Abstract: A system for at least a partial underwriting of insurance policies is described. based on the similarity to previous insurance applications, a decision on the current request for underwriting may be made this decision-making process represents an analogical approach to the placement of an insurance application to an underwriting category, whereby a given insurance application request is compared to previous requests.

Abstract: A robust process for automating the tuning and maintenance of decision-making systems is described. A configurable multi-stage mutation-based evolutionary algorithm optimally tunes the decision thresholds and internal parameters of fuzzy rule-based and case-based systems that decide the risk categories of insurance applications. The tunable parameters have a critical impact on the coverage and accuracy of decision-making, and a reliable method to optimally tune these parameters is critical to the quality of decision-making and maintainability of these systems.

Abstract: A process is described for evaluating the decision-making confidence of a process and system for at least a partial underwriting of insurance policies where placement of an insurance application to an underwriting category is based on its similarity to previous insurance applications. The confidence factor computed is a measure of the correctness of the decision for a given application for insurance.

Abstract: A system is described for evaluating the decision-making confidence of a process and system for at least a partial underwriting of insurance policies where placement of an insurance application to an underwriting category is based on its similarity to previous insurance applications. The confidence factor computed is a measure of the correctness of the decision for a given application for insurance.

Abstract: A robust system for automating the tuning and maintenance of decision-making systems is described. A configurable multi-stage mutation-based evolutionary algorithm optimally tunes the decision thresholds and internal parameters of fuzzy rule-based and case-based systems that decide the risk categories of insurance applications. The tunable parameters have a critical impact on the coverage and accuracy of decision-making, and a reliable method to optimally tune these parameters is critical to the quality of decision-making and maintainability of these systems.

Abstract: A process to structure and summarize the key information required by automated decision-making systems for insurance underwriting is described. The automated underwriting system may be based on rules corresponding to underwriting components, wherein based on the degree of satisfaction of each rule, a component may be assigned to a category, and based on the category for each component, the insurance application may be assigned an underwriting category, or the automated underwriting system may be based on an evaluation of the similarity of a given application to previous application requests, to decide an underwriting category. Most of the key information required for automated insurance underwriting is structured and standardized, except for the Attending Physician Statement (APS), which is almost as unique as each individual physician.

Abstract: A system to structure and summarize the key information required by automated decision-making systems for insurance underwriting is described. The automated underwriting system may be based on rules corresponding to underwriting components, wherein based on the degree of satisfaction of each rule, a component may be assigned to a category, and based on the category for each component, the insurance application may be assigned an underwriting category, or the automated underwriting system may be based on an evaluation of the similarity of a given application to previous application requests, to decide an underwriting category. Most of the key information required for automated insurance underwriting is structured and standardized, except for the Attending Physician Statement (APS), which is almost as unique as each individual physician.

Abstract: The present invention is a method and system for automating the process for valuing a property that produces an estimated value of a subject property, and a quality assessment of the estimated value, that is based on the fusion of multiple processes for valuing a property. In one embodiment, three processes for valuing a subject property are fused. The first process, called LOCVAL, uses the location and living area to provide an estimate of the subject property's value. The second process, called AIGEN, is a generative artificial intelligence method that trains a fuzzy-neural network using a subset of cases from a case-base, and produces a run-time system to provide an estimate of the subject property's value. The third process, called AICOMP, uses a case based reasoning process similar to the sales comparison approach to determine an estimate of the subject property's value.

Abstract: A system and method for diagnosing and validating a machine over a network using waveform data. Historical waveform data are obtained via the network from machines having known faults along with corresponding actions for repairing the machines and are used to develop fault classification rules. The fault classification rules are stored in a diagnostic knowledge database. The database of classification rules are used to diagnose new waveform data from a machine having an unknown fault, via the network.

Abstract: This disclosure describes a system and method for providing stability analysis of profitability calculations of target markets for goods or services. The system and method of this disclosure uses an internal experience database and external demographic databases. The system and method evaluates demographic data and calculates the risk associated with a variety of different goods or services. The system and method comprises a number of steps. First, a data acquisition module associates descriptor variables with different goods or services in a portfolio of goods or services. Second, a risk model module processes the various goods or services into distinct groups based upon specified characteristics. The risk model module also calculates risk values for the various goods or services and sorts the portfolio of goods or services into specified categories. Third, a profitability module calculates the net present value of each of the goods or services in the entire portfolio.

Abstract: System and method for generating a time-to-break prediction for a paper web in a paper machine. This invention uses principal components analysis, neuro-fuzzy systems and trending analysis to form a model for predicting the time-to-break of the paper web from paper mill measurements of paper machine process variables. The model is used to isolate the root cause of the predicted web break.

Abstract: A system and method for predicting web breaks in a paper machine. Principal components analysis (PCA) and classification and regression tree (CART) modeling are used to predict web break sensitivity from measurements taken from the paper mill. Also, the CART model is used to isolate the root cause of the predicted web break sensitivity.

Abstract: System and method for predicting web breaks in a paper machine. Principal components analysis and an aggregated classification and regression tree (CART) model are used to predict web break sensitivity from measurements taken from a paper mill where the paper machine is located.

Abstract: A system and method for predicting web breaks in a paper machine. Principal components analysis and an aggregated classification and regression tree (CART) model are used to predict web break sensitivity from sensor measurements taken from the paper machine.

Abstract: A system and method for predicting web breaks in a paper machine. Principal components analysis (PCA) and classification and regression tree (CART) modeling are used to predict web break sensitivity from sensor measurements taken from the paper machine. Also, the CART model is used to isolate the root cause of the predicted web break sensitivity.

Abstract: The present invention is a method and system for automating the process for valuing a property that progduces an estimated value of a subject property, and a quality assessment of the estimated value. The process is a generative artificial intelligence method that trains a fuzzy-neural network using a subset of cases from a case-base, and produces a run-time system to provide an estimate of the subject property's value. In one embodiment, the system is a network-based implementation of fuzzy inference based on a system that implements a fuzzy system as a five-layer neural network so that the structure of the network can be interpreted in terms of high-level rules. The neural network is trained automatically from data. IF/THEN rules are used to map inputs to outputs by a fuzzy logic inference system. Different models for the same problem can be obtained by changing the inputs to the neuro-fuzzy network, or by varying its architecture.

Abstract: A diagnostic system is provided for identifying faults in a machine (e.g., CT scanner, MRI system, x-ray apparatus) by analyzing a data file generated thereby. The diagnostic system includes a trained database containing a plurality of trained data, each trained data associated with one of plurality of known fault types. Each trained data is represented by a trained set of feature values and corresponding weight values. Once a data file is generated by the machine, a current set of feature values are extracted from the data file by performing various analyses (e.g., time domain analysis, frequency domain analysis, wavelet analysis). The current set of feature values extracted is analyzed by a fault detector which produces a candidate set of faults based on the trained set of feature values and corresponding weight values for each of the fault types. The candidate set of faults produced by the fault detector is presented to a user along with a recommend repair procedure.

Abstract: A system and method for generating a time-to-break prediction for a paper web in a paper machine. This invention uses principal components analysis, neuro-fuzzy systems and trending analysis to form a model for predicting the time-to-break of the paper web from sensor measurements of paper machine process variables. The model is used to isolate the root cause of the predicted web break.

Abstract: System and method for predicting web breaks in a paper machine. Principal components analysis and an aggregated classification and regression tree (CART) model are used to predict web break sensitivity from measurements taken from a paper mill where the paper machine is located.

Abstract: System and method for generating a time-to-break prediction for a paper web in a paper machine. This invention uses principal components analysis, neuro-fuzzy systems and trending analysis to form a model for predicting the time-to-break of the paper web from paper mill measurements of paper machine process variables. The model is used to isolate the root cause of the predicted web break.