44th week of 2021 patent applcation highlights part 50 |
Patent application number | Title | Published |
20210342619 | TRACKING SYSTEM, ARRANGEMENT AND METHOD FOR TRACKING OBJECTS - A tracking system for tracking objects within a field of view is disclosed. The field of view may include a first zone and an adjacent zone of interest where at least two gates are associated with respective sides of the first zone within the field of view. The first camera is configured to detect when an object crosses one of the at least two gates and track the object throughout the first zone and the zone of interest. The tracking system is configured to generate a first event message in response to the object being tracked from one of the gates into the zone of interest and subsequently leaving the first zone through a dedicated gate of the at least two gates. | 2021-11-04 |
20210342620 | GEOGRAPHIC OBJECT DETECTION APPARATUS AND GEOGRAPHIC OBJECT DETECTION METHOD - A geographic object recognition unit ( | 2021-11-04 |
20210342621 | METHOD AND APPARATUS FOR CHARACTER RECOGNITION AND PROCESSING - The disclosure provides a method and an apparatus for character recognition and processing. A character region is labelled for each character contained in each sample image of a sample image set. A character category and a character position code corresponding to each character region are labelled. A preset neural network model for character recognition is trained based on the sample image set having labelled character regions, character categories and character position codes corresponding to the character regions. | 2021-11-04 |
20210342622 | TEXT DETECTION, CARET TRACKING, AND ACTIVE ELEMENT DETECTION - Detection of typed and/or pasted text, caret tracking, and active element detection for a computing system are disclosed. The location on the screen associated with a computing system where the user has been typing or pasting text, potentially including hot keys or other keys that do not cause visible characters to appear, can be identified and the physical position on the screen where typing or pasting occurred can be provided based on the current resolution of where one or more characters appeared, where the cursor was blinking, or both. This can be done by identifying locations on the screen where changes occurred and performing text recognition and/or caret detection on these locations. The physical position of the typing or pasting activity allows determination of an active or focused element in an application displayed on the screen. | 2021-11-04 |
20210342623 | TEXT DETECTION, CARET TRACKING, AND ACTIVE ELEMENT DETECTION - Detection of typed and/or pasted text, caret tracking, and active element detection for a computing system are disclosed. The location on the screen associated with a computing system where the user has been typing or pasting text, potentially including hot keys or other keys that do not cause visible characters to appear, can be identified and the physical position on the screen where typing or pasting occurred can be provided based on the current resolution of where one or more characters appeared, where the cursor was blinking, or both. This can be done by identifying locations on the screen where changes occurred and performing text recognition and/or caret detection on these locations. The physical position of the typing or pasting activity allows determination of an active or focused element in an application displayed on the screen. | 2021-11-04 |
20210342624 | SYSTEM AND METHOD FOR ROBUST IMAGE-QUERY UNDERSTANDING BASED ON CONTEXTUAL FEATURES - A method includes obtaining, using at least one processor of an electronic device, an image-query understanding model. The method also includes obtaining, using the at least one processor, an image and a user query associated with the image, where the image includes a target image area and the user query includes a target phrase. The method further includes retraining, using the at least one processor, the image-query understanding model using a correlation between the target image area and the target phrase to obtain a retrained image-query understanding model. | 2021-11-04 |
20210342625 | TEXT DETECTION, CARET TRACKING, AND ACTIVE ELEMENT DETECTION - Detection of typed and/or pasted text, caret tracking, and active element detection for a computing system are disclosed. The location on the screen associated with a computing system where the user has been typing or pasting text, potentially including hot keys or other keys that do not cause visible characters to appear, can be identified and the physical position on the screen where typing or pasting occurred can be provided based on the current resolution of where one or more characters appeared, where the cursor was blinking, or both. This can be done by identifying locations on the screen where changes occurred and performing text recognition and/or caret detection on these locations. The physical position of the typing or pasting activity allows determination of an active or focused element in an application displayed on the screen. | 2021-11-04 |
20210342626 | Computer Vision Systems and Methods for Geospatial Property Feature Detection and Extraction From Digital Images - Systems and methods for property feature detection and extraction using digital images. The image sources could include aerial imagery, satellite imagery, ground-based imagery, imagery taken from unmanned aerial vehicles (UAVs), mobile device imagery, etc. The detected geometric property features could include tree canopy, pools and other bodies of water, concrete flatwork, landscaping classifications (gravel, grass, concrete, asphalt, etc.), trampolines, property structural features (structures, buildings, pergolas, gazebos, terraces, retaining walls, and fences), and sports courts. The system can automatically extract these features from images and can then project them into world coordinates relative to a known surface in world coordinates (e.g., from a digital terrain model). | 2021-11-04 |
20210342627 | METHOD AND SYSTEM FOR ANALYZING IMAGE - An image analysis method and an image analysis system are disclosed. The method may include extracting training raw graphic data including at least one first node corresponding to a plurality of histological features of a training tissue slide image, and at least one first edge defined by a relationship between the histological features and generating training graphic data by sampling the first node of the training raw graphic data. The method may also include determining a parameter of a readout function by training a graph neural network (GNN) using the training graphic data and training output data corresponding to the training graphic data, and extracting inference graphic data including at least one second node corresponding to a plurality of histological features of an inference tissue slide image, and at least one second edge decided by a relationship between the histological features of the inference tissue slide image. | 2021-11-04 |
20210342628 | BALE DETECTION AND CLASSIFICATION USING STEREO CAMERAS - An apparatus comprises a sensor comprising a left camera and a right camera. A processor is coupled to the sensor. The processor is configured to produce an image and disparity data for the image, and search for a vertical object within the image using the disparity data. The processor is also configured to determine whether the vertical object is a bale of material using the image, and compute an orientation of the bale relative to the sensor using the disparity data. The sensor and processor can be mounted for use on an autonomous bale mover comprising an integral power system, a ground-drive system, a bale loading system, and a bale carrying system. | 2021-11-04 |
20210342629 | IMAGE PROCESSING METHOD, APPARATUS, AND DEVICE, AND STORAGE MEDIUM - An image processing method, apparatus, and device, and a storage medium are provided. The method is performed by a computing device, and includes: determining a first image feature of a first size of an input image, the first image feature having at least two channels; performing weight adjustment on each channel in the first image feature by using a first weight adjustment parameter, to obtain an adjusted first image feature, the first weight adjustment parameter including at least two parameter components, and each parameter component being used for adjusting a pixel of a channel corresponding to each parameter component; downsampling the adjusted first image feature to obtain a second image feature having a second size; combining the first image feature and the second image feature to obtain a combined image feature; and determining an image processing result according to the combined image feature. | 2021-11-04 |
20210342630 | IMAGE DESCRIPTOR NETWORK WITH IMPOSED HIERARCHICAL NORMALIZATION - Techniques are disclosed for using and training a descriptor network. An image may be received and provided to the descriptor network. The descriptor network may generate an image descriptor based on the image. The image descriptor may include a set of elements distributed between a major vector comprising a first subset of the set of elements and a minor vector comprising a second subset of the set of elements. The second subset of the set of elements may include more elements than the first subset of the set of elements. A hierarchical normalization may be imposed onto the image descriptor by normalizing the major vector to a major normalization amount and normalizing the minor vector to a minor normalization amount. The minor normalization amount may be less than the major normalization amount. | 2021-11-04 |
20210342631 | INFORMATION PROCESSING METHOD AND INFORMATION PROCESSING SYSTEM - An information processing method is executed by a computer and includes acquiring a first recognition result that is output as a result of inputting sensing data to a first recognition model trained through machine learning, acquiring reference data for the sensing data, determining a difference in class of a recognition target between the first recognition result and the reference data, generating an additional class for the first recognition model when the difference satisfies a predetermined condition, and outputting the sensing data or processed data obtained by processing the sensing data as training data for the additional class. | 2021-11-04 |
20210342632 | IMAGE PROCESSING METHOD AND APPARATUS, ELECTRONIC DEVICE, AND STORAGE MEDIUM - An image processing method includes: obtaining an image feature of each of a plurality of images for a same object; determining, according to the image feature of each of the plurality of images, a weight coefficient having a one-to-one correspondence with each image feature; and performing feature fusion processing on the image features of the plurality of images based on the weight coefficient of each image feature to obtain a fusion feature of the plurality of images. | 2021-11-04 |
20210342633 | METHOD OF MERGING IMAGES AND DATA PROCESSING DEVICE - Feature points included in input images are extracted and matching information indicating mapping relationships for feature points included in different input images is generated. A reference image is selected among the input images based on the matching information. Valid images are determined among the input images by excluding noise images from the input images based on the matching information. A two-dimensional bundle adjustment is performed to generate synchronized images by aligning the valid images to the reference image. A merged image is generated by merging the reference image and the synchronized images. Image merging performance is enhanced by selecting the reference image highly correlated with the other input images and estimating exact homography based on the reference image. | 2021-11-04 |
20210342634 | PRECOMPUTED SIMILARITY INDEX OF FILES IN DATA PROTECTION SYSTEMS WITH NEURAL NETWORK - Described is a system and method that provides a data protection risk assessment for the overall functioning of a backup and recovery system. Accordingly, the system may provide a single overall risk assessment score that provide an operator with an “at-a-glance” overview of the entire system. Moreover, the system may account for changes that occur over time based on leveraging statistical methods to automatically generate assessment scores for various components (e.g. application, server, network, load, etc.). In order to determine a risk assessment score, the system may utilize a predictive model based on historical data. Accordingly, residual values for newly observed data may be determined using the predictive model and the system may identify potentially anomalous or high risk indicators. | 2021-11-04 |
20210342635 | DENSITY BASED CONFIDENCE MEASURES OF NEURAL NETWORKS FOR RELIABLE PREDICTIONS - Computer-implemented systems and methods for selecting a first neural network model from a set of neural network models for a first dataset, the first neural network model having a set of predictor variables and a second dataset comprising a plurality of datapoints mapped into a multi-dimensional grid that defines one or more neighborhood data regions; applying the first neural network model on the first dataset to generate a model score for one or more datapoints in the second dataset, the model score representing an optimal fit of input predictor variables to a target variable for the set of variables of the first neural network model. | 2021-11-04 |
20210342636 | OPTIMIZATION OF WORKFLOWS FOR MICROSCOPES - A method for optimizing a workflow of at least one microscope or microscope system includes a step a) of implementing a workflow by one or more components of at least one microscope and/or microscope system, wherein the workflow comprises a capture of first data. In a step b), a trained model is determined for the workflow, at least in part based on the captured first data. | 2021-11-04 |
20210342637 | GENERATING GROUND TRUTH FOR MACHINE LEARNING FROM TIME SERIES ELEMENTS - Sensor data, including a group of time series elements, is received. A training data set is determined, including by determining for at least a selected time series element in the group of time series elements a corresponding ground truth. The corresponding ground truth is based on a plurality of time series elements in the group of time series elements. A processor is used to train a machine learning model using the training dataset. | 2021-11-04 |
20210342638 | X-RAY IMAGE SYNTHESIS FROM CT IMAGES FOR TRAINING NODULE DETECTION SYSTEMS - Systems and methods for generating synthesized medical images for training a machine learning based network are provided. An input medical image in a first modality is received. The input medical image comprises a nodule region for each of one or more nodules and a remaining region. The input medical image comprises an annotation for each of the one or more nodules. A synthesized medical image in a second modality is generated from the input medical image. The synthesized medical image comprises the annotation for each of the one or more nodules. A synthesized nodule image of each of the nodule regions and synthesized remaining image of the remaining region are generated in the second modality. It is determined whether each particular nodule of the one or more nodules is visible in the synthesized medical image based on at least one of the synthesized nodule image for the particular nodule and the synthesized remaining image. In response to determining that at least one nodule of the one or more nodules is not visible in the synthesized medical image, the annotation for the at least one not visible nodule is removed from the synthesized nodule image. | 2021-11-04 |
20210342639 | PRODUCT ONBOARDING MACHINE - A method for generating training examples for a product recognition model is disclosed. The method includes capturing images of a product using an array of cameras. A product identifier for the product is associated with each of the images. A bounding box for the product is identified in each of the images. The bounding boxes are smoothed temporally. A segmentation mask for the product is identified in each bounding box. The segmentation masks are optimized to generate an optimized set of segmentation masks. A machine learning model is trained using the optimized set of segmentation masks to recognize an outline of the product. The machine learning model is run to generate a set of further-optimized segmentation masks. The bounding box and further-optimized segmentation masks from each image are stored in a master training set with its product identifier as a training example to be used to train a product recognition model. | 2021-11-04 |
20210342640 | AUTOMATED MACHINE-LEARNING DATASET PREPARATION - A method of preparing a dataset may comprise calculating a pattern relevance for a first field in the dataset and modifying the first field based on the pattern relevance. The method may further comprise detecting a contextual cue in the first field. The method may further comprise retrieving contextual information for a value in the first field and adding that contextual information to the database. Finally, the method may further comprise identifying a numerical scheme for the first field and parsing the first field into a number according to that numerical scheme. | 2021-11-04 |
20210342641 | METHOD AND SYSTEM FOR GENERATING SYNTHETIC TIME DOMAIN SIGNALS TO BUILD A CLASSIFIER - State of the art systems and methods attempting to generate synthetic biosignals such as PPG generate patient specific PPG signatures and do not correlate with pathophysiological changes. Embodiments herein provide a method and system for generating synthetic time domain signals to build a classifier. The synthetic signals are generated using statistical explosion. Initially, a parent dataset of actual sample data of class and non-class subjects is identified, and statistical features are extracted. Kernel density estimate (KDE) is used to vary the feature distribution and create multiple data template from a single parent signal. PPG signal is again reconstructed from the distribution pattern using non-parametric techniques. The generated synthetic data set is used to build the two stage cascaded classifier to classify CAD and Non CAD, wherein the classifier design enables reducing bias towards any class. | 2021-11-04 |
20210342642 | MACHINE LEARNING TRAINING DATASET OPTIMIZATION - A method comprising: receiving a dataset comprising a plurality of data instances; extracting a feature vector representation of each of the data instances in the dataset; choosing a first data instance for adding to a subset of the dataset, wherein the first data instance is removed from the dataset; performing an iterative process comprising: (i) identifying one of the data instances in the dataset which represents a maximal information addition to the subset, based, at least in part, on measuring an information difference parameter between the feature vector representation of the identified data instance and the feature vector representations of all of the data instances in the subset, and (ii) adding the identified data instance to the subset and removing the identified data instance from the dataset, until the information difference parameter is lower than a predetermined threshold; and outputting the subset as a representative subset of the dataset. | 2021-11-04 |
20210342643 | METHOD, APPARATUS, AND ELECTRONIC DEVICE FOR TRAINING PLACE RECOGNITION MODEL - A computer device extracts local features of sample images based on a first part of a convolutional neural network (CNN) model. The sample images comprise a plurality of images taken at the same place. The device; aggregates the local features into feature vectors having a first dimensionality based on a second part of the CNN model. The device obtains compressed representation vectors of the feature vectors based on a third part of the CNN model. The compressed representation vectors have a second dimensionality less than the first dimensionality. The device trains the CNN model, and obtains a trained CNN mode satisfying a preset condition in accordance with the training. | 2021-11-04 |
20210342644 | EXTENDING KNOWLEDGE DATA IN MACHINE VISION - A machine-vision system obtains a trained-synthetic dataset associated with a virtual-sporting event, features of the trained-synthetic dataset including features associated with the virtual sporting event. The machine-vision system can further train by extending the trained-synthetic dataset using a real-life dataset associated with an actual sporting event including recognized results, the extending can include identifying and selecting a portion of the recognized results from the real-life dataset for annotation, an unsupervised machine-learning component annotating the portion, adding the annotated portion to the trained-synthetic dataset to obtain an extended-trained dataset, based on improvement of detection of the features from the extended-trained dataset compared to the trained-synthetic dataset, and availability of another portion of the recognized results, repeating the extending using a part of the extended-trained dataset as the trained-synthetic dataset in a next iteration, and providing the extended-trained dataset to the machine-vision system. | 2021-11-04 |
20210342645 | COMBINING ENSEMBLE TECHNIQUES AND RE-DIMENSIONING DATA TO INCREASE MACHINE CLASSIFICATION ACCURACY - Classifying unlabeled input data is provided. Euclidean distance and cosine similarity are calculated between an unlabeled input data point to be classified and a class label centroid of each class within a set of training data. A confidence value is calculated for each class label centroid based on the Euclidean distance and the cosine similarity between the unlabeled input data point and the class label centroid of each class. A highest confidence value equals a best matching class label centroid to the unlabeled input data point. A class label centroid having the highest confidence value is selected. The computer classifies the unlabeled input data point using a class label corresponding to the class label centroid having the highest confidence value. | 2021-11-04 |
20210342646 | SYSTEMS, METHODS, AND APPARATUSES FOR TRAINING A DEEP MODEL TO LEARN CONTRASTIVE REPRESENTATIONS EMBEDDED WITHIN PART-WHOLE SEMANTICS VIA A SELF-SUPERVISED LEARNING FRAMEWORK - Described herein are means for training a deep model to learn contrastive representations embedded within part-whole semantics via a self-supervised learning framework, in which the trained deep models are then utilized for the processing of medical imaging. For instance, an exemplary system is specifically configured for performing a random cropping operation to crop a 3D cube from each of a plurality of medical images received at the system as input, performing a resize operation of the cropped 3D cubes, performing an image reconstruction operation of the resized and cropped 3D cubes to predict the resized whole image represented by the original medical images received; and generating a reconstructed image which is analyzed for reconstruction loss against the original image representing a known ground truth image to the reconstruction loss function. Other related embodiments are disclosed. | 2021-11-04 |
20210342647 | SEMANTIC ADVERSARIAL GENERATION BASED FUNCTION TESTING METHOD IN AUTONOMOUS DRIVING - A system includes a camera configured to obtain image information from objects. The system also includes a processor in communication with the camera and programmed to receive an input data including the image information, encode the input via an encoder, obtain a latent variable defining an attribute of the input data, generate a sequential reconstruction of the input data utilizing at least the latent variable and an adversarial noise, obtain a residual between the input data and the sequential reconstruction utilizing a comparison of at least the input and the reconstruction to learn a mean shift in latent space, and output a mean shift indicating a test result of the input compared to the adversarial noise based on the comparison. | 2021-11-04 |
20210342648 | ANNOTATION OF INFRARED IMAGES FOR MACHINE LEARNING USING BEAMSPLITTER-BASED CAMERA SYSTEM AND METHODS - Systems and methods include an infrared camera configured to capture an infrared image of a scene, a visible light camera configured to capture a visible light image of the scene, and a logic device configured to simultaneously capture a pair of images of the scene comprising the infrared image of the scene and the visible image of the scene, align the pair of images so that a pixel location in one of the pair of images has a corresponding pixel location in the other image, classify the visible image, annotate the infrared image based, at least in part, on the classification of the visible image, and add the annotated infrared image to a neural network training dataset for use in training a neural network for infrared image classification. | 2021-11-04 |
20210342649 | Systems for Predicting a Terminal Event - In implementations of systems for predicting a terminal event, a computing device implements a termination system to receive input data defining a period of time and a maximum event threshold. This system uses a classification model to generate event scores for a plurality of entity devices. Each of the event scores indicates a probability of an event occurrence for a corresponding entity device within a period of time. The plurality of entity devices are segmented into a first segment and a second segment based on an event score threshold. Entity devices included in the first segment have event scores greater than the event score threshold and entity devices included in the second segment have event scores below the event score threshold. The termination system generates an indication of a probability that a number of event occurrences for the entity devices included in the second segment exceeds the maximum even threshold within the period of time. | 2021-11-04 |
20210342650 | PROCESSING OF LEARNING DATA SETS INCLUDING NOISY LABELS FOR CLASSIFIERS - A method for processing of learning data sets for a classifier. The method includes: processing learning input variable values of at least one learning data set multiple times in a non-congruent manner by one or multiple classifier(s) trained up to an epoch E | 2021-11-04 |
20210342651 | DATA CLASSIFICATION DEVICE, DATA CLASSIFICATION METHOD, AND DATA CLASSIFICATION PROGRAM - A data classification device includes: a known data input unit that receives an input of known data, the known data being data already classified into a class and a subclass subordinate to the class; a feature extraction unit that extracts, from features included in the known data, a feature that causes classification of the known data belonging to the same class into a subclass using the feature to fail; and a classification unit that classifies classification target data into a class using the feature extracted by the feature extraction unit. | 2021-11-04 |
20210342652 | ANOMALY DETECTION SYSTEM USING MULTI-LAYER SUPPORT VECTOR MACHINES AND METHOD THEREOF - A classifier network has at least two distinct sets of refined data, wherein the first two sets of refined data are sets of numbers representing the features values data received from sensors or a manufactured part. Performing, via at least two distinct types of support vector machines using an associated feature selection process for each classifier independently in a first layer, anomaly detection on the manufactured part. Then, using the stored data including refined data of at least two different types of data transforms and performing, via at least a two distinct types of support vector machines in a second layer, an associated feature selection process for each classifier independently. Forming at least four distinct compound classifier types for anomaly detection on the part using the stored data or coefficients. The ensemble of second layer support vector machine outputs compare the results to determine the presence of an anomaly. | 2021-11-04 |
20210342653 | METHOD AND DEVICE FOR ASCERTAINING AN EXPLANATION MAP - A method for ascertaining an explanation map of an image, in which all those pixels of the image are changed which are significant for a classification of the image ascertained with the aid of a deep neural network. The explanation map is selected in such a way that a smallest possible subset of the pixels of the image are changed, and the explanation map preferably does not lead to the same classification result as the image when it is supplied to the deep neural network for classification. The explanation map is selected in such a way that an activation caused by the explanation map does not essentially exceed an activation caused by the image in feature maps of the deep neural network. | 2021-11-04 |
20210342654 | MACHINE-GENERATED EXAMPLES OF COMMAND-LINE COMMANDS WITH PARAMETER VALUES - Examples of the usage of a command of a command line interface includes the command with a set of parameters and corresponding parameter values. The examples are generated from telemetry data, which does not contain parameter values, and from web-based sources that may contain multiple parameter values. A machine learning model is used to predict the data type of a parameter value when the parameter is used with a particular command. The predicted data type is then used to select an appropriate parameter value for the example from multiple known parameter values or to generate a parameter value when no known parameter value exists. | 2021-11-04 |
20210342655 | Method And Apparatus To Classify Structures In An Image - Disclosed is a system and method for segmentation of selected data. In various embodiments, automatic segmentation of fiber tracts in an image data may be performed. The automatic segmentation may allow for identification of specific fiber tracts in an image. | 2021-11-04 |
20210342656 | SYSTEM AND METHOD FOR MULTIMODAL EMOTION RECOGNITION - Systems, methods, apparatuses, and computer program products for providing multimodal emotion recognition. The method may include receiving raw input from an input source. The method may also include extracting one or more feature vectors from the raw input. The method may further include determining an effectiveness of the one or more feature vectors. Further, the method may include performing, based on the determination, multiplicative fusion processing on the one or more feature vectors. The method may also include predicting, based on results of the multiplicative fusion processing, one or more emotions of the input source. | 2021-11-04 |
20210342657 | INFORMATION PROCESSING APPARATUS AND NON-TRANSITORY COMPUTER READBLE MEDIUM STORING PROGRAM - An information processing apparatus includes a processor configured to set a first reference object, and if a second reference object identical or similar to the first reference object is recognized, virtually display a target object in relation to the second reference object. The target object is recognized in advance together with the first reference object. | 2021-11-04 |
20210342658 | POLYSEMANT MEANING LEARNING AND SEARCH RESULT DISPLAY - A polysemant meaning learning method is provided. The method includes extracting a plurality of first target terms and at least one adjacent term combinations of each first target term; obtaining a capsule network model by training by taking the encoding of each first target term as an input vector and the encoding of each adjacent term combination corresponding to each first target term as an output vector; when a to-be-recognized second target term is recognized, inputting the second target term into the capsule network model, and determining a plurality of obtained intermediate vectors as feature vectors of the second target term; and clustering the feature vectors with a cosine similarity greater than a similarity threshold to generate representative terms of one or more categories and determining one or more meanings of the one or more categories. | 2021-11-04 |
20210342659 | HYBRID DOCUMENTS WITH ELECTRONIC INDICIA - A hybrid document includes a flexible document having visible markings. One or more light-controlling elements and a controller are embedded in or on the flexible document. The controller is electrically connected to the one or more light-controlling elements to control the one or more light-controlling elements. A power input connection is electrically connected to the controller, or one or more light-controlling elements, or both. A power source can be connected to the power input connection, for example a piezoelectric or photovoltaic power source. In response to applied power, the controller causes the one or more light-controlling elements to emit light. In some embodiments, the controller includes a memory and a value can be stored in the memory and displayed by the light-controlling element(s). In some embodiments, the value can be assigned or varied by a hybrid currency teller machine. | 2021-11-04 |
20210342660 | Circuit And Method of Improving Energy Harvesting for Radio Frequency Identification (RFID) Tag with Temperature Sensor - The present disclosure provides a circuit and a method for improving energy harvest for an RFID tag with a temperature sensor, where an instruction command sent by a card reader includes a modulated part to invoke temperature sensor functions, and an unmodulated constant-envelop RF signal part with an extended time of duration to charge a switched additional energy storage capacitor embedded in the circuit. The switched additional energy storage capacitor is connected to the circuit upon a mode control signal corresponding to the sensor operation mode of the RFID tag. Thus, the RFID tag with the temperature sensor is ensured to conform to the timing window protocol for regular downlink operations, and at the same time, is capable of meeting higher energy demand for a high accuracy temperature sensor operation. | 2021-11-04 |
20210342661 | RFID TRANSPONDER AND METHOD OF OPERATING AN RFID TRANSPONDER - In accordance with a first aspect of the present disclosure, a radio frequency identification (RFID) transponder is provided, comprising a charge pump and at least one functional component, wherein: the charge pump is configured to convert an input voltage into an output voltage and to supply the output voltage to the functional component; the functional component is configured to perform a function of the RFID transponder using the output voltage of the charge pump; wherein the charge pump comprises a diode or switch transistor and at least one capacitor coupled to said diode or switch transistor, and wherein the capacitor is configured to compensate for a change of an impedance of said diode or switch transistor. In accordance with a second aspect of the present disclosure, a corresponding method of operating an RFID transponder is conceived. | 2021-11-04 |
20210342662 | PRODUCT AUTHENTICATION SYSTEM - A method and system for authenticating a variety of consumer products is provided. The system includes a plurality of near field communication (NFC) tags configured for coupling to a variety of consumer products, the tags programmed to provide identifying data associated with a single consumer product to an NFC-capable mobile computing device, the NFC having a structure that detects opening of the consumer product, a central database for storing said data, a server communicably connected to a communications network and configured to access the database, and a mobile application executing on a mobile computing device, configured to read said identifying data from the NFC tag and communicate said identifying data to the server. | 2021-11-04 |
20210342663 | MULTI-PURPOSE SMART CARD WITH USER TRUSTED BOND - The present disclosure relates a new generation “smart card” designed to create a severable invisible “bond” between the cardholder and the smart card itself where this trusted bond relationship is used to enhance and simplify the authentication process and during the use of the multi-purpose smart card. This new smart card is initiated and connected to a specific user using biometric information added to the card and the user using biometric information connects via a trusted bond with the card by pairing the biometric information which can be severed in one of multiple ways. The trusted bond with the smart card can be broken in one of multiple ways including disconnection from a network, distancing from the user, impact accelerometers, outside parameters, etc. The multi-function smart card also uses this established trusted bond with the user to simplify the authentication of the user for use of the card in encrypted computer network, ground security, or other retail and payment function. | 2021-11-04 |
20210342664 | PRODUCT AUTHENTICATION AND PRODUCT VIEWING SYSTEM - A method and system for authenticating a variety of consumer products is provided. The system includes a plurality of near field communication (NFC) tags configured for coupling to a variety of consumer products, the tags programmed to provide identifying data associated with a single consumer product to an NFC-capable mobile computing device, a central database for storing said data, a server communicably connected to a communications network and configured to access the database, and a mobile application executing on a mobile computing device, configured to read said identifying data from the NFC tag, read an image marker from the consumer product, communicate said identifying data to the server, and view 3D renderings of the consumer product. | 2021-11-04 |
20210342665 | IC TAG - An IC tag according to the present invention includes: a sheet-shaped tag main body having an outer shape that extends in a lengthwise direction and a widthwise direction orthogonal to the lengthwise direction; and a reinforcing member that is arranged along the widthwise direction so as to cover a surrounding area of the tag main body. The tag main body includes: an IC chip; an antenna configured to electrically transmit and receive information stored in the IC chip; and a sheet-shaped substrate that supports the IC chip and the antenna. The reinforcing member is made of a material having a Shore D hardness that is less than or equal to a Shore D hardness of the substrate, and is arranged so as to cover at least the IC chip. | 2021-11-04 |
20210342666 | SYSTEMS AND METHODS FOR CAPTURING VISIBLE INFORMATION - A transaction card construction and computer-implemented methods for a transaction card are described. The transaction card has vector-formatted visible information applied by a laser machining system. In some embodiments, systems and methods are disclosed for enabling the sourcing of visible information using a scalable vector format The systems and methods may receive a request to add visible information to a transaction card and capture an image of the visible information. The systems and methods may capture data representing the image. The systems and methods may also determine an ambient color saturation of the image. Further, systems and methods may translate the image based on the ambient color saturation of the image. The systems and methods may also map the translated image to a bounding box and convert the mapped image into vector format. In addition, the systems and methods may provide the converted image to a laser machining system. | 2021-11-04 |
20210342667 | Method And Apparatus For Constructing Informative Outcomes To Guide Multi-Policy Decision Making - In Multi-Policy Decision-Making (MPDM), many computationally-expensive forward simulations are performed in order to predict the performance of a set of candidate policies. In risk-aware formulations of MPDM, only the worst outcomes affect the decision making process, and efficiently finding these influential outcomes becomes the core challenge. Recently, stochastic gradient optimization algorithms, using a heuristic function, were shown to be significantly superior to random sampling. In this disclosure, it was shown that accurate gradients can be computed-even through a complex forward simulation—using approaches similar to those in dep networks. The proposed approach finds influential outcomes more reliably, and is faster than earlier methods, allowing one to evaluate more policies while simultaneously eliminating the need to design an easily-differentiable heuristic function. | 2021-11-04 |
20210342668 | Methods And Systems For Efficient Processing Of Recurrent Neural Networks - Recurrent neural networks are efficiently mapped to hardware computation blocks specifically designed for Legendre Memory Unit (LMU) cells, Projected LSTM cells, and Feed Forward cells. Iterative resource allocation algorithms are used to partition recurrent neural networks and time multiplex them onto a spatial distribution of computation blocks, guided by multivariable optimizations for power, performance, and accuracy. Embodiments of the invention provide systems for low power, high performance deployment of recurrent neural networks for battery sensitive applications such as automatic speech recognition (ASR), keyword spotting (KWS), biomedical signal processing, and other applications that involve processing time-series data. | 2021-11-04 |
20210342669 | METHOD, SYSTEM, AND MEDIUM FOR PROCESSING SATELLITE ORBITAL INFORMATION USING A GENERATIVE ADVERSARIAL NETWORK - Method, electronic device, system, and computer-readable medium embodiments are disclosed. Some embodiments include a signal processing workflow incorporating a graphical user interface for displaying orbital information for satellites and other spacecraft. In some embodiments, a generative adversarial network (GAN) is employed for evaluating satellite orbital positions, for predicting future orbital movements, for detecting orbital maneuvers of a satellite, and for analyzing such maneuvers for potential nefarious intent. | 2021-11-04 |
20210342670 | PROCESSING SEQUENCES USING CONVOLUTIONAL NEURAL NETWORKS - Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing sequences using convolutional neural networks. One of the methods includes, for each of the time steps: providing a current sequence of audio data as input to a convolutional subnetwork, wherein the current sequence comprises the respective audio sample at each time step that precedes the time step in the output sequence, and wherein the convolutional subnetwork is configured to process the current sequence of audio data to generate an alternative representation for the time step; and providing the alternative representation for the time step as input to an output layer, wherein the output layer is configured to: process the alternative representation to generate an output that defines a score distribution over a plurality of possible audio samples for the time step. | 2021-11-04 |
20210342671 | VERTICAL MAPPING AND COMPUTING FOR DEEP NEURAL NETWORKS IN NON-VOLATILE MEMORY - A non-volatile memory structure capable of storing layers of a deep neural network (DNN) and perform an inferencing operation within the structure is presented. A stack of bonded die pairs is connected by through silicon vias. Each bonded die pair includes a memory die, having one or more memory arrays onto which layers of the neural network are mapped, and a peripheral circuitry die, including the control circuits for performing the convolution or multiplication for the bonded die pair. The multiplications can either be done in-array on the memory die or in-logic on the peripheral circuitry die. The arrays can be formed into columns along the vias, allowing an inferencing operation to be performed by propagating an input up and down the columns, with the output of one level being the input of the subsequent layer. | 2021-11-04 |
20210342672 | CROSSBAR ARRAYS FOR COMPUTATIONS IN MEMORY-AUGMENTED NEURAL NETWORKS - In a hardware-implemented approach for operating a neural network system, a neural network system is provided comprising a controller, a memory, and an interface connecting the controller to the memory, where the controller comprises a processing unit configured to execute a neural network and the memory comprises a neuromorphic memory device with a crossbar array structure that includes input lines and output lines interconnected at junctions via electronic devices. The electronic devices of the neuromorphic memory device are programmed to incrementally change states by coupling write signals into the input lines based on: write instructions received from the controller and write vectors generated by the interface. Data is retrieved from the neuromorphic memory device, according to a multiply-accumulate operation, by coupling read signals into one or more of the input lines of the neuromorphic memory device based on: read instructions from the controller and read vectors generated by the interface. | 2021-11-04 |
20210342673 | INTER-PROCESSOR DATA TRANSFER IN A MACHINE LEARNING ACCELERATOR, USING STATICALLY SCHEDULED INSTRUCTIONS - A compiler generates a computer program implementing a machine learning network on a machine learning accelerator (MLA) including interconnected processing elements. The computer program includes data transfer instructions for non-colliding data transfers between the processing elements. To generate the data transfer instructions, the compiler determines non-conflicting data transfer paths for data transfers based on a topology of the interconnections between processing elements, on dependencies of the instructions and on a duration for execution of the instructions. Each data transfer path specifies a routing and a time slot for the data transfer. The compiler generates data transfer instructions that specify routing of the data transfers and generates a static schedule that schedules execution of the data transfer instructions during the time slots for the data transfers. The static schedule also schedules execution of compute instructions for computations using transferred data that implement the machine learning network. | 2021-11-04 |
20210342674 | METHODS FOR SECURING FILES WITHIN A STORAGE DEVICE USING ARTIFICIAL INTELLIGENCE AND DEVICES THEREOF - The present technology relates to identifying an artificial intelligence model based on a received first key value to write a received first block of data associated with a file. The received first key value is applied to the identified artificial intelligence model which is trained to output one of a plurality of actual index values where the identified artificial intelligence model and the plurality of data blocks are stored as a neural tree. The one of the actual index values is compared to a range within the actual index values to determine when the one of the actual index value points to a first data block of the plurality of data. The received first block of data associated with the file is written into the determined first data block. | 2021-11-04 |
20210342675 | ORDERING COMPUTATIONS OF A MACHINE LEARNING NETWORK IN A MACHINE LEARNING ACCELERATOR FOR EFFICIENT MEMORY USAGE - A compiler efficiently manages memory usage in the machine learning accelerator by intelligently ordering computations of a machine learning network. The compiler identifies a set of partial networks of the machine learning network representing portions of the machine learning network across multiple layers on which an output or set of outputs are dependent. Because any given output may depend on only a limited subset of intermediate outputs from the prior layers, each partial network may include only a small fraction of the intermediate outputs from each layer. Instead of implementing the MLN by computing one layer at a time, the compiler schedules instructions to sequentially implement partial networks. As each layer of a partial network is completed, the intermediate outputs can be released from memory. The described technique enables intermediate outputs to be directly streamed between processing elements of the machine learning accelerator without requiring large transfers to and from external memory. | 2021-11-04 |
20210342676 | VERTICAL MAPPING AND COMPUTING FOR DEEP NEURAL NETWORKS IN NON-VOLATILE MEMORY - Anon-volatile memory structure capable of storing layers of a deep neural network (DNN) and perform an inferencing operation within the structure is presented. A stack of bonded die pairs is connected by through silicon vias. Each bonded die pair includes a memory die, having one or more memory arrays onto which layers of the neural network are mapped, and a peripheral circuitry die, including the control circuits for performing the convolution or multiplication for the bonded die pair. The multiplications can either be done in-array on the memory die or in-logic on the peripheral circuitry die. The arrays can be formed into columns along the vias, allowing an inferencing operation to be performed by propagating an input up and down the columns, with the output of one level being the input of the subsequent layer. | 2021-11-04 |
20210342677 | ARCHITECTURE FOR A HARDWARE BASED EXPLAINABLE NEURAL NETWORK - Explainable neural networks may be designed to be easily implementable in hardware efficiently, leading to substantial speed and space improvements. An exemplary embodiment extends upon possible hardware embodiments of XNNs, making them suitable for low power applications, smartphones, mobile computing devices, autonomous machines, server accelerators, Internet of Things (IoT) and edge computing applications amongst many other applications. The capability of XNNs to be transformed from one form to another while preserving their logical equivalence is exploited to create efficient, secure hardware implementations that are optimized for the desired application domain and predictable in their behavior. | 2021-11-04 |
20210342678 | COMPUTE-IN-MEMORY ARCHITECTURE FOR NEURAL NETWORKS - A compute-in-memory neural network architecture combines neural circuits implemented in CMOS technology and synaptic conductance crossbar arrays. The crossbar memory structures store the weight parameters of the neural network in the conductances of the synapse elements, which define interconnects between lines of neurons of consecutive layers in the network at the crossbar intersection points. | 2021-11-04 |
20210342679 | CROSS ARRAY FERROELECTRIC TUNNEL JUNCTION DEVICES FOR ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING ACCELERATORS - Embodiments of the present disclosure are directed toward techniques and configurations for cross-point integrated circuits (ICs) for an artificial neural network (ANN). In embodiments, an ANN IC includes at least one synaptic structure. The synaptic structure includes a plurality of synapses that are formed from a plurality of wordlines (WL) and a plurality of bitlines (BLs). Each synapse is formed by ferroelectric tunnel junction (FTJ) coupling a portion of a BL and a portion of a WL. Each synapse is configured to perform an ANN operation based on an input voltage applied to the plurality of WLs and output a current on a corresponding BL of the plurality of BLs. Other embodiments may be described and/or claimed. | 2021-11-04 |
20210342680 | CHIP AND CHIP-BASED DATA PROCESSING METHOD - Embodiments of the present specification provide chips and chip-based data processing methods. In an embodiment, a method comprises: obtaining data associated with one or more neural networks transmitted from a server; for each layer of a neural network of the one or more neural networks, configuring, based on the data, a plurality of operator units based on a type of computation each operator unit performs; and invoking the plurality of operator units to perform computations, based on neurons of a layer of the neural network immediately above, of the data for each neuron to produce a value of the neuron. | 2021-11-04 |
20210342681 | Semi-Stochastic Boolean-Neural Hybrids for Solving Hard Problems - Described herein are methods of and systems for finding solutions to hard problems including factorization, subset sum, maximum satisfiability, bitcoin mining, and many other related and unrelated problems based on a novel type of computing circuits—Boolean-neural hybrids—that combine traditional two- or three-state logic gates with semi-stochastic neurons. Semi-stochastic neurons are a new type of artificial neurons that search for a problem solution stochastically and store the solution deterministically when it is found. Boolean-neural hybrids are based on invertible logic gates and operate in reverse: the input data are applied to the output, and the result is read from the input. | 2021-11-04 |
20210342682 | DECODERS FOR ANALOG NEURAL MEMORY IN DEEP LEARNING ARTIFICIAL NEURAL NETWORK - Numerous embodiments of decoders for use with a vector-by-matrix multiplication (VMM) array in an artificial neural network are disclosed. The decoders include bit line decoders, word line decoders, control gate decoders, source line decoders, and erase gate decoders. In certain embodiments, a high voltage version and a low voltage version of a decoder is used. | 2021-11-04 |
20210342683 | COMPANION ANALYSIS NETWORK IN DEEP LEARNING - Systems and methods for analyzing a first machine learning system via a second machine learning system. The first machine learning system comprising a first objective function. The method includes connecting the first machine learning system to an input of the second machine learning system, which includes a second objective function for analyzing an internal characteristic of the first machine learning system. The method further includes providing a data item to the first machine learning system, collecting internal characteristic data from the first machine learning system associated with the internal characteristic, computing partial derivatives of the first objective function through the first machine learning system with respect to the data item, and computing partial derivatives of the second objective function through both the second machine learning system and the first machine learning system with respect to the collected internal characteristic data. | 2021-11-04 |
20210342684 | METHOD AND SYSTEM FOR TABLE RETRIEVAL USING MULTIMODAL DEEP CO-LEARNING WITH HELPER QUERY-DEPENDENT AND QUERY-INDEPENDENT RELEVANCE LABELS - A system and a computer-implemented method for ranking tabular data entities by likelihood of comprising answers for (natural language) queries, based on multimodal descriptions of the tabular data entities, comprising separate representations, which represent different aspects of the tabular data entities. The ranking is based on joint representations, generated from the query representation and separate representations of the tabular data entities' aspects, using gated multimodal units. The computer-implemented method may be used for applications such as web searches, data aggregation, and research tasks. | 2021-11-04 |
20210342685 | Leveraging Simple Model Predictions for Enhancing Computational Performance - A computer-implemented method, system, and non-transitory computer-readable storage medium for enhancing performance of a first model. The first model is trained with a training data set. A second model receives the training data set associated with the first model. The second model provides the first model with a hardness value associated with prediction of each data point of the training data set. The first model determines a confidence value regarding predicting each data point based on the training data set, and determines a ratio of the hardness value of a prediction of each data point by the second model with respect to the confidence value of the first model. The first model is retrained with a re-weighted training data set when the determined ratio is lower than a value of β. | 2021-11-04 |
20210342686 | CONTENT MANAGEMENT USING ONE OR MORE NEURAL NETWORKS - Apparatuses, systems, and techniques are presented to determine whether to render one or more content objects. In at least one embodiment, one or more neural networks can determine whether to render an object to be transmitted in media content based at least in part upon whether those objects were previously rendered for that content. | 2021-11-04 |
20210342687 | Base Station-User Equipment Messaging Regarding Deep Neural Networks - Techniques and apparatuses are described for enabling base station-user equipment messaging regarding deep neural networks. A network entity (base station | 2021-11-04 |
20210342688 | NEURAL NETWORK TRAINING METHOD, DEVICE AND STORAGE MEDIUM BASED ON MEMORY SCORE - The present disclosure relates to a method, devices, and storage medium for training neural networks based on memory scores. The said method comprises: establishing the memory scores of a plurality of first-sample images in the library, from their training ages and training indicators, and a preset discount rate; determining a plurality of second-sample images from these memory scores and a preset first count, and using them to establish the first training set; training the neural network by using the first training set, with the said neural network is used for defect detection. The neural network training method in the disclosed embodiment reduces the size of the training set and shortens the time to converge, thereby improving training efficiency. | 2021-11-04 |
20210342689 | COMPUTER-IMPLEMENTED METHOD, AND DEVICE FOR PRODUCING A KNOWLEDGE GRAPH - A method for producing a knowledge graph having triples, in particular in the form of . The method includes: providing a body of text and input data for a model, determining with the aid of model triples including two entities of the knowledge graph and a relation between the two entities in each case, and determining an explanation for verifying the respective triple using the model. The following steps are carried out for determining a respective triple and for determining an explanation: classifying relevant areas of the body of text and discarding areas of the body of text classified as not relevant, and deriving a relation between the first entity and the second entity from the relevant areas of the body of text. | 2021-11-04 |
20210342690 | SYSTEMS AND METHODS FOR LEARNING-BASED HIGH-PERFORMANCE, ENERGY-EFFICIENT, AND SECURE ON-CHIP COMMUNICATION DESIGN FRAMEWORK - Systems and methods are disclosed for improving on-chip security, while minimizes the latency and cost of security techniques to improve system-level performance and power simultaneously. The framework uses machine learning algorithms, such as an artificial neural network (ANN), for runtime attack detection with higher accuracy. Further, a learning-based attack mitigation method using deep reinforcement learning is disclosed, where the method may be used to isolate the malicious components and to optimize network latency and energy-efficiency. | 2021-11-04 |
20210342691 | SYSTEM AND METHOD FOR NEURAL TIME SERIES PREPROCESSING - Systems and methods for neural time series preprocessing and forecasting, dividing time series data to generate chunks of short time series, inputting each of the short time series to a data preprocessing neural network that includes differencing to transform non-stationary data to stationary data and to filter noise, generating and outputting, from the data preprocessing neural network, processed time series data, and inputting the processed time series data to a forecasting neural network. Parameters of the data preprocessing neural network and parameters of the forecasting neural network are learned end-to-end. | 2021-11-04 |
20210342692 | TECHNOLOGIES FOR SCALING DEEP LEARNING TRAINING - Technologies for artificial neural network training include a computing node with a host fabric interface that sends a message that includes one or more artificial neural network training algorithm values to another computing node in response to receipt of a request to send the message. Prior to sending the message, the host fabric interface may receive a request to quantize the message and quantize the message based on a quantization level included in the request to generate a quantized message. The quantization message includes one or more quantized values such that each quantized value has a lower precision than a corresponding artificial neural network training algorithm value. The host fabric interface then transmits the quantized message, which includes metadata indicative of the quantization level, to another computing node in response to quantization of the message for artificial neural network training. Other embodiments are described and claimed. | 2021-11-04 |
20210342693 | CONTEXT AND DOMAIN SENSITIVE SPELLING CORRECTION IN A DATABASE - A method of operating a health tracking system is disclosed. The method comprises: receiving a first data record comprising at least a first descriptive string regarding a consumable item, the first descriptive string having at least one word thereof incorrectly spelled; generating a vector using the first descriptive string using a machine learning model; identifying a second descriptive string which corresponds to the consumable item and which has a correct spelling of the at least one incorrectly spelled word by applying the machine learning model to the generated vector; calculating a confidence factor regarding the identified second descriptive string using the machine learning model; and when it is determined that the confidence factor exceeds a predetermined threshold, (i) modifying the first data record by replacing the first descriptive string with the second descriptive string, and (ii) storing the modified first data record in the database. | 2021-11-04 |
20210342694 | Machine Learning Network Model Compression - A first aspect relates to a computer-implemented method for performing model compression. The method includes compressing a machine learning (ML) network model comprising a multiple layer structure to produce a compressed ML network model. The compressed ML network model maintains the multiple layer structure of the ML network model. The method generates a model file for the compressed ML network model. The model file includes the compressed ML network model and decoding information for enabling the ML network model to be decompressed and executed layer-by-layer. | 2021-11-04 |
20210342695 | SIGNAL PROCESSING METHOD, SIGNAL PROCESSING DEVICE, AND SIGNAL PROCESSING PROGRAM - A signal processing method includes: obtaining signals of two systems, the signals being a measuring system signal being a measurable time-series signal expressed in a real space, and a referring system signal being referred in a process of the measuring system signal and being expressed in the real space; extracting, based on a process using the signals of the two systems obtained in the obtaining and being performed in the real space, a time-series-feature expressing a feature of the measuring system signal in the real space; and converting the time-series-feature extracted in the extracting into a feature real-expression-feature being an expression of an information space dual with the real space. | 2021-11-04 |
20210342696 | Deep Learning Model Training Method and System - A deep learning model training method includes generating N first gradient sets in a back propagation (BP) calculation in a j | 2021-11-04 |
20210342697 | GENERATING A PERSONALIZED PREFERENCE RANKING NETWORK FOR PROVIDING VISUALLY-AWARE ITEM RECOMMENDATIONS - The present disclosure relates to a fashion recommendation system that employs a task-guided learning framework to jointly train a visually-aware personalized preference ranking network. In addition, the fashion recommendation system employs implicit feedback and generated user-based triplets to learn variances in the user's fashion preferences for items with which the user has not yet interacted. In particular, the fashion recommendation system uses triplets generated from implicit user data to jointly train a Siamese convolutional neural network and a personalized ranking model, which together produce a user preference predictor that determines personalized fashion recommendations for a user. | 2021-11-04 |
20210342698 | SYSTEMS AND METHODS FOR PARAMETER OPTIMIZATION - Methods and systems that provide one or more recommended configurations to planners using large data sets in an efficient manner. These methods and systems provide optimization of objectives using a genetic algorithm that can provide parameter recommendations that optimize one or more objectives in an efficient and timely manner. The methods and systems disclosed herein are flexible enough to satisfy diverse use cases. | 2021-11-04 |
20210342699 | COOPERATIVE EXECUTION OF A GENETIC ALGORITHM WITH AN EFFICIENT TRAINING ALGORITHM FOR DATA-DRIVEN MODEL CREATION - A method includes determining a trainable model to provide to a trainer, the trainable model determined based on modification of one or more models of a plurality of models. The plurality of models is generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method also includes providing the trainable model to the trainer. The method further includes adding a trained model, output by the trainer based on the trainable model, as input to a second epoch of the genetic algorithm, the second epoch subsequent to the first epoch. | 2021-11-04 |
20210342700 | METHOD AND SYSTEM FOR PERFORMING DETERMINISTIC DATA PROCESSING THROUGH ARTIFICIAL INTELLIGENCE - A method for performing deterministic data processing through Artificial Intelligence (AI) is disclosed. The method may include generating, via a deep learning network, a set of input feature vectors based on input data for a deterministic data processing model. The method may further include providing the set of input feature vectors to a trained AI model. The trained AI model may generate a set of output feature vectors that may correspond to an output data of the deterministic data processing model. The method may further include determining a variation between the set of output feature vectors and the output data, and iteratively performing incremental learning of the AI model based on the determined variation. | 2021-11-04 |
20210342701 | DEEP LEARNING BASED VISUAL COMPATIBILITY PREDICTION FOR BUNDLE RECOMMENDATIONS - Embodiments of the present invention provide systems, methods, and computer storage media for predicting visual compatibility between a bundle of catalog items (e.g., a partial outfit) and a candidate catalog item to add to the bundle. Visual compatibility prediction may be jointly conditioned on item type, context, and style by determining a first compatibility score jointly conditioned on type (e.g., category) and context, determining a second compatibility score conditioned on outfit style, and combining the first and second compatibility scores into a unified visual compatibility score. A unified visual compatibility score may be determined for each of a plurality of candidate items, and the candidate item with the highest unified visual compatibility score may be selected to add to the bundle (e.g., fill the in blank for the partial outfit). | 2021-11-04 |
20210342702 | METHOD FOR AUTOMATICALLY ANALYZING TRANSACTION LOGS OF A DISTRIBUTED COMPUTING SYSTEM - An aspect of the invention relates to a method for automatically analysing a transaction log of a distributed computing system, comprising a plurality of lines, the method comprising the following steps:
| 2021-11-04 |
20210342703 | GENERATIVE ADVERSARIAL NETWORKS FOR TIME SERIES - Systems, techniques, and computer-program products are provided to generate synthetic time series using a generative adversarial network. In some embodiment a technique includes configuring a first neural network having a first function representative of an output of the first neural network, and configuring a second neural network having a second function representative of an output of the second neural network. In addition, such a technique includes generating a generative adversarial network by solving an optimization problem with respect to an objective function based at least on the first function and the second function. The generative adversarial network includes a discriminator neural network and a generator neural network. A synthetic time series can be generated using at least the generator neural network. | 2021-11-04 |
20210342704 | System and Method for Detecting Misinformation and Fake News via Network Analysis - A method for detection of misinformation without the need to analyze any articles (HINTS) includes forming a mixed graph containing at least two different node types, such as users and articles, with edges between users and articles, and with user weights for user nodes and article weights for article nodes. Seed nodes are planted for at least one user node and at least one article node. User weights and article weights are manually assigned to the seed nodes, then neighborhoods are defined for the seed nodes. A HITS-like algorithm is then run for a predetermined number of rounds updating both people and articles, while keeping the weights of the seed nodes constant to converge the graph for the weights of articles and users. Finally, a set of highest weights for users and/or articles is outputted and possible remedial action can be taken. | 2021-11-04 |
20210342705 | DEVICE AND METHOD FOR DETECTING A FAULT IN A SPINNING MILL AND FOR ESTIMATING ONE OR MORE SOURCES OF THE FAULT - An electronic device and associated method are used to detect a fault in a spinning mill and to estimate one or more sources of the fault, the spinning mill including a plurality of textile machines that sequentially process textile materials. With the electronic device, the method receives parameter information of one or more of the textile machines and of one or more of the textile materials. The electronic device detects faults and location of the faults by identifying parameter information of the textile materials deviating from reference information. The electronic device is used to access configuration information of the textile machines and knowledge-based information related to possible sources of faults in the spinning mill. The method incudes using the electronic device to apply parameter information, configuration information, and knowledge-based information to one or more machine-learning algorithms to estimate the sources of the faults. | 2021-11-04 |
20210342706 | METHODS AND SYSTEMS FOR MULTI-FACTORIAL PHYSIOLOGICALLY INFORMED REFRESHMENT SELECTION USING ARTIFICIAL INTELLIGENCE - A system for multi-factorial physiologically informed refreshment selection using artificial intelligence, the system comprising a computing device, the computing device designed and configured to retrieve a biological extraction pertaining a user, wherein the biological extraction contains an element of user data; select, a nutritional machine-learning model using the biological extraction; determine a geolocation of the user; identify a provider located within the geolocation of the user, wherein the provider generates a plurality of refreshment possibilities; determine the compatibility of the plurality of refreshment possibilities utilizing the biological extraction and the nutritional model; and display the compatibility of the plurality of refreshment possibilities. | 2021-11-04 |
20210342707 | DATA-DRIVEN TECHNIQUES FOR MODEL ENSEMBLES - Techniques to ensemble machine learning (ML) models are provided. A plurality of residues is generated by processing a plurality of input records using a plurality of ML models. A plurality of data clusters is identified by evaluating, using a clustering model, the plurality of input records and the plurality of residues. A first ensemble is generated for a first data cluster of the plurality of data clusters, where the first ensemble comprises one or more of the plurality of ML models. Upon determining that a new input record corresponds to the first data cluster, the new input record is processed using the first ensemble. | 2021-11-04 |
20210342708 | Use of Machine Learning to Identify Topical Arguments - Technology is described for identifying topical arguments to be provided in order to enable problem solving. The method can include a first operation of storing a topical problem statement at a root of a graph. A plurality of topical arguments may be stored in a plurality of argument nodes and user answers to the topical arguments in the graph. Another operation may be grouping the argument nodes into section groups that define a sub-topic linked by the graph to the topical problem statement. Additionally, use requests for the topical arguments in the graph may be tracked by recording access to the topical arguments. A use pattern of topical arguments may be identified by tracking use requests to the topical arguments. The use pattern may be processed using a machine learning model. Another operation may be sending an additional section group with topical arguments and user answers in the graph based in part on processing of the use pattern using the machine learning model to determine which additional section group to send to a requestor. | 2021-11-04 |
20210342709 | Use of Machine Learning to Provide Answer Patterns and Context Descriptions - Technology is described for providing relevant context in order to assist with solving a problem. The method can include a first operation of identifying a graph with a topical problem statement to be solved and plurality of section groups representing sub-topics. The section groups may contain a plurality of topical arguments in a plurality of nodes. Another operation may be receiving a first request for an answer pattern associated with the topical argument. A first response with the answer pattern for the topical argument and a description of the pattern for a user answer to the topical argument may be provided. A second request for a context explanation field associated with the topical argument may be received. A further operation may be providing a context explanation field which explains a context for asking the topical argument. | 2021-11-04 |
20210342710 | PROBLEM MANIPULATORS FOR LANGUAGE-INDEPENDENT COMPUTERIZED REASONING - A method of improving computing efficiency of a computing device for language-independent problem solving and reasoning includes receiving a query from a user, which is decomposed into one or more sub-queries arranged according to a tree structure. The one or more sub-queries are executed in a knowledge base. The results of the executed one or more sub-queries are received and composed into a query response. The query response is transmitted to the user. | 2021-11-04 |
20210342711 | Assessing Similarity Between Items Using Embeddings Produced Using a Distributed Training Framework - A resource-efficient technique is described for producing and utilizing a set of trained embeddings. With respect to its training phase, the technique receives a group of sparsely-expressed training examples of high dimensionality. The technique processes the training examples using a distributed training framework of computing devices. With respect to its inference stage, the technique draws on the embeddings produced by the training framework. But in one implementation, the inference-stage processing applies a different prediction function than that used by the training framework. One implementation of interference-stage processing involves determining a distance between a query embedding and a candidate item embedding, where each such embedding is obtained or derived from the trained embeddings produced by the training framework. Another manifestation of inference-stage processing involves adjusting click counts based on identified relations among items embeddings. | 2021-11-04 |
20210342712 | Workload-Oriented Prediction of Response Times of Storage Systems - Training examples are created from telemetry data, in which each training example engineered features derived from the telemetry data, storage system characteristics about the storage system that processed the workload associated with the telemetry data, and the response time of the storage system while processing the workload. The training examples are provided to an unsupervised learning process which assigns the training examples to clusters. Training examples of each cluster are used to train/test a separate supervised learning process for the cluster, to cause each supervised learning process to learn a regression between independent variables (system characteristics and workload features) and a dependent variable (storage system response time). To determine a response time of a proposed storage system, the proposed workload is used to select one of the clusters, and then the trained learning process for the selected cluster is used to determine the response time of the proposed storage system. | 2021-11-04 |
20210342713 | ENVIRONMENTAL AND CROP MONITORING SYSTEM - An environmental and crop monitoring system is disclosed, comprising a plurality of sensors disposed in an environment. The plurality of sensors is configured to dynamically detect environmental anomalies (e.g., within crops) and transmit output data to a processing system in communication with the plurality of sensors. The processing system is configured to predict the anomalies associate with environmental or crop monitoring. | 2021-11-04 |
20210342714 | DYNAMIC INTELLIGENT TEST METHOD AND COMPUTER DEVICE EMPLOYING THE METHOD - A method for dynamic intelligent testing of a target, to be tested according to projects, includes calling up a data distribution model of a project in response to a target being tested by the project, and obtaining a test range corresponding to the project based on the data distribution model. The method further includes obtaining a test value when the target is at a minimum power consumption value by testing the target based on the test range, and updating the data distribution model and the test range of the project based on the test value. | 2021-11-04 |
20210342715 | METHOD AND DEVICE FOR GROUP-AWARE MULTI-AGENT MOTION PATH PLANNING - A computer-implemented method for planning a motion path for multiple agents. The method includes: performing a conflict-based motion planning for the multiple agents, wherein conflict-free motion paths for each of the agents are determined depending on movement costs, determining the poses and velocities of one or more individual objects and one or more groups of objects; calculating the movement costs depending on interaction costs of each of the agents with the one or more objects and/or the one or more groups of objects. | 2021-11-04 |
20210342716 | DEVICE AND METHOD FOR DETERMINING A KNOWLEDGE GRAPH - A device and method for determining a knowledge graph. A second embedding is determined for a first embedding for a word including a function. A first classification, which determines whether or not the word is an entity for the knowledge graph, or which defines to which entity or to which type of entity for the knowledge graph the word in the knowledge graph is to be assigned, is determined for the second embedding using a first classifier. A second classification, which defines to which type of embeddings from a plurality of types of embeddings the second embedding is to be assigned, is determined for the second embedding using a second classifier. At least one parameter for the function is trained in a training as a function of a gradient for the training of the first classifier and as a function of a gradient for the training of the second classifier. | 2021-11-04 |
20210342717 | DEVICE AND METHOD FOR DETERMINING A KNOWLEDGE GRAPH - A device and a method for determining a knowledge graph, including: providing a first entity for the knowledge graph; providing a text body; providing input data for a model that are defined as a function of the text body and the first entity of the knowledge graph; determining a prediction for a second entity and a prediction for a relationship for a triple for the knowledge graph, and a prediction for an explanation for the triple using the model as a function of the input data; determining a first probability that the model assigns to the triple and a second probability that the model assigns to the prediction for the explanation; determining a classification for the triple as a function of the first probability and of the second probability. | 2021-11-04 |
20210342718 | METHOD FOR TRAINING INFORMATION RETRIEVAL MODEL BASED ON WEAK-SUPERVISION AND METHOD FOR PROVIDING SEARCH RESULT USING SUCH MODEL - A search method using an artificial intelligence based information retrieval model and a method for training the artificial intelligence based information retrieval model used for the method are provided. | 2021-11-04 |