36th week of 2022 patent applcation highlights part 46 |
Patent application number | Title | Published |
20220284199 | SYSTEM AND METHOD FOR DETERMINING THE CONDITION OF AN ARTICLE - A system and method for determining whether an article is new or used; and if used, the extent of usage. A smart tag, or smart tag, integrates into the article. The smart tag has integrated therein, data, which related to the article. A tag reader communicates a query to the smart tag to obtain the data. A non-volatile memory contains the item manufacturer, a serial number, and an item identity. A one-time-activation circuit determines if the article is ‘NEW’ or ‘USED’. A sensing element contains a ‘wakeup’ process to respond to a query, or, to monitor and log usage. Usage is categorized to a density, a sequence, an interval, and an amplitude algorithm of movement as being representative of the scale of usage. This data stores in the non-volatile memory. A tag reader, or tag reader queries the smart tag to access the logged data. | 2022-09-08 |
20220284200 | METHOD OF EXTENDING A RFID TAG READING RANGE AND DEVICE FOR CARRYING OUT THE SAME - A method of increasing a RFID tag reading range and a device for carrying out the same, said method comprising placing the RFID tag in an electrically polarizable medium, which may be water or another polarizable liquid or a polarizable amorphous plastic or melt. The device comprises a capsule into which the RFID tag is mounted. Then, the capsule is filled with an electrically polarizable medium. | 2022-09-08 |
20220284201 | RFID TAG - An RFID tag includes an IC for RFID to which information for presentation can be written by wireless communication, a display, and a control circuit configured to output presentation data to the display. The control circuit includes a font repository storing font data and a data processor configured to create the presentation data by using one or more character codes and the font data, the one or more character codes being included in the information for presentation. | 2022-09-08 |
20220284202 | APPARATUS HAVING HYBRID MONOCHROME AND COLOR IMAGE SENSOR ARRAY - There is provided in one embodiment an apparatus having an image sensor array. In one embodiment, the image sensor array can include monochrome pixels and color sensitive pixels. The monochrome pixels can be pixels without wavelength selective color filter elements. The color sensitive pixels can include wavelength selective color filter elements. | 2022-09-08 |
20220284203 | APPARATUS HAVING HYBRID MONOCHROME AND COLOR IMAGE SENSOR ARRAY - There is provided in one embodiment an apparatus having an image sensor array. In one embodiment, the image sensor array can include monochrome pixels and color sensitive pixels. The monochrome pixels can be pixels without wavelength selective color filter elements. The color sensitive pixels can include wavelength selective color filter elements. | 2022-09-08 |
20220284204 | APPARATUS HAVING HYBRID MONOCHROME AND COLOR IMAGE SENSOR ARRAY - There is provided in one embodiment an apparatus having an image sensor array. In one embodiment, the image sensor array can include monochrome pixels and color sensitive pixels. The monochrome pixels can be pixels without wavelength selective color filter elements. The color sensitive pixels can include wavelength selective color filter elements. | 2022-09-08 |
20220284205 | INDICIA READER ACOUSTIC FOR MULTIPLE MOUNTING POSITIONS - An indicia reader can include an indicia-capturing system, an indicia-decoding module, and an audio indicator system having a sound source. An indicia-reader housing can support these components, and the housing includes two adjacent mounting surfaces and a sound port opening formed within a portion of the common edge of the two adjacent surfaces. The indicia reader can be operatively mounted in at least two different positions by attaching one of the two adjacent surfaces to a support structure. The reader's sound port opening is in acoustic communication with the sound source of the audio indicator for transmitting audible indications emitted via the audio indicator system when the indicia reader is mounted in either of the at least two different mounting positions. | 2022-09-08 |
20220284206 | CASH COUNTER WITH INFRARED LIGHT SOURCE FOR SCANNING IMAGING - A cash a cash counter with an infrared light source for scanning imaging is provided. The cash counter includes a cash counter body, the cash counter body including a cash feeding table, a cash dispensing assembly, an infrared light source emitting assembly, an infrared light source receiving assembly, a motor, a transmission assembly, a display screen for displaying the number of cashes, a cash receiving assembly and a cash receiving rack; the infrared light source emitting assembly and the infrared light source receiving assembly are provided to face each other; the cash receiving assembly receives the identified cashes and transfers the identified cashes to the cash receiving rack; and the motor supplies power for the cash dispensing assembly, the cash receiving assembly and the transmission assembly to rotate, respectively. | 2022-09-08 |
20220284207 | VIRTUAL AUTHENTICATION DETECTION - Methods, systems, and devices are provided for authentication system configured to authenticate a document. According to one aspect, the system can receive image capture data including one or more virtual images of the document. The system can detect one or more identification indicators in the one or more virtual images. The system can detect one or more authentication indicators in the one or more virtual images. And the system can detect whether the document is authentic based on a result including analyzing the one or more identification indicators and analyzing the one or more authentication indicators. | 2022-09-08 |
20220284208 | OPTICAL INFORMATION READING DEVICE - To suppress an increase in processing time due to a load of inference processing while improving reading accuracy by the inference processing of machine learning. An optical information reading device includes a processor including: an inference processing part that inputs a code image to a neural network and executes inference processing of generating an ideal image corresponding to the code image; and a decoding processing part that executes first decoding processing of decoding the code image and second decoding processing of decoding the ideal image generated by the inference processing part. The processor executes the inference processing and the first decoding processing in parallel, and executes the second decoding processing after completion of the inference processing. | 2022-09-08 |
20220284209 | AUTOMATING COMMUNICATIONS ACROSS A COMMUNICATION PLATFORM - A dynamic communication link provided. Briefly, a process aggregates content from content providers in the form of triggers (e.g., QR codes, newsfeeds, etc.). Here, the content is related to information of interest to users. In the example of newsfeeds, the process curates a newsfeed for a user by assembling articles, each article selected for the user based upon data in a user profile, data associated with the article, combinations thereof, etc. Upon initiation of the trigger, the communication platform initiates an ability to carry out a direct communication between the user and a specific representative of a content provider that is associated with the trigger, where the direct communication is independent of the trigger. | 2022-09-08 |
20220284210 | ANTI-SPOOFING FOR CONTACTLESS FINGERPRINT READERS - Contactless fingerprint reader. The contactless fingerprint reader has one or more light sources adapted to emit light under different lighting conditions, a first camera adapted to successively capture first images of a subject captured with and without flash light, at least one second camera adapted to capture second images from different angles, one or more sensors adapted to generate one or more spatial-temporal signals representing a change of a distance between said subject and one or more locations of the contactless fingerprint reader, and communication module for sending the first images, said second images, and the one or more spatial-temporal signals to an anti-spoofing device | 2022-09-08 |
20220284211 | Technique of Determining a Measure of Proximity between Two Devices - Disclosed is a technique of determining a measure of proximity between two devices ( | 2022-09-08 |
20220284212 | RANDOMIZED MULTI-FINGERPRINT AUTHENTICATION - Some disclosed methods involve randomly or pseudo-randomly selecting one fingerprint authentication case from a plurality of fingerprint authentication cases stored in a memory, each of the fingerprint authentication cases corresponding to one or more fingerprints used during an authentication process, the fingerprint authentication cases including a plurality of multiple-fingerprint authentication cases for which two or more fingerprints are used during the authentication process. Upon determining that the selected fingerprint authentication case is a multiple-fingerprint authentication case, some methods involve controlling a display system to provide a multiple-fingerprint authentication graphical user interface (GUI) indicating at least two digit placement areas corresponding with a fingerprint sensor system area of a fingerprint sensor system, controlling the fingerprint sensor system to obtain fingerprint sensor data corresponding to each of the at least two digit placement areas and performing the authentication process based, at least in part, on the fingerprint sensor data. | 2022-09-08 |
20220284213 | METHODS AND SYSTEMS FOR REAL-TIME ELECTRONIC VERIFICATION OF CONTENT WITH VARYING FEATURES IN DATA-SPARSE COMPUTER ENVIRONMENTS - The systems and methods provide a machine learning model that can exploit long time dependency for time-series sequences, perform end-to-end learning of dimension reduction and clustering, or train on long time-series sequences with low computation complexity. For example, the methods and systems use a novel, unsupervised temporal representation learning model. The model may generate cluster-specific temporal representations for long-history time series sequences and may integrate temporal reconstruction and a clustering objective into a joint end-to-end model. | 2022-09-08 |
20220284214 | HANDHELD ELECTRONIC DEVICE - A portable electronic device may include a housing, a display at least partially within the housing, a front cover coupled to the housing and positioned over the display, and a biometric sensor module configured to illuminate an object and capture an image of the object through the front cover. The biometric sensor module may include a first lens positioned below the front cover, a first light source positioned below the first lens and configured to project, through the first lens, a dot pattern on the object, a second light source positioned below the first lens and configured to illuminate, through the first lens, the object with a flood of light, a second lens positioned below the front cover, and a light sensor positioned below the second lens and configured to capture an image of the object. | 2022-09-08 |
20220284215 | METHODS AND SYSTEMS FOR EXTRACTING INFORMATION FROM DOCUMENT IMAGES - This disclosure relates to a method and system for extracting information from images of one or more templatized documents. A knowledge graph with a fixed schema based on background knowledge is used to capture spatial and semantic relationships of entities present in scanned document. An adaptive lattice-based approach based on formal concepts analysis (FCA) is used to determine a similarity metric that utilizes both spatial and semantic information to determine if the structure of the scanned document image adheres to any of the known document templates, If known document template whose structure is closely matching the structure of the scanned document is detected, then an inductive rule learning based approach is used to learn symbolic rules to extract information present in scanned document image. If a new document template is detected, then any future scanned document images belonging to new document template are automatically processed using the learnt rules. | 2022-09-08 |
20220284216 | METHOD AND COMPUTING SYSTEM FOR GENERATING A SAFETY VOLUME LIST FOR OBJECT DETECTION - A method and computing system for performing the method are presented. The method may include receiving image information representing an object; identifying a set of one or more matching object recognition templates associated with a set of one or more detection hypotheses. The method may further include selecting a primary detection hypothesis associated with a matching object recognition template; generating a primary candidate region based on the matching object recognition template; determining at least one of: (i) whether the set of one or more matching object recognition templates has a subset of one or more remaining matching templates, or (ii) whether the image information has a portion representing an unmatched region; and generating a safety volume list based on at least one of: (i) the unmatched region, or (ii) one or more additional candidate regions that are generated based on the subset of one or more remaining matching templates. | 2022-09-08 |
20220284217 | AUGMENTED REALTY BASED ASSISTANCE SYSTEM AND METHOD THEREOF - The disclosure relates to system and method for providing assistance to a user using augmented reality. The method includes acquiring a video stream and a set of data associated with a task being performed by a user, in real-time, using a camera and/or a sensor device. The video stream includes sequential frames. The method further includes determining a present state associated with the task based on the sequential frames using an Artificial Neural Network (ANN) based action prediction model; determining scenarios and events corresponding to the scenarios based on the video stream and the set of data using an ANN based augmented intelligence model; and determining sequential instructions required for assisting the user to accomplish the task, dynamically, based on the present state and the events associated with the task, using at least one of a rule-based engine and an ANN based instruction prediction model | 2022-09-08 |
20220284218 | VIDEO CLASSIFICATION METHOD, ELECTRONIC DEVICE AND STORAGE MEDIUM - The present disclosure discloses a video classification method, an electronic device and a storage medium, and relates to the field of computer technologies, and particularly to the field of artificial intelligence technologies, such as knowledge graph technologies, computer vision technologies, deep learning technologies, or the like. The video classification method includes: extracting a keyword in a video according to multi-modal information of the video; acquiring background knowledge corresponding to the keyword, and determining a text to be recognized according to the keyword and the background knowledge; and classifying the text to be recognized to obtain a class of the video. | 2022-09-08 |
20220284219 | EVENT SUMMARIZATION FACILITATED BY EMOTIONS/REACTIONS OF PEOPLE NEAR AN EVENT LOCATION - A method, system and computer program product for event summarization facilitated by emotions/reactions of people near an event location is disclosed. The method includes generating a query based at least in part on reaction information and at least in part on primary video metadata. Based on the query, at least one possible event summarization match for the one or more events is retrieved from a database. | 2022-09-08 |
20220284220 | Highlight Video Generated with Adaptable Multimodal Customization - In implementations for highlight video generated with adaptable multimodal customization, a multimodal detection system tracks activities based on poses and faces of persons depicted in video clips of video content. The system determines a pose highlight score and a face highlight score for each of the video clips that depict at least one person, the highlight scores representing a relative level of the interest in an activity depicted in a video clip. The system also determines pose-based emotion features for each of the video clips. The system can detect actions based on the activities of the persons depicted in the video clips, and detect emotions exhibited by the persons depicted in the video clips. The system can receive input selections of actions and emotions, and filter the video clips based on the selected actions and emotions. The system can then generate a highlight video of ranked and filtered video clips. | 2022-09-08 |
20220284221 | DEEP LEARNING BASED PARAMETRIZABLE SURROUND VISION - Systems and methods for generating a virtual view of a scene captured by a physical camera are described. The physical camera captures an input image with multiple pixels. A desired pose of a virtual camera for showing the virtual view is set. The actual pose of the physical camera is determined, and an epipolar geometry between the actual pose of the physical camera and the desired pose of the virtual camera is defined. The input image and depth data of the pixels of the input image are resampled in epipolar coordinates. A controller performs disparity estimation of the pixels of the input image and a deep neural network, DNN, corrects disparity artifacts in the output image for the desired pose of the virtual camera. The complexity of correcting disparity artifacts in the output image by a DNN is reduced by using epipolar geometry. | 2022-09-08 |
20220284222 | SYSTEMS AND METHODS FOR VEHICLE LIGHT SIGNAL CLASSIFICATION - In one embodiment, a vehicle light classification system captures a sequence of images of a scene that includes a front/rear view of a vehicle with front/rear-side lights, determines semantic keypoints, in the images and associated with the front/rear-side lights, based on inputting the images into a first neural network, obtains multiple difference images that are each a difference between successive images from among the sequence of images, the successive images being aligned based on their respective semantic keypoints, and determines a classification of the front/rear-side lights based at least in part on the difference images by inputting the difference images into a second neural network. | 2022-09-08 |
20220284223 | IMPERCEPTIBLE ROAD MARKINGS TO SUPPORT AUTOMATED VEHICULAR SYSTEMS - Disclosed herein are methods and systems for painting driving markings invisible in visible light spectrum, comprising generating driving assistance markings expressing driving information relating to one or more road segments, computing instructions for painting the driving assistance markings on one or more elements of the road segment(s) using one or more paint material(s) characterized by: (1) reflecting light in a visible light spectral range deviating less than a first value from the visible light spectral range reflected by a surface of the element(s) and (2) reflecting light in an infrared spectral range deviating more than a second value from the infrared spectral range reflected by the surface of the element(s), and outputting the painting instructions for applying the one or more paint materials on the element(s) according to the instructions such that the driving assistance markings are visible in the infrared spectrum and significantly invisible in the visible spectrum. | 2022-09-08 |
20220284224 | RELIABLE VISUAL MARKERS BASED ON MULTISPECTRAL CHARACTERISTICS - Disclosed herein are methods and systems for painting driving assistance markings using one or more paint materials which are visible in a plurality of light spectral ranges, in particular, visible light and one or more infrared light spectral ranges. Further disclosed are methods and systems for analyzing images captured in multiple spectral ranges to identify the driving assistance markings and/or part thereof in a plurality of different spectral ranges and identify aggregated driving assistance markings by aggregating the driving assistance markings identified in the plurality of different spectral ranges. Also disclosed herein are methods and systems for presenting and detecting enhanced driving assistance markings on one or more elements under one or more paint materials which are highly transparent in one or more infrared spectral ranges while reflecting visible light conforming to a color of the element(s) surface thus not affecting appearance of the element(s) in the visible light spectrum. | 2022-09-08 |
20220284225 | MARKING AND DETECTING ROAD MARKS - Disclosed herein are methods and system for detecting road marking expressed using alternating infrared reflective tiles comprising high infrared reflective tiles and low infrared red reflective tiles painted on a road surface using paint material(s) characterized by: (1) reflecting light in visible light spectral range deviating less than a first value from the light reflected by the road surface and (2) reflecting light in an infrared spectral range deviating more than a second value from the light reflected by the road surface. Infrared image(s) and visible light image(s) of the road surface which are registered to each other may be analyzed to compute an infrared reflective value and a luminance value for each pixel respectively. A ratio may be computed between the infrared reflective value of and the luminance value of corresponding pixels to identify high and low infrared reflective tiles in pixels having a ratio exceeding a third value. | 2022-09-08 |
20220284226 | ENHANCED DETECTION USING SPECIAL ROAD COLORING - Disclosed herein are methods and systems for detecting dynamic objects using road painted patterns perceptible in infrared spectral range, comprising receiving images captured in one or more infrared spectral ranges depicting a road segment painted with background patterns which are highly imperceptible in visible light spectrum while highly visible in one or more infrared spectral ranges, analyzing the images to detecting one or more dynamic objects located in front of the background patterns. The light reflected by the one or more dynamic objects in the one or more infrared spectral ranges deviating from the light reflected by the one or more background pattern and computing a location of the one or more identified objects. Further disclosed are methods and systems for calibration of systems and/or sensors based on reference markings which are highly imperceptible in visible light spectrum while highly visible in the infrared spectral range(s). | 2022-09-08 |
20220284227 | SYSTEM AND METHOD FOR LEVERAGING A TIME-SERIES OF MICROEXPRESSIONS OF USERS IN CUSTOMIZING MEDIA PRESENTATION BASED ON USERS` SENTIMENTS - A system for customizing media presentation based on user's sentiments is disclosed. The system presents a media item to the user on a platform comprising a website. The system captures a first set of microexpressions of the user reacting to the media item. The system extracts a set of baseline features from the first set of microexpressions. The system determines whether the media item elicits positive or sentiment from the user. If the system determines that the media item elicits positive sentiment from the user, the system classifies the media item into a first class of media items that elicit positive sentiment from the user. The system adjusts contents of the platform to include media items from the first class of media items. | 2022-09-08 |
20220284228 | AUTHENTICATON OF RGB VIDEO BASED ON INFRARED AND DEPTH SENSING - In one aspect, a device may include at least one processor and storage accessible to the at least one processor. The storage may include instructions executable by the at least one processor to access a first frame of RGB video content corresponding to a first time, access a first frame of IR video content corresponding to the first time, and access data from a depth sensor corresponding to the first time. The instructions may also be executable to determine whether at least a portion of the first frame of the RGB video content correlates to at least a portion of the first frame of the IR video content and/or the data from the depth sensor. Responsive to a determination that it does, the instructions may be executable to authenticate the RGB video content and indicate the RGB video content as being authenticated via a graphical user interface. | 2022-09-08 |
20220284229 | RGB-NIR DUAL CAMERA FACE ANTI-SPOOFING METHOD - A method of face anti-spoofing, comprising, receiving a near infra-red facial image, having a near infrared channel, receiving a red-green-blue facial image, having a red channel, a green channel and a blue channel, generating a synthetic three channel image based on the near infrared channel, the red channel, the green channel and the blue channel and training a deep neural network based on the synthetic three channel image. | 2022-09-08 |
20220284230 | SYSTEM AND METHOD FOR ADAPTIVE IMAGE TRANSFORMATION - Image transformation tasks such as cropping, text addition etc. are common across industries. Each industry has different business context and demands the image transformations be performed aligned to the business context. This disclosure relates to a system and method for an adaptive image transformation for a given context and maintaining aesthetic sense of the transformed image. Herein, the system is configurable and adaptive to any business context or domain. The system learns the context from available domain samples and creates an automated workflow of context-aware transformation tasks that maintains both the content and aesthetics demands of the context. Further, a saliency map is extracted for the identified RoI to append a text to the RoI based on the extracted saliency map, the calculated similarity metric for various content and aesthetic factors and various preferences of the user. | 2022-09-08 |
20220284231 | DYNAMIC MODIFICATION OF GEOFENCED DISPLAYS - One or more computer processors establish a geofence surrounding a display having a plurality of pixels capable of change based on one or more display capabilities. The one or more computer processors monitor for a change in at least one pixel of the plurality of pixels. The one or more computer processors identify a photosensitive user within the established geofence. The one or more computer processors responsive to the at least one pixel change associated with the display, calculate a photosensitivity score utilizing a model trained for one or more photosensitive conditions associated with the photosensitive user. The one or more computer processors adjust the display to show a change on the at least one pixel based on the calculated photosensitivity score. | 2022-09-08 |
20220284232 | TECHNIQUES TO IDENTIFY DATA USED TO TRAIN ONE OR MORE NEURAL NETWORKS - Apparatuses, systems, and techniques to identify one or more images used to train one or more neural networks. In at least one embodiment, one or more images used to train one or more neural networks are identified, based on, for example, one or more labels of one or more objects within the one or more images. | 2022-09-08 |
20220284233 | PIXEL CORRESPONDENCE VIA PATCH-BASED NEIGHBORHOOD CONSENSUS - One example provides a computing system comprising a storage machine storing instructions executable by a logic machine to extract features from a source and target images to form source and target feature maps, form a correlation map comprising a plurality of similarity scores, form an initial correspondence map comprising initial mappings between pixels of the source feature map and corresponding pixels of the target feature map, refine the initial correspondence map by, for each of one or more pixels of the source feature map, for each of a plurality of candidate correspondences, inputting a four-dimensional patch into a trained scoring function, the trained scoring function being configured to output a correctness score, and selecting a refined correspondence based at least upon the correctness scores, and output a refined correspondence map comprising a refined correspondence for each of the one or more pixels of the source feature map. | 2022-09-08 |
20220284234 | SYSTEMS AND METHODS FOR IDENTIFYING SEMANTICALLY AND VISUALLY RELATED CONTENT - Systems and methods for selecting items of interest for an organization from a set of feeds, based on the interests that users have demonstrated through their interactions with existing content, are described herein. In some embodiments, the system is part of a content management service that allows users to add and organize files, media, links, and other information. The content can be uploaded from a computer, imported from cloud file systems, added via links, or pulled from various kinds of feeds. | 2022-09-08 |
20220284235 | COMPUTER-BASED SYSTEMS, COMPUTING COMPONENTS AND COMPUTING OBJECTS CONFIGURED TO IMPLEMENT DYNAMIC OUTLIER BIAS REDUCTION IN MACHINE LEARNING MODELS - Systems and methods include processors for receiving training data for a user activity; receiving bias criteria; determining a set of model parameters for a machine learning model including: (1) applying the machine learning model to the training data; (2) generating model prediction errors; (3) generating a data selection vector to identify non-outlier target variables based on the model prediction errors; (4) utilizing the data selection vector to generate a non-outlier data set; (5) determining updated model parameters based on the non-outlier data set; and (6) repeating steps (1)-(5) until a censoring performance termination criterion is satisfied; training classifier model parameters for an outlier classifier machine learning model; applying the outlier classifier machine learning model to activity-related data to determine non-outlier activity-related data; and applying the machine learning model to the non-outlier activity-related data to predict future activity-related attributes for the user activity. | 2022-09-08 |
20220284236 | BLUR CLASSIFICATION AND BLUR MAP ESTIMATION - Systems and methods for image processing are described. Embodiments identify a training set including a first image that includes a ground truth blur classification and second image that includes a ground truth blur map, generate a first embedded representation of the first image and a second embedded representation of the second image using an image encoder, predict a blur classification of the first image based on the first embedded representation using a classification layer, predict a blur map of the second image based on the second embedded representation using a map decoder, compute a classification loss based on the predicted blur classification and the ground truth blur classification, train the image encoder and the classification layer based on the classification loss, compute a map loss based on the blur map and the ground truth blur map, and train the image encoder and the map decoder. | 2022-09-08 |
20220284237 | RESTRICTED BOLTZMANN MACHINE BASED SOURCE-SEPARATION MODEL WITH APPLICATION TO LOAD DISAGGREGATION - Load disaggregation is useful for both the consumers and producers of energy. The present-day supervised learning models for load disaggregation necessitate the learning of models for every appliance load of interest, which incurs high computational costs. Embodiments of the present disclosure implement a Restricted Boltzmann Machine (RBM) based source-separation model with application to load disaggregation of appliances of interest. Representations of appliance of interest are learnt, between the power aggregate data and the appliance signatures, to output the mapping of data representations on the appliance signatures, for load disaggregation. Discriminative ability for each load/appliance of interest is achieved by adding the free energies of softmax layers of the RBM on other loads/appliance, as a discriminating gradient to the approximate gradients obtained on the load under consideration. | 2022-09-08 |
20220284238 | LEARNING APPARATUS, METHOD AND PROGRAM - According to one embodiment, a learning apparatus includes a processor. The processor determines, based on a data resolution of subject data obtained at a subject device, a plurality of data resolutions that differ from one another within a range covering the data resolution of the subject data, the data resolutions each indicating a corresponding amount of information per unit. The processor trains a scalable network with training samples corresponding to each of the plurality of data resolutions, the scalable network being a neural network adapted to change a data resolution of input data. | 2022-09-08 |
20220284239 | OBJECT DETECTION DEVICE, OBJECT DETECTION METHOD, AND RECORDING MEDIUM - Provided is an object detection device or the like which efficiently generates good-quality training data. This object detection device is provided with: a detection unit which uses a dictionary to detect objects from an input image; a reception unit which displays, on a display device, the input image accompanied by a display emphasizing partial areas of detected objects, and receives, from one operation of an input device, a selection of a partial area and an input of the class of the selected partial area; a generation unit which generates training data from the image of the selected partial area and the inputted class; and a learning unit which uses the training data to learn the dictionary. | 2022-09-08 |
20220284240 | METHODS FOR TRAINING AND ANALYSING INPUT DATA USING A MACHINE LEARNING MODEL - Broadly speaking, the present techniques generally relate to machine learning models comprising neural network layers, in which the quantisation level of each layer of the model can be independently selected at run-time. In particular, the present application relates to a computer-implemented method for analysing input data on a device using a trained machine learning, ML, model, comprising independently selecting a quantisation level for each of a plurality of network layers of the model at runtime. The present application also relates to a computer-implemented method of training a machine learning model so that the quantisation level of each of the plurality of network layers is independently selectable at runtime. A single trained model with a single set of weights can therefore be deployed, with the quantisation of each layer selected at runtime to suit the capabilities of the device and available resource. | 2022-09-08 |
20220284241 | MACHINE LEARNING ASSISTANT FOR IMAGE ANALYSIS - Systems, methods, and non-transitory computer readable media are provided for labeling depictions of objects within images. An image may be obtained. The image may include a depiction of an object. A user's marking of a set of dots within the image may be received. The set of dots may include one or more dots. The set of dots may be positioned within or near the depiction of the object. The depiction of the object within the image may be labeled based on the set of dots. | 2022-09-08 |
20220284242 | DEBIASING TRAINING DATA BASED UPON INFORMATION SEEKING BEHAVIORS - One or more computing devices, systems, and/or methods for debiasing training data based upon information seeking behaviors are provided. Users associated with a set of training data are segmented into information seeking behavior groups corresponding to varying degrees of information seeking behaviors of the users. Biases for the information seeking behavior groups may be estimated based upon information seeking behaviors of users within the information seeking behavior groups. The training set of data is debiased using the biases to generate a debiased training set of data. A model may be trained to perform a task based upon the debiased training set of data. | 2022-09-08 |
20220284243 | ENSEMBLE VOTING CLASSIFIERS USING ADJUSTED THRESHOLDS - An example system includes a processor to receive training data used to train an ensemble voting classifier. For each classifier in the ensemble voting classifier, the processor can also set a classification score of a positive training item as a threshold. The processor can further adjust a threshold of at least one of the classifiers based on an analysis of a vote contribution of each classifier on the votes on the training data. The threshold of the at least one of the classifiers is adjusted to increase a voting specificity without impacting sensitivity with respect to the training data. | 2022-09-08 |
20220284244 | PLATFORM, SYSTEMS, AND METHODS FOR IDENTIFYING CHARACTERISTICS AND CONDITIONS OF PROPERTY FEATURES THROUGH IMAGERY ANALYSIS - In an illustrative embodiment, methods and systems for automatically categorizing a condition of a property characteristic may include obtaining aerial imagery of a geographic region including the property, identifying features of the aerial imagery corresponding to the property characteristic, analyzing the features to determine a property characteristic classification, and analyzing a region of the aerial imagery including the property characteristic to determine a condition classification. | 2022-09-08 |
20220284245 | RANDOMIZED METHOD FOR IMPROVING APPROXIMATIONS FOR NONLINEAR SUPPORT VECTOR MACHINES - The disclosed embodiments relate to a system that improves operation of a monitored system. During a training mode, the system uses a training data set comprising labeled data points received from the monitored system to train the SVM to detect one or more conditions-of-interest. While training the SVM model, the system makes approximations to reduce computing costs, wherein the approximations involve stochastically discarding points from the training data set based on an inverse distance to a separating hyperplane for the SVM model. Next, during a surveillance mode, the system uses the trained SVM model to detect the one or more conditions-of-interest based on monitored data points received from the monitored system. When one or more conditions-of-interest are detected, the system performs an action to improve operation of the monitored system. | 2022-09-08 |
20220284246 | METHOD FOR TRAINING CROSS-MODAL RETRIEVAL MODEL, ELECTRONIC DEVICE AND STORAGE MEDIUM - The present disclosure discloses a method for training a cross-modal retrieval model, an electronic device and a storage medium, and relates to the field of computer technologies, and particularly to the field of artificial intelligence technologies, such as knowledge graph technologies, computer vision technologies, deep learning technologies, or the like. The method for training a cross-modal retrieval model includes: determining similarity of a cross-modal sample pair according to the cross-modal sample pair, the cross-modal sample pair including a sample of a first modal and a sample of a second modal, and the first modal being different from the second modal; determining a soft margin based on the similarity, and determining a soft margin loss function based on the soft margin; and determining a total loss function based on the soft margin loss function, and training a cross-modal retrieval model according to the total loss function. | 2022-09-08 |
20220284247 | SECONDARY COLOR UNIFORMITY COMPENSATION MECHANISM - A printing system is disclosed. The printing system includes at least one physical memory device to store calibration logic and one or more processors coupled with the at least one physical memory device to execute the calibration logic to perform uniformity compensation of a plurality of secondary colors printed by pel forming elements, each of the pel forming elements associated with one of a plurality of primary colors, including generating a uniformity compensated first primary color transfer function for each of the pel forming elements associated with a first primary color and a uniformity compensated second color transfer function for each of the pel forming elements associated with a second primary color, generating an updated uniformity compensated first primary color transfer function for each of the pel forming elements associated with the first primary color and a uniformity compensated third primary color transfer function for each of the pel forming elements associated with a third primary color and generating an updated uniformity compensated second primary color transfer function for each of the pel forming elements associated with the second primary color and an updated uniformity compensated third primary color transfer function for each of the pel forming elements associated with a third primary color. | 2022-09-08 |
20220284248 | COMPUTER-READABLE MEDIUM, IMAGE PROCESSING DEVICE, AND METHOD FOR REDUCING TIME TAKEN FROM INPUT OF PRINT INSTRUCTION UNTIL START OF PRINTING - A non-transitory computer-readable medium stores computer-readable instructions executable by a hardware processor communicably connected with a printing device and a user interface. The instructions are configured to, when executed by the hardware processor, cause the hardware processor to perform one or more printing processes. Each printing process includes, after obtaining a data selection instruction via the user interface, obtaining a print instruction corresponding to the data selection instruction via the user interface. Each printing process further includes starting generating the print data using target image data selected based on the data selection instruction, after obtaining the data selection instruction and before obtaining the print instruction. Each printing process further includes, even after the print data has been generated, not starting providing the print data until obtaining the print instruction, but starting providing the print data to the printing device after obtaining the print instruction. | 2022-09-08 |
20220284249 | METHODS AND SYSTEMS FOR OPERATING A PRINTING APPARATUS - Various embodiments illustrated herein disclose a method comprising rendering a buffer image from a first image data received for printing. Further, the method includes scaling the buffer image to generated scaled buffer image. Furthermore, the method includes determining a first location of a machine readable indicia in the scaled buffer image. Additionally, the method includes causing a print head to print the buffer image on a print media to generate a printed content. | 2022-09-08 |
20220284250 | COLOR UNIFORMITY COMPENSATION MECHANISM - A system is disclosed. The system includes at least one physical memory device to store calibration logic and one or more processors coupled with the at least one physical memory device to execute the calibration logic to receive an image having a blended input color comprising a first primary color value and a second primary color value, generate blend weights for the blended input color based on the first primary color value and the second primary color value, receive a plurality of halftone designs corresponding to each of the first primary color value and the second primary color values and apply the blend weights and the plurality of halftone designs to the first primary color value and the second primary color value to generate a uniformity corrected halftone design associated with the first primary color value and a uniformity corrected halftone design associated with the second primary color value. | 2022-09-08 |
20220284251 | MOBILE PHONE WITH MAGNETIC CARD EMULATION - An electronic transaction card communicates with an add-on slot of an intelligent electronic device. The add-on slot may be a memory card slot. The intelligent electronic device may be a mobile phone or other device with or without network connectivity. The electronic transaction card may have magnetic field producing circuitry compatible with magnetic card readers, smartcard circuitry, other point-of-sale interfaces, or any combination thereof. | 2022-09-08 |
20220284252 | CARDS HAVING DYNAMIC REGIONS FOR SELECTIVELY LIMITING VISIBILITY OF CONTENT ON CARD SURFACES - A physical card has a body with dynamic region(s) configured to appear opaque for human viewing in a first phase and translucent for human viewing in a second phase. The card also has a computer readable chip, a power supply configured to power the one or more dynamic regions, a communication device, one or more processors, and memory storing instructions that, when executed, are configured to cause the card to perform a method. The card may receive an authorization signal from a recognized user device associated with a cardholder, direct dynamic region(s) to transition from being opaque in the first phase to being translucent in the second phase, and direct the dynamic region(s) to transition from being translucent in the second phase to being opaque in the first phase upon hitting a predetermined time threshold in the second phase. | 2022-09-08 |
20220284253 | METHODS AND SYSTEMS FOR HEAT APPLIED SENSOR TAG - A method for configuring a sensor tag includes placing the sensor tag in a location on a garment or fabric to be affixed. The method may further include applying a heat source at a temperature to the sensor tag for a time period. The method may further include removing the heat source after the time period has elapsed. The method may further include removing a paper layer between an adhesive layer and the garment of fabric prior to placing the sensor taking in the location. The method may further include removing a top protective layer after removing the heat source. | 2022-09-08 |
20220284254 | RECONFIGURABLE INTERACTION BADGE - An interaction badge is provided that can be reconfigured as necessary to display or otherwise provide identification information or credentials for a specific application. The interaction badge can also provide control or access to when and where the information provided by the badge is accessed. Additionally, the interaction badge can provide additional functionality for a wearer such as location and interaction tracking, audio recording, and language translation. | 2022-09-08 |
20220284255 | Providing Alerts via a Color Changing Transaction Card - Methods and systems disclosed herein may communicate information, such as alerts and notifications, to a cardholder via a color-changing transaction card. In particular, the cardholder may configure one or more thresholds that establish when a transaction card may change colors. For instance, a transaction card may change colors at, or while approaching, a first threshold. The transaction card may change colors at, or while approaching, a second threshold. When the cardholder's balance goes below the thresholds, the transaction card may revert back to the lower threshold color or the original color of the transaction card. Using the techniques described herein a financial institution may convey information to a cardholder in a way that does not require the cardholder to digitally engage with the financial institution. | 2022-09-08 |
20220284256 | TAG - A tag indicating an attribute of the tag by an electromagnetic wave reflection characteristic, the tag including a substrate ( | 2022-09-08 |
20220284257 | Storing and Retrieving Identification Tag Data Associated With an Asset - Systems and methods of the present invention provide for one or more server computers communicatively coupled to a network and configured to receive identification tag data from the identification tag using the identification tag reader, receive and store additional data relating to the identification tag data or an associated asset and received from a GUI, display the identification tag or additional data responsive to scanning the tag, and receiving from a GUI search parameters used to identify a tag or associated resource, once scanned. | 2022-09-08 |
20220284258 | METHOD AND DEVICE FOR DETECTING UNAUTHORIZED TRANFER BETWEEN PERSONS - A method of confirming the identity of a person who issued a token to signify eligibility for a privilege. Possession token is confirmed to be by the same person by using sensors in the token which track the movements of the person. A machine learning system is trained to evaluate the sensor data detecting transfer of possession of the token. The state of continuous possession since the token was issued or set to an enabled state is confirmed and the privilege is granted. The method of identity confirmation is used in various contexts such as for to control entry to a location, use of a facility or service. It is also useful to determine continuous possession of a weapon to prevent misuse after the weapon is stolen, dropped or lost. Servers, beacons and outside sources of data or inputs to be measured by the sensor can also be used. | 2022-09-08 |
20220284259 | COMPUTER AUTOMATED CLASSIFICATION OF NON-STRUCTURED DATA STREAMS - Automated classification of non-structure data streams from a plurality of Internet of Things (IoT) devices includes receiving, by a computer, from the plurality of IoT devices a data stream including a set of labeled readings S with a predetermined sample size n and a predetermined partition size m. The received data stream is partitioned into a partition set S′ including m readings. The computer determines a set of features associated with the data stream based on the partition set S′ by applying feature engineering techniques. A vector representation of the obtained set of features is built by the computer to place each feature on a same range scale. A predetermined minimum number of layers and neurons is then selected based on the set of features for training a neural network. Finally, non-structured data streams from new or unknown data sources can be classified using the trained neural network. | 2022-09-08 |
20220284260 | VARIABLE QUANTIZATION FOR NEURAL NETWORKS - A method for an artificial neural network includes receiving an input. A quantization threshold is determined based on the input, or a characteristic or type of the input. Neural network values, such as weights or activations, of one or more layers of the artificial neural network are quantized according to the quantization threshold. The artificial neural network generates an output based on the quantized neural network values. | 2022-09-08 |
20220284261 | TRAINING-SUPPORT-BASED MACHINE LEARNING CLASSIFICATION AND REGRESSION AUGMENTATION - Machine learning models are provided that consider, during the process of producing output, various aspects of the training data and/or training process from which the models are created. A machine learning model may generate output (e.g., classification determinations or regression output) that is augmented with information regarding the distribution(s) of the corpus of training data upon which the model was trained, the features extracted from the training data, the resulting determinations made by the model, and/or other information. The augmentation may occur internally while generating the model output, or the output itself may be augmented to include distribution-based data in addition to a model output. | 2022-09-08 |
20220284262 | NEURAL NETWORK OPERATION APPARATUS AND QUANTIZATION METHOD - A neural network operation apparatus and method implementing quantization is disclosed. The neural network operation method may include receiving a weight of a neural network, a candidate set of quantization points, and a bitwidth for representing the weight, extracting a subset of quantization points from the candidate set of quantization points based on the bitwidth, calculating a quantization loss based on the weight of the neural network and the subset of quantization points, and generating a target subset of quantization points based on the quantization loss. | 2022-09-08 |
20220284263 | NEURAL NETWORK OPERATION APPARATUS AND METHOD - A neural network operation apparatus and method is provided. The neural network operation apparatus includes a memory configured to store data for a neural network operation, and a processor configured to validate the data based on a determination that the neural network operation should be performed on the data, obtain a real memory address to perform the neural network operation based on a result of the validating and a virtual tensor address of the data, and perform the neural network operation based on the real memory address. | 2022-09-08 |
20220284264 | COMPUTER-READABLE RECORDING MEDIUM STORING PROGRAM, COMPUTER, AND LEARNING METHOD - A non-transitory computer-readable recording medium storing a program for causing a computer to execute a procedure, the procedure includes in learning by a plurality of nodes in deep learning, determining to allocate a number of batches according to a performance of each of the plurality of nodes to the each of the plurality of nodes or to terminate the learning at a predetermined timing, and adjusting a learning rate to be used for the learning according to a ratio of a preset number of batches for the plurality of nodes to a number of execution batches executed by the allocation in the plurality of nodes or number of execution batches executed before the predetermined timing. | 2022-09-08 |
20220284265 | HARDWARE ARCHITECTURE FOR SPIKING NEURAL NETWORKS AND METHOD OF OPERATING - The present invention provides a hardware architecture for spiking neural networks which is characterized in that it combines a fully-parallel architecture with a time-multiplexed architecture. | 2022-09-08 |
20220284266 | REINFORCEMENT LEARNING USING ADVANTAGE ESTIMATES - Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for computing Q values for actions to be performed by an agent interacting with an environment from a continuous action space of actions. In one aspect, a system includes a value subnetwork configured to receive an observation characterizing a current state of the environment and process the observation to generate a value estimate; a policy subnetwork configured to receive the observation and process the observation to generate an ideal point in the continuous action space; and a subsystem configured to receive a particular point in the continuous action space representing a particular action; generate an advantage estimate for the particular action; and generate a Q value for the particular action that is an estimate of an expected return resulting from the agent performing the particular action when the environment is in the current state. | 2022-09-08 |
20220284267 | ARCHITECTURES FOR TEMPORAL PROCESSING ASSOCIATED WITH WIRELESS TRANSMISSION OF ENCODED DATA - Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a transmitting wireless communication device may encode a data set using a single shot encoding operation and a temporal processing operation associated with at least one neural network to produce an encoded data set, wherein a dimensionality of a subset of inputs of a set of inputs to the temporal processing operation is greater than a dimensionality of the encoded data set. The transmitting wireless communication device may transmit the encoded data set to a receiving wireless communication device. Numerous other aspects are described. | 2022-09-08 |
20220284268 | DISTRIBUTED PROCESSING OF SYNAPTIC CONNECTIVITY GRAPHS - In one aspect, there is provided a method performed by multiple data processing units for distributed processing of data defining a synaptic connectivity graph that includes multiple nodes and edges and represents synaptic connectivity between neurons in a brain of a biological organism. The method includes obtaining graph data defining the synaptic connectivity graph that represents synaptic connectivity between neurons in the brain of the biological organism. The method further includes dividing the graph data defining the synaptic connectivity graph into multiple sub-graph datasets that each define a respective sub-graph of the synaptic connectivity graph. The method further includes distributing multiple sub-graph datasets over multiple data processing units and processing multiple sub-graph datasets using multiple data processing units. | 2022-09-08 |
20220284269 | SYSTEM FOR CONTROL AND ANALYSIS OF GAS FERMENTATION PROCESSES - This disclosure relates to analyzing a fermentation process that occurs in a bioreactor. Such a fermentation process may involve microbes consuming a substrate, and producing various metabolites. A computing device may train and execute one or more machine learning models to analyze such a fermentation process. Such a machine learning model may be configured to determine a current fermentation state of such a fermentation process as one example. As another example, a machine learning model may be configured to predict metabolite production of a fermentation process based on historical fermentation data and a window of control decisions for the fermentation process. | 2022-09-08 |
20220284270 | SYSTEMS AND METHODS FOR MODULATION CLASSIFICATION OF BASEBAND SIGNALS USING MULTIPLE DATA REPRESENTATIONS OF SIGNAL SAMPLES - Systems and methods for classifying radio frequency signal modulations include receiving, at a consolidated neural network, a complex quadrature vector of interest representative of a baseband signal derived from a radio frequency signal, generating multiple data representations of the vector of interest, providing each data representation to one of multiple parallel neural networks in the consolidated neural network, and receiving, from the consolidated neural network, a classification result for the baseband signal. The consolidated neural network may be trained to classify baseband signals with respect to known modulation types by receiving complex quadrature training vectors, each including samples of a baseband signal derived from a radio frequency signal of known modulation type, comparing a classification result for the training vector to the known modulation type to determine modulation classification performance, and modifying a configuration parameter of the consolidated neural network dependent on the determined modulation classification performance. | 2022-09-08 |
20220284271 | SPARSITY-BASED NEURAL NETWORK MAPPING TO COMPUTING UNITS IN A SYSTEM-ON-CHIP - A method for an artificial neural network includes receiving a set of input values to be convolved with multiple kernels via multiple computing units. One or more thermally-stressed computing units of the multiple computing units are determined. The multiple kernels are mapped to the multiple computing units of a system-on-chip (SOC) based on the one or more thermally-stressed computing units. A convolution is performed on the set of input values and a most sparse kernel of the multiple kernels on the most thermally-stressed computing unit. | 2022-09-08 |
20220284272 | DYNAMIC DESIGN METHOD TO FORM ACCELERATION UNITS OF NEURAL NETWORKS - A method is disclosed to dynamically design acceleration units of neural networks. The method comprises steps of generating plural circuit description files through a neural network model; reading a model weight of the neural network model to determine a model data format of the neural network model; selecting one circuit description file from the plural circuit description files according to the model data format, so that the chip is reconfigured according to the selected circuit description file to form an acceleration unit adapted to the model data format. The acceleration unit is suitable for running a data segmentation algorithm, which may accelerate the inference process of the neural network model. Through this method the chip may be dynamically reconfigured into an efficient acceleration unit for the different model data format, thereby speeding up the inference process of the neural network model. | 2022-09-08 |
20220284273 | NEURAL PROCESSOR AND CONTROL METHOD OF NEURAL PROCESSOR - A neural processor and a control method of the neural processor are provided. The neural processor includes plurality of processing element groups, wherein each of the processing element groups includes a plurality of processing elements configured to perform a vector operation, an overflow accumulator configured to be engaged by a processing element in which an overflow or underflow occurs from among the plurality of processing elements, and a register configured to store information indicating the processing element as an owner processing element. | 2022-09-08 |
20220284274 | NEURAL PROCESSING DEVICE AND OPERATION METHOD OF THE NEURAL PROCESSING DEVICE - A neural processing device includes a first memory configured to store universal data, a second memory distinguished from the first memory and having a capacity less than that of the first memory, a bandwidth control path configured to reconfigure a memory bandwidth for memory clients to use one of the first memory and the second memory based on a control signal, and a control logic configured to calculate a target capacity for data of a target client of the memory clients determined based on a layer configuration of an artificial neural network, and generate the control signal to store the data of the target client in the second memory based on a result of comparing the target capacity and the capacity of the second memory. | 2022-09-08 |
20220284275 | TASK ACTIVATING FOR ACCELERATED DEEP LEARNING - Techniques in advanced deep learning provide improvements in one or more of accuracy, performance, and energy efficiency. An array of processing elements performs flow-based computations on wavelets of data. Each processing element has a compute element and a routing element. Each router enables communication via wavelets with at least nearest neighbors in a 2D mesh. Routing is controlled by virtual channel specifiers in each wavelet and routing configuration information in each router. Execution of an activate instruction or completion of a fabric vector operation activates one of the virtual channels. A virtual channel is selected from a pool comprising previously activated virtual channels and virtual channels associated with previously received wavelets. A task corresponding to the selected virtual channel is activated by executing instructions corresponding to the selected virtual channel. | 2022-09-08 |
20220284276 | DATA STORAGE METHOD FOR SPEECH-RELATED DNN OPERATIONS - A data storage method for speech-related deep neural network (DNN) operations, characterized by comprising the following steps: 1. determining the configuration parameters by a user; 2. configuring a peripheral storage access interface; 3. configuring a multi-transmitting interface of feature storage array; 4. enabling CPU to store to-be-calculated data in a storage space between the feature storage space start address and the feature storage space end address of the peripheral storage device; 5. after data storage, enabling CPU to check the state of the peripheral storage access interface and the multi-transmitting interface of feature storage array; 6. upon receiving a transportation completion signal of the peripheral storage access interface by CPU, enabling the multi-transmitting interface of feature storage array. 7. upon receiving a transportation completion signal of the multi-transmitting interface of feature storage array by CPU, repeating step 6. | 2022-09-08 |
20220284277 | NETWORK OF TENSOR TIME SERIES - One or more machine learning models for a network of tensor time series can be provided. Co-evolving time series having multiple modes can be received. A tensor graph convolutional network can be trained, using the co-evolving time series and adjacency matrices associated with the multiple modes in the co-evolving time series, to generate node embeddings associated with a snapshot of the co-evolving time series at time t. A tensor recurrent neural network can be trained to generate temporal dynamics associated with the co-evolving time series based on the generated node embeddings. A neural network model can be trained to forecast a prediction for the co-evolving time series based on the generated node embeddings and the generated temporal dynamics. The tensor graph convolutional network, the tensor recurrent neural network and the neural network model can be trained jointly. | 2022-09-08 |
20220284278 | ESTIMATING REMAINING USEFUL LIFE BASED ON OPERATION AND DEGRADATION CHARACTERISTICS - Methods, computer program products, and/or systems are provided that perform the following operations: obtaining asset data; determining an asset class associated with the asset data; initializing a new neural network model, wherein the new neural network model is initialized based on a pretrained model associated with the asset class; training the new neural network model based, at least in part, on the asset data to obtain a trained remaining useful life model; and deploying the trained remaining useful life model to generate prediction data for one or more assets as output of the trained remaining useful life model. | 2022-09-08 |
20220284279 | COMPUTATIONAL TECHNIQUES FOR IDENTIFYING THE SURFACE OF A BRAIN - In one aspect, there is provided a method performed by one or more data processing apparatus that includes obtaining a point cloud dataset representing a brain of a biological organism. The point cloud dataset includes a collection of brain points that each define a respective spatial location in the brain. The method further includes identifying multiple brain points from the point cloud dataset as being located on a surface of the brain by repeatedly performing operations including initializing a current value of a position parameter and iteratively adjusting the current value of the position parameter until a termination criterion is satisfied. The termination criterion is satisfied if at least one brain point from the point cloud dataset is included in an interior of a shape parameterized by the current value of the position parameter. The operations further include, after determining that the termination criterion is satisfied, identifying each brain point from the point cloud dataset that is included in the interior of the shape parameterized by the current value of the position parameter as being located on the surface of the brain. | 2022-09-08 |
20220284280 | DATA LABELING FOR SYNTHETIC DATA GENERATION - Aspects described herein may relate to methods, systems, and apparatuses for labeling data in connection with synthetic data generation. The data labeling may begin with a manual process where a user provides labels for data. Based on the labels provided by the user, modified data may be generated and may include one or more encodings associated with the labels provided by the user. A machine-learning model may be trained to predict labels based on the modified data samples. Accuracy of the model may be determined based on comparing the predicted labels to further labels provided by the user and/or by allowing the user to indicate whether predicted labels are correct or incorrect. Once the model is determined to be accurate, the predicted labels may be used as a basis for generating synthetic data. | 2022-09-08 |
20220284281 | IMITATION LEARNING WITH FITTED Q ITERATION - Methods and systems for learning a policy model include determining an imitation learning expert policy. A policy model neural network is iteratively trained using the determined imitation learning expert policy, including modifying the policy model neural network at iteration to decrease a difference between an output of the policy model neural network and a target signal that is based on the determined imitation learning expert policy. | 2022-09-08 |
20220284282 | ENCODING TECHNIQUES FOR NEURAL NETWORK ARCHITECTURES - Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive an indication of one or more encoding operations to use for encoding a compressed dataset, the one or more encoding operations including a differential encoding operation, or an entropy encoding operation, or both. In some examples, using a neural network, the UE may first encode a dataset based on an additional encoding operation to generate a compressed dataset and then quantize the compressed dataset encoded based on the additional encoding operation. Subsequently, after the dataset has been initially encoded and then quantized, the UE may use the indication of the one or more encoding operations to further encode and compress the dataset. The UE may then transmit the dataset to a second device based on the one or more encoding operations. | 2022-09-08 |
20220284283 | NEURAL NETWORK TRAINING TECHNIQUE - Apparatuses, systems, and techniques to invert a neural network. In at least one embodiment, one or more neural network layers are inverted and, in at least one embodiment, loaded in reverse order. | 2022-09-08 |
20220284284 | Music Release Disambiguation using Multi-Modal Neural Networks - Methods and systems for disambiguating musical artist names are disclosed. Musical-artist-release records (MARRs) may be input to a multi-modal artificial neural network (ANN). Each MARR may be associated with a musical release of an artist, and may include a release ID and an artist ID, and release data in categories including music media content and metadata categories including sub-definitive musician name of the artist and release subcategories. All n-tuples of MARRs may be formed, and for each n-tuple, the ANN may be applied concurrently to each MARR to generate a release feature vector (RFV) that includes a set of sub-feature vectors, each characterizing a different category of release data. For each n-tuple, the ANN may be trained to cluster in a multi-dimensional RFV space RFVs of the same artist ID, and to separate RFVs of different artist IDs. The MARRs and their RFVs may be stored in a release database. | 2022-09-08 |
20220284285 | TRAINING A MACHINE LEARNING-BASED MODEL FOR ACTION RECOGNITION - A device for training a first machine learning-based model (MLM) for action recognition implements a training method. According to the training method, the training device obtains training data that comprises time sequences of data samples, which represent predefined subjects that are performing predefined actions. The training device trains the first MLM based on the training data, to discriminate between the predefined actions and to be adversarial to discrimination between the predefined subjects by a second MLM, and trains the second MLM based on feature data that is extracted by the first MLM for the training data, to discriminate between the predefined subjects. Thereby, the first MLM is encouraged to extract feature data that is unrelated to individual subjects, which improves action recognition performance of the trained first MLM when encountering new subjects. | 2022-09-08 |
20220284286 | METHOD AND APPARATUS FOR PROVIDING RECOMMENDATIONS FOR COMPLETION OF AN ENGINEERING PROJECT - Provided is a recommendation engine to provide automatically recommendations for the completion of an engineering project, the recommendation engine including: a first artificial intelligence, AI, module adapted to provide latent representations of a sequence of selected items; and a second artificial intelligence, AI, module adapted to process the latent representations of the sequence of selected items provided by the first artificial intelligence, AI, module to generate at least one sequence of complementary items required to complement the sequence of selected items to provide a complete sequence of items output via an interface as a recommendation to complete the engineering project. | 2022-09-08 |
20220284287 | ROBUST ARTIFICIAL NEURAL NETWORK HAVING IMPROVED TRAINABILITY - An artificial neural network (ANN), including processing layers which are each configured to process input quantities in accordance with trainable parameters of the ANN to form output quantities. At least one normalizer is inserted into at least one processing layer and/or between at least two processing layers. The normalizer includes a transformation element configured to transform input quantities directed into the normalizer into one or more input vectors, using a predefined transformation. The normalizer also includes a normalizing element configured to normalize the input vector(s) using a normalization function, to form one or more output vectors. The normalization function has at least two different regimes and changes between the regimes as a function of a norm of the input vector at a point and/or in a range, whose position is a function of a predefined parameter. The normalizer also includes an inverse transformation element. | 2022-09-08 |
20220284288 | LEARNING FROM BIOLOGICAL SYSTEMS HOW TO REGULARIZE MACHINE-LEARNING - The present disclosure relates to machine-learning generalization, and in particular to techniques for regularizing machine-learning The present disclosure relates to machine-learning generalization, and in particular to techniques for regularizing machine-learning models using biological systems (e.g. brain data) to engineer machine-learning-algorithms that can generalize better. Particularly, aspects are directed to a computer implemented method that includes measuring a plurality of biological responses (e.g. neural responses to stimuli or other variables such body movements); generating data (e.g. responses to stimuli) using the predictive model which can denoise biological data and extract task relevant information; scaling and transforming these predictions (e.g. measure representational similarities between stimuli); and using the biologically derived data to regularize machine-learning-algorithms. The method is applicable in many domains of computer science and artificial intelligence such as perception, learning, memory, cognition, decision making. | 2022-09-08 |
20220284289 | METHOD FOR DETERMINING AN OUTPUT SIGNAL BY MEANS OF A NEURAL NETWORK - Computer-implemented method for determining an output signal based on an input signal and by means of a neural network. The neural network determines the output signal based on a layer output determined by a first layer of the neural network. The layer output is determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer. | 2022-09-08 |
20220284290 | DATA-DRIVEN WEIGHT INITIALIZATION FOR MACHINE LEARNING MODELS - Certain aspects of the present disclosure provide techniques for provide a method, comprising: receiving input data for a layer of a neural network model; selecting a target code for the input data; and determining weights for the layer based on an autoencoder loss and the target code. | 2022-09-08 |
20220284291 | LEARNING IN TIME VARYING, DISSIPATIVE ELECTRICAL NETWORKS - A method for performing learning in a dissipative learning network is described. The method includes determining a trajectory for the dissipative learning network and determining a perturbed trajectory for the dissipative learning network based on a plurality of target outputs. Gradients for a portion of the dissipative learning network are determined based on the trajectory and the perturbed trajectory. The portion of the dissipative learning network is adjusted based on the gradients. | 2022-09-08 |
20220284292 | METHOD OF TRAINING A NEURAL NETWORK TO CONTROL AN AIRCRAFT SYSTEM - A method of training a neural network to control an aircraft system. The method includes obtaining an operational mode data space representing a set of operational modes for the aircraft system, wherein each operational mode represents a configuration of the aircraft system where performance of an aircraft component is impaired according to an aircraft system model. Each operational mode includes probability data indicating a respective probability of the operational mode occurring, according to the model. The method includes generating a reduced operational mode data space including operational modes having a probability greater than a theoretical operational probability threshold, and generating a training operational mode data space including operational modes within the reduced operational mode data space that have a probability less than a real-world operational probability threshold. The method includes training the neural network using operational modes within the training operational mode data space. | 2022-09-08 |
20220284293 | COMBINING COMPRESSION, PARTITIONING AND QUANTIZATION OF DL MODELS FOR FITMENT IN HARDWARE PROCESSORS - Small and compact Deep Learning models are required for embedded Al in several domains. In many industrial use-cases, there are requirements to transform already trained models to ensemble embedded systems or re-train those for a given deployment scenario, with limited data for transfer learning. Moreover, the hardware platforms used in embedded application include FPGAs, AI hardware accelerators, System-on-Chips and on-premises computing elements (Fog/Network Edge). These are interconnected through heterogenous bus/network with different capacities. Method of the present disclosure finds how to automatically partition a given DNN into ensemble devices, considering the effect of accuracy—latency power—tradeoff, due to intermediate compression and effect of quantization due to conversion to AI accelerator SDKs. Method of the present disclosure is an iterative approach to obtain a set of partitions by repeatedly refining the partitions and generating a cascaded model for inference and training on ensemble hardware. | 2022-09-08 |
20220284294 | ARTIFICIAL NEURAL NETWORKS GENERATED BY LOW DISCREPANCY SEQUENCES - Artificial neural networks (ANNs) are computing systems that imitate a human brain by learning to perform tasks by considering examples. These ANNs are typically created by connecting several layers of neural units using connections, where each neural unit is connected to every other neural unit either directly or indirectly to create fully connected layers within the ANN. However, by representing an artificial neural network utilizing paths from an input of the ANN to an output of the ANN, a complexity of the ANN may be reduced, and the ANN may be trained and implemented in a much faster manner when compared to fully connected layers within the ANN. More specifically, the ANN may be trained sparse from scratch in order to avoid a more expensive procedure of training the ANN and compressing it afterwards. | 2022-09-08 |
20220284295 | ASYNCHRONOUS AGENTS WITH LEARNING COACHES AND STRUCTURALLY MODIFYING DEEP NEURAL NETWORKS WITHOUT PERFORMANCE DEGRADATION - Methods and computer systems improve a trained base deep neural network by structurally changing the base deep neural network to create an updated deep neural network, such that the updated deep neural network has no degradation in performance relative to the base deep neural network on the training data. The updated deep neural network is subsequently training. Also, an asynchronous agent for use in a machine learning system comprises a second machine learning system ML2 that is to be trained to perform some machine learning task. The asynchronous agent further comprises a learning coach LC and an optional data selector machine learning system DS. The purpose of the data selection machine learning system DS is to make the second stage machine learning system ML2 more efficient in its learning (by selecting a set of training data that is smaller but sufficient) and/or more effective (by selecting a set of training data that is focused on an important task). The learning coach LC is a machine learning system that assists the learning of the DS and ML2. Multiple asynchronous agents could also be in communication with each others, each trained and grown asynchronously under the guidance of their respective learning coaches to perform different tasks. | 2022-09-08 |
20220284296 | METHOD FOR PROVIDING AN AGENT FOR CREATING A GRAPH NEURAL NETWORK ARCHITECTURE AND METHOD FOR CREATING, BY AN AGENT, A GRAPH NEURAL NETWORK ARCHITECTURE - Provided is a computer implemented method for providing an agent for creating a graph neural network architecture, which is suitable for providing a prediction of at least one indicator of a complex system and to a computer implemented method for providing such a graph neural network architecture by an agent. Also provide is an agent and a unit for providing an agent a computer program product and computer readable storage media. | 2022-09-08 |
20220284297 | COMPILER-BASED NEURON-AWARE DEEP NEURAL NETWORK ENSEMBLE TRAINING - Extensive training of DNNs may take a significant amount of time due to redundancy in data processing within network nodes. Improvement may be made by a method for training a Deep Neural Network (DNN) ensemble, include the steps of: executing by at least a processor in a computer, program code of a compiler which is stored in a non-transitory computer-readable medium, wherein the compiler configures a Deep Neural Network (DNN) into N networks to perform training steps: (a) receiving, a plurality of inputs i . . . I, by a plurality of neurons ni . . . nx, wherein each neuron ni being a computation node comprised in the N networks of the DNN; (b) utilizing by the compiler, the plurality of inputs i . . . I to train to the N networks to ensemble the DNN through analyzing, identifying and removing inter-network neuron redundancy to obtain savings in training time constraints and a reduction in original memory footprint. | 2022-09-08 |
20220284298 | METHOD AND APPARATUS FOR PRUNING NEURAL NETWORKS - The present invention relates to a method for pruning a neural network comprising a plurality of neurons, said method comprising: an initialization phase, wherein input information is fetched comprising at least parameters ({w | 2022-09-08 |