24th week of 2021 patent applcation highlights part 58 |
Patent application number | Title | Published |
20210182629 | SIMILARITY DETERMINATION APPARATUS, SIMILARITY DETERMINATION METHOD, AND SIMILARITY DETERMINATION PROGRAM - A finding classification unit classifies each pixel of a first medical image into at least one finding. A feature amount calculation unit calculates a first feature amount for each finding. A weighting coefficient setting unit sets a weighting coefficient indicating a degree of weighting, which varies depending on a size of each finding, for each finding. A similarity derivation unit performs a weighting operation for the first feature amount for each finding calculated in the first medical image and a second feature amount for each finding calculated in advance in a second medical image on the basis of the weighting coefficient to derive a similarity between the first medical image and the second medical image. | 2021-06-17 |
20210182630 | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING PROGRAM, AND INFORMATION PROCESSING METHOD - An information processor can logically support prediction based on past statistical information even though the information contains qualitative or non-numerical data. The processor determines whether an input pattern corresponding to an input object (a determination target) belongs to a specific class among multiple classes, based on feature subsets of any combination of a plurality of features, each feature comprises multiple categories. The processor includes a storage storing the input pattern corresponding to the input object and samples corresponding to respective sample objects and a classification determiner determining whether the input pattern belongs to the specific class. The classification determiner calculates a first conditional probability and a second conditional probability based on the number of the samples belonging to each category of the respective features, the first conditional probability is a probability that the data of the input pattern belong to categories corresponding to the respective feature for the specific class, the second conditional probability is a probability that the data of the input pattern belong to categories corresponding to the respective features for a non-specific class which is a class other than the specific class among classes, and the number of the samples is counted for each class based on the feature information on the samples and the class label information on the samples, and determines whether the input pattern belongs to the specific class based on the feature information on the input pattern, the first conditional probability and the second conditional probability. | 2021-06-17 |
20210182631 | CLASSIFICATION USING HYPER-OPINIONS - Systems, devices, methods, and computer-readable media for determining a hyper-opinion classification of an object. A method can include receiving data of an object to be classified, and determining, using a neural network, a hyper-opinion classification of the object including an indication of the probabilities of base classes and composite classes that are “or” combinations of proper subsets of the base classes. | 2021-06-17 |
20210182632 | METHOD AND APPARATUS FOR ESTIMATION ROAD SURFACE TYPE USING ULTRASONIC SIGNAL - The present invention relates to a method and apparatus for estimating a road surface type by using an ultrasonic signal and, more particularly, to a method for estimating a road surface type by using an artificial neural network model machine-learned with respect to a reflected ultrasonic signal and an apparatus for performing same. According to the present invention, provided are a method and apparatus for providing highly accurate road surface information at low cost, by machine-learning both characteristics of an ultrasonic signal reflected from a road surface and a road surface state, establishing a model between the two, and then estimating the type of the road surface by utilizing the model. In particular, even a road surface where thin ice, that is, black ice, is formed, which was not detectable in the conventional method for estimating a road-surface friction coefficient, may be accurately estimated, thereby contributing to safer driving. | 2021-06-17 |
20210182633 | LOCALIZATION METHOD AND HELMET AND COMPUTER READABLE STORAGE MEDIUM USING THE SAME - The present disclosure provides a localization method as well as a helmet and a computer readable storage medium using the same. The method includes: extracting first feature points from a target image; obtaining inertial information of the carrier, and screening the first feature points based on the inertial information to obtain second feature points; triangulating the second feature points of the target image to generate corresponding initial three-dimensional map points, if the target image is a key frame image; performing a localization error loopback calibration on the initial three-dimensional map points according to at least a predetermined constraint condition to obtain target three-dimensional map points; and determining a positional point of the specific carrier according to the target three-dimensional map points. In this manner, the accuracy of the localization of a dynamic object such as a person when moving can be improved. | 2021-06-17 |
20210182634 | APPARATUS AND METHODS FOR MULTI-TARGET DETECTION - A method for multi-target detection and an apparatus for multi-target detection are capable of detecting at least two targets in real time or near real time. The real-time detection or near real time detection can be achieved by at least one of a Recipe Group Approach, an End Member Grouping Approach, and a Pixelated Grouping Based Approach. | 2021-06-17 |
20210182635 | MACHINE LEARNING AND/OR IMAGE PROCESSING FOR SPECTRAL OBJECT CLASSIFICATION - In one embodiment, a method of machine learning and/or image processing for spectral object classification is described. In another embodiment, a device is described for using spectral object classification. Other embodiments are likewise described. | 2021-06-17 |
20210182636 | STRUCTURE LEARNING IN CONVOLUTIONAL NEURAL NETWORKS - The present disclosure provides an improved approach to implement structure learning of neural networks by exploiting correlations in the data/problem the networks aim to solve. A greedy approach is described that finds bottlenecks of information gain from the bottom convolutional layers all the way to the fully connected layers. Rather than simply making the architecture deeper, additional computation and capacitance is only added where it is required. | 2021-06-17 |
20210182637 | IMAGE FORMING APPARATUS - An image forming apparatus includes a reader configured to read a paper used for printing, a controller configured to generate paper information indicating the type of paper read by the reader, and a storage unit configured to store the paper information in association with history information of a paper. The controller updates history information corresponding to target paper information when the target paper information indicating a same type of paper as the paper indicated by newly generated paper information is stored, and causes the storage unit to store the newly generated paper information when the target paper information is not stored. The controller searches for paper information indicating the type of paper input in a search operation, and causes an operation panel to display the history information corresponding to the found paper information. | 2021-06-17 |
20210182638 | COVERT FLOATING IMAGE - A method of producing at least one security element ( | 2021-06-17 |
20210182639 | AUGMENTED CAMERA FOR IMPROVED SPATIAL LOCALIZATION AND SPATIAL ORIENTATION DETERMINATION - An augmented reality system for procedural guidance identifies a fiducial marker object in a frame of a first field of view generated by a camera, determines a pose of the fiducial marker object, applies the fiducial marker pose to generate a first transformation between a first coordinate system of the fiducial marker object and a second coordinate system of the camera, and applies a pose of a headset to determine a second transformation between the first coordinate system and a third coordinate system of the headset. | 2021-06-17 |
20210182640 | OPTICAL SIGNATURE GENERATION, DISTRIBUTION AND DISPLAY AT A CLIENT DEVICE - A system generates, distributes, and displays an optical signature on devices with a display and wearable devices. The optical signature can be provided as part of an optical signature set to one or more users to allow them to communicate information to each other visually. The optical signature is displayed by a device associated with a first user for interpretation by a second user. The optical signature conveys information about the first user such as interaction type being sought with a second user and characteristics of the user being sought to interact with. The optical signature may be composed of any combination of shapes, colors, geometric patterns, pictures or video displayed statically or in motion or holographic images. A user with a displayed optical signature can find other users locally and remotely. | 2021-06-17 |
20210182641 | DATA-TRANSMISSION SYSTEM - A data-transmission system, comprising measurement means, having memories, to create and collect measurement data, an output device having a zero-power passive state, to show in the zero-power passive state, machine-readable code containing the measurement data created using the measurement means, a power supply for the output device and the measurement means, a server arrangement to process and/or store the measurement data, and one or several reader devices to read the code from the output device in the zero-power passive state and arranged for data transfer with the server arrangement. The output device and measurement means with memories are to be arranged at monitoring objects. | 2021-06-17 |
20210182642 | SMART CARD AND CONTROL METHOD THEREOF - A smart card has a light-emitting element, a fingerprint sensor and a microcontroller. An operation of the smart card has a plurality of indication periods to indicate operation statuses of the smart card by the light-emitting element. The control method of the smart card includes generating a light source control signal, controlling a current supplied to the light-emitting element by the microcontroller according to the light source control signal, and decreasing the current supplied to the light-emitting element or stop the current supplied to the light-emitting element during at least one power-saying period in a first indication period of the plurality of indication periods. | 2021-06-17 |
20210182643 | INTEGRATED CIRCUIT, WIRELESS COMMUNICATION CARD AND WIRING STRUCTURE OF IDENTIFICATION MARK - An integrated circuit, a wireless communication card and a wiring structure of an identification mark are provided. The integrated circuit includes a power supply wiring, a ground wiring and at least one identification mark pattern. Each identification mark pattern has a first conductive wiring and a second conductive wiring that overlap each other, wherein the first conductive wiring is electrically connected to the power wiring, and the second conductive wiring is electrically connected to the ground wiring. | 2021-06-17 |
20210182644 | RFID SECURITY TAPE - An RFID security tape comprising: a layer of tamper-evident tape; a layer of an RFID inlay; a blocking layer; a layer of metal foil having an adhesive bottom surface; and a layer of a release liner, wherein all layers of the RFID security tape are in adhesive connection with each other and wherein the layer of the RFID inlay is configured to be damaged when the RFID security tape has been applied to an asset and the layer of tamper-evident tape is subsequently removed from the asset. | 2021-06-17 |
20210182645 | MEDICINAL DOSAGE STORAGE FOR COMBINED ELECTRONIC INVENTORY DATA AND ACCESS CONTROL - Disclosed are apparatuses and methodologies for achieving current inventory data management with an electronic access control system. An access control system provides access control data while a sealed enclosure incorporates an RFID reading system for determining the identity of respective tagged contents therein. Particularly in conjunction with the storage of controlled substances, such as some drugs utilized on an EMS vehicle, a tamper evident RFID tag is fully or partially destroyed or damaged, or otherwise impacted or affected so as to generate a changed ID, whenever the contained medicinal dosage is acquired for administration. Specific container/cap combinations accommodate various drug dosages, and are combinable with tamper evident RFID tags. Usage of tagged drugs may be tracked by reading narcotics box contents before and after a work shift. Intra-shift access and usage reports at each point of consumption maintains a complete record of custody of control. | 2021-06-17 |
20210182646 | MEDICINAL DOSAGE STORAGE METHOD FOR COMBINED ELECTRONIC INVENTORY DATA AND ACCESS CONTROL - Disclosed are apparatuses and methodologies for achieving current inventory data management with an electronic access control system. An access control system provides access control data while a sealed enclosure incorporates an RFID reading system for determining the identity of respective tagged contents therein. Particularly in conjunction with the storage of controlled substances, such as some drugs utilized on an EMS vehicle, a tamper evident RFID tag is fully or partially destroyed or damaged, or otherwise impacted or affected so as to generate a changed ID, whenever the contained medicinal dosage is acquired for administration. Specific container/cap combinations accommodate various drug dosages, and are combinable with tamper evident RFID tags. Usage of tagged drugs may be tracked by reading narcotics box contents before and after a work shift. Intra-shift access and usage reports at each point of consumption maintains a complete record of custody of control. | 2021-06-17 |
20210182647 | FLUIDIC CONDUCTIVE TRACE BASED RADIO-FREQUENCY IDENTIFICATION - In some examples, a fluidic conductive trace based radio-frequency identification device may include a flexible substrate layer including a channel, and a trace formed of a conductive fluid that is disposed substantially within the channel. The fluidic conductive trace based radio-frequency identification device may further include a sealing layer disposed on the flexible substrate layer and the trace to seal the conductive fluid in a liquid state within the channel. | 2021-06-17 |
20210182648 | INSULATING GLAZING UNIT - An insulating glazing unit that has at least two glass panes and a circumferential spacer profile between them near their edges, for use in a window, a door, or a façade glazing, which has in each case a frame surrounding the edges of the insulating glazing, into which the insulating glazing is inserted using spacers, wherein at least one RFID transponder is attached to the insulating glazing unit as an identification element, wherein the a least one transponder is positioned at the edge or on the boundary edge of a glass pane such that, in the installed state of the window, door, or façade glazing, it is positioned on or above a spacer in the surrounding, in particular metallic, frame. | 2021-06-17 |
20210182649 | RFIC MODULE AND RFID TAG - An RFIC module is provided that includes an RFIC and an impedance matching circuit connected to an RFIC side first terminal electrode, an RFIC side second terminal electrode, an antenna side first terminal electrode and an antenna side second terminal electrode. The impedance matching circuit includes a first inductor, a second inductor, a third inductor, and a fourth inductor, and a conductor pattern that configures the first inductor, the second inductor, the third inductor, and the fourth inductor as a single coil-shaped pattern. | 2021-06-17 |
20210182650 | SMARTCARDS WITH MULTIPLE COUPLING FRAMES - RFID devices comprising (i) a transponder chip module (TCM, | 2021-06-17 |
20210182651 | RADIO FREQUENCY IDENTIFICATION ENABLED MIRRORS - A radio frequency identification (RFID) enabled mirror includes a mirror comprising a reflective layer. The reflective layer comprises at least one layer of a metallic material. At least one portion of the reflective layer is removed to form a booster antenna from a remaining portion of the reflective layer. A dielectric coating is applied to the mirror where the reflective layer was removed. The RFID-enabled mirror further includes an RFID chip coupled to the booster antenna. | 2021-06-17 |
20210182652 | EVALUATION SYSTEM FOR MEASURED DATA FROM MULTIPLE DOMAINS - An evaluation system for processing measured data which include physical measured data detected with the aid of one or multiple sensors, and/or realistic synthetic measured data of the sensor(s), into one or multiple evaluation results. The system includes at least two input stages independent from each other, which are designed to receive measured data and process these measured data into precursors. At least one processing stage, receives the precursors from all input stages as inputs and is designed to process one or multiple input precursor(s) into a shared intermediate product. At least one output stage, which is designed to process the intermediate product into one or multiple evaluation result(s) of the evaluation system. A method for training the evaluation system. A method for operating the evaluation system is also provided. | 2021-06-17 |
20210182653 | OUTPUT FROM A RECURRENT NEURAL NETWORK - Application of the output from a recurrent artificial neural network to a variety of different applications. A method can include identifying topological patterns of activity in a recurrent artificial neural network, outputting a collection of digits, and inputting a first digit of the collection to a first application that is designed to fulfil a first purpose and to a second application that is designed to fulfil a second purpose, wherein the first purpose differs from the second purpose. The topological patterns are responsive to an input of data into the recurrent artificial neural network and each topological pattern abstracts a characteristic of the input data. Each digit represents whether one of the topological patterns of activity has been identified in the artificial neural network. | 2021-06-17 |
20210182654 | INPUT INTO A NEURAL NETWORK - Abstracting data that originates from different sensors and transducers using artificial neural networks. A method can include identifying topological patterns of activity in a recurrent artificial neural network and outputting a collection of digits. The topological patterns are responsive to an input, into the recurrent artificial neural network, of first data originating from a first sensor and second data originating from a second sensor. Each topological pattern abstracts a characteristic shared by the first data and the second data. The first and second sensors sense different data. Each digit represents whether one of the topological patterns of activity has been identified in the artificial neural network. | 2021-06-17 |
20210182655 | ROBUST RECURRENT ARTIFICIAL NEURAL NETWORKS - Robust recurrent artificial neural networks and techniques for improving the robustness of recurrent artificial neural networks. For example, a system can include a plurality of nodes and links arranged in a recurrent neural network, wherein either transmissions of information along the links or decisions at the nodes are non-deterministic, and an output configured to output indications of occurrences of topological patterns of activity in the recurrent artificial neural network. | 2021-06-17 |
20210182656 | ARITHMETIC PROCESSING DEVICE - In this arithmetic processing device, during a filter processing and a cumulative addition processing for calculating a specific pixel of an output feature amount map, an arithmetic controller controls so as to temporarily store an intermediate result in a cumulative addition result storing memory and process another pixel, store the intermediate result of the cumulative addition processing for all pixels in the cumulative addition result storing memory, then return to a first pixel, read the value stored in the cumulative addition result storing memory as an initial value of the cumulative addition processing, and continue the cumulative addition processing. | 2021-06-17 |
20210182657 | INTERPRETING AND IMPROVING THE PROCESSING RESULTS OF RECURRENT NEURAL NETWORKS - A method includes defining a plurality of different windows of time in a recurrent artificial neural network, wherein each of the different windows has different durations, has different start times, or has both different durations and different start times, identifying occurrences of topological patterns of activity in the recurrent artificial neural network in the different windows of time, comparing the occurrences of the topological patterns of activity in the different windows, and classifying, based on a result of the comparison, a first decision that is represented by a first topological pattern of activity that occurs in a first of the windows as less robust than a second decision that is represented by a second topological pattern of activity that occurs in a second of the windows. | 2021-06-17 |
20210182658 | Machine-Learning Architectures for Simultaneous Connection to Multiple Carriers - Techniques and apparatuses are described for machine-learning architectures for simultaneous connection to multiple carriers. In implementations, a network entity determines at least one deep neural network (DNN) configuration for processing information exchanged with a user equipment (UE) over a wireless communication system using carrier aggregation that includes at least a first component carrier and a second component carrier. At times, the at least one DNN configuration includes a first portion for forming a first DNN at the network entity, and a second portion for forming a second DNN at the UE. The network entity forms the first DNN based on the first portion and communicates an indication of the second portion to the UE. The network entity directs the UE to form the second DNN based on the second portion, and uses the first DNN to exchange, over the wireless communication system, the information with the UE using the carrier aggregation. | 2021-06-17 |
20210182659 | DATA PROCESSING AND CLASSIFICATION - The present invention discloses a method, a system and a computer program product for data processing and classification. The invention provides warm start and cold start classification tools for classification of data obtained from known or unknown entities. The system and method are also configured to be employed over blockchain based networks. | 2021-06-17 |
20210182660 | DISTRIBUTED TRAINING OF NEURAL NETWORK MODELS - Systems and methods for distributed training of a neural network model are described. Various embodiments include a master device and a slave device. The master device has a first version of the neural network model. The slave device is communicatively coupled to a first data source and the master device, and the first data source is inaccessible by the master device, in accordance with one embodiment. The slave device is remote from the master device. The master device is configured to output first configuration data for the neural network model based on the first version of the neural network model. The slave device is configured to use the first configuration data to instantiate a second version of the neural network model. The slave device is configured to train the second version of the neural network model using data from the first data source and to output second configuration data for the neural network model. The master device is configured to use the second configuration data to update parameters for the first version of the neural network model. | 2021-06-17 |
20210182661 | Neural Network Training From Private Data - Training and enhancement of neural network models, such as from private data, are described. A slave device receives a version of a neural network model from a master. The slave accesses a local and/or private data source and uses the data to perform optimization of the neural network model. This can be done such as by computing gradients or performing knowledge distillation to locally train an enhanced second version of the model. The slave sends the gradients or enhanced neural network model to a master. The master may use the gradient or second version of the model to improve a master model. | 2021-06-17 |
20210182662 | TRAINING OF NEURAL NETWORK BASED NATURAL LANGUAGE PROCESSING MODELS USING DENSE KNOWLEDGE DISTILLATION - Techniques for training a first neural network (NN) model using a pre-trained second NN model are disclosed. In an example, training data is input to the first and second models. The training data includes masked tokens and unmasked tokens. In response, the first model generates a first prediction associated with a masked token and a second prediction associated with an unmasked token, and the second model generates a third prediction associated with the masked token and a fourth prediction associated with the unmasked token. The first model is trained, based at least in part on the first, second, third, and fourth predictions. In another example, a prediction associated with a masked token, a prediction associated with an unmasked token, and a prediction associated with whether two sentences of training data are adjacent sentences are received from each of the first and second models. The first model is trained using the predictions. | 2021-06-17 |
20210182663 | METHODS AND SYSTEMS FOR DEFINING EMOTIONAL MACHINES - A method for training an intelligent agent is disclosed comprising creating a personality matrix, combining a cognitive bias matrix with the personality matrix and generating a behavioral function for a situation based on the combined cognitive bias matrix and personality matrix. | 2021-06-17 |
20210182664 | NEURAL NETWORK TRAINING METHOD AND DEVICE - Provided are an AI system for simulating functions such as recognition, determination, and so forth of human brains by using a mechanical learning algorithm like deep learning, or the like, and an application thereof. In particular, according to the AI system and the application thereof, a neural network training method includes obtaining a plurality of first images belonging to a particular category and a plurality of second images for which a category is not specified, training a neural network model for category recognition, based on the plurality of first images belonging to the particular category, recognizing at least one second image corresponding to the particular category among the plurality of second images, by using the trained neural network model, and modifying and refining the trained neural network model based on the recognized at least one second image. | 2021-06-17 |
20210182665 | SENSOR DEVICE FOR CLASSIFICATION OF DATA - A sensor device comprising one or more sensors configured to measure data, a controller and memory. The controller comprises an arithmetic-logic unit “ALU”. The controller is configured to cause the ALU to carry out computations for implementing an artificial neural network “ANN” comprising a network of interconnected nodes. The memory is coupled to the ALU, and is configured to store integers representing weights associated with interconnects between the nodes. The controller is operable to implement the ANN to: receive data measured by the one or more sensors; and determine a classification of the data based on the network of interconnected nodes and the weights associated with the interconnects. | 2021-06-17 |
20210182666 | WEIGHT DATA STORAGE METHOD AND NEURAL NETWORK PROCESSOR BASED ON THE METHOD - Disclosed are a weight data storage method and a convolution computation method that may be implemented in a neural network. The weight data storage method comprises searching for effective weights in a weight convolution kernel matrix and acquiring an index of effective weights. The effective weights are non-zero weights, and the index of effective weights is used to mark the position of the effective weights in the weight convolution kernel matrix. The weight data storage method further comprises storing the effective weights and the index of effective weights. According to the weight data storage method and the convolution computation method of the present disclosure, storage space can be saved, and computation efficiency can be improved. | 2021-06-17 |
20210182667 | COOKING APPARATUS AND CONTROL METHOD THEREOF - The present disclosure provides a cooking apparatus and a method for controlling the same for analyzing changes in the internal temperature, the external temperature, and the surface of a cooking material that is being cooked, and appropriately heating the cooking material based on the analysis result to cook the same. In particular, the intensity of heat emitted toward the cooking material from a heater or the cooking time may be controlled using an artificial intelligence (AI) model, which executes machine learning (ML) over a 5G network, such that the cooking material is appropriately cooked in accordance with a change in the surface of the cooking material and a change in a thermal image representing the internal temperature and the external temperature of the cooking material. | 2021-06-17 |
20210182668 | TERMINAL DEVICE AND METHOD FOR ESTIMATING FIREFIGHTING DATA - A method for estimating firefighting data includes: obtaining firefighting condition data of a site, wherein the firefighting condition data comprises information on firefighting equipment, information on flammable articles; and estimating firefighting input data and firefighting damage data based on the firefighting condition data using a simulation analysis model, wherein the simulation analysis model is created based on firefighting condition data, firefighting input data and firefighting damage data of different sites. | 2021-06-17 |
20210182669 | INFORMATION SHARING PLATFORM AND METHOD CAPABLE OF PROVIDING BIDIRECTIONAL VEHICLE STATE INFORMATION AND SYSTEM HAVING INFORMATION SHARING PLATFORM - An information sharing platform of providing bidirectional vehicle state information between a driver and a vehicle, the information sharing platform may include a communication controller which collects measured data and vehicle Controller Area Network (CAN) information by sensors installed to components capable of diagnosing a vehicle state; and a graphic controller which provides a driver with diagnosis result output information that is generated based on a predetermined selection criterion among the components through Deep Learning based diagnosis using big data having the collected data. | 2021-06-17 |
20210182670 | METHOD AND APPARATUS WITH TRAINING VERIFICATION OF NEURAL NETWORK BETWEEN DIFFERENT FRAMEWORKS - A processor-implemented method of verifying the training of a neural network between frameworks is provided. The method includes providing test data to a first module operating based on a first framework, and providing the test data to a second module operating based on a second framework. The method further includes obtaining, from the first module, first data generated in the first module, obtaining, from the second module, second data generated in the second module, and comparing the first data with the second data. | 2021-06-17 |
20210182671 | BIG DATA-BASED DRIVING INFORMATION PROVISION SYSTEM AND METHOD THEREOF - A big data-based driving information provision system may include a sensor configured to measure and collect state monitoring data of an engine, vehicle monitoring data, and vibration data; an engine electronic control unit (ECU) configured to generate a combustion characteristic index (CCI) data of the engine; and a graphic controller configured to generate a primary deep learning model which classifies the big data including the state monitoring data, the vehicle monitoring data, the vibration data, and the CCI into at least two categories. | 2021-06-17 |
20210182672 | SELF-ORGANIZING MAP LEARNING DEVICE AND METHOD, NON-TRANSITORY COMPUTER READABLE MEDIUM STORING SELF-ORGANIZING MAP LEARNING PROGRAM AND STATE DETERMINATION DEVICE - A Self-Organizing Map learning device includes a distance calculator that obtains a distance D between an input vector in an observation space and a reference vector of each neuron in a latent space, a smallest value neuron specifier that specifies a smallest value neuron having the smallest distance D, a neuron selector that selects M (M is an integer smaller than L) selection neurons from the L (L is equal to or larger than 2) smallest value neurons in a case where the L smallest value neurons are present, and an updater that updates the reference vector of each neuron in the latent space with the M selection neurons as winner neurons. | 2021-06-17 |
20210182673 | ENCODER DEVICE AND METHOD OF DETERMINING A KINEMATIC VALUE - An encoder device for determining a kinematic value of the movement of a first object relative to a second object is provided, wherein the encoder device comprises a standard associated with the first object and at least one scanning unit associated with the second object for producing at least one scanning signal by detection of the standard and a control and evaluation unit that is configured to determine the kinematic value from the scanning signal. The control and evaluation unit is here further configured to determine the kinematic value by an evaluation of the scanning signal using a method of machine learning, with the evaluation being trained with a plurality of scanning signals and associated kinematic values. | 2021-06-17 |
20210182674 | AUTOMATIC TRAINING AND DEPLOYMENT OF DEEP LEARNING TECHNOLOGIES - Systems and methods for automatically training a machine learning based model are provided. A trigger for automatically training a machine learning based model is received. In response to receiving the trigger, a preprocessing manager for executing preprocessing code for preprocessing training data is automatically invoked. A training manager for executing training code for training the machine learning based model based on the preprocessed training data is automatically invoked. A deployment manager for executing deployment code for converting the trained machine learning based model to a production model is automatically invoked. The production model is output. | 2021-06-17 |
20210182675 | Computer Vision Systems and Methods for End-to-End Training of Convolutional Neural Networks Using Differentiable Dual-Decomposition Techniques - Computer vision systems and methods for end-to end training of neural networks are provided. The system generates a fixed point algorithm for dual-decomposition of a maximum-a-posteriori inference problem and trains the convolutional neural network and a conditional random field with the fixed point algorithm and a plurality of images of a dataset to learn to perform semantic image segmentation. The system can segment an attribute of an image of the dataset by the trained neural network and the conditional random field. | 2021-06-17 |
20210182676 | SYSTEMS AND METHODS FOR GENERATION OF SPARSE CODE FOR CONVOLUTIONAL NEURAL NETWORKS - A system and method may generate code to be used when executing neural networks (NNs), for example convolutional neural networks (CNNs) which may include one or more convolutional layers. For at least one convolutional layer, for each non-zero element in a kernel tensor or matrix associated with the convolutional layer, instructions may be generated or issued. For example, for each non-zero element, a vector broadcast instruction may be generated, and a fused multiply-add (FMA) instruction may be generated, having as parameters a register representing a portion of the output for the convolutional layer, a register storing input data for the convolutional layer, and a register or reference to memory storing the non-zero element. The software or code produced may be executed during convolutional operations, for example as part of a larger application such as a NN inference application. | 2021-06-17 |
20210182677 | Identifying Portions of Electronic Communication Documents Using Machine Vision - In a computer-implemented method, an artificial neural network is trained to identify portions of conversation segments within electronic communication documents, wherein an input layer of the artificial neural network includes a plurality of input parameters each corresponding to a different characteristic of text-based content. The method also includes receiving a first electronic communication document that includes first text-based content, and processing the first text-based content using the trained artificial neural network. Processing the first text-based content includes generating one or more position indicators for the first electronic communication document, and the one or more position indicators include one or more segment portion indicators denoting positions of one or more portions of one or more conversation segments within the first electronic communication document. The method also includes determining an ordered relationship between the first electronic communication document and one or more other electronic communication documents using the position indicator(s). | 2021-06-17 |
20210182678 | DATA PROCESSING SYSTEM AND DATA PROCESSING METHOD - A data processing system includes: a processor including hardware, wherein the processor performs a process determined by a neural network. An optimization parameter of the neural network is optimized based on a comparison between output data output when learning data is subject to the process and ideal output data for the learning data. The processor is configured to: output a feature map having the same width and height as the intermediate data by applying, in an M-th (M is an integer equal to or larger than 1) intermediate layer, an operation to intermediate data representing input data input to the M-th intermediate layer, the operation including a convolutional operation that uses a convolutional kernel comprised of the optimization parameter; multiply the intermediate data and the feature map mutually at each corresponding coordinate, the intermediate data being input to the M-th intermediate layer, and the feature map being output by inputting the intermediate data to the M-th intermediate layer; and execute a pooling process in an (M+1)-th intermediate layer on the intermediate data output by executing multiplication. | 2021-06-17 |
20210182679 | DATA PROCESSING SYSTEM AND DATA PROCESSING METHOD - A data processing system includes: a neural network processing unit that performs a process determined by a neural network including an input layer, one or more intermediate layers, and an output layer; and a learning unit that trains the neural network by optimizing an optimization parameter of the neural network based on a comparison between output data output when the neural network processing unit subjects learning data to the process determined by the neural network and ideal output data for the learning data. The neural network processing unit performs, in a learning process, a coefficient process of multiplying intermediate data representing input data input to an intermediate layer element constituting the intermediate layer of an M-th layer (M is an integer equal to or larger than 1) or representing output data from the intermediate layer element by a coefficient the absolute value of which increases monotonically in accordance with progress of learning. | 2021-06-17 |
20210182680 | PROCESSING SEQUENTIAL INTERACTION DATA - This disclosure relates to processing sequential interaction data through machine learning. In one aspect, a method includes obtaining a dynamic interaction graph constructed based on a dynamic interaction sequence. The dynamic interaction sequence includes interaction feature groups corresponding to interaction events. Each interaction feature group includes a first object, a second object, and an interaction time of an interaction event that involved the first object and the second object. The dynamic interaction graph includes multiple nodes including, for each interaction feature group, a first node that represents the first object of the interaction feature group and a second node that represents the second object of the interaction feature group. A current sequence corresponding to a current node to be analyzed is determined. The current sequence is input into a Transformer-based neural network model. The neural network model determines a feature vector corresponding to the current node. | 2021-06-17 |
20210182681 | DISTANCE METRICS AND CLUSTERING IN RECURRENT NEURAL NETWORKS - Distance metrics and clustering in recurrent neural networks. For example, a method includes determining whether topological patterns of activity in a collection of topological patterns occur in a recurrent artificial neural network in response to input of first data into the recurrent artificial neural network, and determining a distance between the first data and either second data or a reference based on the topological patterns of activity that are determined to occur in response to the input of the first data. | 2021-06-17 |
20210182682 | LEARNING TASK COMPILING METHOD OF ARTIFICIAL INTELLIGENCE PROCESSOR AND RELATED PRODUCTS - The present disclosure relates to a learning task compiling method of artificial intelligence processors and related products. The learning task compiling method of artificial intelligence processors includes fusing a redundant neural network layer to a convolution layer, optimizing a structure of a convolution neural network, and compiling a learning task of an artificial intelligence processor based on the optimized convolution neural network. The method may achieve high efficiency for learning task compiling of artificial intelligence processors, and may reduce data exchange during processing when being executed on a device. | 2021-06-17 |
20210182683 | METHOD AND SYSTEM FOR NEURAL NETWORK SYNTHESIS - According to various embodiments, a method for generating one or more optimal neural network architectures is disclosed. The method includes providing an initial seed neural network architecture and utilizing sequential phases to synthesize the neural network until a desired neural network architecture is reached. The phases include a gradient-based growth phase and a magnitude-based pruning phase. | 2021-06-17 |
20210182684 | DEPTH-FIRST DEEP CONVOLUTIONAL NEURAL NETWORK INFERENCE - A method performed by a computing device includes determining a partition for depth-first processing by a multi-layer artificial neural network (ANN) of the computing device. The computing device comprising a processor, on-chip memory, and off-chip memory. The first partition determined based on an amount of on-chip memory used by the first partition, an available amount of on-chip memory, and a size of a write back to the off-chip memory. The method also includes processing, at the device via the multi-layer ANN, an input, using the depth-first processing in accordance with the partition. | 2021-06-17 |
20210182685 | NEURAL NETWORK BATCH NORMALIZATION OPTIMIZATION METHOD AND APPARATUS - A neural network batch normalization optimization method includes: setting a first network layer in a neural network as a starting layer; sequentially obtaining initial bias values of different network layers backwards starting from the starting layer; calculating equivalent bias values of the different network layers; determining whether there is a target network layer, wherein a ratio of the equivalent bias value corresponding to a previous layer of a target network layer to the equivalent bias value corresponding to the target network layer is no less than a pre-set threshold value; and if the target network layer is present, setting the bias values of the different network layers between the starting layer and the previous layer of the target network layer to zero, and taking the equivalent bias value of the target network layer as a bias value of the target network layer. | 2021-06-17 |
20210182686 | CROSS-BATCH MEMORY FOR EMBEDDING LEARNING - This disclosure includes computer vision technologies, specifically for embeddings and metric learning. In various practical applications, such as product recognition, image retrieval, face recognition, etc., the disclosed technologies use a cross-batch memory mechanism to memorize prior embeddings, so that a pair-based learning model can mine more pairs across multiple mini-batches or even over the whole dataset. The disclosed technologies not only boost the performance for various applications, but considerably improve the computation itself with its memory-efficient approach. | 2021-06-17 |
20210182687 | APPARATUS AND METHOD WITH NEURAL NETWORK IMPLEMENTATION OF DOMAIN ADAPTATION - A processor-implemented neural network operating method, the operating method comprising obtaining a neural network pre-trained in a source domain and a first style feature of the source domain, extracting a second style feature of a target domain from received input data of the target domain, using the neural network, performing domain adaptation of the input data, by performing style matching of the input data based on the first style feature of the source domain and the second style feature of the target domain, and processing the style-matched input data, using the neural network. | 2021-06-17 |
20210182688 | REINFORCEMENT LEARNING WITH AUXILIARY TASKS - Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a reinforcement learning system. The method includes: training an action selection policy neural network, and during the training of the action selection neural network, training one or more auxiliary control neural networks and a reward prediction neural network. Each of the auxiliary control neural networks is configured to receive a respective intermediate output generated by the action selection policy neural network and generate a policy output for a corresponding auxiliary control task. The reward prediction neural network is configured to receive one or more intermediate outputs generated by the action selection policy neural network and generate a corresponding predicted reward. Training each of the auxiliary control neural networks and the reward prediction neural network comprises adjusting values of the respective auxiliary control parameters, reward prediction parameters, and the action selection policy network parameters. | 2021-06-17 |
20210182689 | MACHINE LEARNING THROUGH MULTIPLE LAYERS OF NOVEL MACHINE TRAINED PROCESSING NODES - Some embodiments of the invention provide efficient, expressive machined-trained networks for performing machine learning. The machine-trained (MT) networks of some embodiments use novel processing nodes with novel activation functions that allow the MT network to efficiently define with fewer processing node layers a complex mathematical expression that solves a particular problem (e.g., face recognition, speech recognition, etc.). In some embodiments, the same activation function (e.g., a cup function) is used for numerous processing nodes of the MT network, but through the machine learning, this activation function is configured differently for different processing nodes so that different nodes can emulate or implement two or more different functions (e.g., two or more Boolean logical operators, such as XOR and AND). The activation function in some embodiments is a periodic function that can be configured to implement different functions (e.g., different sinusoidal functions). | 2021-06-17 |
20210182690 | OPTIMIZING NEURAL NETWORKS FOR GENERATING ANALYTICAL OR PREDICTIVE OUTPUTS - Certain embodiments involve generating or optimizing a neural network for generating analytical or predictive outputs. The neural network can be generated using a relationship between various predictor variables and an outcome (e.g., a condition's presence or absence). The neural network can be used to determine a relationship between each of the predictor variables and a response variable. The neural network can be optimized by iteratively adjusting the neural network such that a monotonic relationship exists between each of the predictor variables and the response variable. The optimized neural network can be used both for accurately determining response variables using predictor variables and determining adverse action codes for the predictor variables, which indicate an effect or an amount of impact that a given predictor variable has on the response variable. The neural network can be used to generate adverse action codes upon which consumer behavior can be modified to improve the response variable score. | 2021-06-17 |
20210182691 | COOPERATIVE USE OF A GENETIC ALGORITHM AND AN OPTIMIZATION TRAINER FOR AUTOENCODER GENERATION - A method includes, during an epoch of a genetic algorithm, determining a fitness value for each of a plurality of autoencoders. The fitness value for an autoencoder indicates reconstruction error responsive to data representing a first operational state of one or more devices. The method includes selecting, based on the fitness values, a subset of autoencoders. The method also includes performing a genetic operation with respect to at least one autoencoder to generate a trainable autoencoder. The method includes training the trainable autoencoder to reduce a loss function value to generate a trained autoencoder. The loss function value is based on reconstruction error of the trainable autoencoder responsive to data representative of a second operational state of the device(s). The method includes adding the trained autoencoder to a population to be provided as input to a subsequent epoch of the genetic algorithm. | 2021-06-17 |
20210182692 | Unsupervised Cluster Generation - A method that may include (a) feeding multiple tagged media units to a neural network to provide, from one or more intermediate layers of the neural network, multiple feature vectors of segments of the media units; wherein the neural network was trained to detect current objects within media units; wherein the new category differs from each one of the current categories; wherein at least one media unit comprises at least one segment that is tagged as including the new object; (b) calculating similarities between the multiple feature vectors; (c) clustering the multiple feature vectors to feature vector clusters, based on the similarities; and (d) finding, out of the feature vector clusters, a new feature vector cluster that identifies media unit segments that comprise the new object. | 2021-06-17 |
20210182693 | Method for physical system anomaly detection - A method for detecting anomalies in a physical system generates from a set of physics rules and a process graph representing the system a set of candidate physics models that assign physics rules to portions of the process graph representing sensors. Candidate physics models are rejected if an error between the models and sensor data exceed a predetermined error tolerance. Supervised learning is used to train a machine learning model to predict an error between the physics models and the sensor data. The predicted error and predicted sensor measurements from the physics models are then used to detect anomalies using unsupervised learning on a distribution of error between the predicted sensor measurements and the sensor data. | 2021-06-17 |
20210182694 | SYSTEMS AND METHODS FOR SITUATION AWARENESS - The present disclosure generally provides systems and methods for situation awareness. When executing a set of instructions stored in at least one non-transitory storage medium, at least one processor may be configured to cause the system to perform operations including obtaining, from at least one of one or more sensors, environmental data associated with an environment corresponding to a first time point, generating a first static global representation of an environment corresponding to the first time point based at least in part on the environmental data, generating a first dynamic global representation of the environment corresponding to the first time point based at least in part on the environmental data, and estimating, based on the first static global representation and the first dynamic global representation, a target state of the environment at a target time point using a target estimation model. | 2021-06-17 |
20210182695 | Machine Learning-Based Rule Mining Algorithm - Data is received that defines a rule mining run including a scope of a search and at least one data source to be searched. In response, the at least one data source is polled to obtain rules responsive to the rule mining run. Each rule can specify one or more actions to take as part of a computer-implemented process when certain conditions are met. A list of rules (i.e., a proposed subset of the obtained rules) can then be generated using at least one machine learning model. The generated list of rule can then be displayed in a graphical user interface. Related apparatus, systems, techniques and articles are also described. | 2021-06-17 |
20210182696 | PREDICTION OF OBJECTIVE VARIABLE USING MODELS BASED ON RELEVANCE OF EACH MODEL - It is preferable to predict an objective variable by optimally selecting or combining the output of a plurality of models. A computer-implemented method is provided that calculates, for each of a plurality of models, a relevance of an output of the model with respect to a value of an objective variable based on the value of the objective variable and the output of the model in the past. The method also calculates, for each of the plurality of models, similarities between a current timing and a plurality of past timings based on the output of the model at the current timing, the output of the model at the plurality of past timings, and the relevance. Additionally, the method predicts the value of the objective variable at a target timing based on the similarities. | 2021-06-17 |
20210182697 | NORMALIZING DIGITAL CONTENT ACROSS DATABASES AND GENERATING PERSONALIZED CONTENT RECOMMENDATIONS - Methods and apparatuses are described for normalizing digital content across databases and generating personalized content recommendations. A server normalizes structured text for each content item to generate unstructured text. The server converts the unstructured text into a multidimensional content item feature set. The server trains a model based upon user profile information, historical content consumption information, historical content recommendation information, and the feature sets. The server receives a request including a vector associated with a user of a client device. The server executes the model using the vector as input to generate interaction prediction scores. The server selects scores above a threshold and identifies content items associated with each score. The server retrieves identified items for display, including converting the normalized text for the items into a format compatible with the client device, receives a response to the displayed digital content items, and updates the model based upon the response. | 2021-06-17 |
20210182698 | INTERPRETATION OF MACHINE LEANING RESULTS USING FEATURE ANALYSIS - Techniques and solutions are described for analyzing results of a machine learning model. A result is obtained for a data set that includes a first plurality of features. A plurality of feature groups are defined. At least one feature group contains a second plurality of features of the first plurality of features. The second plurality of features is less than all of the first plurality of features. Feature groups can be defined based on determining dependencies between features of the first plurality of features, including using contextual contribution values. Group contextual contribution values can be determined for feature groups by aggregating contextual contribution values of the constituent features of the feature groups. | 2021-06-17 |
20210182699 | SELF-MANAGING DATABASE SYSTEM USING MACHINE LEARNING - A self-managing database system includes a metrics collector to collect metrics data from one or more databases of a computing system and an anomaly detector to analyze the metrics data and detect one or more anomalies. The system includes a causal inference engine to mark one or more nodes in a knowledge representation corresponding to the metrics data for the one or more anomalies and to determine a root cause with a highest probability of causing the one or more anomalies using the knowledge representation. The system includes a self-healing engine, to take at least one remedial action for the one or more databases in response to determination of the root cause. | 2021-06-17 |
20210182700 | CONTENT ITEM SELECTION FOR GOAL ACHIEVEMENT - One or more computing devices, systems, and/or methods for content item selection for goal achievement are provided herein. A goal of a user is identified. A model is utilized to evaluate the goal, user information, and a set of content items to generate predictions for the content items of how likely each content item will be a causation factor of the user making progress towards the goal in response to the user being provided with each content item. A target content item is selected from the set of content items based upon the target content item having a predicted likelihood of being the causation factor above a threshold. The target content item is provided through a registered media channel accessible through a device of the user. | 2021-06-17 |
20210182701 | VIRTUAL DATA SCIENTIST WITH PRESCRIPTIVE ANALYTICS - A data analytics platform may determine whether a machine learning model is a regression model. The data analytics platform may perform, based on determining that the machine learning model is a regression model, a regression prescription method including acquiring a predicted value of a performance indicator determined by the machine learning model processing data associated with a plurality of features and the performance indicator, acquiring a target value of the performance indicator, determining a rate of change of the performance indicator with respect to each feature to generate first results, determining, based on the regression model and for each feature, a rate of change of each feature with respect to other features to generate second results, and determining, for each feature and based on the predicted value, the target value, the first results, and the second results, a change in each feature to achieve the target value. | 2021-06-17 |
20210182702 | EVALUATION SYSTEM, EVALUATION METHOD, AND EVALUATION PROGRAM - A learning unit 81 generates a plurality of sample groups from samples to be used for learning, and generates a plurality of prediction models while inhibiting overlapping of a sample group to be used for learning among the generated sample groups. An optimization unit 82 generates an objective function based on an explained variable predicted by the prediction model and based on a constraint condition for optimization, and optimizes a generated objective function. An evaluation unit 83 evaluates an optimization result by using a sample group that has not been used in learning of a prediction model used for generating an objective function targeted for the optimization. | 2021-06-17 |
20210182703 | SYSTEM AND METHOD FOR ANTI-PATTERN DETECTION FOR COMPUTING APPLICATIONS - A system and method for anti-pattern detection for computing application prior to deployment in cloud environment is provided. The present invention provides for applying a pre-defined set of rules on one or more applications source code. The pre-defined set of rules are applied in pre-defined order. Further, applying one or more anti-pattern detection models on one or more applications source code. The anti-pattern detection models are applied for determining correlation between one or more syntax patterns of the application source code and the anti-patterns detection models. Further, detecting anti-patterns associated with the syntax patterns of the application source code based on the pre-defined set of rules and the anti-patterns detection models. The detected anti-patterns represent unique anti-patterns. Lastly, generating a migration actionable event for the application source code based on the detected anti-patterns. | 2021-06-17 |
20210182704 | Surface Detection Based on Vehicle Motion Patterns - A system and method are disclosed for a system determining surface types using motion patterns. In an embodiment, the system receives inertial measurements from a sensor of a vehicle operating on a surface of an unknown surface type. The system generates a prediction of a type of the surface based on the inertial measurements. In some embodiments, the system generates the prediction by performing a fast Fourier transform (FFT) operation on the inertial measurements to generate a set of frequency bins that reflect surface features. In other embodiments, the system generates the prediction by inputting the inertial measurements into a trained machine learning model configured to generate the prediction of the surface type. The system provides for display data representing the prediction on a user device. The system may also determine the speed and/or geographic location of the vehicle using the inertial measurements and the surface type prediction. | 2021-06-17 |
20210182705 | MACHINE LEARNING BASED SKIN CONDITION RECOMMENDATION ENGINE - A skin condition recommendation engine identifies skin conditions of a user's face and recommends actions and/or products that increase a likelihood that the skin conditions will be remedied. The skin condition recommendation engine trains a machine learned model using a training set of information that includes images and identified skin conditions of training users' faces. The skin condition recommendation engine inputs images of the user's face into the machine learned model, which outputs identified skin conditions of the user. The skin condition recommendation engine accordingly identifies actions that, if performed by the user, would increase a likelihood of the skin conditions being remedied. The skin condition recommendation engine modifies an interface of a device of the user to show the identified actions. | 2021-06-17 |
20210182706 | SYSTEMS AND METHODS FOR DETERMINING RELATIVE IMPORTANCE OF ONE OR MORE VARIABLES IN A NON-PARAMETRIC MACHINE LEARNING MODEL - Systems and methods for determining relative importance of one or more variables in a non-parametric model include: receiving, raw values of the variables corresponding to one or more entities; processing the raw values using a statistical model to obtain probability values for the variables and an overall prediction value for each entity; determining a plurality of cumulative distributions for the variables based on the raw values and the number of entities having a specific raw value; grouping the variables into a plurality of equally sized buckets based on the cumulative distributions; determining a mean probability value for each bucket; assigning a rank number for each bucket based on the mean probability values; compiling a table for the entities based on the raw values and the buckets corresponding to the raw values; and determining the relative importance of the variables for the entities based on the rank numbers. | 2021-06-17 |
20210182707 | Method for Testing and Testing Device - A method and a device for testing, the device comprising a learning arrangement adapted to provide scenarios for test cases and principles to be tested, in particular comprising a digital representation of one or more of a law, an accident report, a log, or human expertise or a combination thereof, wherein the learning arrangement is adapted to determine at least one rule for test case generation from the scenarios and the principles, and wherein a modelling arrangement is adapted to determine, store and/or output a model for test case generation depending on the at least one rule. A method and a device for testing an at least partially autonomous apparatus or a behavior of a user at an at least partially autonomous apparatus, including a selecting arrangement adapted to determine a scenario for testing depending on a probability defined for the scenario in a probability distribution, and to determine a test case depending on the scenario and depending on information about the at least partial autonomous apparatus, and a testing arrangement adapted to determine an output for the at least partially autonomous apparatus depending on the test case, detect a response to the test case at the at least partially autonomous apparatus and to determine a result of the testing depending on the response. | 2021-06-17 |
20210182708 | TIME SERIES DATA PROCESSING DEVICE AND OPERATING METHOD THEREOF - Disclosed are a time series data processing device and an operating method thereof. The time series data processing device includes a preprocessor, a learner, and a predictor. The preprocessor generates preprocessed data and interval data. The learner may adjust a feature weight, a time series weight, and a weight group of a feature distribution model for generating a prediction distribution, based on the interval data and the preprocessed data. The predictor may generate a feature weight, based on the interval data and the preprocessed data, may generate a time series weight, based on the feature weight and the interval data, and may calculate a prediction result and a reliability of the prediction result, based on the time series weight. | 2021-06-17 |
20210182709 | METHOD AND SYSTEM FOR EXTRACTING CONTEXTUAL INFORMATION FROM A KNOWLEDGE BASE - This disclosure relates generally to method and system for extracting contextual information from a knowledge base. The method receives a user query comprising a request to extract contextual information from the user query. Further, the user query is analyzed based on a plurality of predefined parameters to determine sufficiency of information comprised in the user query. The received user query identifies relevant sources of the structured data, the unstructured data or the semi-structured data storage repositories. The user query is processed using a fine grain approach, where a dictionary of one or more keywords with weights are created through the domain ontology builder from the one or more knowledge articles. Furthermore, an appropriate contextual information related to the user query is extracted using the fine grain approach, based on the knowledge articles associated with the trained knowledge base comprising information required by the user query extracted from the knowledge articles. | 2021-06-17 |
20210182710 | METHOD AND SYSTEM OF USER IDENTIFICATION BY A SEQUENCE OF OPENED USER INTERFACE WINDOWS - A method and a system for user identification of a user in a computer system are provided. The method comprising: obtaining a user identifier associated with the user; assigning a respective window identifier to each user interface window opened by the user on the computing device; for a given working session of a pre-determined number of working sessions: storing a sequence of user interface windows opened by the user; identify, within user interface windows opened over the pre-determined number of working sessions, at least one pattern including a pre-determined number of repetitive sequences of user interface windows; generating a set of parameters characterizing a time elapsed between a transition from a first user interface window to an other user interface window within the at least one pattern; using the set of parameters associated with the at least one pattern and the user identifier to train at least one classifier. | 2021-06-17 |
20210182711 | DETERMINING MODEL PARAMETERS USING SECRET SHARING - This disclosure relates to determining model parameters using secret sharing. In some aspects, a first data party device obtains a first share of a Hessian matrix for a data processing model. The first data party device obtains, using secret sharing with the second data party device, a first share of a product of a random number matrix and the Hessian matrix. The first data party device, determines a first share of a first inverse matrix based on a second inverse matrix and the first share of the random number matrix. The first data party device determines the first inverse matrix, a first share of a product of the first inverse matrix and a gradient of a loss function of the data processing model, and a first share of a new model parameter for the data processing model. | 2021-06-17 |
20210182712 | PREDICTION INTERPRETATION APPARATUS AND PREDICTION INTERPRETATION METHOD - A prediction interpretation apparatus, comprising: a data storage unit configured to store data of a plurality of users; a model storage unit configured to store a prediction model learned from data of the whole of the plurality of users; a vicinity user search unit configured to extract vicinity users for the target user from the data storage unit; a linear regression model learning unit configured to learn a linear regression model approximated to the prediction model for the vicinity users; and an interpretation result output unit configured to output an interpretation result of prediction for the target user based on a partial regression coefficient of the linear regression model, wherein the vicinity user search unit extracts the vicinity user by narrowing vicinity user candidates extracted based on distance between users based on a prediction direction of the target user by the prediction model. | 2021-06-17 |
20210182713 | EXPLAINABLE ARTIFICIAL INTELLIGENCE (AI) BASED IMAGE ANALYTIC, AUTOMATIC DAMAGE DETECTION AND ESTIMATION SYSTEM - An Artificial Intelligence (AI) based automatic damage detection and estimation system receives images of a damaged object. The images are converted into monochrome versions if needed and analyzed by an ensemble machine learning (ML) cause prediction model that includes a plurality of sub-models that are each trained to identify a cause of damage to a corresponding portion for the damaged object from a plurality of causes. In addition, an explanation for the selection of the cause from the plurality of causes is also provided. The explanation includes image portions and pixels of images that enabled the cause prediction model to select the cause of damage. An ML parts identification model is also employed to identify and labels parts of the damaged object which are repairable and parts that are damaged and need replacement. The cost estimation for the repair and restoration of the damaged object can also be generated. | 2021-06-17 |
20210182714 | AUDITABLE RULE ENGINE-BASED DIAGNOSTIC TOOLS - This disclosure describes techniques for diagnostic tools rendering auditable reasoning of underlying rule engines. A method includes a computing system running a software tool executing an iteration of an underlying rule engine of the software tool to compute at least one input fact; sending a command over a communication interface to a fact source and obtaining an output from the fact source over the communication interface; recording the output from the fact source in a diagnostic event construct; recording a reference to the diagnostic event construct in an inference construct; displaying information of the inference construct in a conclusion view user interface; and displaying information of the diagnostic event construct referenced by the inference construct in an expanded view user interface. | 2021-06-17 |
20210182715 | SYSTEMS AND METHODS FOR GENERATING A BOUNDARY OF A FOOTPRINT OF UNCERTAINTY FOR AN INTERVAL TYPE-2 MEMBERSHIP FUNCTION BASED ON A TRANSFORMATION OF ANOTHER BOUNDARY - Systems and methods may generate a boundary of a FOU for an interval type-2 MF based on a transformation of another boundary of the FOU. The systems and methods may receive a plurality of parameters for a type-1 MF that defines a boundary of the FOU for the interval type-2 MF and may receive at least one other parameter. The systems and methods may generate, based on a transformation of the type-1 MF utilizing the at least one parameter, a type-1 MF that defines a different boundary of the FOU. The system and methods may adjust the plurality of parameters and the at least one second parameter to adjust the FOU for use in a model representing, for example, a real-world physical system, where execution of the model executes a fuzzy inference system and generates results representing a behavior of the real-world physical system. | 2021-06-17 |
20210182716 | SYSTEM AND METHOD FOR FIELD VALUE RECOMMENDATIONS BASED ON CONFIDENCE LEVELS IN ANALYZED DATASET - A method of training a predictive model to predict a likely field value for one or more user selected fields within an application. The method comprises providing a user interface for user selection of the one or more user selected fields within the application; analyzing a pre-existing, user provided data set of objects; training, based on the analysis, the predictive model; determining, for each user selected field based on the analysis, a confidence function for the predictive model that identifies the percentage of cases predicted correctly at different applied confidence levels, the percentage of cases predicted incorrectly at different applied confidence levels, and the percentage of cases in which the prediction model could not provide a prediction at different applied confidence levels; and providing a user interface for user review of the confidence functions for user selection of confidence threshold levels to be used with the predictive model. | 2021-06-17 |
20210182717 | WEIGHT ASSIGNMENT FOR FUSION OF PROGNOSTIC ESTIMATORS - A system for predicting remaining useful life of a component implements a set of estimation models that generate future damage estimates for the component. The system detects damage to the component and estimates the magnitude of the current damaged. An error processor estimates the between each future damage estimate and the magnitude of current damage. A weight calculator calculates weights for the future damage estimates, wherein each weight is inversely proportional to the error. A fusion processor applies the weights respectively to future damage estimates of the estimators and combines the weighted future damage estimates | 2021-06-17 |
20210182718 | DETERMINING ACTION SELECTION POLICIES OF AN EXECUTION DEVICE - Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an action selection policy of an execution device for completing a task in an environment. The method includes computing a hybrid sampling policy at a state of the execution device based on a sampling policy and an exploration policy, wherein the exploration policy specifies a respective exploration probability corresponding to each of multiple possible actions in the state, wherein the exploration probability is negatively correlated with a number of times that the each of the multiple possible actions in the state has been sampled; sampling an action among the multiple possible actions in the state according to a sampling probability of the action specified in the hybrid sampling policy; and updating an action selection policy in the state by performing Monte Carlo counterfactual regret minimization based on the action. | 2021-06-17 |
20210182719 | DETERMINING ACTION SELECTION POLICIES OF AN EXECUTION DEVICE - Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an action selection policy for completing a task in an environment. The method includes identifying multiple possible actions in a state, wherein the state corresponds to a vector of information sets; identifying a vector of current action selection policies in the state, wherein each current action selection policy in the vector of current action selection policies corresponds to an information set in the vector of information sets; computing a sampling policy based on the vector of current action selection policies in the state; sampling an action among the multiple possible actions in the state according to a sampling probability of the action specified in the sampling policy; and updating each current action selection policy of the execution device in the state based on the action. | 2021-06-17 |
20210182720 | INFORMATION PROCESSING DEVICE, PUBO SOLVER, INFORMATION PROCESSING METHOD AND NON-TRANSITORY STORAGE MEDIUM - According to one embodiment, an information processing device includes a first storage and a first processing circuit. The first storage is configured to store constraint data which includes a constraint of a combinatorial optimization problem expressed in a formal language. The first processing circuit is configured to generate logical expression data from the constraint data and generate a penalty term data including a penalty term having a binary variable parameter by converting the logical expression data. | 2021-06-17 |
20210182721 | METHOD AND APPARATUS FOR CONSTRUCTING QUANTUM MACHINE LEARNING FRAMEWORK, QUANTUM COMPUTER AND COMPUTER STORAGE MEDIUM - The present disclosure provides a method and an apparatus for constructing a quantum machine learning framework, a quantum computer and a computer storage medium. The method includes: obtaining a Hamiltonian corresponding to a set problem and a number of quantum bits required by the set problem, obtaining target bits according to the number of the quantum bits, obtaining a variational quantum circuit of the set problem according to the target bits and the Hamiltonian, determining a quantum bit to be measured from the target bit, constructing a quantum-operation node class that provides an expectation-value solving interface and a gradient solving interface according to the quantum bit to be measured, the Hamiltonian and the variational quantum circuit, and calling the gradient solving interface and the expectation-value solving interface provided on the quantum-operation node class inserted into a preset machine learning framework to solve the set problem, so as to construct the quantum machine learning framework. With the above method, the quantum machine learning framework may be applied to the quantum computer, so that hybrid programming of a neural network and quantum computing may be realized, and the quantum computer may perform machine learning. | 2021-06-17 |
20210182722 | METHOD AND SYSTEM FOR GENERATING QUANTUM BIT CONTROL SIGNAL - The present disclosure provides a method and a system for generating a quantum bit control signal. The method includes: receiving a first tag code and a first standard signal corresponding to each basic quantum logic gate in a set of reference quantum gates from a master computer; storing the first standard signal, and obtaining a first address code identifying a storage location of the first standard signal; receiving a target tag code and a target time code corresponding to each basic quantum logic gate in a target quantum program from the master computer; and obtaining, according to the target tag code and the target time code, the first standard signal corresponding to the basic quantum logic gate in the target quantum program as a signal to be processed, and processing the signal to be processed to obtain the quantum bit control signal. The present disclosure may satisfy requirements of a multi-bit quantum bit test and provide quantum bit control signals required by the multi-bit quantum bit test, thereby greatly increasing a response speed of a control-signal generation module and ensuring a speed of subsequent quantum operations. | 2021-06-17 |
20210182723 | APPARATUS AND METHOD FOR QUANTUM CIRCUIT SYNTHESIS USING HARDWARE-SPECIFIC CONSTRAINTS - Apparatus and method for hardware-specific quantum circuit synthesis. For example, one embodiment of an apparatus comprises: one or more memory and/or storage devices to store quantum computation specifications and hardware-specific constraints associated with a quantum processor; and a quantum circuit synthesizer to generate a hardware-optimal quantum circuit based on the quantum computation specifications and the hardware-specific constraints. | 2021-06-17 |
20210182724 | APPARATUS AND METHOD FOR SPECIFYING QUANTUM OPERATION PARALLELISM FOR A QUANTUM CONTROL PROCESSOR - Apparatus and method for specifying quantum operation parallelism. For example, one embodiment of an apparatus comprises: instruction fetch circuitry to fetch a plurality of quantum instructions from a memory or a cache; slice-based instruction processing circuitry to identify quantum circuit slices comprising sets of one or more of the plurality of quantum instructions; and one or more instruction decoders to decode the quantum instructions to generate quantum microoperations; and quantum execution circuitry to execute sets of the quantum microoperations in parallel based on the quantum circuit slices. | 2021-06-17 |
20210182725 | APPARATUS AND METHOD INCLUDING SCALABLE REPRESENTATIONS OF ARBITRARY QUANTUM COMPUTING ROTATIONS - Apparatus and method for performing a quantum rotation operation. For example, one embodiment of an apparatus comprises: a decoder to decode a plurality of instructions; execution circuitry to execute a first instruction or first set of the instructions to generate a floating point (FP) value and to store the FP value in a first register; the execution circuitry to execute a second instruction or second set of the one or more of the instructions to read the FP value from the first register and compress the FP value to generate a compressed FP value having a precision selected for performing quantum rotation operations; and quantum interface circuitry to process the compressed FP value to cause a quantum rotation to be performed on one or more qubits of a quantum processor. | 2021-06-17 |
20210182726 | ESTIMATION OF AN EXPECTED ENERGY VALUE OF A HAMILTONIAN - Systems, computer-implemented methods, and computer program products to facilitate estimation of an expected energy value of a Hamiltonian based on data of the Hamiltonian, the quantum state produced by a quantum device and/or entangled measurements 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 selection component that selects a quantum state measurement basis having a probability defined based on a ratio of a Pauli operator in a Hamiltonian of a quantum system. The computer executable components can further comprise a measurement component that captures a quantum state measurement of a qubit in the quantum system based on the quantum state measurement basis. | 2021-06-17 |
20210182727 | QUBIT DETECTION SYSTEM AND DETECTION METHOD - Methods for qubit detection include: imaging, via an imaging device, a qubit to obtain an image; inputting the image to a machine learning model; and outputting, by the machine learning model, prediction information based on the image. Systems for qubit detection include: a test module including an imaging device configured to provide an image of a qubit; and a prediction module communicatively coupled to the test module and including a machine learning model configured to output prediction information based on the image provided by the test module. Devices for qubit detection include: a non-transitory computer-readable storage medium storing an instruction set; and a processor configured to execute the instruction set to cause the device to perform controlling an imaging device to image a qubit to obtain an image; inputting the image to a machine learning model; and controlling the machine learning model to output prediction information based on the image. | 2021-06-17 |
20210182728 | TWO-QUBIT GATES IMPLEMENTED WITH A TUNABLE COUPLER - Methods, systems and apparatus for implementing two-qubit gates using a tunable coupler. In one aspect, a method of implementing a two-qubit gate includes: applying a unitary transformation control signal to a tunable coupler arranged between a first data qubit and a second data qubit to obtain a target unitary transformation of the first data qubit and the second data qubit, w'herein the unitary transformation control signal is applied to the tunable coupler over a predetermined period of time to allow coupling between the first data qubit and the second data qubit through the tunable coupler. | 2021-06-17 |