Abstract: A computer program product, method and system for transforming data into predictive models. The transformation of data into predictive models comprises a multi stage learning process that uses a plurality of algorithms at each stage to select output for use in the next stage. The final predictive model is a linear or nonlinear predictive model. Analyses of the model and the variables associated with it can be used to produce graphs and other management reports.

Abstract: The computer implemented life form (CILF) is a belief program which excludes all three of the classical logic paradigms, it can then (at least for discussion purposes) be considered derived from a form of “non-classical” logic. Certainly, even the mere idea that reality itself could possibly be nothing more than a simulation, could easily be considered a new (neo-classical) and useful form of non-classical thought. The programming methods used by the CILF are non-computational, meaning they will not generate any independent fact or data. Instead, the CILF programming method will merely check data to form a state of belief or doubt upon the input data from which a new and improved data store can be more correctly and effectively established.

Abstract: A method and/or system that can be implemented on a computing device or tables or board game or otherwise uses a rule set to evaluate data about a situation and actors in order to provide advice regarding strategies for influencing actors and/or other outputs.

Abstract: The present invention provides a method and system for constructing one or more a comprehensive summaries of event sequence(s). The present invention approaches the problem of finding the shortest yet most comprehensive summary of an event sequence by transforming this summarization problem into a concrete optimization problem and provides a computer-implementing technique for solving this optimization problem to construct and/or form the basis for constructing the summaries. The summaries describe an entire event sequence while at the same time reveal local associations between events of that sequence. In certain embodiments, the segmentation of the event sequence produced in accordance with the present invention is itself a summary of the event sequence. In other embodiments, the segmentation produced forms a basis for one or more summaries.

Abstract: System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g.

Abstract: Intelligent computer implemented agents are associated with computer user interface tasks by dividing the tasks into statistically distinct clusters based on sampled user assessments. The assessments collect data on multiple user variables. Multivariate statistical analysis is used to divide the tasks into distinct clusters. The clusters are validated using univariate analysis on each of the measured variables. Intelligent agents are associated based on the measured variables to ensure that agents are effective. The objective assessment and association avoids costly creation and overhead of agents applied where not effective.

Abstract: A method for predicting survival rates of medical patients includes providing a set D of survival data for a plurality of medical patients, providing a regression model having an associated parameter vector ?, providing an example x0 of a medical patient whose survival probability is to be classified, calculating a parameter vector {circumflex over (?)} that maximizes a log-likelihood function of ? over the set of survival data, l(?|D), wherein the log likelihood l(?|D) is a strictly concave function of ? and is a function of the scalar x?, calculating a weight w0 for example x0, calculating an updated parameter vector ?* that maximizes a function l(?|D?{(y0,x0,w0)}), wherein data points (y0,x0,w0) augment set D, calculating a fair log likelihood ratio ?f from {circumflex over (?)} and ?* using ?f=?(?*|x0)+sign(?({circumflex over (?)}|x0)){l({circumflex over (?)}|D)?l(?*|D)}, and mapping the fair log likelihood ratio ?f to a fair price y0f, wherein said fair price is a probability that class label y0 for exam

Abstract: A control system for controlling the operation of a useful system comprises at least one controller configured to control an operation of the useful system in response to a well formed command from a group of well formed commands. At least one taxonomy engine of the system is adapted to generate a taxonomy dataset establishing the group of well formed commands, and at least one command generator of the system is adapted to generate a well formed command using the taxonomy dataset. The taxonomy engine is configured to deliver the taxonomy dataset to the command generator, and the command generator is configured to deliver the well formed command to the controller.

Abstract: Cross over (S560) in a genetic algorithm (128) is adapted for deriving an optimal mask (S540), or set of segments of a line. Each mask of a chromosome is subject to cross over with the respective mask of the other parent. Any overlapping part, whether a filtering (320) or pass-through part (350), is retained in the child (334) to preserve commonality. The part is preferably, potentially extended, according to one parent or the other, as decided pseudo-randomly (430). In a preferred application, spectrums of candidate blood constituents are masked for fitting to ensemble spectrums of test blood samples (S610, S620). The developed masks are applicable to constituent spectrums to create masked spectrums (S710) which are then applicable to an actual blood sample to be analyzed (S720).

Abstract: The claimed matter provides systems and/or techniques that develop or use predictive models of human forgetting to effectuate automated reminding. The system includes the use of predictive models that infer the probability that aspects of items will be forgotten, models that evaluate the relevance of recalling aspects of items in different settings, based on contextual information related to user attributes associated with the items, and models of the context-sensitive cost of interrupting users with reminders. The system can combine the probability of users forgetting aspects of an item with an assessed cost of forgetting those aspects to ascertain expected costs for not being reminded about events, compare expected costs for not being reminded with expected costs for interrupting users, and based on comparisons between expected costs for being reminded and expected costs for interrupting users regarding events, generate and deliver reminder notifications to users about items.

Abstract: Methods and apparatus are provided for distributed rule processing in a sense and respond system.

Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.

Abstract: A method for generating object classification models is disclosed. Initially, a set of training data is fed into a training algorithm to generate a first object classification model. A set of field data is then applied to the first object classification model to produce a set of field object classifications. The number of data in the set of field data is significantly less than the number of data in the set of training data. Finally, the set of field object classifications and the set of field data are fed into the training algorithm to generate a second object classification model. The second object classification model can be utilized for predicting object classifications.

Abstract: Systems and methods for image classification are disclosed. In one aspect, embodiments of the present disclosure include a method, which may be implemented on a system, of selecting a predetermined number of training images that are representative of images associable with a particular topic category. One embodiment can include, extracting training image features from the training images, generating a set of descriptors characteristic of images associable with the particular topic category, and generating the particular set of predetermined models that correspond to the particular topic category based on the set of descriptors.

Abstract: A data classification method and apparatus are disclosed for labeling unknown objects. The disclosed data classification system employs a learning algorithm that adapts through experience. The present invention classifies objects in domain datasets using data classification models having a corresponding bias and evaluates the performance of the data classification. The performance values for each domain dataset and corresponding model bias are processed to identify or modify one or more rules of experience. The rules of experience are subsequently used to generate a model for data classification. Each rule of experience specifies one or more characteristics for a domain dataset and a corresponding bias that should be utilized for a data classification model if the rule is satisfied.

Abstract: The present invention relates to a system and method for problem solving using intelligent agents. The intelligent agents may be embodied as processor-readable software code stored on a processor-readable medium. The intelligent agents may include a brain agent to parse the input and direct the parsed input query to other intelligent agents within the system. The apparatus and method may use, for example, a personality agent, a language agent, a knowledge agent, a mood agent, a visual agent, sound agent, a tactile agent, and a smell/taste agent and various connectors to external data sources or other intelligent systems to interpret questions and provide responses back to the user. The apparatus and method may further parse questions in a conceptual manner. The apparatus and method may further optimize its system performance by evolving with and reacting to specific user interactions. Thus, the present invention may be configured to receive a human question and to output a human answer.

Abstract: A flow processing facility, which uses a set of artificial neurons for pattern recognition, such as a self-organizing map, in order to provide security and protection to a computer or computer system supports unified threat management based at least in part on patterns relevant to a variety of types of threats that relate to computer systems, including computer networks. Flow processing for switching, security, and other network applications, including a facility that processes a data flow to address patterns relevant to a variety of conditions are directed at internal network security, virtualization, and web connection security. A flow processing facility for inspecting payloads of network traffic packets detects security threats and intrusions across accessible layers of the IP-stack by applying content matching and behavioral anomaly detection techniques based on regular expression matching and self-organizing maps.

Abstract: A computer implemented method of constructing a computer application for automatically implementing a complex comparison programming task provides a compare design wizard to a display of a user's computer. The user interacts with the compare design wizard to specify (a) at least first and second data groups each containing associated data elements, (b) one or more keys from the first data group, and (c) one or more keys from the second data group, the keys comprising data elements that the user desires to be compared by the computer application. The user further interacts with the compare design wizard to specify one or more actions to be taken by the computer application based on data element comparisons to be performed by the computer application of: the keys matching between the first and second data groups; excess data being found in one of the groups; and excess data being found in a different one of the groups.

Abstract: Computer resources in a computer network can be predictively monitored where those resources are conventionally monitored using a monitoring rule. For predictive monitoring, the current values of the parameters of the monitoring rule are tracked at regular intervals. The current values are used in an “inverted” or predictive form of the conventional monitoring rule to derive a predictive value that is indicative of the imminence of a defined event. The monitoring system may be instructed to report a predictive value that exceeds a predetermined percentage of the final value at which the resource event will be deemed to have occurred. The earlier report increases the chances the network manager will have sufficient time to take appropriate preemptive action to prevent actual occurrence of the event.

Abstract: A method and system for storing episodic sequences (events and actions). The system learns episodic sequencing by observing real-world events and actions or by receiving fact data from a database storing common sense facts. The episodic sequences are classified into events and actions, processed to indicate correlations and causality between the events and actions, and generated into linked graphs. The linked graphs may then be used to draw inferences, recognize patterns, and make decisions.