153 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 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.

Techniques are disclosed for using a combination of explicit and implicit user context modeling techniques to identify and provide appropriate computer actions based on a current context, and to continuously improve the providing of such computer actions. The appropriate computer actions include presentation of appropriate content and functionality. Feedback paths can be used to assist automated machine learning in detecting patterns and generating inferred rules, and improvements from the generated rules can be implemented with or without direct user control. The techniques can be used to enhance software and device functionality, including self-customizing of a model of the user's current context or situation, customizing received themes, predicting appropriate content for presentation or retrieval, self-customizing of software user interfaces, simplifying repetitive tasks or situations, and mentoring of the user to promote desired change.

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.

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.

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.

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.

Disclosed are a system, a method, and a computer program product to provide for the optimization of the output transfer load balance between the peer computers transferring data to one or more storage devices. The peer computers receive, organize and transfer the data to storage devices. The data set is composed of a plurality of data transfers. After an initial division of the data transfers between the two peers, each peer will have assigned responsibility for a number of data transfers. If the one of the peer computers completes offloading transactions earlier than the other peer, then the peer that is still transferring data will employ the other peer to execute a portion of the remaining data transfers. The operation of the system is symmetrical in that either peer may assist the other peer depending upon which peer has idle time. In addition the operation is autonomous and self-adjusting resulting in the peer nodes optimizing the size of the portion of data transfers that are reassigned during the operation of the invention resulting in the minimization of idle time for either peer. The self-adjusting feature allows the system to react to changing conditions that affect data transfer rates to the storage devices.

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.

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.

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.

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.

An auto-learned hierarchy algorithm that learns the hierarchical layout of a power monitoring system. Historical power data from each meter is received and placed into a data table. The main is assumed to be at the top of the hierarchy and is designated as the reference. A check matrix is developed indicating whether a possible connection exists between each meter pair combination. A correlation coefficient matrix (CCM) is calculated based on the data table, and entries in which no connection is possible are zeroed. The column for the reference meter from the CCM is copied to a correlation reference array (CRA), and the meter having the highest correlation with the reference meter in the CRA is marked as connected to the reference meter in a connection table. That meter's power is subtracted from the data table and the procedure is repeated until all meters have been analyzed.

The invention provides a character input method comprising the following steps: the text data associated with an application program in the present system environment is acquired; the text data is analyzed to generate a temporary word bank; the word banks existing in the input method and the temporary word bank are loaded; the input information of users is received; a search is carried out in theword banks existing in the input method and the temporary word bank according to the received input information to get the corresponding candidates; the selection information of users is received andthe pointed candidates are displayed on a screen. With the invention, the input method can automatically learn about the text content corresponding to the environment when users adopt the input method so as to form a temporary word relation bank for the access of users. The user can get the best input experience in each new conversation by the means so as to basically solve the problems of strongcomprehensiveness and poor individuality of the word bank of the existing input method.

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 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 prioritised 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 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.

The present invention relates to a system and methodology that applies automated learning procedures for determining document relevance and assisting information retrieval activities. A system is provided that facilitates a machine-learned approach to determine document relevance. The system includes a storage component that receives a set of human selected items to be employed as positive test cases of highly relevant documents. A training component trains at least one classifier with the human selected items as positive test cases and one or more other items as negative test cases in order to provide a query-independent model, wherein the other items can be selected by a statistical search, for example. Also, the trained classifier can be employed to aid an individual in identifying and selecting new positive cases or utilized to filter or re-rank results from a statistical-based search.

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.

In at least one embodiment, an automated medical data machine learning system and method allow a natural language processing (“NLP”) system to automatically learn via, for example, feedback to improve ongoing performance of the natural language processing system. The particular technology for improving the interpretation by the NLP system of future input data is a matter of design choice. In at least one embodiment, the automated medical data learning system and method includes a linguistics module that determines the part of speech of a particular term or term, such as use as a noun or verb. In at least one embodiment, the system and method learns an interpretation based on a source of the input data. In at least one embodiment, the system and method includes a statistics module that allows the system and method to determine a most probable interpretation or multiple interpretations.

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 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.