152 results about "Automated learning" 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.

A technique is provided for enhancing performance of computer-assisted data operating algorithms in a medical context. Datasets are compiled and accessed, which may include data from a wide range of resources, including controllable and prescribable resources, such as imaging systems. The datasets are analyzed by a human expert or medical professional, and the algorithms are modified based upon feedback from the human expert or professional. Modifications may be made to a wide range of algorithms and based upon a wide range of data, such as available from an integrated knowledge base. Modifications may be made in sub-modules of the algorithms providing enhanced functionality. Modifications may also be made on various bases, including patient-specific changes, population-specific changes, feature-specific changes, and so forth.

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.

Described is a speech recognition dialog management system that allows more open-ended conversations between virtual agents and people than are possible using just agent-directed dialogs. The system uses both novel dialog context switching and learning algorithms based on spoken interactions with people. The context switching is performed through processing multiple dialog goals in a last-in-first-out (LIFO) pattern. The recognition accuracy for these new flexible conversations is improved through automated learning from processing errors and addition of new grammars.

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 that may be efficiently processed to determine correspondence. Applications of the interface and system include a video cassette recorder (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.

Disclosed is a system and method for suggesting messages that are automatically learned, personalized and diversified by leveraging messages sent by a large number of users in order to provide message completions that are as engaging as possible. The disclosed systems and methods learn from recurrent messages sent to a recipient, and suggest message completions based upon user behavior. The systems and methods compute a ranked list of suggested message completions after every key stroke or character input, and if the list satisfies a given confidence threshold, it is returned and displayed to the user.

The invention relates to auxiliary medical diagnoses, and aims to provide a method for automatically identifying whether a thyroid nodule is benign or malignant based on a deep convolutional neural network. The method for automatically identifying whether the thyroid nodule is benign or malignant based on the deep convolutional neural network comprises the following steps: reading B ultrasonic data of thyroid nodules; performing preprocessing for thyroid nodule images; selecting images, and obtaining nodule portions and non-nodule portions through segmentations; averagely dividing the extracted ROIs (regions of interest) into p groups, extracting characteristics of the ROIs by utilizing a CNN (convolutional neural network), and performing uniformization; taking p-1 groups of data as a training set, taking the remaining one group to make a test, and obtaining an identification model through training to make the test; and repeating cross validation for p times, and then obtaining an optimum parameter of the identification model. The method can obtain the thyroid nodules through the automatic segmentations by means of the deep convolutional neural network, and makes up for the deficiency that a weak boundary problem cannot be solved based on a movable contour and the like; and the method can automatically lean and extract valuable feature combinations, and prevent the complexity of an artificial feature selection.

The subject invention relates to systems and methods that employ automated learning techniques to database and information retrieval systems in order to facilitate knowledge capabilities for users and systems. In one aspect, an adaptive information retrieval system is provided. The system includes a database component to store structured and unstructured data values. A search component queries the data values from the database, wherein a learning component associated with the search component or the database component is provided to facilitate retrieval of desired information.

The invention relates to the field of auxiliary medical diagnosis and aims to provide an auxiliary diagnosis system for interpreting medical image features based on the deep learning method. The system comprises the following steps of: reading medical image data of a lesion and preprocessing the medical image data; selecting an image, establishing a convolutional neural network architecture, automatically learning and segmenting a lesion area, and refining the shape of the lesion; constructing a CNN model of a convolutional neural network architecture to automatically interpret the characteristics of benign and malignant lesions, and acquiring an auxiliary diagnosis system for interpreting medical image features based on the depth learning method after training. The invention not only canautomatically divide the focus area by means of the depth convolution neural network, make up the deficiency that the weak boundary problem cannot be solved based on the active contour and the like, but also can automatically learn the characteristic combination extracted with the value, thereby avoiding the complex of manually selecting the feature.

The present teaching relates to method system, and medium for generating knowledge for a chat bot. Training data are used to learn and generating knowledge and are received with at least some labeled training seeds and unlabeled conversation data. The training data are parsed and various linguistic elements are extracted therefrom. Such linguistic elements are then used to perform automated learning in accordance with at least one label used in labeling the training seeds. Based on the automated learning, at least one model associated with the at least one label is generated from the training data.

The invention is a method for retrieving answers to questions from an information retrieval system. The method involves automatically learning phrase features for classifying questions into different types, automatically generating candidate query transformations from a training set of question/answer pairs, and automatically evaluating the candidate transforms on information retrieval systems. At run time, questions are transformed into a set of queries, and re-ranking is performed on the documents retrieved.

Systems and techniques are described for a centralized management system operating within a virtual machine which configures, monitors, analyzes, and manages an adaptive private network (APN) to provide a discovery process that learns about changes to the APN through a network control node (NCN) that is a single point of control of the APN. The discovery process automatically learns a new topology of the network without relying on configuration information of nodes in the APN. Network statistics are based on a timeline of network operations that a user selected to review. Such discovery and timeline review is separate from stored configuration information. If there was a network change, the changes either show up or not show up in the discovery process based on the selected time line. Configuration changes can be made from the APN VM system by loading the latest configuration on the APN under control of the NCN.

A computer-implemented method for automatically generating learning exercises, including determining a target learning item in response to an event, obtaining a knowledge level of a learner in relation to the target learning item based on a model of the learner as produced by an automated learner model, associating a level of difficulty with the obtained knowledge level of the learner, retrieving a learning exercise pattern from an exercise pattern database, automatically generating a learning exercise relating to the retrieved learning exercise pattern based on the model of the learner and the associated level of difficulty; and presenting the learning exercise to the learner via an exercise interface.

A static gesture recognition method based on a multi-scale convolution neural network is firstly proposed. The invention is based on the Caffe frame of depth learning to carry out optimization design,and uses the technical principle of image processing to recognize the static gesture picture. Firstly, the static gesture image data in simple background and complex background are collected and preprocessed. The data are divided into training data and test data. After setting up the experiment and testing environment, the convolution neural network based on multi-scale is designed, that is, determining the number of neural network layers, selecting the appropriate scale features, and so on. The training data are put into the network structure for learning and then the test data samples are input for testing, and the recognition accuracy is obtained. The invention can automatically learn gesture features by using a convolution layer and overcomes the shortcomings of manual feature extraction and the shortcomings that common convolution neural network feature extraction is not precise and comprehensive enough and the stability is not good enough, and the recognition accuracy is higher,and the training time is equal. The method has strong flexibility and wide applicability.

The present invention relates to a method of implementing, using and also testing a machine learning system. Preferably the system employs the Naïve Bayesian prediction algorithm in conjunction with a feature data structure to provide probability distributions for an input record belonging to one or more categories. Elements of the feature data structure may be prioritized and sorted with a view to selecting relevant elements only for use in the calculation of a probability indication or distribution. A method of testing is also described which allows the influence of one input learning data record to be removed from the system with the same record being used to subsequently test the accuracy of the system.

The invention is a method for detecting events in an imaged scene by analyzing the occlusion of linear features in the background image. Linear features, curved or straight, in specific scene locations are either manually specified or automatically learned from an image or image sequence of the background scene. For each linear feature, an occlusion model determines whether the line or part of it is occluded. The locations of the lines of interest in the scene, together with their occlusion characterizations, collectively form a description of the scene for a particular image. An event, defined as a series of descriptions of the scene over an image sequence, can then be initially defined and subsequently detected automatically by the system. An example application of this is counting cars or people passing in front of a video camera.

An automated integrated monitoring (IM) algorithm that automatically puts data from a utility monitoring system into context by temporally aligning the data to a common reference point and by identifying the location of each monitoring device in a hierarchy relative to other devices. Frequency variation data is received from all meters. The data is automatically aligned to a common reference point, such as a precise zero crossing, using a cross-correlation algorithm to determine the time delay at which the data is most correlated. Once the data is aligned, power data is received from all meters in a hierarchy, and the monitoring system layout is auto-learned using a correlation algorithm to determine which two meters are most likely correlated with one another based upon their historical power readings. Once the layout is complete, additional decisions regarding hardware and software configuration can automatically be made by the IM algorithm.