404results about "Dynamic trees" patented technology

Systems and methods for data analysis and trend prediction. Multiple networks are combined for analysis to improve the accuracy of the evaluation by broadening the type of criteria considered. Relevant features are extracted from a dataset and at least one network is formed representing various relationships identified among the items contained in the dataset according to heuristics. Statistical analyses are applied to the relationships and the results output to a user via one or more reports to permit a user to evaluate each of the items in the dataset relative to each other. The trend of the relationships may be predicted based on the results of statistical analysis applied to the features over successive discrete time periods.

A method for searching potential solutions within a solution network. The method includes authoring a solution to solve an issue, storing the solution within a decision tree relating to the issue, and searching the solution network based upon the issue. The searching includes accessing the decision tree relating to the issue.

A method and system for selecting and presenting questions to users is provided. The method and system provides for a decision-tree architecture that functionally changes form as users traverse the tree, depending on the responses provided by the users.

A system and method for developing a rule-based named entity extraction system is provided. The method includes analyzing requirements of business users. The method further includes designing the rule-based named entity extraction system based on the requirement analysis. Further, the method includes implementing the design of rule-based named entity extraction system using one or more GUI-based tools. Thereafter, regression testing of the rule-based named entity extraction system is conducted. Finally, rule-based named entity extraction system is deployed.

A computer system is arranged to automatically calculate a path along nodes in a virtual world. After the co-ordinates for the virtual world environment have been initially defined (40), including nodes along which a path may travel, the system automatically increases the density of nodes in the environment up to a desired density. New nodes are added between each pair of nodes which have line of sight to each other (44 to 47), dramatically increasing the number of available links and nodes. This is repeated until a sufficient density of nodes is reached and no more nodes are being added (48 and 49).

Markov Chain Monte Carlo (MCMC) sampling of elements of a domain to be sampled is performed to generate a set of samples. The MCMC sampling is performed over a search tree of decision sequences representing the domain to be sampled and having terminal nodes corresponding to elements of the domain. In some embodiments the MCMC sampling is performed by Metropolis-Hastings (MH) sampling. The MCMC sampling is constrained using a bound on nodes of the search tree. The constraint may entail detecting a node whose bound value ensures that an acceptable element cannot be identified by continuing traversal of the tree past that node, and terminating the traversal in response. The constraint may entail selecting a node to serve as a starting node for a sampling attempt in accordance with a statistical promise distribution indicating likelihood that following a decision sequence rooted at the node will identify an acceptable element.

Computer-implemented methods, systems, and articles of manufacture for determining the importance of a data item. A method includes: (a) receiving a node graph; (b) approximating a number of neighbor nodes of a node; and (c) calculating a average shortest path length of the node to the remaining nodes using the approximation step, where this calculation demonstrates the importance of a data item represented by the node. Another method includes: (a) receiving a node graph; (b) building a decomposed line graph of the node graph; (c) calculating stationary probabilities of incident edges of a node graph node in the decomposed line graph, and (d) calculating a summation of the stationary probabilities of the incident edges associated with the node, where the summation demonstrates the importance of a data item represented by the node. Both methods have at least one step carried out using a computer device.

A method and apparatus that allow business users to dynamically add, modify, and delete business rules and associated constraints, and then to apply these changes in a very efficient manner without needing to recompile and/or restart e-business applications, is disclosed. According to one embodiment, hybrid use of the business rules and constraints with multiple decision-support engines provides the practical solution for the described problems. Rules are used to define the generic search space only, without needing to describe all special cases. Then an optimization (not rule-based) engine can automatically create and analyze all possible branches of the tree specified by the rules to select the best one. Because one universal rule engine is not sufficient to handle online decision support, in one embodiment there is a family of intelligent engines (including the rule engine) that can resolve the optimization problem generated automatically based on the applicable dynamic rules.

A system and method for processing a large constraint satisfaction problem quickly, including a subset generating module (1), which divides a set of alternatives provided for a plurality of parts of a given problem into a plurality of subsets, such that each subset has not more than two alternatives for each part. For each subset generated by the division, a solution calculation module (2) finds a solution by calculating combinations of alternatives satisfying a constraint between alternatives selected for each two parts. The calculation of a solution for a subset, such that each subset has not more than two alternatives for each part, requires a very short period of processing time, even if the parts are many. Thus, the sum of the times required for finding a solution for all the subsets is much shorter than the time required for finding a solution for the original problem without such processing.

Embodiments are directed to a method of performing autonomous learning for updating input features used for an artificial intelligence model, the method comprising receiving updated data of an information space that includes a graph of nodes having a defined topology, the updated data including historical data of requests to the artificial intelligence model and output results associated with the requests, wherein different categories of input data corresponds to different input nodes of the graph. The method may further comprise updating edge connections between the nodes of the graph by performing path optimizations that each use a set of agents to explore the information space over cycles to reduce a cost function, each connection including a strength value, wherein during each path optimization, path information is shared between the rest of agents at each cycle for determining a next position value for each of the set of agents in the graph.

Methods for predicting states of a subject are presented. For example, a method for predicting states of a subject includes obtaining training data comprising a plurality of variables, obtaining training states associated with the training data, and forming a predictive model according to the training data and the training states, the predictive model predictive of the training states. The forming of the predictive model includes extracting one or more hidden components from the training data. The extracting of the one or more hidden components includes regression analysis including determining one or more relationships between the one or more hidden components and the plurality of variables, and determining one or more relationships between the one or more hidden components and the training states. A number of the one or more hidden components is less than a number of the plurality of variables and greater than a number of the training states.

Methods, systems and computer program products are provided for cross-media recommendation by store a plurality of taste profiles corresponding to a first domain and a plurality of media item vectors corresponding to a second domain. An evaluation taste profile in the first domain is applied to a plurality of models that have been generated based on relationship among the plurality of taste profiles and the plurality of media item vectors, and obtain a plurality of resulting codes corresponding to at least one of the plurality of media item vectors in the second domain.

Methods and systems for knowledge extraction and prediction are described. In an example, a computerized method, and system for performing the method, can include receiving historical data pertaining to a domain of interest, receiving predetermined heuristics design data associated with the domain of interest, and using the predetermined heuristics design and historical data, automatically creating causal maps including a hierarchy of nodes, each node of the hierarchy of nodes being associated with a plurality of quantization points and reference temporal patterns, the plurality quantization points being known reference spatial patterns. In an example the computerized method, and system for performing the method, can further include receiving, at each node, a plurality of unknown patterns pertaining to a cause associated with the domain of interest, automatically mapping the plurality of unknown patterns to the quantization points using spatial similarities of the unknown patterns and the quantization points, automatically pooling the quantization points into a temporal pattern, the temporal pattern being a sequence of spatial patterns that represent the cause, automatically mapping the temporal pattern to a reference temporal pattern, automatically creating a sequence of the temporal patterns, and automatically recognizing the cause using the sequence of temporal patterns.

Computer-implemented systems and methods for identifying an object in an image are provided. In one example, the method includes identifying a first object related to an electronic image. The image includes at least a second object. Based at least in part on the identity of the first object, social networking information related to the first object is used to programmatically identify the second object. The first object and/or the second object may be a person. In some embodiments, metadata associated with the image may be used to identify the second object. Based at least in part on the identifications, social networking information may be associated between the first object and the second object.

The method is used in a computer and includes the steps of providing a logical theory (12, 30) that has clauses. A rule (14) is generated that is a resolvent of clauses in the logical theory. An example (16) is retrieved. A proof tree (18, 40) is generated from the example (16) using the logical theory (12, 30). The proof tree (18, 40) is transformed into a database (20, 42) of a coverage check apparatus (28). The rule (14) is converted into a partial proof tree (60) that has nodes (62, 54, 66). The partial proof tree is transformed into a database query (22) of the coverage check apparatus (28). The query (22, 72) is executed to identify tuples in the database (20, 42) that correspond to the nodes of the partial proof tree.

In one embodiment, the invention includes sorting a set of constraints based on weights associated with each constraint, the constraints being capable of being satisfied based on values selected for corresponding associated parameters, selecting a constraint that is unsatisfied and that has the greatest weight, selecting parameters associated with the constraint and finding parameter values to satisfy the constraint, after satisfying the selected unsatisfied constraint, selecting a further unsatisfied constraint having the next greatest weight and finding values for associated parameters to satisfy the further constraint, and repeating the selecting of further constraints and the finding of values for associated parameters.

The present disclosure relates to systems and methods for an adaptive pipelining composition service that can identify and incorporate one or more new models into the machine learning application. The machine learning application with the new model can be tested off-line with the results being compared with ground truth data. If the machine learning application with the new model outperforms the previously used model, the machine learning application can be upgraded and auto-promoted to production. One or more parameters may also be discovered. The new parameters may be incorporated into the existing model in an off-line mode. The machine learning application with the new parameters can be tested off-line and the results can be compared with previous results with existing parameters. If the new parameters outperform the existing parameters as compared with ground-truth data, the machine learning application can be auto-promoted to production.

A novel approach to test-suite reduction based on network maximum flows. Given a test suite T and a set of test requirements R, the method identifies a minimal set of test cases which maintains the coverage of test requirements. The approach encodes the problem with a bipartite directed graph and computes a minimum cardinality subset of T that covers R as a search among maximum flows, using the classical Ford-Fulkerson algorithm in combination with efficient constraint programming techniques. Test results have shown that the method outperforms the Integer Linear Programming (ILP) approach by 15-3000 times, in terms of the time needed to find the solution. At the same time, the method obtains the same reduction rate as ILP, because both approaches compute optimal solutions. When compared to the simple greedy approach, the method takes on average 30% more time and produces from 5% to 15% smaller test suites.

Data structures, systems, methods and computer program products that enable precise information retrieval and extraction, and thus facilitate relational and associative discovery are disclosed. The present invention utilizes a novel data structure termed a “Knowlet” which combines multiple attributes and values for relationships between concepts. While texts contain many re-iterations of factual statements, Knowlets record relationships between two concepts only once and the attributes and values of the relationships change based on multiple instances of factual statements, increasing co-occurrence or associations. The present invention's approach results in a minimal growth of the Knowlet space as compared to the text space and it thus useful where there is a vast data store, a relevant ontology/thesaurus, and a need for knowledge navigation and (relational, associative, and/or other) knowledge discovery.