478 results about "Confidence measures" patented technology

A data confidence indicator includes a plurality of physiological data and a plurality of signal quality measures derived from a physiological sensor output, and a plurality of comparator outputs each responsive to one of the measures and a corresponding one of a plurality of thresholds. An alert trigger output combines the comparator outputs. A low signal quality warning is generated in response to the alert trigger output, wherein the thresholds are set so that the warning occurs during a time period when there is low confidence in the data. The alert may be in the form of a message generated on the pulse oximeter display to warn that the accuracy of saturation and pulse rate measurements may be compromised. A confidence-based alarm utilizes signal quality measures to reduce the probability of false alarms when data confidence is low and to reduce the probability of missed events when data confidence is high.

In a method for determining the general semantic theme of a group of images, whereby each digitized image is identified as belonging to a specific group of images, one or more image feature measurements are extracted from each of the digitized images in an image group, and then used to produce an individual image confidence measure that an individual image belongs to one or more semantic classifications. Then, the individual image confidence measures for the images in the image group are used to produce an image group confidence measure that the image group belongs to one or more semantic classifications, and the image group confidence measure is used to decide whether the image group belongs to one or to none of the semantic classifications, whereby the selected semantic classification constitutes the general semantic theme of the group of images. Additionally, a plurality of semantic theme processors, one for each semantic classification, are provided to produce enhanced value imaging services and products for image groups that fall into an appropriate semantic theme.

An apparatus for providing dynamic vocabulary prediction for setting up a speech recognition network of resource constrained portable devices may include a recognition network element. The recognition network element may be configured to determine a confidence measure for each candidate recognized word for a current word to be recognized. The recognition network element may also be configured to select a subset of candidate recognized words as selected candidate words based on the confidence measure of each one of the candidate recognized words, and determine a recognition network for a next word to be recognized, the recognition network including likely follower words for each of the selected candidate words, e.g. using language model and highly frequently used words.

A system and method for providing continuous confidence-based authentication. The present invention may be implemented in an incremental access authentication system for controlling access to secured data having various levels of security. During the course of a conversational session between user and machine, a confidence-based authentication system according to the present invention will periodically analyze the input speech of a user interacting with the system to compute a “confidence measure” for the validity of an original identity claim i provided by the user at the commencement of the dialog session. The “confidence measure” computation process according to the present invention is seamlessly integrated into the incremental access authentication system so that the system can tailor its interaction with the user based on its confidence in the original identity claim.

A method for content recommendation for a user, wherein a song or a video is recommended to the user and a confidence measure is determined for the recommended song. The confidence measure is displayed to the user, so the user may get more confidence into the recommendation of the system.

Proprietary rights logos are detected in a video. The video is divided into a plurality of regions that are analyzed for generic proprietary rights logo features. A confidence mask is generated that comprises a plurality of scaling factors, each scaling factor corresponding to a region of the video and indicating a likelihood that the corresponding region of the video includes a proprietary rights logo. The scaling factors of the confidence mask are applied to the video data to generate an altered video. The altered video is analyzed to determine a confidence measure that the video includes a reference proprietary rights logo.

A DeepQA engine is enhanced to provide a digital medical investigation tool which assists a medical professional in researching potential causes of a set of patient conditions, including clues, facts and factoids about the patient. The DeepQA engine provides one or more answers to a natural language question with confidence levels for each answer. If a confidence level falls below a threshold, the enhanced DeepQA engine performs a crowd sourcing operation to gather additional information from one or more domain experts. The domain expert responses are provided to the medical professional, and are learned by the enhanced DeepQA system to provide for better research of similar patient conditions in future queries.

A system and method for speech recognition includes generating a set of likely hypotheses in recognizing speech, rescoring the likely hypotheses by using semantic content by employing semantic structured language models, and scoring parse trees to identify a best sentence according to the sentence's parse tree by employing the semantic structured language models to clarify the recognized speech.

Techniques for providing an automated conversational name dialing system for placing a call in response to an input by a user. One technique begins with the step of analyzing an input from a user, wherein the input includes information directed to identifying an intended recipient of a telephone call from the user. At least one candidate for the intended recipient is identified in response to the input, wherein the at least one candidate represents at least one potential match between the intended recipient and a predetermined vocabulary. A confidence measure indicative of a likelihood that the at least one candidate is the intended recipient is determined, and additional information is obtained from the user to increase the likelihood that the at least one candidate is the intended recipient, based on the determined confidence measure.

The invention discloses a depth camera based on structured light and stereo vision A depth camera system uses a structured light illuminator and multiple sensors such as infrared light detectors, such as in a system which tracks the Motion of a user in a field of view. One sensor can be optimized for shorter range detection while another sensor is optimized for longer range detection. The sensors can have a different baseline distance from the illuminator, as well as a different spatial resolution, exposure time and sensitivity. In one approach, depth values are obtained from each sensor by matching to the structured light pattern, and the depth values are merged to obtain a final depth map which is provided as an input to an application. The merging can involve unweighted averaging, weighted averaging, accuracy measures and / or confidence measures. In another approach, additional depth values which are included in the merging are obtained using stereoscopic matching among pixel data of the sensors.

Systems and methods to generate business recommendations for specific business actions based on weather element forecasts and known relationships between a business activity and weather elements are provided. The system includes a confidence level filter, an opportunity matrix filter, a weather decision point generator, a business rule recommendation engine and a business rule knowledge database. Methods to generate a business recommendation for a business activity are also provided. The methods include receiving weather driven demand data, assigning opportunity measures to each of the data points, identifying weather decision points based on opportunity measures, and applying business weather rules to the weather decision points to generate business recommendations. In a further feature, a weather element relationship and / or a weather element forecast confidence level is assigned to each data point within the weather driven demand data. These confidence levels are then factored in to determine weather decision points.

An algorithm for removing motion artifacts from a PPG signal in the frequency domain and a harmonic template classifier (HTC) algorithm and unit to determine heart rate are disclosed. In some examples, PPG signals can be processed in combination with accelerometer signals to remove unwanted artifacts in the frequency domain. For example, an acceleration mask can be generated and used to filter out acceleration contributions represented in the PPG signal. Additionally or alternatively, in some examples, an HTC unit can be configured to generate a heart rate correlation curve based on the correlation between frequency domain PPG signals and spectral templates. In some examples, the HTC unit can be configured to implement an algorithm to determine a predicted heart rate and an associated confidence measure. In some examples, heuristics can be used to determine a predicted heart rate based on the correlation curve and / or the confidence measure.

The invention discloses a pedestrian detection method based on an end-to-end convolutional neural network in order to solve the problem that the existing pedestrian detection algorithm has the disadvantages of low detection precision, complex algorithm and difficult multi-module fusion. A novel end-to-end convolutional neural network is adopted, a training sample set with marks is constructed, and end-to-end training is performed to get a convolutional neural network model capable of predicting a pedestrian candidate box and the confidence of the corresponding box. During test, a test picture is input into a trained model, and a corresponding pedestrian detection box and the confidence thereof can be obtained. Finally, non-maximum suppression and threshold screening are performed to get an optimal pedestrian area. The invention has two advantages compared with previous inventions. First, through end-to-end training and testing, the whole model is very easy to train and test. Second, pedestrian scale and proportion problems are solved by constructing a candidate box regression network, the pyramid technology adopted in previous inventions is not needed, and a lot of computing resources are saved.

Disclosed is directed an apparatus and method to reduce recognition errors through context relations among multiple dialogue turns. The apparatus includes a rule set storage unit having a rule set containing one or more rules, an evolutionary rule generation module connected to the rule storage unit, and a rule trigger unit connected to the rule storage unit. The rule set uses dialogue turn as a unit for the information described by each rule. The method analyzes a dialogue history through an evolutionary massive parallelism approach to get a rule set describing the context relation among dialogue turns. Based on the rule set and recognition result of an ASR system, it reevaluates the recognition result, and measures the confidence measure of the reevaluated recognition result. After each successful dialogue turn, the rule set is dynamically adapted.

A method locates an object in a sequence of frames of a video. A feature vector is constructed for every pixel in each frame. The feature vector is used to training the weak classifiers. The weak classifiers separate pixels that are associated with the object from pixels that are associated with the background. The set of weak classifiers are combined into a strong classifier. The strong classifier labels pixels in a frame to generate a confidence map. A ‘peak’ in the confidence is located using a mean-shift operation. The peak indicates a location of the object in the frame. That is, the confidence map distinguishes the object from the background in the video.

A system for automatically recognizing a handwriting image and converting such image to text data including a sequence of validated words, has an image input device, a number of handwriting recognition engines, and control unit. A first handwriting recognition engine is responsive to the image input device, for analyzing the data file and providing one or more possible text words for each successive word in the data file. The first handwriting recognition engine further provides a resemblance indication for each possible text word indicating a level of resemblance between its appearance and the appearance of the handwritten word in the data file. In the event that there is not a high level of confidence in the selection of the first handwriting recognition engine, a selection of a validated word is based on the selections of one or more of the other handwriting recognition engines.

The invention discloses a customizable voice wake-up method and a system. Through using a long and short memory network and a connection time sequence classification model, phoneme information of the voice information is modeled, the model is trained, the model after training is adopted for testing, and a possible phoneme sequence most similar to a customized wake-up word is searched in a generated Lattice network structure and serves as a judgment basis. The feature of CTC model output posteriori probability sparsity is used for high-efficiency searching, and the technique of calculating the confidence of the wake-up word is completed. On one hand, high wake-up performance can be obtained, that is, high accuracy and low error wake-up can be obtained; and on the other hand, the calculation resources of the application system are relatively little consumed.

A well monitoring system particularly useful in detecting kicks in the well includes a well, a well system, and a computing apparatus. The well defines a wellbore and the well system includes at least one sensor measuring at least one well condition. The computing apparatus hosts a well monitoring software component that performs a method to detect a kick in a well. The method includes: storing a set of real-time data from a measurement of a well condition by the sensor, the measurements being correlative to an unplanned fluid influx into the well; modeling the operation of the well with a physics-based, state space model of the well system to obtain an estimate of the well condition; and applying the real-time data set and the estimate to a probabilistic estimator to yield a probability of an occurrence of a kick and a confidence measure for the probability.

The invention discloses an image recognition method and an image recognition device. According to one concrete embodiment, the method comprises the following steps of: obtaining an image to be recognized containing an object to be recognized; sending the image to be recognized to a server, and receiving a confidence degree parameter and marker information of a target object corresponding to the object to be recognized, wherein the confidence degree parameter and the marker information are returned by a server and are obtained through recognition on the image to be recognized; when the confidence degree parameter is greater than a confidence threshold, using the marker information of the target object as a recognition result; when the confidence degree parameter is smaller than the confidence degree threshold, obtaining marking information associated with the image to be recognized from a third party platform; and using the marking information as a recognition result. The image recognition method and the image recognition device have the advantages that the effect that the server automatic recognition and the third party marking information are combined is achieved; the recognition accuracy is improved; the third part marking information is used for training a recognition model corresponding to a machine learning recognition mode used by the server; and the training effect of the recognition model is improved.

The invention determines the correct orientation of text blocks being processed with an optical character recognition computer program. The method determines a recognition confidence of characters in a text block being analyzed by the optical character recognition computer program and evaluates an orientation of the text block based on the recognition confidence.

A device of the present invention obtains a character string of a speech recognition result and a confidence factor thereof. A time monitor monitors time and determines whether or not processing is delayed by checking the confidence factor and time status. When the processing is not delayed, a checker is asked to perform manual judgment. In this event, speech is processed and the manual judgment of the speech recognition result is performed on the basis of the processed speech. When the processing is delayed, automatic judgment is performed by use of the confidence factor. When the character string is judged to be correct as a result of the manual judgment or the automatic judgment, the character string is displayed as a confirmed character string. When the character string is judged to be incorrect, automatic correction is performed by matching on the basis of a next candidate obtained by the speech recognition, texts and attributes of the presentation, a script text, and the like. Character string after the automatic correction is displayed as an unconfirmed character string.

A non-transitory computer readable storage medium having stored thereon instructions when executable by a processor perform operations including responsive to receiving an email including a URL, conducting an analysis of the email including: (i) analyzing a header and a body, and (ii) analyzing the URL; analyzing contents of a web page directed to by the URL; generating a score indicating a level of confidence the email is associated with a phishing attack based on at least one of the analysis of the email or the analysis of the contents of the web page; and responsive to the score being below a threshold, virtually processing the web page to determine whether the web page is associated with the phishing attack is shown.

An apparatus that calculates a confidence measure of a target word string specified in a recognition result includes: an alternative candidate generator which generates an alternative candidate word string in the position of the target word string; a classifier training unit which trains a classifier which is configured to discriminate between the target word string and the alternative candidate word string; a feature extractor which extracts a feature value representing an adjacent context in the position of the target word string; and a confidence measure calculator which determining whether the true word string in the position of the target word string is the target word string or the alternative candidate word string by using the classifier and the feature value and calculates a confidence measure of the target word string on the basis of the determination result.

Systems, methods, and media for rating websites for safe advertising are provided. In accordance with some embodiments of the disclosed subject matter, the method comprises: extracting one or more features from a piece of web content; applying a plurality of statistical models to the extracted features to generate a plurality of ordinomial estimates, wherein each ordinomial estimate represents a probability that the web content is a member of one of a plurality of severity groups; determining a posterior ordinomial estimate for the web content by combining the plurality of ordinomial estimates; generating a risk rating that encodes severity and confidence based on the determined posterior ordinomial estimate, wherein the risk rating identifies whether the web content is likely to contain objectionable content of a given category; and providing the risk rating for determining whether an advertisement should be associated with the web content.

The application provides a character recognition method and a character recognition device. The character recognition method includes: acquiring an image containing to-be-recognized characters; carrying out area segmentation on the image and acquiring a text block of each area; sequentially inputting each text block into at least two recognition modules at the same time for recognition, wherein each of the recognition modules has a respective confidence degree, and the confidence degrees of the recognition modules are different; taking a recognition result of the recognition module with the high confidence degree as a recognition result of the current text block when recognition results of the recognition modules are different; and integrating recognition results of all the text blocks to obtain a result of recognition for the characters in the image.

A high-definition map system receives sensor data from vehicles travelling along routes and combines the data to generate a high definition map for use in driving vehicles, for example, for guiding autonomous vehicles. A pose graph is built from the collected data, each pose representing location and orientation of a vehicle. The pose graph is optimized to minimize constraints between poses. Points associated with surface are assigned a confidence measure determined using a measure of hardness / softness of the surface. A machine-learning-based result filter detects bad alignment results and prevents them from being entered in the subsequent global pose optimization. The alignment framework is parallelizable for execution using a parallel / distributed architecture. Alignment hot spots are detected for further verification and improvement. The system supports incremental updates, thereby allowing refinements of sub-graphs for incrementally improving the high-definition map for keeping it up to date

Computer software for and a method of determining location of a target image within a two-dimensional input image by employing a three-dimensional reference image comprising determining object edge points in the input image; given estimated seeker line-of-sight orientation and range data, computing a two-dimensional reference image from the three-dimensional reference image; applying a weighted-direction-cross-product Hough Transform to the object edge points and points of the two-dimensional reference image; classifying the input image as containing a target image or not; if the image contains a target image, identifying a location of the target image within the two-dimensional input image; computing confidence measure as to the selected location of the target image; and in the case of sequential decision, also performing confidence accumulation through multi-frames or multi-looks.