6181results about "Mathematical models" patented technology

An adaptive interface for a programmable system, for predicting a desired user function, based on user history, as well as machine internal status and context. The apparatus receives an input from the user and other data. A predicted input is presented for confirmation by the user, and the predictive mechanism is updated based on this feedback. Also provided is a pattern recognition system for a multimedia device, wherein a user input is matched to a video stream on a conceptual basis, allowing inexact programming of a multimedia device. The system analyzes a data stream for correspondence with a data pattern for processing and storage. The data stream is subjected to adaptive pattern recognition to extract features of interest to provide a highly compressed representation which may be efficiently processed to determine correspondence. Applications of the interface and system include a VCR, medical device, vehicle control system, audio device, environmental control system, securities trading terminal, and smart house. The system optionally includes an actuator for effecting the environment of operation, allowing closed-loop feedback operation and automated learning.

Systems and methods for discovery and classification of denial of service attacks in a distributed computing system may employ local agents on nodes thereof to detect resource-related events. An information later agent may determine if events indicate attacks, perform clustering analysis to determine if they represent known or unknown attack patterns, classify the attacks, and initiate appropriate responses to prevent and/or mitigate the attack, including sending warnings and/or modifying resource pool(s). The information layer agent may consult a knowledge base comprising information associated with known attack patterns, including state-action mappings. An attack tree model and an overlay network (over which detection and/or response messages may be sent) may be constructed for the distributed system. They may be dynamically modified in response to changes in system configuration, state, and/or workload. Reinforcement learning may be applied to the tuning of attack detection and classification techniques and to the identification of appropriate responses.

A method and apparatus are provided for building an intelligent automated assistant. Embodiments of the present invention rely on the concept of “active ontologies” (e.g., execution environments constructed in an ontology-like manner) to build and run applications for use by intelligent automated assistants. In one specific embodiment, a method for building an automated assistant includes interfacing a service-oriented architecture that includes a plurality of remote services to an active ontology, where the active ontology includes at least one active processing element that models a domain. At least one of the remote services is then registered for use in the domain.

The present invention relates to a method and apparatus for exploiting human feedback in an intelligent automated assistant. One embodiment of a method for conducting an interaction with a human user includes inferring an intent from data entered by the human user, formulating a response in accordance with the intent, receiving feedback from a human advisor in response to at least one of the inferring and the formulating, wherein the human advisor is a person other than the human user, and adapting at least one model used in at least one of the inferring and the formulating, wherein the adapting is based on the feedback.

A method of presenting digital content to a user on a display of a computing device displaying a continuous stream of digital content in a stream portion of the display, obtaining a current context of the user and the computing device, providing contextual sensitivities for the digital content items, determining a value for the digital content item's relevance of being displayed, calculating a probability of displaying the digital content item based upon the value of the digital content item's relevance of being displayed, performing a random selection of the digital content item based upon the probability of displaying the digital content item, and presenting the digital content item in the stream of digital content being displayed.

Sound, robust methods identify the most suitable, parsimonious set of tests to use with respect to prioritized, sequential anomaly detection in a collected batch of sample data. While the focus is on detecting anomalies in network traffic flows and classifying network traffic flows into application types, the methods are also applicable to other anomaly detection and classification application settings, including detecting email spam, (e.g. credit card) fraud detection, detecting imposters, unusual event detection (for example, in images and video), host-based computer intrusion detection, detection of equipment or complex system failures, as well as of anomalous measurements in scientific experiments.

The present invention relates to methods for achieving an optimal function of a biochemical reaction network. The methods can be performed in silico using a reconstruction of a biochemical reaction network of a cell and iterative optimization procedures. The methods can further include laboratory culturing steps to confirm and possibly expand the determinations made using the in silico methods, and to produce a cultured cell, or population of cells, with optimal functions. The current invention includes computer systems and computer products including computer-readable program code for performing the in silico steps of the invention.

The present disclosure provides methods for determining the ploidy status of a chromosome in a gestating fetus from genotypic data measured from a mixed sample of DNA comprising DNA from both the mother of the fetus and from the fetus, and optionally from genotypic data from the mother and father. The ploidy state is determined by using a joint distribution model to create a plurality of expected allele distributions for different possible fetal ploidy states given the parental genotypic data, and comparing the expected allelic distributions to the pattern of measured allelic distributions measured in the mixed sample, and choosing the ploidy state whose expected allelic distribution pattern most closely matches the observed allelic distribution pattern. The mixed sample of DNA may be preferentially enriched at a plurality of polymorphic loci in a way that minimizes the allelic bias, for example using massively multiplexed targeted PCR.

Interactive geographic information systems (GIS) and techniques are disclosed that provide users with a greater degree of flexibility, utility, and information. A markup language is provided that facilitates communication between servers and clients of the interactive GIS, which enables a number of GIS features, such as network links (time-based and/or view-dependent dynamic data layers), ground overlays, screen overlays, placemarks, 3D models, and stylized GIS elements, such as geometry, icons, description balloons, polygons, and labels in the viewer by which the user sees the target area. Also, “virtual tours” of user-defined paths in the context of distributed geospatial visualization is enabled. Streaming and interactive visualization of filled polygon data are also enabled thereby allowing buildings and other such features to be provided in 3D. Also, techniques for enabling ambiguous search requests in a GIS are provided.

A multipurpose hemofiltration system (10) and method are disclosed for the removal of fluid and/or soluble waste from the blood of a patient. The system (10) continuously monitors the flow rates of drained fluid, blood, and infusate. When necessary, the pumping rates of the infusate, drained fluid and blood are adjusted to remove a preselected amount of fluid from the blood in a preselected time period. A supervisory controller (160) can monitor patient parameters, such as heart rate (120) and blood pressure (130), and adjust the pumping rates accordingly. The supervisory controller (160) uses fuzzy logic to make expert decisions, based upon a set of supervisory rules, to control each pumping rate to achieve a desired flow rate and to respond to fault conditions. An adaptive controller (162) corrects temporal variations in the flow rate based upon an adaptive law and a control law.

A quantitative risk assessment system (QRAS) builds a risk model of a system for which risk of failure is being assessed, then analyzes the risk of the system corresponding to the risk model. The QRAS performs sensitivity analysis of the risk model by altering fundamental components and quantifications built into the risk model, then re-analyzes the risk of the system using the modifications. More particularly, the risk model is built by building a hierarchy, creating a mission timeline, quantifying failure modes, and building/editing event sequence diagrams. Multiplicities, dependencies, and redundancies of the system are included in the risk model. For analysis runs, a fixed baseline is first constructed and stored. This baseline contains the lowest level scenarios, preserved in event tree structure. The analysis runs, at any level of the hierarchy and below, access this baseline for risk quantitative computation as well as ranking of particular risks. A standalone Tool Box capability exists, allowing the user to store application programs within QRAS.

Systems and techniques are provided for tracking puts and takes of inventory items by subjects in an area of real space. A plurality of cameras with overlapping fields of view produce respective sequences of images of corresponding fields of view in the real space. In one embodiment, the system includes first image processors, including subject image recognition engines, receiving corresponding sequences of images from the plurality of cameras. The first image processors process images to identify subjects represented in the images in the corresponding sequences of images. The system includes second image processors, including background image recognition engines, receiving corresponding sequences of images from the plurality of cameras. The second image processors mask the identified subjects to generate masked images. Following this, the second image processors process the masked images to identify and classify background changes represented in the images in the corresponding sequences of images.

A system and for providing reference documents as a suggestion for classifying uncoded documents is provided. A set of reference electronically stored information items, each associated with a classification code, is designated. One or more of the reference electronically stored information items is combined with a set of uncoded electronically stored information items. Clusters of the uncoded electronically stored information items and the one or more reference electronically stored information items are generated. Relationships between the uncoded electronically stored information items and the one or more reference electronically stored information items in at least one cluster are visually depicted as suggestions for classifying the uncoded electronically stored information items in that cluster.

Methods and systems are described for improved drilling operations through the use of real-time drilling data to predict bit wear, lithology, pore pressure, a rotating friction coefficient, permeability, and cost in real-time and to adjust drilling parameters in real-time based on the predictions. The real-time lithology prediction is made by processing the real-time drilling data through a multilayer neural network. The real-time bit wear prediction is made by using the real-time drilling data to predict a bit efficiency factor and to detect changes in the bit efficiency factor over time. These predictions may be used to adjust drilling parameters in the drilling operation in real-time, subject to override by the operator. The methods and systems may also include determining various downhole hydraulics parameters and a rotary friction factor. Historical data may be used in combination with real-time data to provide expert system assistance and to identify safety concerns.

Systems and methods are disclosed for segmenting a digital image to identify an object portrayed in the digital image from background pixels in the digital image. In particular, in one or more embodiments, the disclosed systems and methods use a first neural network and a second neural network to generate image information used to generate a segmentation mask that corresponds to the object portrayed in the digital image. Specifically, in one or more embodiments, the disclosed systems and methods optimize a fit between a mask boundary of the segmentation mask to edges of the object portrayed in the digital image to accurately segment the object within the digital image.

The present invention relates to methods for evaluating and/or predicting the outcome of a clinical condition, such as cancer, metastasis, AIDS, autism, Alzheimer's, and/or Parkinson's disorder. The methods can also be used to monitor and track changes in a patient's DNA and/or RNA during and following a clinical treatment regime. The methods may also be used to evaluate protein and/or metabolite levels that correlate with such clinical conditions. The methods are also of use to ascertain the probability outcome for a patient's particular prognosis.

Apparatus and methods for high-level neuromorphic network description (HLND) using tags. The framework may be used to define nodes types, define node-to-node connection types, instantiate node instances for different node types, and/or generate instances of connection types between these nodes. The HLND format may be used to define nodes types, define node-to-node connection types, instantiate node instances for different node types, dynamically identify and/or select network subsets using tags, and/or generate instances of one or more connections between these nodes using such subsets. To facilitate the HLND operation and disambiguation, individual elements of the network (e.g., nodes, extensions, connections, I/O ports) may be assigned at least one unique tag. The tags may be used to identify and/or refer to respective network elements. The HLND kernel may comprises an interface to Elementary Network Description.

Intelligent multi-scale image parsing determines the optimal size of each observation by an artificial agent at a given point in time while searching for the anatomical landmark. The artificial agent begins searching image data with a coarse field-of-view and iteratively decreases the field-of-view to locate the anatomical landmark. After searching at a coarse field-of view, the artificial agent increases resolution to a finer field-of-view to analyze context and appearance factors to converge on the anatomical landmark. The artificial agent determines applicable context and appearance factors at each effective scale.

A method controls a motion of a vehicle using a model of the motion of the vehicle that includes an uncertainty. The method samples a control space of possible control inputs to the model of the motion of the vehicle to produce a set of sampled control inputs and determines a probability of each sampled control input to move the vehicle into state satisfying constraints on the motion of the vehicle. The method determines, using the probabilities of the sampled control inputs, a control input having the probability to move the vehicle in the state above a threshold. The control input is mapped to a control command to at least one actuator of the vehicle to control the motion of the vehicle.

A document annotation method includes modeling data elements of an input document and dependencies between the data elements as a dependency network. Static features of at least some of the data elements are defined, each expressing a relationship between a characteristic of the data element and its label. Dynamic features are defined which define links between an element and labels of the element and of a second element. Parameters of a collective probabilistic model for the document are learned, each expressing a conditional probability that a first data element should be labeled with information derived from a label of a neighbor data element linked to the first data element by a dynamic feature. The learning includes decomposing a globally trained model into a set of local learning models. The local learning models each employ static features to generate estimations of the neighbor element labels for at least one of the data elements.

Provided herein is an electronic message management platform that enables management and execution of electronic message campaigns while appropriately managing challenges presented by spam filters, black lists, and domain blocking technologies, and that includes elements for managing an electronic message campaign based on dynamic conditions, quality measures, engagement factors, and other measures, factors and conditions.