24th week of 2020 patent applcation highlights part 59 |
Patent application number | Title | Published |
20200184271 | ITERATIVE DATA PATTERN PROCESSING ENGINE LEVERAGING DEEP LEARNING TECHNOLOGY - An artificial intelligence system and method leveraging deep learning technology for data pattern processing and identifying misappropriation are provided herein comprising a deep learning engine comprising a data patterning component and a reasoning component. A controller is configured to: monitor a data stream comprising user interaction data; extract the interaction data from the data stream; determine, using the data patterning component, a data pattern from the extracted interaction data, wherein the data pattern is output to the reasoning component; analyze, using the reasoning component, the data pattern by comparing the data pattern to predetermined rules and factual reference data; identify an anomaly in the data pattern based on comparing the data pattern, wherein the anomaly is associated with misappropriation resources; in response, generate a revised data pattern, wherein the revised data pattern is output to the data patterning component; and confirm the revised data pattern using the data patterning component. | 2020-06-11 |
20200184272 | FRAMEWORK FOR BUILDING AND SHARING MACHINE LEARNING COMPONENTS - One embodiment of the present invention sets forth a technique for managing machine learning. The technique includes organizing a set of reusable components for performing machine learning under a framework. The technique also includes representing, within the framework, a machine learning model as a graph-based structure that includes nodes representing a subset of the reusable components and edges representing input-output relationships between pairs of the nodes. The technique further includes validating the machine learning model based on inputs and outputs associated with the nodes and the input-output relationships represented by the edges in the graph-based structure. Finally, the technique includes generating the machine learning model according to the graph-based structure and configurations for the subset of the reusable components. | 2020-06-11 |
20200184273 | CONTINUOUS LEARNING IMAGE STREAM PROCESSING SYSTEM - Systems and methods for continuous adaptive development of a model of a real world environment through data acquired by sensors disposed to observe that environment. The sensors provide a sensor data stream, e.g., audio/video data, to compute resources that are configured to archive the data stream, select portions of the data stream for analysis, annotate items of interest in the portions of the data stream, and analyze the items of interest according to an iteratively refining model. The model constitutes a digital summarized representation of an environment and subjects represented in the data stream, and is amenable to quality control, and thus to incremental improvement. The ever-updating model enables annotation and analysis of the data stream by the compute resources. | 2020-06-11 |
20200184274 | APPARATUS AND METHOD FOR GENERATING MEDICAL IMAGE SEGMENTATION DEEP-LEARNING MODEL, AND MEDICAL IMAGE SEGMENTATION DEEP-LEARNING MODEL GENERATED THEREFROM - There is provided a medical image segmentation deep-learning model generation apparatus including a training data generation/allocation unit configured to generate a training dataset through a segmentation result value acquired by inputting a given medical image to an original medical image segmentation deep-learning model and a learning control unit configured to acquire temporary weights using output data corresponding to primary learning by inputting good task data and bad task data sampled from primary learning training datasets to the medical image segmentation deep-learning model and configured to update weights by adding gradients acquired using weights acquired using output data corresponding to secondary learning by inputting good task data and bad task data sampled from secondary learning training datasets to the medical image segmentation deep-learning model, wherein the primary learning and the secondary learning are repeated. | 2020-06-11 |
20200184275 | METHOD AND SYSTEM FOR GENERATING AND CORRECTING CLASSIFICATION MODELS - Data having some similarities and some dissimilarities may be clustered or grouped according to the similarities and dissimilarities. The data may be clustered using agglomerative clustering techniques. The clusters may be used as suggestions for generating groups where a user may demonstrate certain criteria for grouping. The system may learn from the criteria and extrapolate the groupings to readily sort data into appropriate groups. The system may be easily refined as the user gains an understanding of the data. | 2020-06-11 |
20200184276 | METHOD AND SYSTEM FOR GENERATING AND CORRECTING CLASSIFICATION MODELS - Data having some similarities and some dissimilarities may be clustered or grouped according to the similarities and dissimilarities. The data may be clustered using agglomerative clustering techniques. The clusters may be used as suggestions for generating groups where a user may demonstrate certain criteria for grouping. The system may learn from the criteria and extrapolate the groupings to readily sort data into appropriate groups. The system may be easily refined as the user gains an understanding of the data. | 2020-06-11 |
20200184277 | RECORDING MEDIUM THAT STORES REINFORCEMENT LEARNING PROGRAM, REINFORCEMENT LEARNING METHOD, AND REINFORCEMENT LEARNING APPARATUS - A reinforcement learning method is performed by a computer. The method includes: acquiring an input value related to a state and an action of a control target and a gain of the control target that corresponds to the input value; estimating coefficients of state-action value function that becomes a polynomial for a variable that represents the action of the control target, or becomes a polynomial for a variable that represents the action of the control target when a value is substituted for a variable that represents the state of the control target, based on the acquired input value and the gain; and obtaining an optimum action or an optimum value of the state-action value function with the estimated coefficients by using a quantifier elimination. | 2020-06-11 |
20200184278 | System and Method for Extremely Efficient Image and Pattern Recognition and Artificial Intelligence Platform - Specification covers new algorithms, methods, and systems for: Artificial Intelligence; the first application of General-AI. (versus Specific, Vertical, or Narrow-AI) (as humans can do) (which also includes Explainable-AI or XAI); addition of reasoning, inference, and cognitive layers/engines to learning module/engine/layer; soft computing; Information Principle; Stratification; Incremental Enlargement Principle; deep-level/detailed recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, tilted or partial-face, OCR, relationship, position, pattern, and object); Big Data analytics; machine learning; crowd-sourcing; classification; clustering; SVM; similarity measures; Enhanced Boltzmann Machines; Enhanced Convolutional Neural Networks; optimization; search engine; ranking; semantic web; context analysis; question-answering system; soft, fuzzy, or un-sharp boundaries/impreciseness/ambiguities/fuzziness in class or set, e.g., for language analysis; Natural Language Processing (NLP); Computing-with-Words (CWW); parsing; machine translation; music, sound, speech, or speaker recognition; video search and analysis (e.g., “intelligent tracking”, with detailed recognition); image annotation; image or color correction; data reliability; Z-Number; Z-Web; Z-Factor; rules engine; playing games; control system; autonomous vehicles or drones; self-diagnosis and self-repair robots; system diagnosis; medical diagnosis/images; genetics; drug discovery; biomedicine; data mining; event prediction; financial forecasting (e.g., for stocks); economics; risk assessment; fraud detection (e.g., for cryptocurrency); e-mail management; database management; indexing and join operation; memory management; data compression; event-centric social network; social behavior; drone/satellite vision/navigation; smart city/home/appliances/IoT; and Image Ad and Referral Networks, for e-commerce, e.g., 3D shoe recognition, from any view angle. | 2020-06-11 |
20200184279 | INFORMATION PROCESSING APPARATUS AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING PROGRAM - An information processing apparatus includes an acquisition unit that acquires first impression information representing a first impression and second impression information representing a second impression for each of plural images including an image in which a subject is imaged and plural partial images including a part of the subject, the first impression being an impression received by a person, and the second impression being an impression received by the person and different from the first impression, a setting unit that sets a weight corresponding to the corresponding second impression information for the first impression information related to each of the plural images based on each of the plural images and the second impression information, and an output unit that outputs the first impression of the image in which the subject is imaged from the first impression information related to each of the plural images using the weight set by the setting unit. | 2020-06-11 |
20200184280 | DIFFERENTIAL CLASSIFICATION USING MULTIPLE NEURAL NETWORKS - A classification engine generates, using a weighted graph, a plurality of sets of confused graphemes based on recognition data for a plurality of document images; receives an input grapheme image associated with a document image comprising a plurality of grapheme images; determines a set of recognition options for the input grapheme image, where the set of recognition options comprises a set of target characters that are similar to the input grapheme image; identifies a neural network trained to recognize a first set of confused graphemes, where the first set of confused graphemes comprises at least a portion of the set of recognition options for the input grapheme image; and determines a grapheme class for the input grapheme image using the identified neural network. | 2020-06-11 |
20200184281 | GRAPHICAL APPROACH TO MULTI-MATCHING - Methods, systems, and computer-readable storage media for providing, by a machine learning (ML) platform, a first binary classifier, processing, by the first binary classifier a super-set of invoices to provide a plurality of sets of invoices based on matching pairs of invoices in the super-set of invoices, providing, by the ML platform, a second binary classifier, processing, by the second binary classifier, a bank statement and the plurality of sets of invoices to define two or more super-invoices based on aggregate features of invoices in the plurality of sets of invoices, and match the bank statement to a super-invoice of the two or more super-invoices, and outputting a match of the bank statement to the super-invoice. | 2020-06-11 |
20200184282 | AUTOMATIC GENERATION OF A NEW CLASS IN A CLASSIFICATION SYSTEM - A system and computer-implemented method for automatically recognizing a new class in a classification system. The method includes accessing components of a trained convolutional neural network (CNN) that has been trained with available classes. The components are provided in a kernel space and include at least one of a plurality of kernels and a plurality of neurons of one or more layers of the CNN. Furthermore, the components are assigned to a class in accordance with the training. The method further includes applying a covariance matrix to map the components in the kernel space to eigenspace; determining, for each of the available classes, an eigen-distance between a sample and the components mapped to eigenspace; based on the eigen-distance, determining whether the sample is an outlier that does not belong to one of the classes; and creating a new class that includes the sample if determined that the sample is an outlier. | 2020-06-11 |
20200184283 | OBJECT RECOGNITION DEVICE AND OBJECT RECOGNITION METHOD - Provided is an object recognition device capable of preventing a change of fusion data from a previous value to a current value that exceeds a tolerable range. The object recognition device is configured to execute specialized tracking processing when a change of the fusion data from the previous value to the current value exceeds a tolerable range, and generate tracking data that is equivalent to prediction data in the specialized tracking processing by setting an adjustment physical quantity for solving a fusion data discontinuity state. | 2020-06-11 |
20200184284 | DEVICE FOR ENSEMBLING DATA RECEIVED FROM PREDICTION DEVICES AND OPERATING METHOD THEREOF - Provided is a device for ensembling data received from prediction devices and a method of operating the same. The device includes a data manager, a learner, and a predictor. The data manager receives first and second device prediction results from first and second prediction devices, respectively. The learner may adjust a weight group of a prediction model for generating first and second item weights, first and second device weights, based on the first and second device prediction results. The first and second item weights depend on first and second item values, respectively, of the first and second device prediction results. The first device weight corresponds to the first prediction device, and the second device weight corresponds to the second prediction device. The predictor generates an ensemble result of the first and second device prediction results, based on the first and second item weights and the first and second device weights. | 2020-06-11 |
20200184286 | SMART MEDICATION IDENTIFYING SYSTEM - A smart medication identifying system is disclosed herein. It comprises a processing device including a first processing module, a scanning module electrically connected to the first processing module and a first reminding module electrically connected to the first processing module; a cloud storage device electrically connected to the processing device and having a storage module, a login module electrically connected to the storage module, and a medication information database electrically connected to the storage module; and a medication identifying device electrically connected to the processing device and the cloud storage device and having a second processing module, an image identifying module electrically connected to the second processing module and a second reminding module electrically connected to the second processing module. | 2020-06-11 |
20200184287 | SYSTEM AND METHOD FOR IMPROVING RECOGNITION OF CHARACTERS - System and method for improving recognition of characters. A system for improving recognition of characters is disclosed. The system comprises at least one processor ( | 2020-06-11 |
20200184288 | SYSTEM AND METHOD FOR PRINTING CUSTOMIZED ITEMS - A printing system and method of printing are provided herein. The printing system may generate custom user designs for printing. The designs may be associated with a selected print medium. The print medium may have a particular layout. The printing system may convert the design for printing on other print media without requiring user alteration or input of the design. In another aspect, the printing system may facilitate printing the designs via local printers and/or via professional printers. Accordingly, printing of designs on different print-receptive media items can be accomplished. | 2020-06-11 |
20200184289 | IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, AND STORAGE MEDIUM - Provided is quantization processing that can reduce color development defect due to dot overlapping and can output an image with reduced granularity when the image is printed by using multiple kinds of colorants. To this end, dot arrangement information for a colorant for which dot arrangement is already determined among multiple kinds of colorants is acquired for a predetermined region of the image, and an evaluation value of each pixel included in the predetermined region is derived based on the arrangement information. In addition, for the predetermined region, a target value for a predetermined colorant for which dot arrangement is yet to be determined is derived based on the image. Then, whether or not to arrange a dot of the predetermined colorant in the predetermined region is determined based on the target value and the evaluation value. | 2020-06-11 |
20200184291 | TRACKING SYSTEMS, METHODS AND APPARATUS - A tracking system is disclosed. The system includes a wireless tracker having a machine-readable identification and an antenna configured to receive and transmit wireless signals; a fastener that includes a machine-readable identification; and a container capable of receiving and containing physical items. The fastener is capable of coupling with the wireless tracker and container to secure the wireless tracker to the container and secure the container in a closed configuration. Also disclosed are method of remotely monitoring items, and management software for tracking items using blockchain technology. | 2020-06-11 |
20200184292 | METHOD, SYSTEM AND APPARATUS FOR DIMENSIONING ITEMS - A method of generating dimensioning assist information for an item includes: obtaining data associated with a physical size of a graphical dimensioning aid associated with the item; generating dimensioning assist information by encoding the data into a machine-readable data object carried by the item. A method of dimensioning an item includes, at a dimensioning system: capturing an image of the item and the graphical dimensioning aid carried by the item; detecting an edge of the item within the captured image; determining a measurement of the edge; detecting the graphical dimensioning aid within the captured image; determining a measurement of the graphical dimensioning aid; decoding a physical size of the graphical dimensioning aid from a machine-readable data object carried by the item; and dimensioning the edge based on the measurement of the edge, the measurement of the graphical dimensioning aid, and the physical size. | 2020-06-11 |
20200184293 | SECURE QR CODE USING NONLINEARITY OF SPATIAL FREQUENCY IN LIGHT - This invention relates to a secure QrCode communication method based on nonlinear spatial frequency characteristics, comprising: camera modeling: modeling according to the spatial frequency of the color filter matrix of the scanning device's camera; QrCode encryption: using the CFA spatial frequency of scanning device and modeling results, as well as the spatial frequency of the display device, generate an encrypted picture of the target QrCode on the display device; QrCode decryption: the camera of the scanning device takes the picture of the display at a specified position and a specified angle, and parses the image to recover the target QrCode. Compared with the prior art, the present invention utilizes the nonlinear characteristics of the optical spatial frequency, and uses the spatial frequency of the camera's own color filter array and the spatial frequency of the display to modulate the target two-dimensional code through phase to achieve the effect of encryption. | 2020-06-11 |
20200184294 | Dual code authentication process - A dual code authentication process combining a visible QR code with an invisible randomly generated code which can be alpha, numeric, symbol or image that can only be read with a reading device. A data generation engine is used to create the generated code which is assigned to the QR code and stored in a cloud based database. The QR code is decodable by a handheld reading device which communicates with the cloud based database releasing a copy of the generated code to the reading device. A reader is then used to decode the invisible printed code wherein the user can compare the printed code on the document and the code stored on the cloud based database to determine a match and authenticity. | 2020-06-11 |
20200184295 | BATTERY CHARGER FOR A TRANSACTION CARD - Examples described herein describe a battery charger for a transaction card. According to some implementations, a charging device may detect a transaction card is received within a charging slot when an integrated circuit (IC) chip of the transaction card is in contact with a charging terminal; request a user device to provide power to charge the transaction card via the charging terminal, wherein the user device is communicatively coupled to the charging terminal; receive the power from the user device; and provide the power to the transaction card to charge a battery of the transaction card. | 2020-06-11 |
20200184296 | FINANCIAL CARD WITH STATUS INDICATORS - Embodiments of the present disclosure relate to financial cards and related systems and methods configured to provide status indicators to a user of the financial card. In some embodiments, the status indicators are provided as the financial card is engaged with an electronic terminal. In some embodiments, a financial card includes a light emitting diode module including one or more light emitting diodes, the light emitting diode module configured to indicate a status of the financial card, and a microprocessor chip configured to engage with an electronic terminal, determine the status of the financial card and modify the operation of the light emitting diode module in accordance with the determined status of the financial card. | 2020-06-11 |
20200184297 | NEAR-FIELD COMMUNICATION DEVICE WITH ANTENNA ON ELONGATED PRINTED INTERCONNECT - A near-field communication tag includes a logic section that responds to a radio-frequency identification interrogation signal. An elongated, printed, interconnect has a first end coupled to the integrated circuit. An antenna is on a second end of the interconnect. The antenna is electrically coupled to the conductive lines of the interconnect and operable to send and receive wireless signals of the radio-frequency identification interrogation and communicate the wireless signals with the integrated circuit via the interconnect. | 2020-06-11 |
20200184298 | MOBILE PHONE WITH NFC APPARATUS THAT DOES NOT RELY ON POWER DERIVED FROM AN INTERROGATING RF FIELD - A mobile phone includes a smartcard controller that does not rely on power received from an interrogating RF field. The mobile phone also includes a small inductive device capable of inductive coupling with an RFID reader. The smartcard controller includes circuitry to modulate an impedance of a port coupled to the inductive element when in the presence of an interrogating RF field at substantially 13.56 MHz. | 2020-06-11 |
20200184299 | CARD-MAKING SUBSTRATE AND PREPARATION METHOD THEREOF AND IC CARD OR ELECTRONIC TAG CONTAINING THE SAME - Provided is a card-making substrate, a preparation method thereof and an IC card or electronic tag containing the same. The card-making substrate comprises an aluminum laminated film layer, a bonding adhesive layer and a base material layer that are stacked in sequence. At least one continuous scratch is formed on a surface of the aluminum laminated film layer. The scratch has a depth greater than the thickness of the aluminum film in the aluminum laminated film layer. The aluminum film in the aluminum laminated film layer is divided into at least two regions that are not connected with each other by the scratch and the edge of the aluminum laminated film layer. According to the present invention, at least one continuous scratch is simply added on the surface of the aluminum laminated film layer in a common card-making substrate, and the aluminum film therein is divided into at least 2 regions which are not connected with each other, which allows the contactless IC card or electronic tag made using the card-making substrate to have a longer read and write distance without damaging the appearance effect of the aluminum laminated film having a laser effect. | 2020-06-11 |
20200184300 | SHIELDING AND/OR ENHANCEMENT OF TEMPERATURE-SENSING RFID DEVICES - A temperature-sensing RFID device includes an RFID chip and an antenna electrically coupled thereto. The RFID chip includes a temperature sensor, while the antenna is adapted to receive energy from an RF field and produce a signal. A shielding structure and/or a thermally conductive or absorbent structure may be associated with the RFID chip. The shielding structure is oriented so as to be positioned between at least a portion of the RFID chip and an outside environment and configured to shield the temperature sensor from at least one environmental factor capable of affecting a temperature sensed by the temperature sensor of an article to which the RFID device is secured. The thermally conductive or absorbent structure is oriented so as to be positioned between at least a portion of the RFID chip and the article and configured to enhance thermal coupling between the temperature sensor and the article. | 2020-06-11 |
20200184301 | SMART TAG AND LABEL METHOD, SYSTEM, AND APPARATUS - A tag or label incorporating embedded trigger technology, such as RFID, QR codes, or barcodes is disclosed to effectively integrate RFID or a trigger directly into the garment. Typically, the embedded technology is embedded via a digital manufacturing process, and the labels and tags enable a data connection via the embedded triggers and a unique identifier corresponding to the embedded triggers. Thus, once the trigger technology is enabled via scanning, visual recognition, UHF/NFC RFID, etc., data items in the data management platform are enabled. Specifically, the data items within the data management platform include sustainability, consumer engagement, authentication/brand protection, merchandising/marketing, and data management and can be leveraged to drive an interactive consumer experience. | 2020-06-11 |
20200184302 | RFID DEVICE PROGRAMMING - A radio frequency identification (RFID) device programming apparatus includes a transport system ( | 2020-06-11 |
20200184303 | CERAMIC-CONTAINING AND CERAMIC COMPOSITE TRANSACTION CARDS - A process for making a card includes the steps of forming a core layer having a first surface and a second surface, disposing an uncured decorative ceramic layer of ceramic particles disposed in a resin binder over the first surface of the core layer, such as by spray coating, and curing the uncured decorative ceramic layer to form a cured decorative ceramic layer. Card products of the process may have a core layer of metal, ceramic, or a combination thereof that form a bulk of the card. | 2020-06-11 |
20200184304 | ELECTRONIC DEVICE FOR ATTACHMENT TO A BEVERAGE CONTAINER - An electronic device, system, and method for sending and receiving a communication relating to a beverage bottle to and from an electronic device, in which the electronic device may include a receiving portion including an aperture configured to receive a neck of a beverage container; a processor; memory; wireless communication circuitry; at least one actuator; and at least one indicator. The electronic device may be configured to attach to the neck of the beverage container via the receiving portion. The wireless communication circuitry may be configured to receive a wireless communication. The processor may be configured to activate the at least one indicator in response to the communication. | 2020-06-11 |
20200184305 | SPOOLABLE SMALL-FORM-FACTOR RFID-ENABLED WRISTBAND - Spoolable RFID-enabled wristbands with maximized read range. In an embodiment, a wristband comprises flexible material formed into a flag portion and a strap portion. The flag portion comprises a radio-frequency identification (RFID) inlay embedded within the material. The strap portion extends from the flag portion, and is perforated in a line along a longitudinal axis of the wristband from a distal end of the strap portion that is distal to the flag portion to a hole at a proximal end of the strap portion that is proximal to the flag portion, such that the strap portion may be torn, from the distal end to the hole at the proximal end, along the perforated line, into two sections of substantially equal dimension, which each extend from the flag portion. | 2020-06-11 |
20200184306 | SIMULATED HUMAN-LIKE AFFECT-DRIVEN BEHAVIOR BY A VIRTUAL AGENT - A system for simulating human-like affect-driven behavior includes a hardware processor and a system memory storing software code providing a virtual agent. The hardware processor executes the software code to identify a character assumed by the virtual agent and having a personality, a target state of motivational fulfillment, a baseline mood, and emotions. The software code identifies current physical and motivational fulfillment states, and currently active emotions of the character, and determines a current mood of the character based on the baseline mood and the currently active emotions. The software code further detects an experience by the character and plans multiple behaviors including a first behavior based on the experience and the current physical state, the second behavior based on the experience, the personality, the current mood, and the currently active emotions, and a third behavior based on the target and current states of motivational fulfillment. | 2020-06-11 |
20200184307 | UTILIZING RECURRENT NEURAL NETWORKS TO RECOGNIZE AND EXTRACT OPEN INTENT FROM TEXT INPUTS - The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize recurrent neural networks to determine the existence of one or more open intents in a text input, and then extract the one or more open intents from the text input. In particular, in one or more embodiments, the disclosed systems utilize a trained intent existence neural network to determine the existence of an actionable intent within a text input. In response to verifying the existence of an actionable intent, the disclosed systems can apply a trained intent extraction neural network to extract the actionable intent from the text input. Furthermore, in one or more embodiments, the disclosed systems can generate a digital response based on the intent identified from the text input. | 2020-06-11 |
20200184308 | METHODS, SYSTEMS, AND COMPUTER READABLE MEDIUMS FOR DETERMINING A SYSTEM STATE OF A POWER SYSTEM USING A CONVOLUTIONAL NEURAL NETWORK - Methods, systems, and computer readable mediums determining a system state of a power system using a convolutional neural network using a convolutional neural network are disclosed. One method includes converting power grid topology data corresponding to a power system into a power system matrix representation input and applying the power system matrix representation input to a plurality of convolutional layers of a deep convolutional neural network (CNN) structure in a sequential manner to generate one or more feature maps. The method further includes applying the one or more feature maps to a fully connected layer (FCL) operation for generating a respective one or more voltage vectors representing a system state of the power system. | 2020-06-11 |
20200184309 | NEURAL NETWORK PROCESSING USING SPECIALIZED DATA REPRESENTATION - Techniques for neural network processing using specialized data representation are disclosed. Input data for manipulation in a layer of a neural network is obtained. The input data includes image data, where the image data is represented in bfloat16 format without loss of precision. The manipulation of the input data is performed on a processor that supports single-precision operations. The input data is converted to a 16-bit reduced floating-point representation, where the reduced floating-point representation comprises an alternative single-precision data representation mode. The input data is manipulated with one or more 16-bit reduced floating-point data elements. The manipulation includes a multiply and add-accumulate operation. The manipulation further includes a unary operation, a binary operation, or a conversion operation. A result of the manipulating is forwarded to a next layer of the neural network. | 2020-06-11 |
20200184310 | APPARATUS AND METHOD FOR DEEP NEURAL NETWORK MODEL PARAMETER REDUCTION USING SPARSITY REGULARIZED FACTORIZED MATRIX - Provided is an apparatus and method for reducing the number of deep neural network model parameters, the apparatus including a memory in which a program for DNN model parameter reduction is stored, and a processor configured to execute the program, wherein the processor represents hidden layers of the model of the DNN using a full-rank decomposed matrix, uses training that is employed with a sparsity constraint for converting a diagonal matrix value to zero, and determines a rank of each of the hidden layers of the model of the DNN according to a degree of the sparsity constraint. | 2020-06-11 |
20200184311 | EXECUTION OF TRAINED NEURAL NETWORKS USING A DATABASE SYSTEM - In an embodiment, a computer-implemented method for efficient execution of a trained neural network using a database system, the trained neural network comprising a plurality of layers each comprising weight values and bias values and programmed at each of the layers to execute an affine transformation of an activation function and an input value, comprises: for a particular layer of the trained neural network, dividing the affine transformation input a plurality of transformation pieces; executing each of the transformation pieces to result in computed pieces and writing the computed pieces to a first database table; using one or more database queries, combining the computed pieces and applying the activation function to generate a set of output data; writing the output data to one of a plurality of different second database tables that respectively correspond to the layers; repeating the dividing, executing, combining, applying and writing for all layers of the trained neural network. | 2020-06-11 |
20200184312 | APPARATUS AND METHOD FOR GENERATING SAMPLING MODEL FOR UNCERTAINTY PREDICTION, AND APPARATUS FOR PREDICTING UNCERTAINTY - There is provided an uncertainty prediction apparatus including an artificial neural network model trained based on deep learning, sampling models modeled by at least two weights obtained through sampling during a training process for the artificial neural network model, and an output generation unit configured to generate a result value reflecting an uncertainty degree by aggregating values output from the artificial neural network model and the sampling models after the same data is input to the artificial neural network model and the sampling models. | 2020-06-11 |
20200184313 | ADAPTIVE SAMPLING IN MONTE CARLO RENDERINGS USING ERROR-PREDICTING NEURAL NETWORKS - A modular architecture is provided for denoising Monte Carlo renderings using neural networks. The temporal approach extracts and combines feature representations from neighboring frames rather than building a temporal context using recurrent connections. A multiscale architecture includes separate single-frame or temporal denoising modules for individual scales, and one or more scale compositor neural networks configured to adaptively blend individual scales. An error-predicting module is configured to produce adaptive sampling maps for a renderer to achieve more uniform residual noise distribution. An asymmetric loss function may be used for training the neural networks, which can provide control over the variance-bias trade-off during denoising. | 2020-06-11 |
20200184314 | GENERATION OF CAPSULE NEURAL NETWORKS FOR ENHANCING IMAGE PROCESSING PLATFORMS - Embodiments of the present invention provide a system for generating capsule neural networks for enhancing image processing platforms. The system is configured for generate capsule neural network based on instructions received form at least one user, transfer learning from an existing image processing platform to train the capsule neural network, receive input from one or more devices and provide the input to the existing image processing platform comprising a convolutional neural network, wherein the convolutional neural network processes the input, activate the capsule neural network to validate the processing of the convolutional neural network, and retrain the capsule neural network based on the validations associated with the convolutional neural network. | 2020-06-11 |
20200184315 | DIVIDING NEURAL NETWORKS - A method of implementing a neural network in a neuromorphic apparatus having a memory and processing circuitry, where the method includes dividing, by the processing circuitry, the neural network into a plurality of sub-networks based on a size of a core of the memory to implement the neural network, initializing, by the processing circuitry, a hyper-parameter used in the sub-networks, and training, by the processing circuitry, the sub-networks by using the hyper-parameter. | 2020-06-11 |
20200184316 | GENERATING DISCRETE LATENT REPRESENTATIONS OF INPUT DATA ITEMS - Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating discrete latent representations of input data items. One of the methods includes receiving an input data item; providing the input data item as input to an encoder neural network to obtain an encoder output for the input data item; and generating a discrete latent representation of the input data item from the encoder output, comprising: for each of the latent variables, determining, from a set of latent embedding vectors in the memory, a latent embedding vector that is nearest to the encoded vector for the latent variable. | 2020-06-11 |
20200184317 | METHOD AND APPARATUS FOR GENERATING STORY FROM PLURALITY OF IMAGES BY USING DEEP LEARNING NETWORK - Disclosed herein are a visual story generation method and apparatus for generating a story from a plurality of images by using a deep learning network. The visual story generation method includes: extracting features from a plurality of respective images by using the first extraction unit of a deep learning network; generating the structure of a story based on the overall feature of the plurality of images by using the second extraction unit of the deep learning network; and generating the story by using outputs of the first and second extraction units. | 2020-06-11 |
20200184318 | DATA PROCESSING DEVICE, DATA PROCESSING METHOD, AND NON-TRANSITORY COMPUTER-READBLE STORAGE MEDIUM - A data processing unit ( | 2020-06-11 |
20200184319 | SYSTEMS AND DEVICES FOR CONFIGURING NEURAL NETWORK CIRCUITRY - Subject matter disclosed herein may relate to storage and/or processing of signals and/or states representative of neural network parameters in a computing device, and may relate more particularly to configuring circuitry in a computing device to process signals and/or states representative of neural network parameters. | 2020-06-11 |
20200184320 | NEURAL NETWORK PROCESSING - A data processing system operable to process a neural network, and comprising a plurality of processors. The data processing system is operable to determine whether to perform neural network processing using a single processor or using plural processors. When it is determined that plural processors should be used, a distribution of the neural network processing among two or more of the processors is determined and the two or more processors are each assigned a portion of the neural network processing to perform. A neural network processing output is provided as a result of the processors performing their assigned portions of the neural network processing. | 2020-06-11 |
20200184321 | MULTI-PROCESSOR NEURAL NETWORK PROCESSING APPARATUS - A multi-processor neural network processing apparatus comprises: a plurality of network processing engines, each for processing one or more layers of a neural network according to a network configuration. A memory at least temporarily stores network configuration information, input image information, intermediate image information and output information for the network processing engines. At least one of the network processing engines is configured, when otherwise idle, to identify configuration information and input image information to be processed by another target network processing engine and to use the configuration information and input image information to replicate the processing of the target network processing engine. The apparatus is configured to compare at least one portion of information output by the target network processing engine with corresponding information generated by the network processing engine to determine if either the target network processing engine or the network processing engine is operating correctly. | 2020-06-11 |
20200184322 | REALIGNING STREAMS OF NEURON OUTPUTS IN ARTIFICIAL NEURAL NETWORK COMPUTATIONS - Systems and methods for realigning streams of neuron outputs are provided. An example method may include generating, by a processing unit, neuron outputs including at least a first neuron output and a second neuron output, generating, by at least one further processing unit, further neuron outputs including at least a further first neuron output and a further second neuron output, receiving, by a synchronization module communicatively coupled to the processing unit and the further processing unit, the neuron outputs, wherein the neuron outputs and the further neuron outputs are received in an arbitrary order, and ordering, by the synchronization module, the first neuron output, the further first neuron output, the second neuron output and the further second neuron output according to a further order, the further order being different from the arbitrary order. | 2020-06-11 |
20200184323 | MEMRISTIVE NANOFIBER NEURAL NETWORKS - Disclosed are various embodiments of memristive devices comprising a number of nodes. Memristive fibers are used to form conductive and memristive paths in the devices. Each memristive fiber may couple one or more nodes to one or more other nodes. In one case, a memristive device includes a first node, a second node, and a memristive fiber. The memristive fiber includes a conductive core and a memristive shell surrounding at least a portion of the conductive core along at least a portion of the memristive fiber. The memristive fiber couples the first node to the second node through a portion of the memristive shell and at least a portion of the conductive core | 2020-06-11 |
20200184324 | NETWORK COMPOSITION MODULE FOR A BAYESIAN NEUROMORPHIC COMPILER - Described is a system for specifying control of a device based on a Bayesian network model. The system includes a Bayesian neuromorphic compiler having a network composition module having probabilistic computation units (PCUs) arranged in a hierarchical composition containing multi-level dependencies. The Bayesian neuromorphic compiler receives a Bayesian network model as input and produces a spiking neural network topology and configuration that implements the Bayesian network model. The network composition module learns conditional probabilities of the Bayesian network model. The system computes a conditional probability and controls a device based on the computed conditional probability. | 2020-06-11 |
20200184325 | MULTI-VARIABLES PROCESSING NEURONS AND UNSUPERVISED MULTI-TIMESCALE LEARNING FOR SPIKING NEURAL NETWORKS - The present disclosure relates to a method of generating spikes by a neuron of a spiking neural network. The method comprises generating at each time, wherein the spike generation encodes at each time instant at least two variable values at the neuron. Synaptic weights may be optimized for a spike train generated by a given presynaptic neuron of a spiking neural network, wherein the spike train being indicative of features of at least one timescale. | 2020-06-11 |
20200184326 | DE-CONFLICTING DATA LABELING IN REAL TIME DEEP LEARNING SYSTEMS - Systems, computer program products, and methods are described herein for de-conflicting data labeling in real-time deep learning systems. The present invention is configured to retrieve one or more dynamically generated expert profiles; and determine an optimal expert mix of experts to classify the transaction into a transaction types, wherein the expert profiles comprises: (i) shared information metrics, (ii) divergence metrics, (iii) characteristics associated with the one or more experts, (iv) a predictive accuracy of the one or more experts, (v) an exposure score associated with the one or more experts, and (vi) information associated with the transaction, wherein the optimal expert mix comprises: (i) a best expert for classifying the transaction, (ii) a combination score from at least the portion of the one or more experts evaluating the transaction simultaneously, and (iii) a sequence of at least the portion of the one or more experts analyzing the transaction. | 2020-06-11 |
20200184327 | AUTOMATED GENERATION OF MACHINE LEARNING MODELS - This document relates to automated generation of machine learning models, such as neural networks. One example system includes a hardware processing unit and a storage resource. The storage resource can store computer-readable instructions cause the hardware processing unit to perform an iterative model-growing process that involves modifying parent models to obtain child models. The iterative model-growing process can also include selecting candidate layers to include in the child models based at least on weights learned in an initialization process of the candidate layers. The system can also output a final model selected from the child models. | 2020-06-11 |
20200184328 | ACCELERATING ARTIFICIAL NEURAL NETWORK COMPUTATIONS BY SKIPPING INPUT VALUES - Systems and methods for accelerating artificial neural network computation are disclosed. An example may comprise selecting, by a controller communicatively coupled to a selector and an arithmetic unit and based on a criterion, an input value from the stream of input values of a neuron, configuring, by the controller, the selector to provide, dynamically, the selected input value to the arithmetic unit, providing, by the controller to the arithmetic unit, an information of the selected input value, acquiring, by the arithmetic unit and based on the information, a weight from a set of weights, and performing, by the arithmetic unit a mathematical operation on the selected input value and the weight to obtain a result, wherein the result is to be used to compute an output of the neuron. The criterion may include a comparison between the input value and a reference value. The reference value may include zero. | 2020-06-11 |
20200184329 | ENVIRONMENT CONTROLLER AND METHOD FOR IMPROVING PREDICTIVE MODELS USED FOR CONTROLLING A TEMPERATURE IN AN AREA - Method and environment controller for improving predictive models used for controlling a temperature in an area. The environment controller executes a neural network inference engine using first and second predictive models for respectively inferring temperature increase and decrease values based on environmental inputs. The environment controller calculates a temperature adjustment value based on the temperature increase and decrease values, and the temperature in the area is adjusted based on the temperature adjustment value. The environment controller receives a vote related to the temperature in the area transmitted by a user device. The environment controller determines, based on the received vote, values of a first and second reinforcement signals. The environment controller executes a neural network training engine to update the first and second predictive models based on the inputs, respectively the temperature increase and decrease values, and respectively the values of the first and second reinforcement signals. | 2020-06-11 |
20200184330 | SYSTEMS AND METHODS FOR LEGAL DOCUMENT GENERATION - A system is configured to receive first training data, train a first neural network (NN) based on the first training data, receive second training data, train a second NN based on the second training data, receive a first plain English phrase, provide the first plain English phrase to the first NN, generate, via the first NN, one or more first legal clauses based on the first plain English phrase, receive a second plain English phrase, provide the second plain English phrase to the first NN, generate, via the first NN, one or more second legal clauses based on the second plain English phrase, provide the one or more first legal clauses and the one or more second legal clauses to the second NN, and generate, via the second NN, a legal document based on the one or more first legal clauses and the one or more second legal clauses. | 2020-06-11 |
20200184331 | METHOD AND DEVICE FOR PROCESSING DATA THROUGH A NEURAL NETWORK - A method can be used to process an initial set of data through a convolutional neural network that includes a convolution layer followed by a pooling layer. The initial set is stored in an initial memory along first and second orthogonal directions. The method includes performing a first filtering of the initial set of data by the convolution layer using a first sliding window along the first direction. Each slide of the first window produces a first set of data. The method also includes performing a second filtering of the first sets of data by the pooling layer using a second sliding window along the second direction. | 2020-06-11 |
20200184332 | CONVOLUTIONAL NEURAL NETWORK PROCESSOR AND DATA PROCESSING METHOD THEREOF - A convolutional neural network processor includes an information decode unit and a convolutional neural network inference unit. The information decode unit is configured to receive a program input and weight parameter inputs and includes a decoding module and a parallel processing module. The decoding module receives the program input and produces an operational command according to the program input. The parallel processing module is electrically connected to the decoding module, receives the weight parameter inputs and includes a plurality of parallel processing sub-modules for producing a plurality of weight parameter outputs. The convolutional neural network inference unit is electrically connected to the information decode unit and includes a computing module. The computing module is electrically connected to the parallel processing module and produces an output data according to an input data and the weight parameter outputs. | 2020-06-11 |
20200184333 | APPARATUS AND METHOD OF COMPRESSING NEURAL NETWORK - Provided are an apparatus and method of compressing an artificial neural network. According to the method and the apparatus, an optimal compression rate and an optimal operation accuracy are determined by compressing an artificial neural network, determining a task accuracy of a compressed artificial neural network, and automatically calculating a compression rate and a compression ratio based on the determined task accuracy. The method includes obtaining an initial value of a task accuracy for a task processed by the artificial neural network, compressing the artificial neural network by adjusting weights of connections among layers of the artificial neural network included in information regarding the connections, determining a compression rate for the compressed artificial neural network based on the initial value of the task accuracy and a task accuracy of the compressed artificial neural network, and re-compressing the compressed artificial neural network according to the compression rate. | 2020-06-11 |
20200184334 | MODIFICATION OF NEURAL NETWORK TOPOLOGY - A method may include obtaining data representative of an NNT of a graph-based model that includes multiple components. The multiple components may include multiple neural nodes and at least one connection. The at least one connection may associate two or more of the neural nodes. The method may include displaying the NNT including the multiple components in a GUI via a display screen. The method may include receiving user input effective to indicate that at least one of the components of the NNT is to be modified. The user input may be received via the GUI. The method may include modifying the at least one of the components of the NNT. The at least one of the components may be modified based on the user input. The method may include displaying the NNT that comprises the modification of the at least one of the components via the GUI. | 2020-06-11 |
20200184335 | NON-VOLATILE MEMORY DIE WITH DEEP LEARNING NEURAL NETWORK - Exemplary methods and apparatus are provided for implementing a deep learning accelerator (DLA) or other neural network components within the die of a non-volatile memory (NVM) apparatus using, for example, under-the-array circuit components within the die. Some aspects disclosed herein relate to configuring the under-the-array components to implement feedforward DLA operations. Other aspects relate to backpropagation operations. Still other aspects relate to using an NAND-based on-chip copy with update function to facilitate updating synaptic weights of a neural network stored on a die. Other aspects disclosed herein relate to configuring a solid state device (SSD) controller for use with the NVM. In some aspects, the SSD controller includes flash translation layer (FTL) tables configured specifically for use with neural network data stored in the NVM. | 2020-06-11 |
20200184336 | METHOD AND APPARATUS FOR DETECTING SMALL OBJECTS WITH AN ENHANCED DEEP NEURAL NETWORK - Various methods are provided for training and subsequently utilizing a convolutional neural network (CNN) to detect small pedestrians (e.g., pedestrians located away a large distance). One example method may comprise performing a first training stage in which a first CNN is trained to detect objects of a first size, the first CNN trained using a first set of images comprised of objects of the first size, and configured to output a first set of parameters, performing a second training stage in which a second CNN is trained using a second set of images, the second set of images comprising objects of a second size, and the first CNN is initialized with the first set of parameters and is re-trained using the second set of images, and determining parameters of the first CNN by minimizing error between the first CNN and the second CNN. | 2020-06-11 |
20200184337 | LEARNING COACH FOR MACHINE LEARNING SYSTEM - A machine learning system includes a coach machine learning system that uses machine learning to help a student machine learning system learn its system. By monitoring the student learning system, the coach machine learning system can learn (through machine learning techniques) “hyperparameters” for the student learning system that control the machine learning process for the student learning system. The machine learning coach could also determine structural modifications for the student learning system architecture. The learning coach can also control data flow to the student learning system. | 2020-06-11 |
20200184338 | REGULARIZATION OF RECURRENT MACHINE-LEARNED ARCHITECTURES - A modeling system trains a recurrent machine-learned model by determining a latent distribution and a prior distribution for a latent state. The parameters of the model are trained based on a divergence loss that penalizes significant deviations between the latent distribution the prior distribution. The latent distribution for a current observation is a distribution for the latent state given a value of the current observation and the latent state for the previous observation. The prior distribution for a current observation is a distribution for the latent state given the latent state for the previous observation independent of the value of the current observation, and represents a belief about the latent state before input evidence is taken into account. | 2020-06-11 |
20200184339 | REPRESENTATION LEARNING FOR INPUT CLASSIFICATION VIA TOPIC SPARSE AUTOENCODER AND ENTITY EMBEDDING - Described herein are embodiments of a unified neural network framework to integrate Topic modeling, Word embedding and Entity Embedding (TWEE) for representation learning of inputs. In one or more embodiments, a novel topic sparse autoencoder is introduced to incorporate discriminative topics into the representation learning of the input. Topic distributions of inputs are generated from a global viewpoint and are utilized to enable autoencoder to learn topical representations. A sparsity constraint may be added to ensure that the most discriminative representations are related to topics. In addition, both words and entity related information may be embedded into the network to help learn a more comprehensive input representation. Extensive empirical experiments show that embodiments of the TWEE framework outperform the state-of-the-art methods on different datasets. | 2020-06-11 |
20200184340 | HYBRID MODEL FOR DATA AUDITING - Implementations include processing a set of documents using an auto-encoder to provide a first sub-set of documents, the first sub-set of documents including electronic documents with a relatively high likelihood of providing true positives in an auditing process, processing a sub-set of documents using a set of auto-generated rules to provide a second sub-set of documents, the second sub-set of documents including electronic documents with a relatively high likelihood of providing false positives in an auditing process, and defining a master set of documents for the auditing process based on the sub-set of documents, the first sub-set of documents, and the second sub-set of documents, the master set of documents including at least a portion of the sub-set of documents, and at least a portion of the first sub-set of documents, and being absent the second sub-set of documents. | 2020-06-11 |
20200184341 | PROGRAM, INFORMATION PROCESSING METHOD, AND INFORMATION PROCESSING APPARATUS - The present technology relates to a program, an information processing method, and an information processing apparatus that make it possible to easily design network. | 2020-06-11 |
20200184343 | Prediction of Business Outcomes by Analyzing Voice Samples of Users - A method and a system for predicting business outcomes by analyzing voice data of users are provided. The method includes generation of predictor models based on test data of test users. The test data includes historical data of the test users, voice samples of the test users, and answers provided by the test users to psychometric questions. The predictor models are then used to predict psychometric features and business outcomes based on target data of a target user. The target data includes voice samples of the target user, historical data of the target user, and answers provided by the target user to the psychometric questions. | 2020-06-11 |
20200184344 | SYSTEM AND METHOD FOR MEASURING MODEL EFFICACY IN HIGHLY REGULATED ENVIRONMENTS - Systems and methods for measuring efficacy of prediction models are described. A processor generates champion scores, variation scores, and challenger scores based on data analyzed using a champion model and a challenger model. The processor uses the variation scores to define a control group and a test group based on the relationship of the variation scores to the champion scores and the challenger scores. The control group and test group are measured to determine an attributable impact on completed actions and based on the attributable impact, one of the champion model and challenger model is selected. | 2020-06-11 |
20200184345 | METHOD AND SYSTEM FOR GENERATING A TRANSITORY SENTIMENT COMMUNITY - A method and system of generating a transitory sentiment community. The method comprises receiving data in a database memory of a server computing device, the data extracted from a plurality of data sources, pre-processing the data based on text character removal and text character replacement, to provide pre-processed data that includes keywords used in a descriptive manner, performing a sentiment analysis on the keywords based at least in part upon a training model, the sentiment analysis identifying a conformance to at least one of a set of sentiment classifications recognized by the training model, and a sentiment intensity rating associated with the conformance, modifying the sentiment intensity rating associated with the sentiment classification upon detecting a sarcasm sentiment above a sarcasm sentiment threshold, and generating the transitory sentiment community based at least in part on the sentiment classification and the modified sentiment intensity rating. | 2020-06-11 |
20200184346 | DYNAMIC PREDICTION MODEL ESTABLISHMENT METHOD, ELECTRIC DEVICE, AND USER INTERFACE - A dynamic prediction model establishment method, an electric device and a user interface are provided. The dynamic prediction model establishment method includes the following steps. An integration model is established by a processing device according to at least one auxiliary data set. The integration model is modified as a dynamic prediction model by the processing device according to a target data set. A sampling point recommendation information is provided by the processing device according to an error degree or an uncertainty degree between the at least one auxiliary data set and the target data set. | 2020-06-11 |
20200184347 | Structurally Defining Knowledge Elements Within a Cognitive Graph - A computer-implementable method for managing a cognitive graph comprising: receiving data from a plurality of data sources; processing the data from the plurality of data sources, the processing the data from the plurality of data sources identifying a plurality of knowledge elements; and, storing the knowledge elements within the cognitive graph as a collection of knowledge elements, the storing universally representing knowledge obtained from the data, each knowledge element being structurally defined within the cognitive graph. | 2020-06-11 |
20200184348 | Communication Generation in Complex Computing Networks - This disclosure is directed to communication generation by traversing routes of a graph in a complex computing network. The communication generation is used for determining whether certain input data has certain desired data attributes. | 2020-06-11 |
20200184349 | SYSTEM AND METHOD FOR DESIGNING MACHINE AND DEEP LEARNING MODELS FOR AN EMBEDDED PLATFORM - A system for designing machine and deep learning models for an embedded platform is disclosed. The system includes a storage subsystem configured to store a plurality of datasets, one or more augmented datasets, a plurality of models and one or more frameworks. The storage subsystem includes a database module and a proposal module. The system also includes a code generation subsystem operatively coupled to the storage subsystem. The code generation subsystem is configured to generate a code in a predefined computer language for a selected model. The system also includes an implementation subsystem operatively coupled to the code generation subsystem. The implementation subsystem is configured to generate one or more test cases and one or more use-cases to test the selected model based on a generated code. | 2020-06-11 |
20200184350 | POST-HOC IMPROVEMENT OF INSTANCE-LEVEL AND GROUP-LEVEL PREDICTION METRICS - A post-processing method, system, and computer program product for post-hoc improvement of instance-level and group-level prediction metrics, including training a bias detector that learns to detect a sample that has an individual bias greater than a predetermined individual bias threshold value with constraints on a group bias, applying the bias detector on a run-time sample to select a biased sample in the run-time sample having a bias greater than the predetermined individual bias threshold bias value, and suggesting a de-biased prediction for the biased sample. | 2020-06-11 |
20200184351 | SYNTHESIZING HIGH-FIDELITY SIGNALS WITH SPIKES FOR PROGNOSTIC-SURVEILLANCE APPLICATIONS - The system receives original time-series signals from sensors in a monitored system. Next, the system detects and removes spikes from the original time-series signals to produce despiked original time-series signals, which involves using the original time-series data to optimize a damping factor, which is applied to a threshold for a spike-detection technique, and using the spike-detection technique with the optimized damping factor to detect the spikes. The system then generates despiked synthetic time-series signals, which are statistically indistinguishable from the despiked original time-series signals. The system also includes synthetic spikes, which have the same temporal, amplitude and width distributions as the spikes in the original time-series signals, in the despiked synthetic time-series signals to produce synthetic time-series signals with spikes. The system uses the synthetic time-series signals with spikes to train an inferential model, and uses the inferential model to perform prognostic-surveillance operations on subsequently-received signals from the monitored system. | 2020-06-11 |
20200184352 | INFORMATION OUTPUT SYSTEM, INFORMATION OUTPUT METHOD, AND RECORDING MEDIUM - An information output system for leading a person in such a way as to rapidly achieve a predetermined state for investigation of crime using communication means is provided. An information output system | 2020-06-11 |
20200184353 | LEARNING OF A FEATURE BASED ON BETTI SEQUENCES OBTAINED FROM TIME SERIES DATA - An apparatus obtains time series data to which a label indicating a category of classification is attached, where the time series data contains variations of target values during a period to which the label is attached. The apparatus divides the time series data into a plurality of sections, and generates an attractor for each of the plurality of sections where the attractor indicates a set of numerical values toward which the variations of the target values tends to evolve. The apparatus calculates a Betti sequence in accordance with the generated attractor for each of the plurality of sections, and obtains a feature for the Betti sequences of the plurality of sections by clustering the Betti sequences or by calculating a statistical quantity of the Betti sequences. The apparatus performs machine learning by using the obtained feature as an input. | 2020-06-11 |
20200184354 | PROFILE DATA CAMERA ADJUSTMENT - Camera settings are managed. A candidate subject for capture by a camera is detected. The detection is based on historical profile data related to a user of the camera. One or more adjustments of the camera are identified for capture of the candidate subject. The one or more adjustments are identified based on the detected candidate subject. A proper capture action is performed based on the identified adjustments. | 2020-06-11 |
20200184355 | SYSTEM AND METHOD FOR PREDICTING INCIDENTS USING LOG TEXT ANALYTICS - Systems and methods for predicting and preventing system incidents such as outages or failures based on advanced log analytics are described. A processing center comprising an incident prediction server and log database may receive application server logs generated by an application server and historical incident data generated by an incident database server. The processing center may be configured to cluster a subset of application server logs and based on the subset of application server logs and the incident data, determine in real time or near real time the likelihood of occurrence of an incident such as a system outage or failure. | 2020-06-11 |
20200184356 | Auxiliary Analysis System Using Expert Information and Method Thereof - An auxiliary analysis system using expert information comprises a user interface and a determination module. The user interface includes a plurality of analysis items which can be triggered by a user. The determination module coupled with the user interface includes a plurality of preliminary results corresponding to the analysis items. Each of the analysis items corresponds to at least one of the preliminary results. The user selects a plurality of analysis items to generate multiple preliminary results, and the preliminary results further generate a final judgment result by an operation process. | 2020-06-11 |
20200184357 | SYSTEM AND METHOD FOR OBTAINING RECOMMENDATIONS USING SCALABLE CROSS-DOMAIN COLLABORATIVE FILTERING - Aspects of the present disclosure involve systems, methods, devices, and the like for presenting a recommendation. In one embodiment, a system is introduced that includes a plurality of models for obtaining a recommendation score. The recommendation score may be obtained using one or more models which can include supervised and unsupervised learning as well as a combination of user information and transactions. In another embodiment, the system is introduced that can provide a total recommendation score and recommendation generated by an ensemble model whose input can include the one or more recommendation scores previously obtained. | 2020-06-11 |
20200184358 | Video Content Valuation Prediction Using A Prediction Network - In some embodiments, a method receives a plurality of inputs for a video for a plurality of times at a prediction network that includes a plurality of cells. The prediction network generates a plurality of predictions of watch behavior of the video for the plurality of inputs at the plurality of cells. The plurality of predictions predicts a performance of the video on a video delivery service for the plurality of times. Actual performance data generated from users viewing the video on the video delivery service is received before a time. A time series residual for at least a portion of the plurality of predictions is generated from the actual performance data and prior predictions. The portion of the predictions after the time using values in the time series residual is adjusted. The adjusted predictions of watch behavior are output for the video. | 2020-06-11 |
20200184359 | METHOD FOR PREDICTING PERMEABILITY AND OIL CONTENT IN A GEOLOGICAL FORMATION - Systems, methods, and apparatuses are provided for permeability prediction. The method acquires data associated with one or more geological formations, calculates, using processing circuitry and a trained Hidden Markov model, log-likelihood values to group the data into a plurality of clusters, and trains an artificial neural network for each of the plurality of clusters when the mode of operation is training mode. Further, the method acquires one or more formation properties corresponding to a geological formation, determines using the trained Hidden Markov model, a log-likelihood score associated with the one or more formation properties, identifies a cluster associated with the one or more formation properties as a function of the log-likelihood score, and predicts a permeability based at least in part on the one or more formation properties and a trained artificial neural network associated with the identified cluster when the mode of operation is forecasting mode. | 2020-06-11 |
20200184360 | PROCESSING APPARATUS, PROCESSING METHOD, ESTIMATING APPARATUS, ESTIMATING METHOD, AND PROGRAM - A processing apparatus is disclosed for representing cognitively biased selection behavior of a consumer as a learnable model with high prediction accuracy taking into account even feature values of a product and the consumer. The processing apparatus generates a selection model obtained by modeling selection behavior of a selection entity that selects at least one choice out of presented input choices. The processing apparatus includes an acquiring unit to acquire training data including a plurality of input feature vectors that indicate features of a plurality of the choices presented to the selection entity and an output feature vector that indicates a feature of an output choice. The processing apparatus further includes an input combining unit to combine the plurality of input vectors to generate an input combined vector, and a learning processing unit to learn a selection model on the basis of the input combined vector and the output vector. | 2020-06-11 |
20200184361 | CONTROLLED NOT GATE PARALLELIZATION IN QUANTUM COMPUTING SIMULATION - Techniques facilitating controlled NOT gate parallelization in quantum computing simulation are provided. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a selector component that can select a first qubit and a second qubit. The first qubit can be a control qubit. The computer executable components can also comprise a parallelization component that can reorder the first qubit with the second qubit and a replication component that can simulate a controlled NOT gate during the reordering by the parallelization component. | 2020-06-11 |
20200184362 | EXTENDED COHERENCE AND SINGLE-SHOT READOUT OF A SILICON-VACANCY SPIN IN DIAMOND - Systems and methods are disclosed for preparing and evolving atomic defects in diamond. Silicon vacancy spins may be cooled to temperatures equal to or below 500 mK to reduce the influence of phonons. The cooling, manipulation, and observation systems may be designed to minimize added heat into the system. A CPMG sequence may be applied to extend coherence times. Coherence times may be extended, for example, to 13 ms. | 2020-06-11 |
20200184363 | QUANTUM COMPUTER HARDWARE WITH REFLECTIONLESS FILTERS FOR THERMALIZING RADIO FREQUENCY SIGNALS - A quantum computer hardware apparatus may include a first stage, which is connected to one or more signal generators, and a second stage adapted to be cooled down at a lower temperature than the first stage. Superconducting qubits are arranged in the second stage. The signal generators are configured, each, to generate radio frequency (RF) signals to drive the qubits, in operation. The apparatus may further include an intermediate stage between the first stage and the second stage, wherein the intermediate stage comprises one or more coolable filters, the latter configured for thermalizing RF signals from the signal generators. Related methods for thermalizing radio frequency signals in a quantum computer hardware apparatus are also disclosed. | 2020-06-11 |
20200184364 | ROUTING QUANTUM SIGNALS IN THE MICROWAVE DOMAIN USING TIME DEPENDENT SWITCHING - A technique relates to configuring a superconducting router. The superconducting router is operated in a first mode. Ports are configured to be in reflection in the first mode in order to reflect a signal. The superconducting router is operated in a second mode. A given pair of the ports is connected together and in transmission in the second mode, such that the signal is permitted to pass between the given pair of the ports. | 2020-06-11 |
20200184365 | INTEGRATED CIRCUIT DEVICE AND CIRCUITRY - The present disclosure provides an integrated circuit (IC) device and a circuitry. The IC includes a measurement circuit and a classifier circuit. The measurement circuit is configured to acquire a practical voltage. The classifier circuit is configured to: generate an information on an immature classification by comparing a default voltage and the practical voltage; receive an information on a reference classification, wherein the reference classification is acquired by manually comparing the default voltage and the practical voltage; update the default voltage to a learned voltage based on the immature classification and the reference classification; and generate a prediction, based on the learned voltage, for adjusting a slew rate. | 2020-06-11 |
20200184366 | SCHEDULING TASK GRAPH OPERATIONS - According to an aspect of an embodiment, a method may include obtaining a task graph that represents operations for a task. The task graph may include multiple sub-task graphs. The method may further include obtaining a first computation time to perform a subset of the operations corresponding to a set of the multiple sub-task graphs based on parallel performance of the subset of the operations. The method may further include obtaining a second computation time to perform the subset of the operations using multiple resources according to a resource schedule of the multiple resources and determining a difference between the first computation time and the second computation time. The method may further include in response to the difference satisfying a threshold, performing the operations of the task graph using the multiple resources based on the resource schedule of the multiple resources. | 2020-06-11 |
20200184367 | AUTOMATING CLUSTER INTERPRETATION IN SECURITY ENVIRONMENTS - Disclosed herein are methods, systems, and processes to automate cluster interpretation in computing environments to develop targeted remediation security actions. To interpret clusters that are generated by a clustering methodology without subjecting clustered data to classifier-based processing, separation quantifiers that indicate a spread in feature values across clusters are determined and used to discover relative feature importances of features that drive the formation of clusters, permitting a security server to identify features that discriminate between clusters. | 2020-06-11 |
20200184368 | MACHINE LEARNING IN HETEROGENEOUS PROCESSING SYSTEMS - Computer-implemented methods are provided for implementing training of a machine learning model in a heterogeneous processing system that includes a host computer operatively interconnected to an accelerator unit. The training operation involves an iterative optimization process for optimizing a model vector defining the model. Such a method includes, in the host computer, storing a matrix of training data and partitioning the matrix into a plurality of batches of data vectors. For each of successive iterations of the optimization process, a selected subset of the batches is provided to the accelerator unit. In the accelerator unit, each iteration of the optimization process is performed to update the model vector in dependence on vectors in the selected subset for that iteration. In the host computer, batch importance values are calculated for respective batches. The batch importance value is dependent on contributions of vectors in that batch to sub-optimality of the model vector. | 2020-06-11 |
20200184369 | MACHINE LEARNING IN HETEROGENEOUS PROCESSING SYSTEMS - Computer-implemented methods are provided for implementing training of a machine learning model in a heterogeneous processing system comprising a host computer operatively interconnected with an accelerator unit. The training includes a stochastic optimization process for optimizing a function of a training data matrix X, having data elements X | 2020-06-11 |
20200184370 | SYSTEM AND METHOD FOR AUTOMATIC LABELING OF CLUSTERS CREATED BY UNSUPERVISED MACHINE LEARING METHODS - Aspects of the present disclosure involve systems, methods, devices, and the like for auto-labeling clusters generated by machine learning models. In one embodiment, a system is introduced that can perform a series of operations for determining comprehensive labels for clusters output from machine learning methods used to classify data sets. The auto-labeling system may include generating labels determined using a computation of a frequency count, ratio, and coverage. These computations may use feature-based dictionaries which aid in the determination, storage, and analysis of the relevant features useful in labeling the clusters. | 2020-06-11 |
20200184371 | METHOD AND SYSTEM FOR FACILITATING COMBINING CATEGORICAL AND NUMERICAL VARIABLES IN MACHINE LEARNING - One embodiment of the subject matter combines categorical and numerical variables in machine learning based on a difference table for categorical variables. During operation, the system performs the following steps. First, the system receives an input value of a categorical variable. Next, the system determines a prediction based on the input value of the categorical variable, a most likely value of the categorical variable, and a difference table for the categorical variable, where the most likely value of the categorical variable is based on a plurality of values of the categorical variable and where the difference table for the categorical variable comprises a number for each pair of values of the categorical variable. Subsequently, the system produces a result that indicates the prediction. | 2020-06-11 |
20200184372 | Loosely-Coupled Inspection and Metrology System for High-Volume Production Process Monitoring - A metrology system is disclosed. In one embodiment, the metrology system includes a controller communicatively coupled to a reference metrology tool and an optical metrology tool, the controller including one or more processors configured to: generate a geometric model for determining a profile of a test HAR structure from metrology data from a reference metrology tool; generate a material model for determining one or more material parameters of a test HAR structure from metrology data from the optical metrology tool; form a composite model from the geometric model and the material model; measure at least one additional test HAR structure with the optical metrology tool; and determine a profile of the at least one additional test HAR structure based on the composite model and metrology data from the optical metrology tool associated with the at least one HAR test structure. | 2020-06-11 |
20200184373 | Recurrent Gaussian Mixture Model For Sensor State Estimation In Condition Monitoring - A computer-implemented method for monitoring a system includes training a recurrent Gaussian mixture model to model a probability distribution for each sensor of the system based on a set of training data. The recurrent Gaussian mixture model applies a Gaussian process to each sensor dimension to estimate current sensor values based on previous sensor values. Measured sensor data is received from the sensors of the system and an expectation maximization technique is performed to determine an expected value for a particular sensor based on the recurrent Gaussian mixture model and the measured sensor data. A measured sensor value is identified for the particular sensor in the measured sensor data. If the measured sensor value and the expected sensor value deviate by more than a predetermined amount, a fault detection alarm is generated to indicate that the system is not operating within a normal operating range. | 2020-06-11 |