1306 results about "Predictive analytics" patented technology

Apparatuses and methods for medical monitoring physiological characteristics values such as blood glucose levels for the treatment of diabetes, are presented. The apparatuses and methods provide dynamic glucose monitoring functions that perform predictive analysis to anticipate harmful conditions, such as glucose crash and hyperglycemic incidents for a patient. The dynamic functions can also be used to maximize athletic performance and warn of inadequate nocturnal basal rate. Other aspects include advanced alarm and reminder functions, as well as advanced data presentation tools to further facilitate convenient and efficient management of various physiological conditions.

Apparatuses and methods for medical monitoring physiological characteristics values such as blood glucose levels for the treatment of diabetes, are presented. The apparatuses and methods provide dynamic glucose monitoring functions that perform predictive analysis to anticipate harmful conditions, such as glucose crash and hyperglycemic incidents for a patient. The dynamic functions can also be used to maximize athletic performance and warn of inadequate nocturnal basal rate. Other aspects include advanced alarm and reminder functions, as well as advanced data presentation tools to further facilitate convenient and efficient management of various physiological conditions.

The invention, referred to herein as PeaCoCk, uses a unique blend of technologies from statistics, information theory, and graph theory to quantify and discover patterns in relationships between entities, such as products and customers, as evidenced by purchase behavior. In contrast to traditional purchase-frequency based market basket analysis techniques, such as association rules which mostly generate obvious and spurious associations, PeaCoCk employs information-theoretic notions of consistency and similarity, which allows robust statistical analysis of the true, statistically significant, and logical associations between products. Therefore, PeaCoCk lends itself to reliable, robust predictive analytics based on purchase-behavior.

A system and method for measuring a performance of an entity and for predicting its future performance is disclosed. Raw information about the entity is collected from internal and external sources. The information is cleaned to exclude false positives. Using performance categories, the information is organized and transformed into meaningful data for the performance analysis engine. The information is normalized by scaling the meaningful data by industry type. A performance score is calculated by the performance analysis engine based on the normalized data. Further, a competitive relationship score is calculated based on the performance score and the normalized data. These scores are reported in a user interface displaying the performance of the entity and are used as inputs, among other factors, to a predictive analysis engine that assesses the future performance of the entities.

A mobile communication system based on digital content including images and video that may be acquired, processed, and displayed using a plurality of mobile device, smartphones, tablet computers, stationary computers, intelligent electronic glasses, smart glasses, headsets, watches, smart devices, vehicles, and servers. Content may be acquired continuously by input and output devices and displayed in a social network. Images may have associated additional properties including nature of voice, audio, data and other information. This content may be displayed and acted upon in an augmented reality or virtual reality system. The imaged base network system may have the ability to learn and form intelligent association between aspects, people and other entities; between images and the associated data relating to both animate and inanimate entities for intelligent image based communication in a network. Acquired content may be processed for predictive analytics. Individuals may be imaged for targeted and personalized advertising.

A method for predictive analytics for a process includes receiving at least one trace of the process, building a probabilistic graph modeling the at least one trace, determining content at each node of the probabilistic graph, wherein a node represents an activity of the process and at least one node is a decision node, modeling each decision node as a respective decision tree, and predicting, for an execution of the process, a path in the probabilistic graph from any decision node to a prediction target node of a plurality of prediction target nodes given the content.

The method and system of the present invention provides for data gathering of energy systems, calculation of billing information for the energy systems to include both traditional energy sources and renewable energy sources, and creation of various outputs to include various user interfaces applicable to overall management of the energy systems. The user interfaces can include integrated, real-time billings to a consumer that measure the cost of the primary energy as well as savings provided by renewable energy sources. The present invention also provides predictive analysis for various aspects of energy use in order to better manage particular facilities that may incorporate renewable energy sources. The gathered data and the various user interfaces enables a user to modify or adjust the use of the renewable energy sources at selected sites to maximize their impact on reducing the cost of the primary energy bill.

A computer-implemented method, a system, and a network include receiving network data from a network and non-network sourced data from one or more external sources relative to the network; performing data mining on the network data and the non-network sourced data; developing a predictive analytics model based on the data mining; and performing predictive analytics on the network data and the non-network sourced data using the predictive analytics model to detect likely future failures in the network. The network can include a Software Defined Network (SDN) operating at any of Layers 0, 1, 2 and / or 3.

Nonlinear generalized dynamic regression analysis system and method of the present invention preferably uses all available data at all time points and their measured time relationship to each other to predict responses of a single output variable or multiple output variables simultaneously. The present invention, in one aspect, is a system and method for predicting whether an intervention administered to a patient changes the physiological, pharmacological, pathophysiological, or pathopsychological state of the patient with respect to a specific medical condition. The present invention uses the theory of martingales to derive the probabilistic properties for statistical evaluations. The approach uniquely models information in the following domains: (1) analysis of clinical trials and medical records including efficacy, safety, and diagnostic patterns in humans and animals, (2) analysis and prediction of medical treatment cost-effectiveness, (3) the analysis of financial data, (4) the prediction of protein structure, (5) analysis of time dependent physiological, psychological, and pharmacological data, and any other field where ensembles of sampled stochastic processes or their generalizations are accessible. A quantitative medical condition evaluation or medical score provides a statistical determination of the existence or onset of a medical condition.

System and methods for selectively or automatically migrating resources between storage operation cells are provided. In accordance with one aspect of the invention, a management component within the storage operation system may monitor system operation and migrate components from storage operation cell to another to facilitate failover recovery, promote load balancing within the system and improve overall system performance as further described herein. Another aspect of the invention may involve performing certain predictive analyses on system operation to reveal trends and tendencies within the system. Such information may be used as the basis for potentially migrating components from one storage operation cell to another to improve system performance and reduce or eliminate resource exhaustion or congestion conditions.

A system for providing real-time modeling of an electrical system under management is disclosed. The system includes a data acquisition component, a virtual system modeling engine, and an analytics engine. The data acquisition component is communicatively connected to a sensor configured to provide real-time measurements of data output from an element of the system. The virtual system modeling engine is configured to generate a predicted data output for the element. The analytics engine is communicatively connected to the data acquisition system and the virtual system modeling engine and is configured to monitor and analyze a difference between the real-time data output and the predicted data output.

A system for predictive analysis of very large data sets using a distributed computational graph has been developed. Data receipt software receives streaming data from one or more sources. In a batch data pathway, data formalization software formats input data for storage. A batch event analysis server inspects stored data for trends, situations, or knowledge. Aggregated data is passed to message handler software. System sanity software receives status information from message handler and optimizes system performance. In the streaming pathway, transformation pipeline software manipulates the data stream, provides results back to the system, receives directives from the system sanity and retrain software.

An apparatus and method for determining at least one operational condition of a premises based system including at least one premises device. The apparatus includes a processor configured to perform a diagnostic procedure. The diagnostic procedure includes determining operational data of the premises based system, the operational data indicating at least one of a premises device and the apparatus is operating outside a failure range and performing predictive analysis based at least in part on the received operational data. The predictive analysis indicates whether the at least one of premises device and apparatus is likely to operate within the failure range within a predefined period of time. The diagnostic procedure includes causing a notification alert to be transmitted to at least one of a user interface device and remote monitoring center based on the predictive analysis.

Dynamically updating neural network systems may be implemented to generate, train, evaluate and update artificial neural network data structures used by content distribution networks. Such systems and methods described herein may include generating and training neural networks, using neural networks to perform predictive analysis and other decision-making processes within content distribution networks, evaluating the performance of neural networks, and generating and training pluralities of replacement candidate neural networks within cloud computing architectures and / or other computing environments.

The statistical analysis described and claimed is a predictive statistical tree model that overcomes several problems observed in prior statistical models and regression analyses, while ensuring greater accuracy and predictive capabilities. Although the claimed use of the predictive statistical tree model described herein is directed to the prediction of a disease in individuals, the claimed model can be used for a variety of applications including the prediction of disease states, susceptibility of disease states or any other biological state of interest, as well as other applicable non-biological states of interest. This model first screens genes to reduce noise, applies k-means correlation-based clustering targeting a large number of clusters, and then uses singular value decompositions (SVD) to extract the single dominant factor (principal component) from each cluster. This generates a statistically significant number of cluster-derived singular factors, that we refer to as metagenes, that characterize multiple patterns of expression of the genes across samples. The strategy aims to extract multiple such patterns while reducing dimension and smoothing out gene-specific noise through the aggregation within clusters. Formal predictive analysis then uses these metagenes in a Bayesian classification tree analysis. This generates multiple recursive partitions of the sample into subgroups (the “leaves” of the classification tree), and associates Bayesian predictive probabilities of outcomes with each subgroup. Overall predictions for an individual sample are then generated by averaging predictions, with appropriate weights, across many such tree models. The model includes the use of iterative out-of-sample, cross-validation predictions leaving each sample out of the data set one at a time, refitting the model from the remaining samples and using it to predict the hold-out case. This rigorously tests the predictive value of a model and mirrors the real-world prognostic context where prediction of new cases as they arise is the major goal.

The invention, referred to herein as PeaCoCk, uses a unique blend of technologies from statistics, information theory, and graph theory to quantify and discover patterns in relationships between entities, such as products and customers, as evidenced by purchase behavior. In contrast to traditional purchase-frequency based market basket analysis techniques, such as association rules which mostly generate obvious and spurious associations, PeaCoCk employs information-theoretic notions of consistency and similarity, which allows robust statistical analysis of the true, statistically significant, and logical associations between products. Therefore, PeaCoCk lends itself to reliable, robust predictive analytics based on purchase-behavior.

A method and system for performing continuous (real-time) physics based prognostics analysis as a function of actual engine usage and changing operating environment. A rule-based mission profile analysis is conducted to determine the mission variability which yields variability in the type of thermal-mechanical loads that an engine is subjected to during use. This is followed by combustor modeling to predict combustion liner temperatures and combustion nozzle plane temperature distributions as a function of engine usage which is followed by off-design engine modeling to determine the pitch-line temperatures in hot gas path components and thermodynamic modeling to compute the component temperature profiles of the components for different stages of the turbine. This is automatically followed by finite element(FE) based non-linear stress-strain analysis using an real-time FE solver and physics based damage accumulation, life consumption and residual life prediction analyses using microstructural modeling based damage and fracture analysis techniques.

The monitoring apparatus includes administrator level software installed in one computer of a computer network, and server agent level software installed in other computers of the computer network having corresponding data storage devices. Log page data of monitored data storage devices is retrieved by the server agent level software and then transmitted to the administrator level software. The log page data is stored in a database at the administrator level software and user interface information is generated from the data stored in the database to provide information to a user regarding the status of each monitored data storage device in the computer network. The user interface information may include explanatory text, predictive analysis, and / or graphical information of both realtime and historical performance of the data storage devices. Accordingly, a very large computer network can be monitored at a single location to determine the general status of each data storage device in the network thereby providing early warning of actual or potential failures of the data storage devices.

Aspects of the present disclosure generally relate to systems and methods for managing and monitoring a plurality of emergency power supply systems (EPSS's) at a facility via an emergency power management system (EPMS). The EPMS generally comprises EPSS equipment, a management computer system for managing, monitoring, and testing the operational characteristics of the EPSS equipment, and a plurality of interface modules for providing unified communication capabilities between the management computer system and the EPSS equipment. Additional aspects relate to methods for easily and efficiently creating and installing an EPMS at a facility. Further aspects are directed to providing predictive analyses related to the EPSS equipment. Also, aspects of the present disclosure relate to normalizing EPSS equipment information across varying vendors, makes, and models of equipment so as to provide a unified view of all equipment across a given facility.

The invention, referred to herein as PeaCoCk, uses a unique blend of technologies from statistics, information theory, and graph theory to quantify and discover patterns in relationships between entities, such as products and customers, as evidenced by purchase behavior. In contrast to traditional purchase-frequency based market basket analysis techniques, such as association rules which mostly generate obvious and spurious associations, PeaCoCk employs information-theoretic notions of consistency and similarity, which allows robust statistical analysis of the true, statistically significant, and logical associations between products. Therefore, PeaCoCk lends itself to reliable, robust predictive analytics based on purchase-behavior.

Presented herein is a tool for analyzing spectrum in a geographic region for a variety of uses. One such use is in monitoring the operation of a wireless network and making adjustments to the wireless network components to augment operation. The tool can be used for mapping network coverage, analyzing network problems, performing network expansion planning, performing predictive analysis and maintaining reliable operation and quality of service in a network. The tool includes multiple RF sensors deployed throughout the region, and a sensor analyzer that receives input from the RF sensors, as well as other information such as mapping information and user input to control the network and report the operational state.

System and methods for selectively or automatically migrating resources between storage operation cells are provided. In accordance with one aspect of the invention, a management component within the storage operation system may monitor system operation and migrate components from storage operation cell to another to facilitate failover recovery, promote load balancing within the system and improve overall system performance as further described herein. Another aspect of the invention may involve performing certain predictive analyses on system operation to reveal trends and tendencies within the system. Such information may be used as the basis for potentially migrating components from one storage operation cell to another to improve system performance and reduce or eliminate resource exhaustion or congestion conditions.

A real-time Internet data mining system comprising a database, data processing, clustering, segmentation, and classification algorithms, and a networking server. The system receives customer account data from subscriber servers and prepares it for analysis. The data is transmitted to third-party data depositories. The third-parties append selected consumer behavioral information matched by a key, such as a physical or an e-mail address. The appended information is returned to the data mining system where multiple algorithms analyze the accounts based on a desired prediction. The scored accounts and analyses are returned to the originating subscriber servers for use in marketing communications.

A method for managing an airplane fleet is described. The method includes: (i) developing a gold body database for an airplane model for each non-destructive inspection system implemented to detect defects; (ii) inspecting, over a period of time, a plurality of candidate airplanes of the airplane model, using different types of non-destructive inspection systems and the gold body database associated with each of the different types of non-destructive inspection systems, to identify defects present on the plurality of candidate airplanes; (iii) repairing or monitoring defects detected on the plurality of candidate airplanes; (iv) conducting a trend analysis by analyzing collective defect data obtained from inspecting of plurality of candidate airplanes; and (v) maintaining the airplane fleet, which includes plurality of candidate airplanes, by performing predictive analysis using results of trend analysis.

The subject invention relates to systems and methods to extend the capabilities of declarative data modeling languages. In one aspect, a declarative data modeling language system is provided. The system includes a data modeling language component that generates one or more data mining models to extract predictive information from local or remote databases. A language extension component facilitates modeling capability in the data modeling language by providing a data sequence model or a time series model within the data modeling language to support various data mining applications.

Methods, apparatus, non-transitory computer readable storage medium, computer systems, networks, and / or systems using a wireless device for detection and tracking of user's data that uses electromagnetic frequency (EMF) identification (EMFID) technologies to provide data transfer and communications for EMFID sensors for automatic identification data collection of personal data for one or more individuals or end user, multiple EMFID tag interactions, remotely storing, monitoring and retrieving data and location data, physical, emotional and mental state data, integration of biometric data, healthcare, physical health conditions, medical conditions, diseases and conditions for disease control and prevention, pharmaceutical data and other data to develop a profile for one or more individuals, using radio and other frequency tags and relaying data from EMFID tag interactions to a database that can be accessed by members of a network, wherein predictive analytics are used for one or more individuals analysis, marketing, monitoring, behavior, diagnosis and promotions, of interest, of medical care, drugs, products, illegal activity, or other services, of interest, of past, present or future customers, users, targets and / or target markets.

One embodiment of the present invention provides an energy-asset control system for utilizing an energy asset to provide one of more modes of operation services. The system includes an economic optimizer configured to identify at least one mode of operation opportunity based on current and / or future market conditions; a prognostics module configured to perform a prognostic analysis associated with the mode of operation opportunity for the energy asset using an existing model, and determine a confidence level associated with the prognostic analysis; and an operation controller. The economic optimizer is further to configured to, in response to the prognostics module determining the confidence level exceeding a predetermined threshold, determine an expected profit of the mode of operation opportunity based on outcomes of the prognostic analysis; and optimize, over a predetermined time period, a usage of the energy asset based on the expected profit of the mode of operation opportunity.