18th week of 2021 patent applcation highlights part 61 |
Patent application number | Title | Published |
20210133551 | Programmable Impedance - A programmable impedance element consists of a plurality of nominally identical two-port elements, each two-port element having an impedance element and two switches, the two-port elements arranged in a chain fashion with a structured set of switches such that a range of impedances can be obtained from each cell by dynamically changing the connections between the impedance elements in the cell. The common cell is constructed by connecting the nominally identical two-port impedance elements in a way that the number of possible combinations of the impedance elements is reduced to the subset of all possible combinations that uses the minimum possible number of connections. This structure allows the creation of matched impedances using industry standard devices. The connections between impedance elements are switches that may be “field-programmable,” i.e., that may be set on the chip after manufacture and configured during operation of the circuit, or alternatively may be mask programmable. | 2021-05-06 |
20210133552 | NEURAL NETWORK LEARNING DEVICE, NEURAL NETWORK LEARNING METHOD, AND RECORDING MEDIUM ON WHICH NEURAL NETWORK LEARNING PROGRAM IS STORED - A neural network learning device | 2021-05-06 |
20210133553 | TRAINING A MODEL - There is provided a computer-implemented method ( | 2021-05-06 |
20210133554 | Cognitive Device Management Using Artificial Intelligence - Methods, apparatus, and processor-readable storage media for cognitive device management using artificial intelligence are provided herein. An example computer-implemented method includes determining an initial telemetry data collection frequency value for a given device by applying machine learning techniques to historic data pertaining to additional devices; collecting an initial set of telemetry data associated with the given device and one or more subsequent sets of telemetry data associated with the given device in accordance with the initial telemetry data collection frequency value; performing a comparison of the one or more subsequent sets of telemetry data to the initial set of telemetry data; updating the initial telemetry data collection frequency value by applying the machine learning techniques to information resulting from the comparison; determining automated actions related to the given device by utilizing a neural network in connection with the collected telemetry data; and automatically initiating the automated actions. | 2021-05-06 |
20210133555 | DISTRIBUTED LEARNING OF COMPOSITE MACHINE LEARNING MODELS - Computer-implemented techniques for learning composite machine learned models are disclosed. Benefits to implementors of the disclosed techniques include allowing non-machine learning experts to use the techniques for learning a composite machine learned model based on a learning dataset, reducing or eliminating the explorative trial and error process of manually tuning architectural parameters and hyperparameters, and reducing the computing resource requirements and model learning time for learning composite machine learned models. The techniques improve the operation of distributed learning computing systems by reducing or eliminating straggler effects and by reducing or minimizing synchronization latency when executing a composite model search algorithm for learning a composite machine learned model. | 2021-05-06 |
20210133556 | FEATURE-SEPARATED NEURAL NETWORK PROCESSING OF TABULAR DATA - Methods and systems for classifying tabular data include clustering columns from one or more input tables into column groups. The column groups are processed using a neural network that has a set of input layers, each input layer accepting a respective one column group from the column groups as input, to generate a classification output. A classification task is performed on the one or more input tables using the classification output. | 2021-05-06 |
20210133557 | COGNITIVE DATA PSEUDONYMIZATION - Computer systems, methods and program products for automating pseudonymization of personal identifying information (PII) using machine learning, metadata, and crowdsourcing patterns to identify and replace PII. Machine learning models are trained for classifying known column names or key names for processing, using metadata. Column or key names are classified to be unprocessed, anonymized or pseudonymized by a pseudonymizer without revealing PII or scrubbing data into a useless format. A library of crowdsourced patterns are utilized for matching PII to data values within column or key names and PII is mapped to replacement methods. Feedback from user annotations retrains the algorithms to improve classification accuracy and Deep Learning algorithms automate the identification of PII using regular expression generation to concisely articulate how pseudonymizers search for PII patterns within a data set. PII replacement is mapped consistently across entire data packages and the crowdsourced pattern library is updated with generated regular expressions. | 2021-05-06 |
20210133558 | DEEP-LEARNING MODEL CREATION RECOMMENDATIONS - One embodiment provides a method, including: accessing historical deployment information for a plurality of deep-learning models, wherein the historical deployment information identifies values for model parameters of a deep-learning model during deployment of the deep-learning model; receiving information related to a target deep-learning model that a developer is creating, wherein the received information identifies components being utilized in the target deep-learning model; determining, by comparing the received information to the historical deployment information, expected values for target model parameters of the target deep-learning model based upon the components utilized within the target deep-learning model; and providing a recommendation for a modification to the target deep-learning model based upon the expected values, wherein the modification comprises a change to at least one component of the target deep-learning model. | 2021-05-06 |
20210133559 | IOT-BASED NETWORK ARCHITECTURE FOR DETECTING FAULTS USING VIBRATION MEASUREMENT DATA - In one embodiment, a device in a network receives a machine learning encoder and decoder trained by a supervisory service. The service trains the encoder and decoder using vibration measurement data sent to the service by a plurality of devices. The device trains, based on the received encoder, a classifier to determine whether vibration measurement data is indicative of a behavioral anomaly. The device receives vibration measurement data captured by a particular set of one or more vibration sensors of a monitored system. The device evaluates, using the trained decoder, the received vibration measurement data to determine whether the data is indicative of a structural anomaly in the monitored system. The device evaluates, using the trained classifier, the received vibration measurement data to determine whether the data is indicative of a behavioral anomaly in the monitored system. | 2021-05-06 |
20210133560 | ARTIFICIAL INTELLIGENCE SERVER - Disclosed herein is an artificial intelligence server which receives a user dictionary, calculates rankings of a plurality of keywords included in the user dictionary, generates a cloud user dictionary according to the rankings of the plurality of keywords, and trains a natural language processing model. | 2021-05-06 |
20210133561 | ARTIFICIAL INTELLIGENCE DEVICE AND METHOD OF OPERATING THE SAME - An artificial intelligence device may acquire fine dust flow information indicating change in fine dust state over time in a space, in which an air cleaner is located, based on weather information and a fine dust information set, determine an operation time of the air cleaner based on the acquired fine dust flow information, and transmit a notification for requesting operation at the determined operation time to the air cleaner via a communication interface. | 2021-05-06 |
20210133562 | ARTIFICIAL INTELLIGENCE SERVER - An artificial intelligence (AI) server is provided. The AI server includes a communication interface configured to communicate with an electronic device, and at least one processor configured to update a classification layer by training an artificial intelligence model in such a manner that classification training data and classification labeling data are provided to the artificial intelligence model including a feature extraction layer for extracting a feature vector and a classification layer for classifying input data using the feature vector, and transmit the updated classification layer to the electronic device. | 2021-05-06 |
20210133563 | METHOD AND APPARATUS FOR TRAINING NEURAL NETWORK, AND STORAGE MEDIUM - A method for training a neural network, includes: training a super network to obtain a network parameter of the super network, wherein each network layer of the super network includes multiple candidate network sub-structures in parallel; for each network layer of the super network, selecting, from the multiple candidate network sub-structures, a candidate network sub-structure to be a target network sub-structure; constructing a sub-network based on target network sub-structures each selected in a respective network layer of the super network; and training the sub-network, by taking the network parameter inherited from the super network as an initial parameter of the sub-network, to obtain a network parameter of the sub-network. | 2021-05-06 |
20210133564 | SPACE TIME ELECTRON-HOLE CHARGE TRANSPORT NETWORK FOR SOLID-STATE MATERIAL STUDIES - A method of training a neural network modeling physical phenomena of semiconductor material includes receiving plurality of training pairs corresponding to a semiconductor material. Each training pair comprises an input charge to a distinct voxel of the semiconductor material and one or more output signals generated by the distinct voxel in response to the input charge. A neural network is trained using the training pairs. The neural network models the semiconductor material and each voxel is represented in the neural network by a tensor field defined by (i) a location of the voxel within the semiconductor material and (ii) one or more physics-based phenomena within the voxel at the location. | 2021-05-06 |
20210133565 | Smooth Continuous Piecewise Constructed Activation Functions - Aspects of the present disclosure are directed to novel activation functions which enable improved reproducibility and accuracy tradeoffs in neural networks. In particular, the present disclosure provides a family of activation functions that, on one hand, are smooth with continuous gradient and optionally monotonic but, on the other hand, also mimic the mathematical behavior of a Rectified Linear Unit (ReLU). As examples, the activation functions described herein include a smooth rectified linear unit function and also a leaky version of such function. In various implementations, the proposed functions can provide both a complete stop region and a constant positive gradient (e.g., that can be 1) pass region like a ReLU, thereby matching accuracy performance of a ReLU. Additional implementations include a leaky version and/or functions that feature different constant gradients in the pass region. | 2021-05-06 |
20210133566 | DECISION-MAKING DEVICE, UNMANNED SYSTEM, DECISION-MAKING METHOD, AND PROGRAM - A decision-making device ( | 2021-05-06 |
20210133567 | DETERMINING AN OUTPUT SIGNAL BY AGGREGATING PARENT INSTANCES - A computer-implemented method of training a function for use in controlling or monitoring a physical system operating in an environment. The function maps an input instance comprising sensor measurements to an output signal. The function is parameterized by a set of parameters including representations of multiple reference instances. Given a training input instance, a number of reference instances are identified as being similar to the training input instance, and their representations and/or output signals are aggregated into an aggregate latent representation for the training input instance. Based on this aggregate latent representation, an output signal for the training input instance is determined, which is compared to a training output signal to derive a training signal. At least a representation of a reference instance is adjusted according to the training signal, obtaining a reference instance not comprised in the training dataset. | 2021-05-06 |
20210133568 | METHODS AND SYSTEMS FOR TRAINING MULTI-BIT SPIKING NEURAL NETWORKS FOR EFFICIENT IMPLEMENTATION ON DIGITAL HARDWARE - The present invention relates to methods of sparsifying signals over time in multi-bit spiking neural networks, methods of training and converting these networks by interpolating between spiking and non-spiking regimes, and their efficient implementation in digital hardware. Four algorithms are provided that encode signals produced by nonlinear functions, spiking neuron models, supplied as input to the network, and any linear combination thereof, as multi-bit spikes that may be compressed and adaptively scaled in size, in order to balance metrics including the desired accuracy of the network and the available energy in hardware. | 2021-05-06 |
20210133569 | METHODS, COMPUTING DEVICES, AND STORAGE MEDIA FOR PREDICTING TRAFFIC MATRIX - The disclosure provides a method for predicting a traffic matrix, a computing device, and a storage medium. The method includes: establishing a dataset based on continuous historical traffic matrices; and inputting one or more historical traffic matrices in the dataset into a trained model for predicting traffic matrices, to obtain one or more predicted traffic matrices. The trained model for predicting traffic matrices is obtained by the following actions: establishing a model for predicting traffic matrices based on a correlation-modeling neural network and a temporal-modeling neural network; and training the model for predicting traffic matrices based on a set of training samples, in which the set of training samples includes sample traffic matrices and label traffic matrices corresponding to the sample traffic matrices at prediction moment samples. | 2021-05-06 |
20210133570 | NEURAL NETWORK METHOD AND APPARATUS - A method and apparatus for processing data of a neural network. The method includes: obtaining one or more bit representations of data used for processing a neural network; generating a plurality of candidate profiles based on the bit representations; determining a final profile by comparing compression performances for each of the candidate profiles; and determining an optimal configuration for compressing data of the neural network based on the determined final profile. | 2021-05-06 |
20210133571 | Systems and Methods for Training Neural Networks - Systems and methods for training models in accordance with embodiments of the invention are illustrated. One embodiment includes a method for training an overparameterized model. The method includes steps for initializing an overparameterized model, receiving a set of one or more training samples, determining losses for the set of training samples based on a loss function by computing a loss component of the loss function, and computing a regularizing component of the loss function, wherein computing the regularizing component includes applying a potential function to weights of the overparameterized model, and updating weights of the model based on the determined losses for the set of training samples. | 2021-05-06 |
20210133572 | IMAGE ANALYSIS APPARATUS USING MACHINE LEARNING-BASED ARTIFICIAL INTELLIGENCE INCLUDING PREPROCESSING MODULES - Disclosed herein is an image preprocessing/analysis apparatus using machine learning-based artificial intelligence. The image preprocessing apparatus includes a computing system, and the computing system includes: a processor; a communication interface configured to receive an input image; and an artificial neural network configured to generate first and second preprocessing conditions through inference on the input image. The processor includes a first preprocessing module configured to generate a first preprocessed image and a second preprocessing module configured to generate a second preprocessed image. The processor is configured to control the first preprocessing module, the second preprocessing module, the artificial neural network, and the communication interface so that the first preprocessed image and the second preprocessed image are transferred to an image analysis module configured to perform image analysis on the input image based on the first preprocessed image and the second preprocessed image. | 2021-05-06 |
20210133573 | SYSTEM AND METHOD FOR TRAINING NEURAL NETWORKS - Systems and methods for training a neural network or an ensemble of neural networks are described. A hyper-parameter that controls the variance of the ensemble predictors is used to address overfitting. For larger values of the hyper-parameter, the predictions from the ensemble have more variance, so there is less overfitting. This technique can be applied to ensemble learning with various cost functions, structures and parameter sharing. A cost function is provided and a set of techniques for learning are described. | 2021-05-06 |
20210133574 | Network Anomaly Detection - A method for detecting network anomalies includes receiving a control message from a cellular network and extracting one or more features from the control message. The method also includes predicting a potential label for the control message using a predictive model configured to receive the one or more extracted features from the control message as feature inputs. Here, the predictive model is trained on a set of training control messages where each training control message includes one or more corresponding features and an actual label. The method further includes determining that a probability of the potential label satisfies a confidence threshold. The method also includes analyzing the control message to determine whether the control message corresponds to a respective network performance issue. When the control message impacts network performance, the method includes communicating the network performance issue to a network entity responsible for the network performance issue. | 2021-05-06 |
20210133575 | WATER TREATMENT PLANT AND METHOD FOR OPERATING WATER TREATMENT PLANT - A water treatment plant which performs water treatment using a water treatment device includes an imaging device, a processing device, and a control device. The imaging device images a water treatment environment of the water treatment device and outputs image data obtained by imaging. The processing device causes an arithmetic device which performs an arithmetic operation using one or more calculation models generated by machine learning to execute the arithmetic operation employing the image data output from the imaging device as input data of the one or more calculation models. The control device controls the water treatment device on the basis of output information output from the arithmetic device by executing the arithmetic operation. | 2021-05-06 |
20210133576 | METHOD AND APPARATUS FOR AUTOMATICALLY PRODUCING AN ARTIFICIAL NEURAL NETWORK - A method for automatically generating an artificial neural network that encompasses modules and connections that link those modules, successive modules and/or connections being added to a current starting network. Modules and/or connections that are to be added are selected randomly from a predefinable plurality of possible modules and connections that can be added. A plurality of possible refinements of the current starting network respectively are generated by adding to the starting network modules and/or connections that are to be added. One of the refinements from the plurality of possible refinements is then selected in order to serve as a current starting network in a subsequent execution of the method. | 2021-05-06 |
20210133577 | PROTECTING DEEP LEARNED MODELS - Apparatus and methods are disclosed for using machine learning models with private and public domains. Operations can be applied to transform input to a machine learning model in a private domain that is kept secret or otherwise made unavailable to third parties. In one example of the disclosed technology, a method includes applying a private transform to produce transformed input, providing the transformed input to a machine learning model that was trained using a training set modified by the private transform, and generating inferences with the machine learning model using the transformed input. Examples of suitable transforms that can be employed include matrix multiplication, time or spatial domain to frequency domains, and partitioning a neural network model such that an input and at least one hidden layer form part of the private domain, while the remaining layers form part of the public domain. | 2021-05-06 |
20210133578 | COMPOUND MODEL SCALING FOR NEURAL NETWORKS - A method for determining a final architecture for a neural network to perform a particular machine learning task is described. The method includes receiving a baseline architecture for the neural network, wherein the baseline architecture has a network width dimension, a network depth dimension, and a resolution dimension; receiving data defining a compound coefficient that controls extra computational resources used for scaling the baseline architecture; performing a search to determine a baseline width, depth and resolution coefficient that specify how to assign the extra computational resources to the network width, depth and resolution dimensions of the baseline architecture, respectively; determining a width, depth and resolution coefficient based on the baseline width, depth, and resolution coefficient and the compound coefficient; and generating the final architecture that scales the network width, network depth, and resolution dimensions of the baseline architecture based on the corresponding width, depth, and resolution coefficients. | 2021-05-06 |
20210133579 | NEURAL NETWORK INSTRUCTION STREAMING - An artificial neural network is implemented via an instruction stream. A header of the instruction stream and a format for instructions in the instruction stream are defined. The format includes an opcode, an address, and data. The instruction stream is created using the header, the opcode, the address, and the data. The artificial neural network is implemented by providing the instruction stream to a computer processor for execution of the instruction stream. | 2021-05-06 |
20210133580 | UPGRADING A MACHINE LEARNING MODEL'S TRAINING STATE - A method for upgrading a training state of a machine learning model is described, the machine learning model being configured for supporting a model update. The method comprises predicting a set of target data elements based on the input data structure using the machine learning model, a target data element corresponding to a respective characteristic of the input data structure, and determining, for at least one of the predicted target data elements, whether or not a respective target data element is presumably erroneous. The method further comprises determining, for each presumably erroneous target data element detected in the previous step, an estimated corrected target data element, and performing, based on at least one estimated corrected target data element, a step of updating the training state of the machine learning model. | 2021-05-06 |
20210133581 | METHOD AND SYSTEM FOR FACILITATING USER SUPPORT USING MULTIMODAL INFORMATION - A method for facilitating user support using multimodal information involves obtaining an interaction between a user and a support agent, generating a question embedding from the interaction, obtaining a clickstream associated with the interaction, and generating a clickstream embedding from the clickstream. The question embedding and the clickstream embedding form a shared latent space representation. The method further involves decoding a problem summary from the shared latent space representation and providing the problem summary to the support agent. | 2021-05-06 |
20210133582 | TRAINING TRAJECTORY SCORING NEURAL NETWORKS TO ACCURATELY ASSIGN SCORES - Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network having a plurality of sub neural networks to assign respective confidence scores to one or more candidate future trajectories for an agent. Each confidence score indicates a predicted likelihood that the agent will move along the corresponding candidate future trajectory in the future. In one aspect, a method includes using the first sub neural network to generate a training intermediate representation; using the second sub neural network to generate respective training confidence scores; using a trajectory generation neural network to generate a training trajectory generation output; computing a first loss and a second loss; and determining an update to the current values of the parameters of the first and second sub neural networks. | 2021-05-06 |
20210133583 | DISTRIBUTED WEIGHT UPDATE FOR BACKPROPAGATION OF A NEURAL NETWORK - Speed of training a neural network is improved by updating the weights of the neural network in parallel. In at least one embodiment, after back propagation, gradients are distributed to a plurality of processors, each of which calculate a portion of the updated weights of the neural network. | 2021-05-06 |
20210133584 | ANALYSIS APPARATUS, NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM FOR STORING ANALYSIS PROGRAM, AND ANALYSIS METHOD - A method includes: generating a refine image from an incorrect image from which an incorrect label is inferred by a neural network; generating a third map by superimposing a first map and a second map, the first map indicating pixels to each of which a change is made in generating the refine image, of plural pixels in the incorrect image, the second map indicating a degree of attention for each local region in the refine image, each local region being a region that has drawn attention at the time of inference by the neural network, and the third map indicating a degree of importance for each pixel for inferring a correct label; and obtaining an added value for respective divided region in the third map by summing pixel values within the respective divided region, the respective divided region being a region divided according to a predetermined index. | 2021-05-06 |
20210133585 | APPARATUS AND METHOD FOR UNSUPERVISED DOMAIN ADAPTATION - An apparatus is for unsupervised domain adaptation for allowing a deep learning model with supervised learning on a source domain completed to be subjected to unsupervised domain adaptation to a target domain. The apparatus includes a first learning unit to perform a forward pass by inputting a pair (x | 2021-05-06 |
20210133586 | SYSTEM AND METHOD FOR UNSUPERVISED ABSTRACTION OF SENSITIVE DATA FOR REALISTIC MODELING - An abstraction system for generating a standard customer profile in a data processing system has a processing device and a memory. The abstraction system may receive customer data from a computing device over a network and perform unsupervised learning on the customer data to produce a plurality of clusters of customers with a first feature in common. The abstraction system performs unsupervised learning on the plurality of clusters of customers to produce a plurality of sub-clusters of customers with a second feature in common, and repeats the unsupervised learning on the plurality of sub-clusters produced to produce further sub-clusters with a plurality of features in common. The abstraction system determines that a sub-cluster represents a standard customer and stores a plurality of standard customer profiles based on the determined standard customers. The abstraction system provides the standard customer profiles to a cognitive system for generating synthetic transaction data. | 2021-05-06 |
20210133587 | TWO-SERVER PRIVACY-PRESERVING CLUSTERING - Described herein are systems and techniques for privacy-preserving unsupervised learning. The disclosed system and methods can enable separate computers, operated by separate entities, to perform unsupervised learning jointly based on a pool of their respective data, while preserving privacy. The system improves efficiency and scalability, while preserving privacy and avoids leaking a cluster identification. The system can jointly compute a secure distance via privacy-preserving multiplication of respective data values x and y from the computers based on a 1-out-of-N oblivious transfer (OT). In various embodiments, N may be 2, 4, or some other number of shares. A first computer can express its data value x in base-N. A second computer can form an | 2021-05-06 |
20210133588 | METHOD FOR MODEL ADAPTATION, ELECTRONIC DEVICE AND COMPUTER PROGRAM PRODUCT - A method for model adaptation, an electronic device, and a computer program product are disclosed. For example, the method comprises processing first input data by using a first machine learning model having first parameter set values, to obtain first feature information of the first input data, the first machine learning model having a capability of self-ordering and the first parameter set values being updated after the processing of the first input data; generating a first classification result for the first input data based on the first feature information by using a second machine learning model having second parameter set values; processing second input data by using the first machine learning model having the updated first parameter set values, to obtain second feature information of the second input data; and generating a second classification result for the second input data based on the second feature information by using the second machine learning model having the second parameter set values. As such, the machine learning model for classification can be adapted to changes in features of input data to provide better classification results. | 2021-05-06 |
20210133589 | INVERSE NEURAL NETWORK FOR PARTICLE DETECTION IN A SOLID-STATE -DEVICES - For training to and/or estimating location, energy level, and/or time of occurrence of incident radiation on a solid-state detector, a machine-learned model, such as a neural network, performs the inverse problem. An estimate of the location, energy level, and/or time is output by the machine-learned model in response to input of the detected signal (e.g., voltage over time). The estimate may account for material property variation of the solid-state detector in a rapid and easily calculated way, and with a minimal amount of data. | 2021-05-06 |
20210133590 | SYSTEM AND METHOD FOR MACHINE LEARNING ARCHITECTURE WITH DIFFERENTIAL PRIVACY - Differential private dictionary learning privatizes input data by training an autoencoder to learn a dictionary, the autoencoder including an encoder and a decoder, and weights of channels in a layer in the decoder defining dictionary atoms forming the dictionary; inputting the input data to the trained autoencoder; projecting, using the encoder, the input data on the learned dictionary to generate a sparse representation of the input data, the sparse representation including coefficients for each dictionary atom; adding noise to the sparse representation to generate a noisy sparse representation; and mapping, using the decoder, the noisy sparse representation to a reconstructed differentially private output. | 2021-05-06 |
20210133591 | REDUCING TRAINING TIMES OF DEEP NEURAL NETWORKS THROUGH EFFICIENT HYBRID PARALLELISM - Presented are systems and methods to automatically find efficient parallelization strategies for deep neural networks (DNNs). A computation graph comprising an efficiently ordered sequence of vertices aids in computing the best parallelizing strategy in a relatively short time. Effectiveness of the parallelization strategies is evaluated on various DNNs, and the performance of the strategies proposed by various embodiments is compared against data parallelism, expert-designed strategies, and other state-of-the-art approaches. Experimental results demonstrate that the proposed strategies outperform a baseline data parallelism strategy and achieve better performance than expert-designed strategies and state-of-the-art approaches. | 2021-05-06 |
20210133592 | METHODS AND SYSTEMS OF PROCESSING COMPLEX DATA SETS USING ARTIFICIAL INTELLIGENCE AND DECONVOLUTION - Disclosed herein, are systems and methods for analyzing complex data signals using artificial intelligence and/or deconvolution algorithms to determine output pertaining to the state or status of one or more parameters. Data sets may include signals from various sources that can confound or distort the signals of interest. Accordingly, disclosed herein are deconvolution algorithms that enable the determination of the status of sources that correspond to the signals of interest. | 2021-05-06 |
20210133593 | ANALYSIS OF ANOMALIES IN A FACILITY - There is provided a system and method of analysing anomalies in one or more electronic appliances including at least one computer. The method comprises, by a processor and memory circuitry, upon detection of a deviation of a given parameter representative of the one or more electronic appliances from an operational state, providing a model associated with the given parameter, wherein the model links one or more other parameters to the given parameter, wherein the one or more other parameters affect the given parameter, and based at least on the model, identifying, among the one or more other parameters, at least one parameter P | 2021-05-06 |
20210133594 | Augmenting End-to-End Transaction Visibility Using Artificial Intelligence - Methods, apparatus, and processor-readable storage media for augmenting end-to-end transaction visibility using artificial intelligence are provided herein. An example computer-implemented method includes obtaining data related to multiple transaction flows across multiple data sources within an enterprise system, and forecasting anomalies in connection with at least one of the transaction flows by applying one or more of a first set of artificial intelligence techniques to portions of the obtained data, wherein applying the artificial intelligence techniques is based on which of the multiple data sources correspond to the portions of the obtained data. Such a method further includes determining automated actions to be performed in connection with the forecasted anomalies by applying one or more of a second set of artificial intelligence techniques to portions of the obtained data related to the forecasted anomalies, and performing the automated actions in connection with the at least one transaction flow. | 2021-05-06 |
20210133595 | METHOD FOR DESCRIBING PREDICTION MODEL, NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM FOR STORING PREDICTION MODEL DESCRIPTION PROGRAM, AND PREDICTION MODEL DESCRIPTION DEVICE - A method includes: selecting a plurality of models by using data set and a prediction result of a prediction model for the data set, each model being configured to linearly separate data included in the data set input to the prediction model; creating a decision tree such that a leaf of the decision tree corresponds to each selected model and a node of the decision tree corresponds to each of logics classifying the data from a root to each leaf of the decision tree; specifying a branch to be pruned by using variation in the data belonging to each leaf of the created decision tree; recreating the decision tree by using the data set corresponding to the decision tree in which the specified branch has been pruned; and outputting each of the logics corresponding to each node of the recreated decision tree as a description result of the prediction model. | 2021-05-06 |
20210133596 | RANKING IMAGE SOURCES FOR TRANSFER LEARNING - A system for ranking machine learning base models for transfer learning purposes is described. The system receives image data in the form an image or an image set and extracts image tags from the images. The image tags are expanded into a set of associated terms using a word embedding database and model. The associated terms are used to query a knowledge database for parent or categorical terms used to rank various matching machine learning base models that may be improved or trained by the image data. | 2021-05-06 |
20210133597 | WEATHER TRIGGER MAPPING ENGINE - In an approach to mapping marketing triggers across geographies, a computer receives one or more variables associated with one or more rulesets. A computer retrieves data corresponding to the one or more variables. A computer sorts the retrieved data by season. A computer computes a seasonal average for each variable by associated postal code. A computer trains a self-organizing map to arrange the data associated with the source geography postal codes into one or more source clusters. A computer maps the retrieved data and the computed data associated with the target geography to the one or more source clusters, thereby creating target counterpart clusters that correspond with at least one of the one or more source clusters. A computer applies one or more cluster rulesets associated with the one or more source clusters to the corresponding one or more target counterpart clusters. A computer outputs one or more bucket sheets. | 2021-05-06 |
20210133598 | MACHINE LEARNING METHOD, APPARATUS, AND COMPUTER PROGRAM FOR PROVIDING PERSONALIZED EDUCATIONAL CONTENT BASED ON LEARNING EFFICIENCY - Disclosed herein is a method of providing user-customized learning content in a service server, which includes a) for a specific subject, configuring a problem database including at least one of multiple-choice problems each including at least one example, providing the problem to user devices, and collecting example selection data of users for the problem from the user devices, b) estimating a probability of right answer to the problem for each of the users using the example selection data of each of the users, and assuming that any user selects an example of any problem, calculating, for each problem, a change rate of probabilities of right answer to all problems contained in the problem database for the user, and sorting the problems contained in the problem database in the order of the high change rate to recommend them to the user. | 2021-05-06 |
20210133599 | METHOD AND SYSTEM FOR PREDICTING RESOURCE REALLOCATION IN A RESOURCE POOL - A method for managing pool device resources, the method comprising obtaining, by a resource use manager, a plurality of data points, generating a resource prediction model based on the plurality of data points, and initiating access to a PCI bus device operating on a pool device using a virtual switch operating on a second pool device based on the resource prediction model. | 2021-05-06 |
20210133600 | SYSTEMS AND METHODS FOR VALIDATION OF ARTIFICIAL INTELLIGENCE MODELS - Systems and methods are described which relate to machine learning model validation. A first machine learning model may be trained to dependent variable data for a first population. A second machine learning model may be trained to simulate dependent variable data for the first population. The second machine learning model may then be applied to student activity data of a second population having different characteristics from the first population to produce simulated dependent variable data. The first machine learning model may then generate predictions for the second population, which may be validated via comparison to the simulated dependent variable data. A given simulated dependent variable value may be generated by the second machine learning model at a specific time T | 2021-05-06 |
20210133601 | SYSTEMS AND METHODS FOR IDENTIFYING UNKNOWN PROTOCOLS ASSOCIATED WITH INDUSTRIAL CONTROL SYSTEMS - A device may receive a hash table that includes lists of protocol detectors, wherein the hash table is generated based on historical process data identifying potential process variables associated with an industrial control system. The device may receive a packet identifying potential process variables associated with the industrial control system, and may extract, from the packet, packet data identifying a source address, a destination address, a port, and a transport protocol. The device may compare the packet data with data in the hash table to identify a set of lists of protocol detectors, and may process the packet data, with the set of lists of protocol detectors, to determine a matching protocol, no matching protocol, or a potential matching protocol for the packet. The device may perform one or more actions based on determining the matching protocol, no matching protocol, or the potential matching protocol for the packet. | 2021-05-06 |
20210133602 | CLASSIFIER TRAINING USING NOISY SAMPLES - An example system includes a processor to receive input data comprising noisy positive data and clean negative data. The processor is to cluster the input data. The processor is to compute a potential score for each cluster of the clustered input data. The processor is to iteratively refine cluster quality of the clusters using the potential scores of the clusters as weights. The processor is to train a classifier by sampling the negative dataset uniformly and the positive set in a non-uniform manner based on the potential score. | 2021-05-06 |
20210133603 | PARKING SPOT LOCATOR BASED ON PERSONALIZED PREDICTIVE ANALYTICS - In an approach to creating and using a reinforcement learning model for personalizing a recommendation of a parking spot, one or more computer processors receive a first destination associated with the vehicle. One or more computer processors determine a parking spot availability in proximity to the destination. One or more computer processors determine a recommended parking spot location. One or more computer processors display the recommended parking spot location to the user. One or more computer processors determine a first parking spot selection. One or more computer processors receive a first satisfaction rating associated with the recommended parking spot location. | 2021-05-06 |
20210133604 | SYSTEMS AND METHODS FOR CLASSIFYING MEDIA ACCORDING TO USER NEGATIVE PROPENSITIES - A system for classifying media according to user negative propensities, includes a computing device configured to identify a negative behavioral propensity associated with a human subject, generate, using a classification algorithm, a media theme classifier, wherein the media theme classifier inputs media items and outputs principal themes of the media items, receive a media item to be transmitted to a device operated by the human subject, identify, using the media theme classifier, a principal theme of the media item, and determine if the principle theme matches the negative behavioral propensity. Identifying the principal theme further includes extracting, from the media item, a plurality of media item content elements, classifying each content element of the plurality of media item content elements to a media item object of a plurality of media item objects using an object classifier, and inputting the plurality of objects to the media theme classifier. | 2021-05-06 |
20210133605 | METHOD, APPARATUS, AND COMPUTER PROGRAM PRODUCTS FOR HIERARCHICAL MODEL FEATURE ANALYSIS AND DECISION SUPPORT - Various embodiments of the present disclosure are directed to model feature classification, analysis, and updating for analyzing one or more risk determination model(s). Embodiments include an improved apparatus configured to generate an actionable feature data object subset of a risk model feature set for a risk determination model, for example using an actionable determination model. The apparatus may provide the actionable feature data object subset for rendering to an interface of a client device. The apparatus may additionally or alternatively be configured to utilize user feedback, provided either directly or identified by the apparatus based on user interactions, to update the actionable determination model. The apparatus may additionally or alternatively be configured to maintain and utilize linked claim data object(s) for use in rendering a linked claim scores analysis interface that provides additional insight regarding the risk level of a particular entity. | 2021-05-06 |
20210133606 | METHOD AND SYSTEM FOR SELECTING LABEL FROM PLURALITY OF LABELS FOR TASK IN CROWD-SOURCED ENVIRONMENT - There is disclosed a method system for selecting a label for a task, the method comprising: receiving a plurality of labels, each of the label included within the plurality of labels being indicative of a given assessor's perceived preference of a first object of over a second object; analyzing the comparison task to determine a set of latent biasing features; executing a MLA configured to generating a respective latent score parameter for the first object and the second object, the respective latent score parameter indicative of a probable offset between the given assessor's perceived preference and an unbiased preference parameter of the first object over the second object; generating a predicted bias degree parameter for the given assessor; generating the unbiased preference parameter; using, by the server, the unbiased preference parameter as the label for the comparison task for the given assessor. | 2021-05-06 |
20210133607 | SYSTEMS AND METHODS FOR SELF-LEARNING ARTIFICIAL INTELLIGENCE OF THINGS (AIOT) DEVICES AND SERVICES - The invention is generally directed to systems and methods of monitoring or predicting a service event for an industrial asset using an artificial intelligence of things (AIoT) system including an AIoT device, AIoT cloud, and a self-learning AI classification and analytics engine. The device may include one or more sensors and an inference engine for reducing power consumption and detecting anomalies at the edge and sending data associated with anomalies to a signal processor for classification and AI-driven automatic configuration. Classification may be based on narrow-band analysis and/or machine learning models. If an anomaly is detected power may be provided to a communication module to send sensor data to the signal processor for classification and/or further processing. Classifications or determinations made by the signal processor or detected through a work-order system may be used to automatically retrain the inference model on the edge, so that the system is self-learning. | 2021-05-06 |
20210133608 | MEDICAL DOCUMENT MANAGEMENT SYSTEM - [Problem] To allow for systemically describing knowledge without fluctuations in expression by forming a knowledge entry using a set of entry attribute descriptions about the knowledge entry and including reference links to other knowledge entries or entry attribute descriptions thereof in the entry attribute descriptions. | 2021-05-06 |
20210133609 | ARTIFICIAL INTELLIGENCE DEVICE - An artificial intelligence device according to an embodiment of the present disclosure may receive voice data corresponding to viewing information and a search command from a display device, convert the received voice data into text data, obtain a first query indicating intention of the converted text data, convert the first query into a second query based on the viewing information, obtain a search result corresponding to the converted second, and transmit the obtained search result to the display device. | 2021-05-06 |
20210133610 | LEARNING MODEL AGNOSTIC MULTILEVEL EXPLANATIONS - A method, system and apparatus of using a computing device to explain one or more predictions of a machine learning model including receiving by a computing device a pre-trained artificial intelligence model with one or more predictions, generating by the computing device a multilevel explanation tree, linking neighborhood of datapoints around each of a plurality of training datapoints to the one or more predictions, and utilizing by the computing device the multilevel explanation tree to explain one or more predictions of the machine learning model. | 2021-05-06 |
20210133611 | METHODS AND SYSTEMS FOR PROVIDING DYNAMIC CONSTITUTIONAL GUIDANCE - A system for providing dynamic constitutional guidance. The system includes a label generator module configured to receive a periodic longevity factor, retrieve a user periodic longevity factor training set, and generate a naïve Bayes classification algorithm utilizing the user periodic longevity factor training set. The system includes a clustering module configured to receive a user adherence factor, retrieve a user adherence factor training set, and generate a k-means clustering algorithm using the user adherence factor training set. The system includes a processing module the processing module configured to retrieve a user ameliorative plan, evaluate a user ameliorative plan, generate an updated user ameliorative plan, and display the updated user ameliorative plan on a graphical user interface. | 2021-05-06 |
20210133612 | GRAPH DATA STRUCTURE FOR USING INTER-FEATURE DEPENDENCIES IN MACHINE-LEARNING - This disclosure involves generating graph data structures that model inter-feature dependencies for use with machine-learning models to predict end-user behavior. For example, a processing device receives an input dataset and a request to modify a first input feature of the input dataset. The processing device uses a graph data structure that models the inter-feature dependencies to modify the input dataset by propagating the modification of the first input feature to a second input feature dependent on the first input feature. The modification to the second input feature is a function of at least (a) the value of the first input feature and (b) a weight assigned to an edge linking the first input feature to the second input feature within the directed graph. The processing device then applies a trained machine-learning model to the modified input dataset to generate a prediction of an outcome. | 2021-05-06 |
20210133613 | QUANTUM STATE PREPARATION OF A PROBABILITY DISTRIBUTION FACILITATING QUANTUM AMPLITUDE ESTIMATION - Systems, computer-implemented methods, and computer program products to facilitate quantum state preparation of a probability distribution to perform amplitude estimation are provided. According to an embodiment, 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 data loader component that prepares a quantum state of a probability distribution based on structure of a quantum amplitude estimation algorithm. The computer executable components can further comprise an operator component that constructs a quantum operator based on the quantum state to perform quantum amplitude estimation. | 2021-05-06 |
20210133614 | MULTI-PHOTON, MULTI-DIMENSIONAL HYPER-ENTANGLEMENT USING HIGHER-ORDER RADIX QUDITS WITH APPLICATIONS TO QUANTUM COMPUTING, QKD AND QUANTUM TELEPORTATION - A system for use with quantum system comprises a light source for generating a first light beam, wherein the first light beam is modulated by a data stream. Entanglement circuitry receives the first light beam from the light source and generates at least two second light beams responsive to the first light beam. The at least two second light beams are entangled. Multistate photon processing circuitry processes each of the at least two second light beams to apply n-states to photons within the at least two light beams and create hyperentangled qudits, where n is greater than 2. | 2021-05-06 |
20210133615 | MATRIX-BASED QUANTUM-RESILIENT SERVER-CLUSTER - Methods for randomly storing data received at a plurality of silicon-based devices included in a matrix-computer-cluster are provided. The silicon-based devices may be arranged in predetermined rows within the matrix-computer-cluster. The matrix-computer-cluster may include a matrix formation of x, y and z coordinates. Methods may encapsulate a first device in a first quantum case. Methods may receive a data element at the first device. Methods may intercept the data element at the first case. Methods may generate a random number sequence at a first quantum random number generator included in the first case. The random number sequence may identify a set of x, y and z coordinates. Methods may determine a second device located within the matrix-computer-cluster that corresponds to the identified set of x, y and z coordinates. Methods may include transmitting the data element to second device, and storing the data element at the second device. | 2021-05-06 |
20210133616 | FAST COOLING OF ION MOTION IN A LONG CHAIN USING LOCAL MODES - Aspects of the present disclosure describe techniques for fast cooling of ion motion in a long chain using local motional modes. For example, a method is described for cooling down ions in a chain of ions that includes performing a cooling down sequence in which phonons are removed from the ions in the chain of ions by exciting and de-exciting local motional modes associated with individual ions, wherein sideband transitions that are part of the cooling down sequence are driven faster for the local motional modes than for collective motional modes for the same chain of ions; and completing the cooling down sequence when the local motional modes reach a ground state. A corresponding system and computer-readable storage medium for fast cooling of ion motion in a long chain using local motional modes are also described. | 2021-05-06 |
20210133617 | Hybrid Quantum-Classical Computer System for Parameter-Efficient Circuit Training - A method includes improved techniques for preparing the initial state of a quantum computer by reducing the number of redundant or unnecessary gates in a quantum circuit. Starting from an initial state preparation circuit ansatz, the method recursively removes gates and re-optimizes the circuit parameters to generate a reduced-depth state preparation. | 2021-05-06 |
20210133618 | Quantum Computer System and Method for Partial Differential Equation-Constrained Optimization - A computer (such as a classical computer, a quantum computer, or a hybrid quantum-classical computer) which performs PDE-constrained optimization of problems in cases in which, for a fixed {right arrow over (w)}, there is an explicit expression for {right arrow over (s)} that is either optimal or an approximation to the optimal solution. This enables embodiments of the present invention to eliminate {right arrow over (s)} from the optimization problem and to formulate the optimization as a polynomial unconstrained binary optimization (PUBO) problem. | 2021-05-06 |
20210133619 | MULTI-SAMPLE SYSTEM FOR EMULATING A QUANTUM COMPUTER AND METHODS FOR USE THEREWITH - A system is presented for emulating sampling of a quantum computer having a plurality of qubits arranged in a grid topology with N columns. The system includes a classical processor that is configured by operational instructions to perform operations that include producing final weights and variable assignments for the N columns based on N iterative passes through the grid topology, wherein each of the N iterative passes generates preliminary weights and variable assignments for a corresponding subset of the N columns, wherein the preliminary weights and variable assignments for a selected column of the corresponding subset based on the preliminary weights and variable assignments generated for a column adjacent to the selected column of the corresponding subset, and wherein the sampling of the quantum computer having the plurality of qubits is emulated by producing a plurality of samples from the N iterative passes based on the final weights and variable assignments for each of the N columns. | 2021-05-06 |
20210133620 | A METHOD FOR EXECUTION OF A MACHINE LEARNING MODEL ON MEMORY RESTRICTED INDUSTRIAL DEVICE - Provided is a method for executing a machine learning, model on a field device including executing basic operations of the machine learning, model divided into operation groups of basic operations according to a schedule, wherein basic operations of an operation group are executed while model parameters of a subsequent operation group are loaded. | 2021-05-06 |
20210133621 | ARTIFICIAL INTELLIGENCE TRANSPARENCY - A computer-implemented method for generating a group of representative model cases for a trained machine learning model may be provided. The method comprising determining an input space, determining an initial plurality of model cases, and expanding the initial plurality of model cases by stepwise modifying field values of the records representing the initial plurality of model cases resulting in an exploration set of model cases. Additionally, the method comprises obtaining a model score value for each record of the exploration set of model cases, continuing the expansion of the exploration set of model cases thereby generating a refined model case set, and selecting the records in the refined model case set based on relative record distance values and related model score values between pairs of records, thereby generating the group of representative model cases. | 2021-05-06 |
20210133622 | ML-BASED EVENT HANDLING - The invention relates to a computer-implemented method for processing events. The method provides a database comprising original event objects stored in association with canonical event objects. The method executes a learning algorithm on the associated original and canonical event objects for generating a trained ML program adapted to transform an original event object of any one of the one or more original data formats into a canonical event object having the canonical data format and uses the trained machine learning program for automatically transforming original event objects generated by an active IT-monitoring system into canonical event objects processable by an event handling system. | 2021-05-06 |
20210133623 | SELF-SUPERVISED OBJECT DETECTOR TRAINING USING RAW AND UNLABELED VIDEOS - An example system includes a processor to receive raw and unlabeled videos. The processor is to extract speech from the raw and unlabeled videos. The processor is to extract positive frames and negative frames from the raw and unlabeled videos based on the extracted speech for each object to be detected. The processor is to extract region proposals from the positive frames and negative frames. The processor is to extract features based on the extracted region proposals. The processor is to cluster the region proposals and assign a potential score to each cluster. The processor is to train a binary object detector to detect objects based on positive samples randomly selected based on the potential score. | 2021-05-06 |
20210133624 | RESILIENCY FOR MACHINE LEARNING WORKLOADS - In exemplary aspects, a golden data structure can be used to validate the stability of machine learning (ML) models and weights. The golden data structure includes golden input data and corresponding golden output data. The golden output data represents the known correct results that should be output by a ML model when it is run with the golden input data as inputs. The golden data structure can be stored in a secure memory and retrieved for validation separately or together with the deployment of the ML model for a requested ML operation. If the golden data structure is used to validate the model and/or weights concurrently with the performance of the requested operation, the golden input data is combined with the input data for the requested operation and run through the model. Relevant outputs are compared with the golden output data to validate the stability of the model and weights. | 2021-05-06 |
20210133625 | 3D PRINTER DEVICE MANAGEMENT USING MACHINE LEARNING - Systems and methods for 3D printer management can allow or reject printing of an object based on a model that is trained with machine learning. In one example, the model classifies the object according to object type. The object type can be compared against a list, such as a whitelist or blacklist, to determine whether to block the object from printing. The lists can be specific to users, such as based on an organizational group to which the user belongs. A print server can apply the model prior to forwarding the object to a 3D printer for printing. Both the models and the lists can evolve based on machine learning, such as based on which print decisions receive override from administrators. | 2021-05-06 |
20210133626 | APPARATUS AND METHOD FOR OPTIMIZING QUANTIZED MACHINE-LEARNING ALGORITHM - Disclosed herein are an apparatus and method for optimizing a quantized machine-learning algorithm. The apparatus includes one or more processors and executable memory for storing at least one program executed by the one or more processors. The at least one program sets the learning rate of the quantized machine-learning algorithm using at least one of an Armijo rule and golden search methods, calculates a quantized orthogonal compensation search vector from the search direction vector of the quantized machine-learning algorithm, compensates for the search performance of the quantized machine-learning algorithm using the quantized orthogonal compensation search vector, and calculates an optimized quantized machine-learning algorithm using the learning rate and the quantized machine-learning algorithm, the search performance of which is compensated for. | 2021-05-06 |
20210133627 | METHODS AND SYSTEMS FOR CONFIRMING AN ADVISORY INTERACTION WITH AN ARTIFICIAL INTELLIGENCE PLATFORM - A system for confirming an advisory interaction with an artificial intelligence platform. The system includes a constitutional generator module configured to receive a first advisory input, retrieve an expert input, select a machine-learning process as a function of the expert input, and generate a therapeutic corrector. The system includes a constitutional advisory module configured to display a therapeutic corrector on a graphical user interface and receive a second advisory input. The system includes a best practices module the best practices module designed and configured to retrieve from an expert database a best practices training set, calculate an optimal vector output, generate an optimal vector output containing an expected therapeutic corrector implementation response, authenticate a second advisory input, and update the best practices module. | 2021-05-06 |
20210133628 | Analysing Machine-Learned Classifier Models - A computer-implemented method comprises inputting a data item for processing by a machine-learned classifier model and receiving, in response to inputting the data item, a plurality of confidence scores for a plurality of respective classes, the plurality of confidence scores having been generated by the machine-learned classifier model based on the data item. The method further comprises determining a distance in dependence on a highest confidence score that is generated for the data item, and causing display of a class distribution diagram, wherein the class distribution diagram comprises: a graphical representation corresponding to a first class, said first class being one of said plurality of classes; a graphical representation corresponding to a second class, said second class being another of said plurality of classes; and a graphical representation corresponding to the data item, wherein the graphical representation corresponding to the data item is located at said distance between the graphical representation of the first class and the graphical representation of the second class. | 2021-05-06 |
20210133629 | Coastal Aquatic Conditions Reporting System Using A Learning Engine - The present invention relates to a software system that incorporates a digital learning engine comprised of machine learning algorithms that efficiently speeds up and expands the extraction of practically useful information from massively large data sets of observations and measurements of coastal aquatic environmental and human health conditions for the purpose of planning and implementing sustainable, preventative or mitigation actions by commercial, consumer, citizen, government, and research organizations. | 2021-05-06 |
20210133630 | MODEL INDUCTION METHOD FOR EXPLAINABLE A.I. - A model induction method for explainable artificial intelligence (XAI) may be shown and described. A model of a black-box AI may be an input to the model induction method, along with a set of sample input data. A linear or non-linear predictor function may be used to predict the output of the black-box model, producing a set of data points. The data points may be partitioned by a partitioning function, and each partition may represent one or more rules. The data may also be transformed using a number of transformation functions, such as a polynomial expansion. A local model may be fitted to the transformed function or functions. A set of rules may be interpreted from the local models and may form a white-box AI model. Linear or non-linear data may be modeled by the white-box model. Further, the white-box model may be implemented on a low-power device. | 2021-05-06 |
20210133631 | COMPUTER METHOD AND SYSTEM FOR AUTO-TUNING AND OPTIMIZATION OF AN ACTIVE LEARNING PROCESS - In one embodiment, a method includes a procedure for the Auto-Tuning and Optimization of an Active Learning Process including receiving unlabeled training set data; processing the unlabeled training set data using a selection process to yield a labeled training set; training a machine learning model using the labeled training set; inferring metadata elements from the model and storing metadata based on the model; iterating the foregoing steps two or more times, including using the metadata to influence how other unlabeled training set data is selected; all of the foregoing implementing one or more of: data and model privacy; optimal initialization; early abort; multi-loop querying strategy; dynamic-evolving querying strategy; querying strategy memorization; optimization and tuning. | 2021-05-06 |
20210133632 | SYSTEMS AND METHODS FOR MODEL MONITORING - Improved systems and methods for improved management of models for data science can facilitate seamless collaboration of data science teams and integration of data science workflows. Systems and methods provided herein can provide an open, unified platform to build, validate, deliver, and monitor models at scale. Systems and methods of the present disclosure may accelerate research, spark collaboration, increase iteration speed, and remove deployment friction to deliver impactful models. In particular, users may be allowed to visualize statistics about models and monitor models in real-time via a graphical user interface provided by the systems. | 2021-05-06 |
20210133633 | AUTONOMOUS MACHINE KNOWLEDGE TRANSFER - A controller for an automated machine may include including: one or more processors configured to: determine that a group affiliation of the automated machine switched from a first group of automated machines to a second group of automated machines, the first group of automated machines being assigned to one or more first tasks, the second group of automated machines being assigned to one or more second tasks; generate a message for one or more network devices of the second group of automated machines in accordance with a communication protocol, the message including information about a task performing model of the automated machine, the task performing model being based on a result of performing at least one task of the one or more first tasks by the automated machine. | 2021-05-06 |
20210133634 | EFFICIENTLY EXECUTING COMMANDS AT EXTERNAL COMPUTING SERVICES - Embodiments of the present invention are directed to facilitating distributed data processing for machine learning. In accordance with aspects of the present disclosure, a set of commands in a query to process at an external computing service is identified. For each command in the set of commands, at least one compute unit including at least one operation to perform at the external computing service is identified. Each of the at least one compute unit associated with each command is analyzed to identify an optimized manner in which to execute the set of commands at the external computing service. An indication of the optimized manner in which to execute the set of commands and a corresponding set of data is provided to the external computing service to utilize for executing the set of commands at the external computing service. | 2021-05-06 |
20210133635 | MATERIAL DESCRIPTOR GENERATION METHOD, MATERIAL DESCRIPTOR GENERATION DEVICE, RECORDING MEDIUM STORING MATERIAL DESCRIPTOR GENERATION PROGRAM, PREDICTIVE MODEL CONSTRUCTION METHOD, PREDICTIVE MODEL CONSTRUCTION DEVICE, AND RECORDING MEDIUM STORING PREDICTIVE MODEL CONSTRUCTION PROGRAM - A material descriptor generation method includes: acquiring a composition formula of a material; generating, from the composition formula, a formula expressing a base material and a dopant list including one or more formulas expressing one or more dopants used to dope the base material; computing descriptors needed to predict a predetermined property value of the material, the descriptors corresponding to the dopant list and the formula expressing the base material; and outputting a material descriptor consolidating the descriptors. The material descriptor is input into a predictive model that predicts the predetermined property value of the material. | 2021-05-06 |
20210133636 | ASYNCHRONOUS MULTIPLE SCHEME META LEARNING - Building machine learning models by receiving, a plurality of training process scores associated with the model parameter lists, determining, a best model parameter list according to the training process scores, determining a descendant model parameter list according to the best model parameter list, wherein the descendant parameter list comprises a portion of the best model parameter list, distributing the descendant model parameter list, conducting a model training process according to the descendant model parameter list, determining a training process score according to the descendant model parameter list, and sending the training process score for the descendant model parameter list. | 2021-05-06 |
20210133637 | SYSTEM FOR SCHEDULING SUMMONING ASSISTANCE - A scheduling and summoning system that provides users with specialty services providers that are available and within their geographic location. The system is composed of a central data and processing system, a user interface and a provider interface. The central data and processing system has a data capturing module, a user data module, a provider data module, a background screening module, a provider qualification module, a geographic location module, a provider availability module and a service completion module. The user interface allows a user to communicate with the central data processing system. The provider interface allows the provider to communicate with the central data processing system. The system allows user's requiring a service to be paired with qualified providers that can provide a service. | 2021-05-06 |
20210133638 | Workspace Managing Method and Workspace Managing System Capable of Improving a Scheduling Efficiency - A workspace managing method includes checking a reservation state of a workspace during a time interval for generating a checking result signal, indicating the reservation state of the workspace by using a display device of the workspace according to the checking result signal, detecting a presence of a person in the workspace for generating a detection result signal, and controlling the display device of the workspace according to the detection result signal. | 2021-05-06 |
20210133639 | BIDDING FOR A REQUEST TO RESERVE A SERVICE - Methods and corresponding system are provided herewith that, in at least one embodiment, include the act or acts of: determining that a service provided by a restaurant is available for bidding. The service is provided for a particular time and a particular date. The methods and system also include receiving a bid for the service provided by the restaurant, in which the bid is associated with a first amount; storing the bid with a plurality of other bids in a storage device; and comparing the bid with at least one other bid that is submitted for reserving the service. The at least one other bid is associated with a second amount. The methods and system also include determining that the first amount is greater than the second amount; and outputting an indicia that grants the request to reserve the service provided by the restaurant to a highest bidder, in which the first amount submitted by the highest bidder is greater than the second amount. | 2021-05-06 |
20210133640 | COMPUTER IMPLEMENTED METHODS AND SYSTEMS FOR CONNECTING PRE-AUTHORISED AND VERIFIED INDIVIDUALS FOR ACCOMPANIED TRANSPORTATION - A system and method are provided for connecting individuals for accompanied transportation. A method includes receiving input from a traveller, including transportation parameters, receiving input from a pre-authorised companion, including transportation parameters, and executes a transportation optimisation step including comparing transportation parameters and an assessment of one or more safety related parameters. The method then presents one or more optimised transportation proposal for acceptance by the traveller. Upon registering traveller selections and acceptance of the transportation proposal, the method notifies the traveller and selected companion and provides to the traveller or companion verification means prior to or at the accepted meeting time to enable verification of identities of the traveller or companion upon meeting. The method further monitors the safety related parameters to determine whether those parameters are satisfied and effects communication with the traveller or companion in response to the monitored one or more safety related parameters. | 2021-05-06 |
20210133641 | MULTI-PASSENGER AND MULTIATTRIBUTE TRAVEL BOOKING PLATFORM - Systems and methods for facilitating multi-passenger and multiattribute travel reservations are presented. The system has a database, a scheduler, a parser, and a processor. The database stores a travel lexicon having attribute names and a travel taxonomy having look-up tables. The scheduler instantiates travel attributes in terms of the travel lexicon to generate instantiated user preferences, indexes the instantiated user preferences, and searches for feasible travel itineraries. The parser parses user preferences to derive a vector function having attribute values and ascertains relevant attribute sets for the user preferences. The processor receives the user preferences from a user device, ranks and weights the attribute values based on encoded preference information derived from the user preferences, associates values to the feasible travel itineraries, and transmits the feasible itineraries to the user device to present a multiattributed travel itinerary to visualize itinerary information associated with each of the travel attributes. | 2021-05-06 |
20210133642 | USER-NOTIFICATION SCHEDULING - Methods, systems, and computer programs are presented for scheduling user notifications to maximize short-term and long-term benefits from sending the notifications. One method includes an operation for identifying features of a state used for reinforcement learning. The state is associated with an action to decide if a notification to a user is to be sent and a reward for sending the notification to the user. Further, the method includes capturing user responses to notifications sent to users to obtain training data and training a machine-learning (ML) algorithm with reinforcement learning based on the features and the training data to obtain an ML model. Additionally, the method includes receiving a request to send a notification to the user, and deciding, by the ML model, whether to send the notification based on a current state. The notification is sent to the user based on the decision. | 2021-05-06 |
20210133643 | Transit-Routing Expediter Systems And Methods For Multi-Modal Journey Optimization - Transit-routing expediter systems and methods for multi-modal journey optimization are disclosed herein. An example method includes determining journey parameters having an origin location, a destination location, and a time of departure; determining paths based on the journey parameters, each of the paths including any combination of route-based options, non-route-based options, or walking options; determining arrival times of the paths relative to the destination location; and identifying a set of solutions for the paths based on the arrival times using an iterative analysis. | 2021-05-06 |
20210133644 | SYSTEM AND METHOD FOR UNSUPERVISED ABSTRACTION OF SENSITIVE DATA FOR CONSORTIUM SHARING - An abstraction system for generating a standard customer profile in a data processing system has a processing device and a memory. The abstraction system may receive customer data from a computing device over a network, perform unsupervised learning on the customer data to produce a plurality of clusters of customers with a plurality of features in common, and determine that a cluster represents a standard customer, and store a plurality of standard customer profiles based on the determined standard customers, wherein the standard customer profiles comprise a plurality of data distributions for the plurality of features in common. The abstraction system additionally provides the standard customer profiles and the additional standard customer profiles to a cognitive system for generating synthetic transaction data. | 2021-05-06 |
20210133645 | AUTOMATED GENERATION OF DOCUMENTS AND LABELS FOR USE WITH MACHINE LEARNING SYSTEMS - Systems and methods for automated generation of documents. In one system, different databases, each having a different type of data, are used in conjunction with a database of document templates. Each template has a number of empty data fields, each data field being associated with a specific type of data present in at least one of the different databases. A document generation module retrieves a document template from the template database and determines which data fields need data. Databases containing the type of data needed by the data fields in the retrieved template are then accessed and suitable data is then retrieved/used and inserted into the retrieved template. Once the template is suitably complete, a document is then output from system and the image of this generated document can then be used with machine learning systems. | 2021-05-06 |
20210133646 | Permit Compliance System - A system and method is disclosed that enables the display of permits and/or permit information related to a specific location, collection of permitting data onsite, comparison of the onsite data to permitted constraints, and reporting the results of the inspection, as well as sending immediate notifications, as appropriate, to decision makers. | 2021-05-06 |
20210133647 | METHODS AND SYSTEMS FOR PREDICTIVE MARKETING PLATFORM IN DATA MANAGEMENT PLATFORM FOR CONTACT CENTER - A method for predictive marketing includes: receiving information that an event is expected to happen or is happening; predicting which attributes of customers will be associated with calls or chats directed to the event; predicting which customers will contact a contact center based on the predicted attributes; and taking action based on the predictions. A system for predictive marketing includes: an intelligent database for storing interaction data of customers; and a data management platform (DMP) configured to receive customer data from the intelligent database, analyze the customer data, determine which of the customers will be affected by an event, determine how the customers will be affected by the event, and take action. A predictive marketing platform includes: data sources; and a marketing and ad platform configured to receive data from the data sources, make predictions based on the data; and take action based on the predictions. | 2021-05-06 |
20210133648 | SYSTEMS AND METHODS FOR EVALUATING DATA SECURITY OF A TARGET SYSTEM - A data security evaluation computing device for evaluating data security of a target system is coupled to a plurality of data sources including the target system, and receives a request message requesting data security review, the request message including an identifier of the target system. The computing device queries a first data source to receive data representing whether the target system has previously breached, and locally caches the data. The computing device queries a second data source to receive data associated with a potential for a future data breach, and locally caches the data. The computing device also generates a data security score by analyzing the locally cached data, the data security score representing a likelihood the target system was or will be the subject of a data breach, compiles the data security score and additional data into a data security report, and transmits a response message including the report. | 2021-05-06 |
20210133649 | Multi-User Asset Sharing and Risk Assessment System and Method - A system for multi-user asset sharing and risk assessment including one or more controlled assets, a key generator that generates a key, wherein the key enables at least one of a locking and start function for the one or more controlled assets; and wherein a shift assigned to a user is stored in database connectively associated with a processor connectively associated with a non-transitory computer-readable medium having stored thereon software instructions that, when executed by a processor, cause the processor to check if the key was requested from the user during the assigned shift and only provides the key to the user for the assigned shift. | 2021-05-06 |
20210133650 | CONTROL TOWER AND ENTERPRISE MANAGEMENT PLATFORM WITH UNIFIED SET OF ROBOTIC PROCESS AUTOMATION SYSTEMS FOR COORDINATED AUTOMATION AMONG VALUE CHAIN APPLICATIONS - An information technology system generally including a cloud-based management platform with a micro-services architecture having a unified set of robotic process automation systems that provide coordinated automation among at least two types of applications from among a set of demand management applications, a set of supply chain applications, a set of intelligent product applications, and a set of enterprise resource management applications for a category of goods with respect to the value chain network entities of the platform. | 2021-05-06 |