161 results about "Predictive systems" patented technology

By using monitoring data, feedback data, and pooling of failure data from a plurality of electronic devices, real-time failure prediction and diagnoses of electronic systems operating in a network environment can be achieved. First, the diagnostic system requests data on the state of a machine and/or its components and collections thereof as part of the machine's normal operation. Secondly, real-time processing of the data either at the machine site or elsewhere in the distributed network allows for predicting or diagnosing system failures. Having determined and/or predicted a system failure, a communication to one or more remote observers in the network allows the remote observers to view the diagnostic information and/or required action to repair the failure. Furthermore, interrogation of either the particular electronic system, or a database containing data on similar electronic systems by the diagnostic server allows the diagnostic server to refine original diagnoses based on this population data to achieve a comprehensive failure predication/diagnosing system.

A system for intelligent monitoring and management of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a real-time energy pricing engine, virtual system modeling engine, an analytics engine, a machine learning engine and a schematic user interface creator engine. The real-time energy pricing engine generates real-time utility power pricing data. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The machine learning engine stores and processes patterns observed from the real-time data output and the predicted data output to forecast an aspect of the electrical system.

A quality prognostics system and a quality prognostics method for predicting the product quality during manufacturing processes are disclosed. The present invention utilizes the current production tool parameters sensed during the manufacturing process and several previous quality data collected from the measurement tool to predict the future product quality. The quality prognostics system is composed of conjecture modeling means and prediction modeling means. The conjecture modeling means itself also can be applied for the purpose of virtual metrology. Further, the quality prognostics method possesses a self-searching means and a self-adjusting means for searching the best combination of various parameters/functions used by the conjecture algorithm or prediction algorithm; and meeting the requirements of new equipment parameters and conjecture/prediction accuracy.

A computer-implemented process of developing a person-level cost model for forecasting future costs attributable to claims from members of a book of business, where person-level data are available for a substantial portion of the members of the book of business for an actual underwriting period, and the forecast of interest is for a policy period is disclosed. The process uses development universe data comprising person-level enrollment data, historical base period health care claims data and historical next period claim amount data for a statistically meaningful number of individuals. The process also provides at least one claim-based risk factor for each historical base period claim based on the claim code associated with the health care claim and provides at least one enrollment-based risk factor based on the enrollment data. The process also develops a cost forecasting model by capturing the predictive ability of the main effects and interactions of claim based risk factors and enrollment-based risk factors, with the development universe data through the application of an interaction capturing technique to the development universe data.

It has been discovered that system operational characteristics (e.g., power level, clock frequency, processor utilization, operating system time slice utilization, size and age of queued jobs) may be used to predict storage access requirements for the system. By predicting the storage access requirements of a system, a storage subsystem may be advantageously controlled to anticipate storage accesses. A storage device or array of such devices can be configured to operate, for example, at selected speeds no greater than that required to process the predicted storage access requirements. The storage access prediction may be based, for example, on the frequency and voltage at which a processor is running or based on other system performance indicators such as job backlog and age and size thereof. Various controllable characteristics such as the speed of a hard drive's storage media, the current applied to a read/write head, etc., can be increased or decreased continuously or to discrete values in response to moving average indicators which provide advance warning of potential processing, and therefore potential storage access, swings.

A vehicle fault diagnosis and prognosis system includes a computing platform configured to receive a classifier from a remote server, the computing platform tangibly embodying computer-executable instructions for evaluating data sequences received from a vehicle control network and applying the classifier to the data sequences, wherein the classifier is configured to determine if the data sequences define a pattern that is associated with a particular fault.

A plurality of ECG Superscore formulae, created from multiple parameter ECG measurements including those from advanced ECG techniques, can be optimized using additive multivariate statistical models or pattern recognition procedures, with the results compared against a large database of ECG measurements from individuals with known cardiac conditions and/or previous cardiac events. Superscore formulae utilize multiple ECG parameters and accompanying weighting coefficients and allow data obtained from any given patient to be used in calculating that patient's ECG Superscore results. ECG Superscores have retrospectively optimized accuracy for identifying and screening individuals for underlying heart disease and/or for determining the risk of future cardiac events. They thus have greater predictive value than that of any conventional or advanced ECG measurement alone or of any non-optimized combinations of conventional or advanced ECG measurements that have been used in the past. Ongoing optimization of ECG Superscore diagnostic and predictive accuracy may be realized through the iterative adjustment of Superscore formulae based on the incorporation of data from new patients into the database and/or from longitudinal follow-up of the disease and cardiac event status of existing patients.

The present invention applies a slope predictive control method to a Variable Temperature Control (VTC) system, measuring the slope of the discharge temperature as a function of flow regulation device position, and using this slope to predict the position of the flow regulation device needed to achieve the desired discharge temperature. The present invention also monitors the response of the VTC over time and utilizes a self-learning algorithm to predict the response time of the system in order to determine when further control adjustments need to be taken.

Detecting patterns and sequences associated with an anomaly in predictions made a predictive system. The predictive system makes predictions by learning spatial patterns and temporal sequences in an input data that change over time. As the input data is received, the predictive system generates a series of predictions based on the input data. Each prediction is compared with corresponding actual value or state. If the prediction does not match or deviates significantly from the actual value or state, an anomaly is identified for further analysis. A corresponding state or a series of states of the predictive system before or at the time of prediction are associated with the anomaly and stored. The anomaly can be detected by monitoring whether the predictive system is placed in the state or states that is the same or similar to the stored state or states.

The software, methods and system of the current invention creates an interactive, auto-execution financial trading platform with unique forecasting algorithms, trading graphs and data mining features. The platform uses a univariate and multivariate architecture that is designed to improve performance of predictors and speed up calculations. Trading graphs, data mining features and predictive algorithms are predominantly based on fractal mathematics and Chaos theory. Even the unique software architecture is fractal in nature. All of these features are intended to be used individually or collectively to improve forecasting performance of financial markets. The above mentioned components also make it easier to manage a portfolio of securities and/or futures.

Disclosed is a system and method for the automated summarizing and reporting of key credit data elements of a consumer's multiple credit reports into an electronic, condensed, human-readable summary report including those key credit data elements most of interest to a loan originator. The generated electronic summary document displays the consumer's current credit mid-score, a credit mid-score forecast 30 days in the future, a mid-score projection based upon increases in the consumer's revolving debt, and an indication of the presence of negative credit information in the consumer's native credit reports.

A method of optimising a model Mx suitable for use in data analysis and determining a prognostic outcome specific to a particular subject (input vector x), the subject comprising a number of variable features in relation to a scenario of interest for which there is a global dataset D of samples also having the same features relating to the scenario, and for which the outcome is known is disclosed. In one implementation, the method includes: (a) determining what number and a subset Vx of variable features will be used in assessing the outcome for the input vector x; (b) determining what number Kx of samples from within the global data set D will form a neighbourhood about x; (c) selecting suitable Kx samples from the global data set which have the variable features that most closely accord to the variable features of the particular subject x to form the neighbourhood Dx; (d) ranking the Vx variable features within the neighbourhood Dx in order of importance to the outcome of vector x and obtaining a weight vector Wx for all variable features Vx; (e) creating a prognostic model Mx, having a set of model parameters Px and the other parameters from (a)-(d); (f) testing the accuracy of the model Mx at e) for each sample from Dx; (g) storing both the accuracy from (f), and the model parameters developed in (a) to (e); (h) repeating (a) and/or (b) whilst applying an optimisation procedure to optimise Vx and/or Kx, to determine their optimal values, before repeating (c)-(h) until maximum accuracy at (f) is achieved.

The invention provides a posting predication system based on a neural network technique. The system adopts phase-space reconstruction for realizing non-linear time sequence analysis and adopts a Browser/Server structure based on a J2EE platform. The system structurally includes a data pre-processing module, a predication analysis management module and predication modeling and simulation interface software and has characteristics of high convergence rate and low training error. The predication precision of established models is good. However, selection of a range of neural network training samples should be paid attention. Sample quantity can be reduced appropriately for predication of posting concerning to emergencies and samples of too early time may not be applied. In application, damage of models due to abnormal values should be paid attention and the abnormal values should be checked and adjusted, so that the model predication precision can be guaranteed. The established models adopt quantitative analysis and acquire a certain precision. Besides, the perception performance is good.

A method of operating Automated Teller Machines, ATMs. An ATM dispenses currency, thereby depleting its supply, and requiring replenishment. Replenishment currently involves humans who (1) measure the currency content of an ATM, (2) compute an amount to be replenished, and (3) report the amounts replenished, when replenishment occurs. These human actions are error-prone. The invention eliminates all three steps. Under the invention, no actual measurement is made of currency content, for replenishment purposes. Instead, the content is estimated. Then, the estimate is used to compute an amount for replenishment. Replenishment occurs, but the fact of replenishment is inferred from signals received from the ATM, and not from a report from a human.

A multivariate predictive system processes usage based data that includes a database engine that provides access to a plurality of database management systems that mine diverse sources of usage based data. A digital filter selectively filters data streams that include usage based data. A sampler automatically divides the filtered data into sample groups and stores the sample groups in a memory. The sampler divides the filtered data according to insurance rating variables processed by a processor programmed to underwrite an insurance product and rate that insurance product. Some samplers store the divided filter data in a local memory. A multiplier multiples rating factors preprogrammed with the insurance rating variables with a base rate of insurance to determine an insured's insurance premium.

The invention discloses a sales forecasting system and method, wherein the system comprises a classification module, a history correction module, a statistics forecasting module, a disassembling module and a period rolling module. The sales forecasting system helps enterprises to promote scientifically forecasting and finely forecasting capabilities by building a rolling standard forecasting system based on the historical data, data warehouse technology, statistical technology, classification forecasting technology, disassembling technology and period rolling technology.

A predictive device system includes a first device motion control input, determines a desired first device motion using the first device motion control input, and provides actual first device motion using the first device motion control input. The predictive system also determines motion inherent in a received signal using the actual first device motion, determines a difference to be simulated in a second device signal using the desired first device motion and the motion inherent in the received signal, and outputs a predictive signal using the first device motion control input and the difference to be simulated in the second device signal.

Overall system performance improvements resulting from improvements to individual system components may be predicted by analyzing the effects of artificial performance degradations inserted into the individual components. Measuring and analyzing a negative overall system effect caused by negative changes to an individual component may predict a positive overall system performance improvement resulting from an improvement to the individual component of similar magnitude to the inserted negative changes. System performance prediction may be used to analyze and predict system performance changes related to various computing resources, such as execution time (speed), memory usage, network bandwidth, file I/O, and/or disk storage space, among others. Additionally, by comparing predicted system performance improvements resulting from potential improvements to various components of a software system, the individual components may be ranked according to the amount of improvement required to realize the predicted system performance improvements.

A machine learning system for matching job candidates' resumes to one or more job opening requirements based on a predictive system that includes machine learning from a large number of resume profile data sets and job opening requirements data sets. The machine learning system includes a resume data training engine that receives a plurality of resume profiles data having a plurality of time slices of job requirement data. The received data is used to determine a plurality of features and generate a predictive model. The system also includes a resume matching runtime engine that utilizes the predictive model to generate matching data regarding a plurality of resume records data relative to the one or more job descriptions using the predictive model.