Abstract: An exemplary method for identifying process elements includes the steps of obtaining a plurality of relevant request-response pairs including request identifiers, mapping the request identifiers to corresponding artifacts, identifying actions based on the artifacts, and mapping the actions to process elements such that each given one of the process elements has an associated artifact.

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 workflow management system with continuous status management includes an apparatus for detecting fuzzy process definitions, an apparatus which controls activity stages in a workflow for the purpose of processing the process definitions, and a device for evaluating the process definitions for each process instance. A method is for implementing a workflow with continuous status management through fuzzy process definitions using continuous instructions and states and logic combinations or continuous mapping operations operating thereon.

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 and a system for discovering knowledge from text documents are disclosed, which involve extracting from text documents semi-structured meta-data, wherein the semi-structured meta-data includes a plurality of entities and a plurality of relations between the entities; identifying from the semi-structured meta-data a plurality of key entities and a corresponding plurality of key relations; deriving from a domain knowledge base a plurality of attributes relating to each of the plurality of entities relating to one of the plurality of key entities for forming a plurality of pairs of key entity and a plurality of attributes related thereto; formulating a plurality of patterns, each of the plurality of patterns relating to one of the plurality of pairs of key entity and a plurality of attributes related thereto; analyzing the plurality of patterns using an associative discoverer; and interpreting the output of the associative discoverer for discovering knowledge.

Abstract: System and method for rule location, ordering, and combining in a polyhierarchical environment are described. In one embodiment, a polyhierarchical environment contains at least one rule, at least one logical structure representable by a graph and at least two connections between one or more of the logical structures and a rule set evaluator (RSE). The RSE retrieves an assembly definition associated with a particular ordering or combination of rules. Each assembly definition is associated with one or more location chains, and each location chain is associated with one of the connections to a logical structure. For each location chain, a rule location policy is invoked, returning a rule. An assembly policy is invoked upon the returned rules, forming the returned rule into a dataset that conforms to the ordering or combination associated with the assembly definition.

Abstract: A method of facilitating calculations of the changes in three mutually dependent variables, in which an initial condition of all the variables is represented by a triangle, calculated changes are then displayed by modifying the triangle, and the modified form of the triangle then provides a new basis for further calculations.

Abstract: A system and method for online reinforcement learning is provided. In particular, a method for performing the explore-vs.-exploit tradeoff is provided. Although the method is heuristic, it can be applied in a principled manner while simultaneously learning the parameters and/or structure of the model (e.g., Bayesian network model). The system includes a model which receives an input (e.g., from a user) and provides a probability distribution associated with uncertainty regarding parameters of the model to a decision engine. The decision engine can determine whether to exploit the information known to it or to explore to obtain additional information based, at least in part, upon the explore-vs.-exploit tradeoff (e.g., Thompson strategy). A reinforcement learning component can obtain additional information (e.g., feedback from a user) and update parameter(s) and/or the structure of the model.

Abstract: A technique is provided for resolving a reportable condition. Upon detection of a reportable condition on a processor-based medical system, machine data is provided to a knowledge engine. One or more identified solutions are received from the knowledge engine and implemented on the processor-based medical system. The one or more solutions are verified as they are implemented to determine if the currently implemented solution resolves the reportable condition. Routines implementing some or all of the technique may be provided on a processor-based system or on a machine-readable medium.

Abstract: An objective function is dynamically chosen from a pool of available objective functions, and a prediction model is dynamically chosen from a pool of available prediction models. Points of inflection are determined for the chosen objective function, based on past values of a metric, to obtain a set of equations that can be solved to obtain model parameters associated with the chosen prediction model. The equations are solved to obtain the model parameters, and a future value of the metric is predicted based on (i) at least some of the past values of the metric and (ii) the chosen prediction model, with the obtained associated model parameters.

Abstract: A method for detecting errors includes obtaining input data, applying a knowledge base to the input data, identifying diagnostics associated with errors in the input data, encapsulating data snippets corresponding to errors with associated diagnostic codes to obtain encapsulated data snippets, and outputting encapsulated data snippets.

Abstract: Object recognition techniques are disclosed that provide both accuracy and speed. One embodiment of the present invention is an identification system. The system is capable of locating objects in images by searching for local features of an object. The system can operate in real-time. The system is trained from a set of images of an object or objects. The system computes interest points in the training images, and then extracts local image features (tokens) around these interest points. The set of tokens from the training images is then used to build a hierarchical model structure. During identification/detection, the system, computes interest points from incoming target images. The system matches tokens around these interest points with the tokens in the hierarchical model. Each successfully matched image token votes for an object hypothesis at a certain scale, location, and orientation in the target image. Object hypotheses that receive insufficient votes are rejected.

Abstract: A computerized method of representing a dataset with a taxonomy includes augmenting a dataset containing a plurality of records with a plurality of predetermined exemplars; representing the plurality of records and predetermined exemplars within the augmented dataset as a plurality of clusters in an initial taxonomy layer; generating a truncated hierarchy of cluster sets based on clusters within the initial taxonomy layer, wherein clusters within the truncated hierarchy contain no more than a predetermined number of exemplars; and labeling clusters within the truncated hierarchy.

Abstract: Missing data is addressed in a medical decision support system. The classifier applied to the patient record with missing data is obtained as a function of the available data. For example, one of a plurality of different classifiers is selected based on the features available in the patient record to be classified. The different classifiers are developed using different feature sets. The classifier developed using a feature set closest to or a sub-set of the features available in the patient record is selected for classifying the patient record. As another example, features in a training set corresponding to features available in the patient record are used to build a classifier. The classifier is applied to the patient record by inputting the available features of the patient record.

Abstract: Rule based data object matching to determine if two or more objects are similar to allow the objects to be merged into one master data object. The rules explicitly state what conditions are required to consider two or more objects equal. The certainty of the rule may be specified. Examples for the certainty are automatic, manual high, medium and low probability. For example, the certainty “automatic” specifies that the objects can be considered matches and no additional manual verification is needed. The certainty “manual low probability” specifies that there is a low probability that the objects are matches and that a manual verification is needed. Matching rules may be grouped by priority. If matches are not found, the next highest priority group of rules is processed. Hierarchical matching rules may be specified that are able to express similarity of objects considering structured objects containing sub-objects in a hierarchical manner.

Abstract: For a first feature of a dataset having a plurality of features, training a classifier to predict the first feature in terms of other features in the data set to obtain a trained classifier; scrambling the values of a second feature in the data set to obtain a scrambled data set, executing the trained classifier on the scrambled data set, determining predictive importance of the seconds feature in predicting the first feature based at least in part on the accuracy of the trained classifier in predicting the first feature when executed with the scrambled data set and creating a graph of the data set in which each of the first and the second features is a node of the graph and a label on an edge between the first node and the second node is based at least in part on the predictive importance of the first feature in terms of the second feature.

Abstract: A computerized method of representing a dataset with an optimal layer of a taxonomy includes obtaining a taxonomy including a hierarchical arrangement of layers, wherein each layer represents a cluster set containing at least one cluster, wherein each cluster represents at least one record within a dataset; identifying a range of taxonomy layers based on a measure of intra-cluster homogeneity of each cluster within the taxonomy; selecting a taxonomy layer within the identified range as an optimal layer of the taxonomy, the selecting being based on a measure of inter-cluster heterogeneity between clusters of a taxonomy layer within the identified range; and labeling clusters within the optimal layer of the taxonomy.

Abstract: A system for achieving a desired goal in a domain. The system may comprise a device operable to receive information and simulate the domain therefrom; a device operable to simulate one or more effects due to one or more operators; a device operable to specify a number of items and/or classes of items and whether each item and/or each class of items is an affectable obstacle or a non-affectable obstacle; a device operable to automatically generate a candidate plan to achieve the desired goal by utilizing the simulated domain and the simulated effect(s), wherein the candidate plan could involve one or more affectable obstacles but does not involve any non-affectable obstacles; and a device operable to automatically refine the candidate plan to change at least one of the affectable obstacles involved in the candidate plan.

Abstract: For the prognosis of a value of a characteristic of a product which is to be produced with the aid of a neuronal network, the history of the product is taken into account when determining an input variable of an input neuron of the neuronal network.

Abstract: When transmitting an on-road location on a digital map, an information provider transmits, as on-road location information, string of coordinates information representing the road shape of a road section including the on-road location having a predetermined length, additional information including at least one information item chosen from a group of the attribute information on the road including the road location and detailed information on the nodes in the road section, and relative information indicating the on-road location in the road section. Receiving party receiving this on-road location information performs shape matching to identify the road section on a digital map and uses relative data to identify the on-road location in this road section. The receiving party can identify the road shape with high accuracy even when only a small amount of string of coordinates data is available. This makes it possible to reduce the data transmission amount and identify an on-load location with high accuracy.