upon the emotion interest level determined by the importance inference engine.

Abstract: A semantic network includes a number of nodes are interconnected to one another through links (e.g., in a subject/verb/target form) representing relationships between the nodes and one or more of the links have one or more variants representing qualifications of the relationships between the nodes. For each link having one or more variants, the variants may be ordered in configurations. Such ordering of the variants in the configurations may be self-described within the semantic network and may determine precedence of those links belonging to the variants. Some of the links of the network may be nodes of others of the links. The interconnection of at least some of the nodes may define a meta-meta model that defines terms in which particular meta models can be defined, each meta model comprising meta facts regarding the nodes of the semantic network.

Abstract: An object oriented rule-based system and method that combines rule-based technology and object-oriented programming in order to provide an efficient approach for adding decision-making and inferencing capabilities to different applications. The rule-based system can include a RETE match algorithm, which can be modified to support pure object-based variable pattern matching. The LHS of a rule base can be compiled into several condition patterns, which includes a transformer integer array in order to record object variables positions. A RETE node can propagate tokens smoothly by asserting the object variable based on inheritance type hierarchy and the rules can be fired when the conditions are satisfied. The object oriented rule system can be utilized to add or remove rules at run time.

Abstract: Systems and methods for extracting or analyzing time-series behavior are described. Some embodiments of computer-implemented methods include generating fuzzy rules from time series data. Certain embodiments also include resolving conflicts between fuzzy rules according to how the data is clustered. Some embodiments further include extracting a model of the time-series behavior via defuzzification and making that model accessible. Advantageously, to resolve conflicts between fuzzy rules, some embodiments define Gaussian functions for each conflicting data point, sum the Gaussian functions according to how the conflicting data points are clustered, and resolve the conflict based on the results of summing the Gaussian functions. Some embodiments use both crisp and non-trivially fuzzy regions and/or both crisp and non-trivially fuzzy membership functions.

Abstract: A method for evaluating a behavior of an entity, the method includes: converting a state machine representation of a behavior of an entity to a complex event processing (CEP) representation of the behavior of the entity; wherein the converting comprises defining a transition between multiple states of the state machine representation as a situation; and evaluating the behavior of the entity in response to the CEP representation.

Abstract: A configuration mapping system and method increase the effectiveness of mapping of information from an established product line to a new product offering. In at least one embodiment, the configuration mapping system herein uses configuration mapping rules to map individual product features and entire configurations from established products to a new product offering. The configuration mapping system also provides a way to appropriately map, for example, demand and sales information for the purpose of demand estimation and sales prediction. Conventionally, mapping can be ineffective because the configuration mapping rules usually focus on one part of the product at a time, and, if applied in isolation, the impact on other parts is missed. The systems and method herein provide a way to integrate configuration mapping rules across feature parts, time periods, and product lines into a unified, holistic view, allowing for new insights.

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: Disclosed is a method and apparatus for learning behavior in a software robot.

Abstract: A system and method for support vector machine plus (SVM+) computations include selecting a set of indexes for a target function to create a quadratic function depending on a number of variables, and reducing the number of variables to two in the quadratic function using linear constraints. An extreme point is computed for the quadratic function in closed form. A two-dimensional set is defined where the indexes determine whether a data point is in the two-dimensional set or not. A determination is made of whether the extreme point belongs to the two-dimensional set. If the extreme point belongs to the two-dimensional set, the extreme point defines a maximum and defines a new set of parameters for a next iteration. Otherwise, the quadratic function is restricted on at least one boundary of the two-dimensional set to create a one-dimensional quadratic function. The steps are repeated until the maximum is determined.

Abstract: A system and method for estimating the a priori probability of a class-of-interest in an input-data-set and a system and method for evaluating the performance of the adaptive Bayes classifier in classifying unlabeled samples from an input-put-data-set. The adaptive Bayes classifier provides a capability to classify data into two classes, a class-of-interest or a class-other, with minimum classification error in an environment where a priori knowledge, through training samples or otherwise, is only available for a single class, the class-of-interest. This invention provides a method and system for estimating the a priori probability of the class-of-interest in the data set to be classified and evaluating adaptive Bayes classifier performance in classifying data into two classes, a class-of-interest and a class-other, using only labeled training samples, or otherwise, from the class-of-interest and unlabeled samples from the data set to be classified.

Abstract: Data of at least one of past values and present values of a system is consolidated from a plurality of sources. Virtual data of future values of the system is generated by applying the acquired data to a predictive model. Additional acquired data is received. The virtual data is dynamically updated by applying the additional acquired data to the predictive model.

Abstract: A group of features that has been identified as “significant” in being able to separate data into classes is evaluated using a support vector machine which separates the dataset into classes one feature at a time. After separation, an extremal margin value is assigned to each feature based on the distance between the lowest feature value in the first class and the highest feature value in the second class. Separately, extremal margin values are calculated for a normal distribution within a large number of randomly drawn example sets for the two classes to determine the number of examples within the normal distribution that would have a specified extremal margin value. Using p-values calculated for the normal distribution, a desired p-value is selected. The specified extremal margin value corresponding to the selected p-value is compared to the calculated extremal margin values for the group of features.

Abstract: According to an example embodiment, a technique may be provided for managing a label data base of which each entry comprises a data field for insertion into a respective packet in a multicast transmission and a validity field. The technique may include, for example, (a) establishing a fixed size for a cluster of labels, (b) associating with each cluster a pointer to another cluster, (c) establishing a sentinel cluster of which all entries are indicated to be invalid and of which the cluster pointer points to this sentinel cluster, (d) establishing a root table which defines the number of replications of the packet in a multicast group and identifies a first label cluster associated with the multicast group and (e) establishing a chain of clusters of which each cluster which is full of valid entries points to the next cluster in the chain and the last cluster which contains valid entries points to the sentinel cluster.

Abstract: In embodiments of the present invention improved capabilities are described for helping a user make a decision through the use of a computing facility, where the computing facility may be a machine learning facility. The process may begin with an initial question being received by the computing facility from the user. The user may then be provided with a dialog consisting of questions from the computing facility and answers provided by the user. The computing facility may then provide a decision to the user based on the dialog and pertaining to the initial question, such as a recommendation, a diagnosis, a conclusion, advice, and the like. In embodiments, future questions and decisions provided by the computing facility may be improved through feedback provided by the user. In embodiments, the present invention may be utilized in conjunction with a third-party application.

Abstract: In embodiments of the present invention improved capabilities are described for helping a user make a decision through the use of a machine learning facility. The process may begin with an initial question being received by the machine learning facility from the user. The user may then be provided with a dialog consisting of questions from the machine learning facility and answers provided by the user. The machine learning facility may then provide a decision to the user based on the dialog and pertaining to the initial question, such as a recommendation, a diagnosis, a conclusion, advice, and the like. In embodiments, future questions and decisions provided by the machine learning facility may be improved through feedback provided by the user.

Abstract: Ideas, groups of ideas, projects, documents, and related materials may be depicted, organized, modified, and shared according to Idea Page systems, methods, and user interfaces. Published workflow templates may additionally be copied into a user context, modified, and depicted in a multi-level visual space, including first-level ideas organized and visually arranged in a first-level idea page, and second-level ideas organized and visually arranged in a second-level idea page.

Abstract: Methods for classifying documents are presented. Methods for analyzing documents associated with legal discovery are also presented. Methods for cleaning up data are also presented. Methods for verifying an association of an invoice with an entity are also presented. Method for managing medical records are presented. Method for face recognition are presented.

Abstract: A method of determining ranges for algorithmic variables for a processor that uses fixed point arithmetic is provided. The method comprises expressing overflow requirements of processor instructions as inequalities. The method also expresses precision requirements and expressiveness requirements as inequalities and merit functions. A global constraint and optimizer tool is used to find ranges for algorithmic variables based on the inequalities and the merit functions. The use of constraint equation solving and optimization finds optimal algorithmic ranges that provide overflow-free arithmetic as well as optimal expressiveness and precision.